cv2 ¶

computeBitmaps(img[, tb[, eb]]) → tb, eb¶

Computes median threshold and exclude bitmaps of given image.

@param img input image
@param tb median threshold bitmap
@param eb exclude bitmap

Parameters:

self –
img (cv2.typing.MatLike) –
tb (cv2.typing.MatLike | None) –
eb (cv2.typing.MatLike | None) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

computeBitmaps(img[, tb[, eb]]) → tb, eb¶

Computes median threshold and exclude bitmaps of given image.

@param img input image
@param tb median threshold bitmap
@param eb exclude bitmap

Parameters:

self –
img (UMat) –
tb (UMat | None) –
eb (UMat | None) –

Return type:

getMaxBits() → retval¶

Parameters:: self –
Return type:: int

setMaxBits(max_bits) → None¶

Parameters:

self –
max_bits (int) –

Return type:

None

getExcludeRange() → retval¶

Parameters:: self –
Return type:: int

setExcludeRange(exclude_range) → None¶

Parameters:

self –
exclude_range (int) –

Return type:

None

getCut() → retval¶

Parameters:: self –
Return type:: bool

setCut(value) → None¶

Parameters:

self –
value (bool) –

Return type:

None

class cv2.AsyncArray¶

get([dst]) → dst¶

Fetch the result. @param[out] dst destination array

Waits for result until container has valid result.
Throws exception if exception was stored as a result.

Throws exception on invalid container state.

@note Result or stored exception can be fetched only once.

Retrieving the result with timeout @param[out] dst destination array @param[in] timeoutNs timeout in nanoseconds, -1 for infinite wait

@returns true if result is ready, false if the timeout has expired

@note Result or stored exception can be fetched only once.

Parameters:

self –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

get([dst]) → dst¶

Fetch the result. @param[out] dst destination array

Waits for result until container has valid result.
Throws exception if exception was stored as a result.

Throws exception on invalid container state.

@note Result or stored exception can be fetched only once.

Retrieving the result with timeout @param[out] dst destination array @param[in] timeoutNs timeout in nanoseconds, -1 for infinite wait

@returns true if result is ready, false if the timeout has expired

@note Result or stored exception can be fetched only once.

Parameters:

self –
dst (UMat | None) –

Return type:

get([dst]) → dst¶

Fetch the result. @param[out] dst destination array

Waits for result until container has valid result.
Throws exception if exception was stored as a result.

Throws exception on invalid container state.

@note Result or stored exception can be fetched only once.

Retrieving the result with timeout @param[out] dst destination array @param[in] timeoutNs timeout in nanoseconds, -1 for infinite wait

@returns true if result is ready, false if the timeout has expired

@note Result or stored exception can be fetched only once.

Parameters:

self –
timeoutNs (float) –
dst (cv2.typing.MatLike | None) –

Return type:

tuple[bool, cv2.typing.MatLike]

get([dst]) → dst¶

Fetch the result. @param[out] dst destination array

Waits for result until container has valid result.
Throws exception if exception was stored as a result.

Throws exception on invalid container state.

@note Result or stored exception can be fetched only once.

Retrieving the result with timeout @param[out] dst destination array @param[in] timeoutNs timeout in nanoseconds, -1 for infinite wait

@returns true if result is ready, false if the timeout has expired

@note Result or stored exception can be fetched only once.

Parameters:

self –
timeoutNs (float) –
dst (UMat | None) –

Return type:

__init__(self)¶

Parameters:: self –
Return type:: None

release() → None¶

Parameters:: self –
Return type:: None

wait_for(timeoutNs) → retval¶

Parameters:

self –
timeoutNs (float) –

Return type:

valid() → retval¶

Parameters:: self –
Return type:: bool

class cv2.BFMatcher¶

classmethod create([normType[, crossCheck]]) → retval¶

Brute-force matcher create method. @param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor description). @param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the matcher’s collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent pairs. Such technique usually produces best results with minimal number of outliers when there are enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.

Parameters:

cls –
normType (int) –
crossCheck (bool) –

Return type:

BFMatcher

__init__(self, normType: int = ..., crossCheck: bool = ...)¶

Parameters:

self –
normType (int) –
crossCheck (bool) –

Return type:

None

class cv2.BOWImgDescriptorExtractor¶

__init__(self, dextractor: Feature2D, dmatcher: DescriptorMatcher)¶

Parameters:

self –
dextractor (Feature2D) –
dmatcher (DescriptorMatcher) –

Return type:

None

setVocabulary(vocabulary) → None¶

Sets a visual vocabulary.

@param vocabulary Vocabulary (can be trained using the inheritor of BOWTrainer ). Each row of the
vocabulary is a visual word (cluster center).

Parameters:

self –
vocabulary (cv2.typing.MatLike) –

Return type:

None

getVocabulary() → retval¶

Returns the set vocabulary.

Parameters:: self –
Return type:: cv2.typing.MatLike

compute(image, keypoints[, imgDescriptor]) → imgDescriptor¶

@overload @param keypointDescriptors Computed descriptors to match with vocabulary. @param imgDescriptor Computed output image descriptor. @param pointIdxsOfClusters Indices of keypoints that belong to the cluster. This means that pointIdxsOfClusters[i] are keypoint indices that belong to the i -th cluster (word of vocabulary) returned if it is non-zero.

Parameters:

self –
image (cv2.typing.MatLike) –
keypoints (_typing.Sequence[KeyPoint]) –
imgDescriptor (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

descriptorSize() → retval¶

Returns an image descriptor size if the vocabulary is set. Otherwise, it returns 0.

Parameters:: self –
Return type:: int

descriptorType() → retval¶

Returns an image descriptor type.

Parameters:: self –
Return type:: int

class cv2.BOWKMeansTrainer¶

cluster() → retval¶

Parameters:: self –
Return type:: cv2.typing.MatLike

cluster() → retval¶

Parameters:

self –
descriptors (cv2.typing.MatLike) –

Return type:

cv2.typing.MatLike

__init__(self, clusterCount: int, termcrit: cv2.typing.TermCriteria = ..., attempts: int = ..., flags: int = ...)¶

Parameters:

self –
clusterCount (int) –
termcrit (cv2.typing.TermCriteria) –
attempts (int) –
flags (int) –

Return type:

None

class cv2.BOWTrainer¶

cluster() → retval¶

Clusters train descriptors.

@overload

@param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor.
Descriptors are not added to the inner train descriptor set.

The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first
variant of the method, train descriptors stored in the object are clustered. In the second variant,
input descriptors are clustered.

Parameters:: self –
Return type:: cv2.typing.MatLike

cluster() → retval¶

Clusters train descriptors.

@overload

@param descriptors Descriptors to cluster. Each row of the descriptors matrix is a descriptor.
Descriptors are not added to the inner train descriptor set.

The vocabulary consists of cluster centers. So, this method returns the vocabulary. In the first
variant of the method, train descriptors stored in the object are clustered. In the second variant,
input descriptors are clustered.

Parameters:

self –
descriptors (cv2.typing.MatLike) –

Return type:

cv2.typing.MatLike

add(descriptors) → None¶

Adds descriptors to a training set.

@param descriptors Descriptors to add to a training set. Each row of the descriptors matrix is a
descriptor.

The training set is clustered using clustermethod to construct the vocabulary.

Parameters:

self –
descriptors (cv2.typing.MatLike) –

Return type:

None

getDescriptors() → retval¶

Returns a training set of descriptors.

Parameters:: self –
Return type:: _typing.Sequence[cv2.typing.MatLike]

descriptorsCount() → retval¶

Returns the count of all descriptors stored in the training set.

Parameters:: self –
Return type:: int

clear() → None¶

Parameters:: self –
Return type:: None

class cv2.BRISK¶

classmethod create([thresh[, octaves[, patternScale]]]) → retval¶

The BRISK constructor for a custom pattern, detection threshold and octaves

@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
keypoint.

@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).

@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).

Parameters:

cls –
thresh (int) –
octaves (int) –
patternScale (float) –
indexChange – index remapping of the bits.

Return type:

BRISK

classmethod create([thresh[, octaves[, patternScale]]]) → retval¶

The BRISK constructor for a custom pattern, detection threshold and octaves

@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
keypoint.

@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).

@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).

Parameters:

cls –
radiusList (_typing.Sequence[float]) –
numberList (_typing.Sequence[int]) –
dMax (float) –
dMin (float) –
indexChange (_typing.Sequence[int]) – index remapping of the bits.

Return type:

BRISK

classmethod create([thresh[, octaves[, patternScale]]]) → retval¶

The BRISK constructor for a custom pattern, detection threshold and octaves

@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
keypoint.

@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).

@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).

Parameters:

cls –
thresh (int) –
octaves (int) –
radiusList (_typing.Sequence[float]) –
numberList (_typing.Sequence[int]) –
dMax (float) –
dMin (float) –
indexChange (_typing.Sequence[int]) – index remapping of the bits.

Return type:

BRISK

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

setThreshold(threshold) → None¶

Set detection threshold. @param threshold AGAST detection threshold score.

Parameters:

self –
threshold (int) –

Return type:

None

getThreshold() → retval¶

Parameters:: self –
Return type:: int

setOctaves(octaves) → None¶

Set detection octaves. @param octaves detection octaves. Use 0 to do single scale.

Parameters:

self –
octaves (int) –

Return type:

None

getOctaves() → retval¶

Parameters:: self –
Return type:: int

setPatternScale(patternScale) → None¶

Set detection patternScale. @param patternScale apply this scale to the pattern used for sampling the neighbourhood of a keypoint.

Parameters:

self –
patternScale (float) –

Return type:

None

getPatternScale() → retval¶

Parameters:: self –
Return type:: float

class cv2.BackgroundSubtractor¶

apply(image[, fgmask[, learningRate]]) → fgmask¶

Computes a foreground mask.

@param image Next video frame.
@param fgmask The output foreground mask as an 8-bit binary image.
@param learningRate The value between 0 and 1 that indicates how fast the background model is
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
rate. 0 means that the background model is not updated at all, 1 means that the background model
is completely reinitialized from the last frame.

Parameters:

self –
image (cv2.typing.MatLike) –
fgmask (cv2.typing.MatLike | None) –
learningRate (float) –

Return type:

cv2.typing.MatLike

apply(image[, fgmask[, learningRate]]) → fgmask¶

Computes a foreground mask.

@param image Next video frame.
@param fgmask The output foreground mask as an 8-bit binary image.
@param learningRate The value between 0 and 1 that indicates how fast the background model is
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
rate. 0 means that the background model is not updated at all, 1 means that the background model
is completely reinitialized from the last frame.

Parameters:

self –
image (UMat) –
fgmask (UMat | None) –
learningRate (float) –

Return type:

getBackgroundImage([backgroundImage]) → backgroundImage¶

Computes a background image.

@param backgroundImage The output background image.

@note Sometimes the background image can be very blurry, as it contain the average background
statistics.

Parameters:

self –
backgroundImage (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

getBackgroundImage([backgroundImage]) → backgroundImage¶

Computes a background image.

@param backgroundImage The output background image.

@note Sometimes the background image can be very blurry, as it contain the average background
statistics.

Parameters:

self –
backgroundImage (UMat | None) –

Return type:

class cv2.BackgroundSubtractorKNN¶

getHistory() → retval¶

Returns the number of last frames that affect the background model

Parameters:: self –
Return type:: int

setHistory(history) → None¶

Sets the number of last frames that affect the background model

Parameters:

self –
history (int) –

Return type:

None

getNSamples() → retval¶

Returns the number of data samples in the background model

Parameters:: self –
Return type:: int

setNSamples(_nN) → None¶

Sets the number of data samples in the background model.

The model needs to be reinitalized to reserve memory.

Parameters:

self –
_nN (int) –

Return type:

None

getDist2Threshold() → retval¶

Returns the threshold on the squared distance between the pixel and the sample

The threshold on the squared distance between the pixel and the sample to decide whether a pixel is
close to a data sample.

Parameters:: self –
Return type:: float

setDist2Threshold(_dist2Threshold) → None¶

Sets the threshold on the squared distance

Parameters:

self –
_dist2Threshold (float) –

Return type:

None

getkNNSamples() → retval¶

Returns the number of neighbours, the k in the kNN.

K is the number of samples that need to be within dist2Threshold in order to decide that that
pixel is matching the kNN background model.

Parameters:: self –
Return type:: int

setkNNSamples(_nkNN) → None¶

Sets the k in the kNN. How many nearest neighbours need to match.

Parameters:

self –
_nkNN (int) –

Return type:

None

getDetectShadows() → retval¶

Returns the shadow detection flag

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for
details.

Parameters:: self –
Return type:: bool

setDetectShadows(detectShadows) → None¶

Enables or disables shadow detection

Parameters:

self –
detectShadows (bool) –

Return type:

None

getShadowValue() → retval¶

Returns the shadow value

Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
in the mask always means background, 255 means foreground.

Parameters:: self –
Return type:: int

setShadowValue(value) → None¶

Sets the shadow value

Parameters:

self –
value (int) –

Return type:

None

getShadowThreshold() → retval¶

Returns the shadow threshold

A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara,
*Detecting Moving Shadows...*, IEEE PAMI,2003.

Parameters:: self –
Return type:: float

setShadowThreshold(threshold) → None¶

Sets the shadow threshold

Parameters:

self –
threshold (float) –

Return type:

None

class cv2.BackgroundSubtractorMOG2¶

apply(image[, fgmask[, learningRate]]) → fgmask¶

Computes a foreground mask.

@param image Next video frame. Floating point frame will be used without scaling and should be in range $[0,255]$.
@param fgmask The output foreground mask as an 8-bit binary image.
@param learningRate The value between 0 and 1 that indicates how fast the background model is
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
rate. 0 means that the background model is not updated at all, 1 means that the background model
is completely reinitialized from the last frame.

Parameters:

self –
image (cv2.typing.MatLike) –
fgmask (cv2.typing.MatLike | None) –
learningRate (float) –

Return type:

cv2.typing.MatLike

apply(image[, fgmask[, learningRate]]) → fgmask¶

Computes a foreground mask.

@param image Next video frame. Floating point frame will be used without scaling and should be in range $[0,255]$.
@param fgmask The output foreground mask as an 8-bit binary image.
@param learningRate The value between 0 and 1 that indicates how fast the background model is
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
rate. 0 means that the background model is not updated at all, 1 means that the background model
is completely reinitialized from the last frame.

Parameters:

self –
image (UMat) –
fgmask (UMat | None) –
learningRate (float) –

Return type:

getHistory() → retval¶

Returns the number of last frames that affect the background model

Parameters:: self –
Return type:: int

setHistory(history) → None¶

Sets the number of last frames that affect the background model

Parameters:

self –
history (int) –

Return type:

None

getNMixtures() → retval¶

Returns the number of gaussian components in the background model

Parameters:: self –
Return type:: int

setNMixtures(nmixtures) → None¶

Sets the number of gaussian components in the background model.

The model needs to be reinitalized to reserve memory.

Parameters:

self –
nmixtures (int) –

Return type:

None

getBackgroundRatio() → retval¶

Returns the “background ratio” parameter of the algorithm

If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's
considered background and added to the model as a center of a new component. It corresponds to TB
parameter in the paper.

Parameters:: self –
Return type:: float

setBackgroundRatio(ratio) → None¶

Sets the “background ratio” parameter of the algorithm

Parameters:

self –
ratio (float) –

Return type:

None

getVarThreshold() → retval¶

Returns the variance threshold for the pixel-model match

The main threshold on the squared Mahalanobis distance to decide if the sample is well described by
the background model or not. Related to Cthr from the paper.

Parameters:: self –
Return type:: float

setVarThreshold(varThreshold) → None¶

Sets the variance threshold for the pixel-model match

Parameters:

self –
varThreshold (float) –

Return type:

None

getVarThresholdGen() → retval¶

Returns the variance threshold for the pixel-model match used for new mixture component generation

Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the
existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it
is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg
value generates more components. A higher Tg value may result in a small number of components but
they can grow too large.

Parameters:: self –
Return type:: float

setVarThresholdGen(varThresholdGen) → None¶

Sets the variance threshold for the pixel-model match used for new mixture component generation

Parameters:

self –
varThresholdGen (float) –

Return type:

None

getVarInit() → retval¶

Returns the initial variance of each gaussian component

Parameters:: self –
Return type:: float

setVarInit(varInit) → None¶

Sets the initial variance of each gaussian component

Parameters:

self –
varInit (float) –

Return type:

None

getVarMin() → retval¶

Parameters:: self –
Return type:: float

setVarMin(varMin) → None¶

Parameters:

self –
varMin (float) –

Return type:

None

getVarMax() → retval¶

Parameters:: self –
Return type:: float

setVarMax(varMax) → None¶

Parameters:

self –
varMax (float) –

Return type:

None

getComplexityReductionThreshold() → retval¶

Returns the complexity reduction threshold

This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05
is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the
standard Stauffer&Grimson algorithm.

Parameters:: self –
Return type:: float

setComplexityReductionThreshold(ct) → None¶

Sets the complexity reduction threshold

Parameters:

self –
ct (float) –

Return type:

None

getDetectShadows() → retval¶

Returns the shadow detection flag

If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for
details.

Parameters:: self –
Return type:: bool

setDetectShadows(detectShadows) → None¶

Enables or disables shadow detection

Parameters:

self –
detectShadows (bool) –

Return type:

None

getShadowValue() → retval¶

Returns the shadow value

Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
in the mask always means background, 255 means foreground.

Parameters:: self –
Return type:: int

setShadowValue(value) → None¶

Sets the shadow value

Parameters:

self –
value (int) –

Return type:

None

getShadowThreshold() → retval¶

Returns the shadow threshold

A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara,
*Detecting Moving Shadows...*, IEEE PAMI,2003.

Parameters:: self –
Return type:: float

setShadowThreshold(threshold) → None¶

Sets the shadow threshold

Parameters:

self –
threshold (float) –

Return type:

None

class cv2.BaseCascadeClassifier¶

class cv2.CLAHE¶

apply(src[, dst]) → dst¶

Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization.

@param src Source image of type CV_8UC1 or CV_16UC1.
@param dst Destination image.

Parameters:

self –
src (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

apply(src[, dst]) → dst¶

Equalizes the histogram of a grayscale image using Contrast Limited Adaptive Histogram Equalization.

@param src Source image of type CV_8UC1 or CV_16UC1.
@param dst Destination image.

Parameters:

self –
src (UMat) –
dst (UMat | None) –

Return type:

setClipLimit(clipLimit) → None¶

Sets threshold for contrast limiting.

@param clipLimit threshold value.

Parameters:

self –
clipLimit (float) –

Return type:

None

getClipLimit() → retval¶

Parameters:: self –
Return type:: float

setTilesGridSize(tileGridSize) → None¶

Sets size of grid for histogram equalization. Input image will be divided into equally sized rectangular tiles.

@param tileGridSize defines the number of tiles in row and column.

Parameters:

self –
tileGridSize (cv2.typing.Size) –

Return type:

None

getTilesGridSize() → retval¶

Parameters:: self –
Return type:: cv2.typing.Size

collectGarbage() → None¶

Parameters:: self –
Return type:: None

class cv2.CalibrateCRF¶

process(src, times[, dst]) → dst¶

Recovers inverse camera response.

@param src vector of input images
@param dst 256x1 matrix with inverse camera response function
@param times vector of exposure time values for each image

Parameters:

self –
src (_typing.Sequence[cv2.typing.MatLike]) –
times (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src, times[, dst]) → dst¶

Recovers inverse camera response.

@param src vector of input images
@param dst 256x1 matrix with inverse camera response function
@param times vector of exposure time values for each image

Parameters:

self –
src (_typing.Sequence[UMat]) –
times (UMat) –
dst (UMat | None) –

Return type:

class cv2.CalibrateDebevec¶

getLambda() → retval¶

Parameters:: self –
Return type:: float

setLambda(lambda_) → None¶

Parameters:

self –
lambda_ (float) –

Return type:

None

getSamples() → retval¶

Parameters:: self –
Return type:: int

setSamples(samples) → None¶

Parameters:

self –
samples (int) –

Return type:

None

getRandom() → retval¶

Parameters:: self –
Return type:: bool

setRandom(random) → None¶

Parameters:

self –
random (bool) –

Return type:

None

class cv2.CalibrateRobertson¶

getMaxIter() → retval¶

Parameters:: self –
Return type:: int

setMaxIter(max_iter) → None¶

Parameters:

self –
max_iter (int) –

Return type:

None

getThreshold() → retval¶

Parameters:: self –
Return type:: float

setThreshold(threshold) → None¶

Parameters:

self –
threshold (float) –

Return type:

None

getRadiance() → retval¶

Parameters:: self –
Return type:: cv2.typing.MatLike

class cv2.CascadeClassifier¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, filename: str)¶

Parameters:

self –
filename (str) –

Return type:

None

detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) → objects¶

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

@param image Matrix of the type CV_8U containing an image where objects are detected.
@param objects Vector of rectangles where each rectangle contains the detected object, the
rectangles may be partially outside the original image.
@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
to retain it.
@param flags Parameter with the same meaning for an old cascade as in the function
cvHaarDetectObjects. It is not used for a new cascade.
@param minSize Minimum possible object size. Objects smaller than that are ignored.
@param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.

Parameters:

self –
image (cv2.typing.MatLike) –
scaleFactor (float) –
minNeighbors (int) –
flags (int) –
minSize (cv2.typing.Size) –
maxSize (cv2.typing.Size) –

Return type:

_typing.Sequence[cv2.typing.Rect]

detectMultiScale(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) → objects¶

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.

@param image Matrix of the type CV_8U containing an image where objects are detected.
@param objects Vector of rectangles where each rectangle contains the detected object, the
rectangles may be partially outside the original image.
@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
to retain it.
@param flags Parameter with the same meaning for an old cascade as in the function
cvHaarDetectObjects. It is not used for a new cascade.
@param minSize Minimum possible object size. Objects smaller than that are ignored.
@param maxSize Maximum possible object size. Objects larger than that are ignored. If `maxSize == minSize` model is evaluated on single scale.

Parameters:

self –
image (UMat) –
scaleFactor (float) –
minNeighbors (int) –
flags (int) –
minSize (cv2.typing.Size) –
maxSize (cv2.typing.Size) –

Return type:

_typing.Sequence[cv2.typing.Rect]

detectMultiScale2(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) → objects, numDetections¶

@overload @param image Matrix of the type CV_8U containing an image where objects are detected. @param objects Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image. @param numDetections Vector of detection numbers for the corresponding objects. An object’s number of detections is the number of neighboring positively classified rectangles that were joined together to form the object. @param scaleFactor Parameter specifying how much the image size is reduced at each image scale. @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have to retain it. @param flags Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade. @param minSize Minimum possible object size. Objects smaller than that are ignored. @param maxSize Maximum possible object size. Objects larger than that are ignored. If maxSize == minSize model is evaluated on single scale.

Parameters:

self –
image (cv2.typing.MatLike) –
scaleFactor (float) –
minNeighbors (int) –
flags (int) –
minSize (cv2.typing.Size) –
maxSize (cv2.typing.Size) –

Return type:

tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int]]

detectMultiScale2(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize]]]]]) → objects, numDetections¶

Parameters:

self –
image (UMat) –
scaleFactor (float) –
minNeighbors (int) –
flags (int) –
minSize (cv2.typing.Size) –
maxSize (cv2.typing.Size) –

Return type:

tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int]]

detectMultiScale3(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]) → objects, rejectLevels, levelWeights¶

@overload This function allows you to retrieve the final stage decision certainty of classification. For this, one needs to set outputRejectLevels on true and provide the rejectLevels and levelWeights parameter. For each resulting detection, levelWeights will then contain the certainty of classification at the final stage. This value can then be used to separate strong from weaker classifications.

A code sample on how to use it efficiently can be found below:
@code
Mat img;
vector<double> weights;
vector<int> levels;
vector<Rect> detections;
CascadeClassifier model("/path/to/your/model.xml");
model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
@endcode

Parameters:

self –
image (cv2.typing.MatLike) –
scaleFactor (float) –
minNeighbors (int) –
flags (int) –
minSize (cv2.typing.Size) –
maxSize (cv2.typing.Size) –
outputRejectLevels (bool) –

Return type:

tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int], _typing.Sequence[float]]

detectMultiScale3(image[, scaleFactor[, minNeighbors[, flags[, minSize[, maxSize[, outputRejectLevels]]]]]]) → objects, rejectLevels, levelWeights¶

A code sample on how to use it efficiently can be found below:
@code
Mat img;
vector<double> weights;
vector<int> levels;
vector<Rect> detections;
CascadeClassifier model("/path/to/your/model.xml");
model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
@endcode

Parameters:

self –
image (UMat) –
scaleFactor (float) –
minNeighbors (int) –
flags (int) –
minSize (cv2.typing.Size) –
maxSize (cv2.typing.Size) –
outputRejectLevels (bool) –

Return type:

tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int], _typing.Sequence[float]]

empty() → retval¶

Checks whether the classifier has been loaded.

Parameters:: self –
Return type:: bool

load(filename) → retval¶

Loads a classifier from a file.

@param filename Name of the file from which the classifier is loaded. The file may contain an old
HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
traincascade application.

Parameters:

self –
filename (str) –

Return type:

read(node) → retval¶

Reads a classifier from a FileStorage node.

@note The file may contain a new cascade classifier (trained by the traincascade application) only.

Parameters:

self –
node (FileNode) –

Return type:

isOldFormatCascade() → retval¶

Parameters:: self –
Return type:: bool

getOriginalWindowSize() → retval¶

Parameters:: self –
Return type:: cv2.typing.Size

getFeatureType() → retval¶

Parameters:: self –
Return type:: int

static convert(oldcascade, newcascade) → retval¶

Parameters:

oldcascade (str) –
newcascade (str) –

Return type:

class cv2.CirclesGridFinderParameters¶

__init__(self)¶

Parameters:: self –
Return type:: None

densityNeighborhoodSize: cv2.typing.Size2f¶

minDensity: float¶

kmeansAttempts: int¶

minDistanceToAddKeypoint: int¶

keypointScale: int¶

minGraphConfidence: float¶

vertexGain: float¶

vertexPenalty: float¶

existingVertexGain: float¶

edgeGain: float¶

edgePenalty: float¶

convexHullFactor: float¶

minRNGEdgeSwitchDist: float¶

squareSize: float¶

maxRectifiedDistance: float¶

class cv2.DISOpticalFlow¶

classmethod create([preset]) → retval¶

Creates an instance of DISOpticalFlow

@param preset one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM

Parameters:

cls –
preset (int) –

Return type:

DISOpticalFlow

getFinestScale() → retval¶

Finest level of the Gaussian pyramid on which the flow is computed (zero level corresponds to the original image resolution). The final flow is obtained by bilinear upscaling.

See also: setFinestScale

Parameters:: self –
Return type:: int

setFinestScale(val) → None¶

@copybrief getFinestScale @see getFinestScale

Parameters:

self –
val (int) –

Return type:

None

getPatchSize() → retval¶

Size of an image patch for matching (in pixels). Normally, default 8x8 patches work well enough in most cases.

See also: setPatchSize

Parameters:: self –
Return type:: int

setPatchSize(val) → None¶

@copybrief getPatchSize @see getPatchSize

Parameters:

self –
val (int) –

Return type:

None

getPatchStride() → retval¶

Stride between neighbor patches. Must be less than patch size. Lower values correspond to higher flow quality.

See also: setPatchStride

Parameters:: self –
Return type:: int

setPatchStride(val) → None¶

@copybrief getPatchStride @see getPatchStride

Parameters:

self –
val (int) –

Return type:

None

getGradientDescentIterations() → retval¶

Maximum number of gradient descent iterations in the patch inverse search stage. Higher values may improve quality in some cases.

See also: setGradientDescentIterations

Parameters:: self –
Return type:: int

setGradientDescentIterations(val) → None¶

@copybrief getGradientDescentIterations @see getGradientDescentIterations

Parameters:

self –
val (int) –

Return type:

None

getVariationalRefinementIterations() → retval¶

Number of fixed point iterations of variational refinement per scale. Set to zero to disable variational refinement completely. Higher values will typically result in more smooth and high-quality flow.

See also: setGradientDescentIterations

Parameters:: self –
Return type:: int

setVariationalRefinementIterations(val) → None¶

@copybrief getGradientDescentIterations @see getGradientDescentIterations

Parameters:

self –
val (int) –

Return type:

None

getVariationalRefinementAlpha() → retval¶

Weight of the smoothness term

See also: setVariationalRefinementAlpha

Parameters:: self –
Return type:: float

setVariationalRefinementAlpha(val) → None¶

@copybrief getVariationalRefinementAlpha @see getVariationalRefinementAlpha

Parameters:

self –
val (float) –

Return type:

None

getVariationalRefinementDelta() → retval¶

Weight of the color constancy term

See also: setVariationalRefinementDelta

Parameters:: self –
Return type:: float

setVariationalRefinementDelta(val) → None¶

@copybrief getVariationalRefinementDelta @see getVariationalRefinementDelta

Parameters:

self –
val (float) –

Return type:

None

getVariationalRefinementGamma() → retval¶

Weight of the gradient constancy term

See also: setVariationalRefinementGamma

Parameters:: self –
Return type:: float

setVariationalRefinementGamma(val) → None¶

@copybrief getVariationalRefinementGamma @see getVariationalRefinementGamma

Parameters:

self –
val (float) –

Return type:

None

getUseMeanNormalization() → retval¶

Whether to use mean-normalization of patches when computing patch distance. It is turned on by default as it typically provides a noticeable quality boost because of increased robustness to illumination variations. Turn it off if you are certain that your sequence doesn’t contain any changes in illumination.

See also: setUseMeanNormalization

Parameters:: self –
Return type:: bool

setUseMeanNormalization(val) → None¶

@copybrief getUseMeanNormalization @see getUseMeanNormalization

Parameters:

self –
val (bool) –

Return type:

None

getUseSpatialPropagation() → retval¶

Whether to use spatial propagation of good optical flow vectors. This option is turned on by default, as it tends to work better on average and can sometimes help recover from major errors introduced by the coarse-to-fine scheme employed by the DIS optical flow algorithm. Turning this option off can make the output flow field a bit smoother, however.

See also: setUseSpatialPropagation

Parameters:: self –
Return type:: bool

setUseSpatialPropagation(val) → None¶

@copybrief getUseSpatialPropagation @see getUseSpatialPropagation

Parameters:

self –
val (bool) –

Return type:

None

class cv2.DMatch¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, _queryIdx: int, _trainIdx: int, _distance: float)¶

Parameters:

self –
_queryIdx (int) –
_trainIdx (int) –
_distance (float) –

Return type:

None

__init__(self, _queryIdx: int, _trainIdx: int, _imgIdx: int, _distance: float)¶

Parameters:

self –
_queryIdx (int) –
_trainIdx (int) –
_imgIdx (int) –
_distance (float) –

Return type:

None

queryIdx: int¶

trainIdx: int¶

imgIdx: int¶

distance: float¶

class cv2.DenseOpticalFlow¶

calc(I0, I1, flow) → flow¶

Calculates an optical flow.

@param I0 first 8-bit single-channel input image.
@param I1 second input image of the same size and the same type as prev.
@param flow computed flow image that has the same size as prev and type CV_32FC2.

Parameters:

self –
I0 (cv2.typing.MatLike) –
I1 (cv2.typing.MatLike) –
flow (cv2.typing.MatLike) –

Return type:

cv2.typing.MatLike

calc(I0, I1, flow) → flow¶

Calculates an optical flow.

@param I0 first 8-bit single-channel input image.
@param I1 second input image of the same size and the same type as prev.
@param flow computed flow image that has the same size as prev and type CV_32FC2.

Parameters:

self –
I0 (UMat) –
I1 (UMat) –
flow (UMat) –

Return type:

collectGarbage() → None¶

Releases all inner buffers.

Parameters:: self –
Return type:: None

class cv2.DescriptorMatcher¶

classmethod create(descriptorMatcherType) → retval¶

Creates a descriptor matcher of a given type with the default parameters (using default constructor).

@param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
supported:
-   `BruteForce` (it uses L2 )
-   `BruteForce-L1`
-   `BruteForce-Hamming`
-   `BruteForce-Hamming(2)`
-   `FlannBased`

Parameters:

cls –
descriptorMatcherType (str) –

Return type:

DescriptorMatcher

classmethod create(descriptorMatcherType) → retval¶

Creates a descriptor matcher of a given type with the default parameters (using default constructor).

@param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
supported:
-   `BruteForce` (it uses L2 )
-   `BruteForce-L1`
-   `BruteForce-Hamming`
-   `BruteForce-Hamming(2)`
-   `FlannBased`

Parameters:

cls –
matcherType (DescriptorMatcher_MatcherType) –

Return type:

DescriptorMatcher

add(descriptors) → None¶

Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor collection.

If the collection is not empty, the new descriptors are added to existing train descriptors.

@param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
train image.

Parameters:

self –
descriptors (_typing.Sequence[cv2.typing.MatLike]) –

Return type:

None

add(descriptors) → None¶

Adds descriptors to train a CPU(trainDescCollectionis) or GPU(utrainDescCollectionis) descriptor collection.

If the collection is not empty, the new descriptors are added to existing train descriptors.

@param descriptors Descriptors to add. Each descriptors[i] is a set of descriptors from the same
train image.

Parameters:

self –
descriptors (_typing.Sequence[UMat]) –

Return type:

None

match(queryDescriptors, trainDescriptors[, mask]) → matches¶

Finds the best match for each descriptor from a query set.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.

In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.

@overload @param queryDescriptors Query set of descriptors. @param matches Matches. If a query descriptor is masked out in mask , no match is added for this descriptor. So, matches size may be smaller than the query descriptors count. @param masks Set of masks. Each masks[i] specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image trainDescCollection[i].

Parameters:

self –
queryDescriptors (cv2.typing.MatLike) –
trainDescriptors (cv2.typing.MatLike) –
mask (cv2.typing.MatLike | None) –

Return type:

_typing.Sequence[DMatch]

match(queryDescriptors, trainDescriptors[, mask]) → matches¶

Finds the best match for each descriptor from a query set.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.

In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.

Parameters:

self –
queryDescriptors (UMat) –
trainDescriptors (UMat) –
mask (UMat | None) –

Return type:

_typing.Sequence[DMatch]

match(queryDescriptors, trainDescriptors[, mask]) → matches¶

Finds the best match for each descriptor from a query set.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.

In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.

Parameters:

self –
queryDescriptors (cv2.typing.MatLike) –
masks (_typing.Sequence[cv2.typing.MatLike] | None) –

Return type:

_typing.Sequence[DMatch]

match(queryDescriptors, trainDescriptors[, mask]) → matches¶

Finds the best match for each descriptor from a query set.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Matches. If a query descriptor is masked out in mask , no match is added for this
descriptor. So, matches size may be smaller than the query descriptors count.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.

In the first variant of this method, the train descriptors are passed as an input argument. In the
second variant of the method, train descriptors collection that was set by DescriptorMatcher::add is
used. Optional mask (or masks) can be passed to specify which query and training descriptors can be
matched. Namely, queryDescriptors[i] can be matched with trainDescriptors[j] only if
mask.at\<uchar\>(i,j) is non-zero.

Parameters:

self –
queryDescriptors (UMat) –
masks (_typing.Sequence[UMat] | None) –

Return type:

_typing.Sequence[DMatch]

knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) → matches¶

Finds the k best matches for each descriptor from a query set.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.

These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.

@overload @param queryDescriptors Query set of descriptors. @param matches Matches. Each matches[i] is k or less matches for the same query descriptor. @param k Count of best matches found per each query descriptor or less if a query descriptor has less than k possible matches in total. @param masks Set of masks. Each masks[i] specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image trainDescCollection[i]. @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Parameters:

self –
queryDescriptors (cv2.typing.MatLike) –
trainDescriptors (cv2.typing.MatLike) –
k (int) –
mask (cv2.typing.MatLike | None) –
compactResult (bool) –

Return type:

_typing.Sequence[_typing.Sequence[DMatch]]

knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) → matches¶

Finds the k best matches for each descriptor from a query set.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.

These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.

Parameters:

self –
queryDescriptors (UMat) –
trainDescriptors (UMat) –
k (int) –
mask (UMat | None) –
compactResult (bool) –

Return type:

_typing.Sequence[_typing.Sequence[DMatch]]

knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) → matches¶

Finds the k best matches for each descriptor from a query set.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.

These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.

Parameters:

self –
queryDescriptors (cv2.typing.MatLike) –
k (int) –
masks (_typing.Sequence[cv2.typing.MatLike] | None) –
compactResult (bool) –

Return type:

_typing.Sequence[_typing.Sequence[DMatch]]

knnMatch(queryDescriptors, trainDescriptors, k[, mask[, compactResult]]) → matches¶

Finds the k best matches for each descriptor from a query set.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.
@param matches Matches. Each matches[i] is k or less matches for the same query descriptor.
@param k Count of best matches found per each query descriptor or less if a query descriptor has
less than k possible matches in total.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.

These extended variants of DescriptorMatcher::match methods find several best matches for each query
descriptor. The matches are returned in the distance increasing order. See DescriptorMatcher::match
for the details about query and train descriptors.

Parameters:

self –
queryDescriptors (UMat) –
k (int) –
masks (_typing.Sequence[UMat] | None) –
compactResult (bool) –

Return type:

_typing.Sequence[_typing.Sequence[DMatch]]

radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) → matches¶

For each query descriptor, finds the training descriptors not farther than the specified distance.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.

For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.

@overload @param queryDescriptors Query set of descriptors. @param matches Found matches. @param maxDistance Threshold for the distance between matched descriptors. Distance means here metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured in Pixels)! @param masks Set of masks. Each masks[i] specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image trainDescCollection[i]. @param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is false, the matches vector has the same size as queryDescriptors rows. If compactResult is true, the matches vector does not contain matches for fully masked-out query descriptors.

Parameters:

self –
queryDescriptors (cv2.typing.MatLike) –
trainDescriptors (cv2.typing.MatLike) –
maxDistance (float) –
mask (cv2.typing.MatLike | None) –
compactResult (bool) –

Return type:

_typing.Sequence[_typing.Sequence[DMatch]]

radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) → matches¶

For each query descriptor, finds the training descriptors not farther than the specified distance.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.

For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.

Parameters:

self –
queryDescriptors (UMat) –
trainDescriptors (UMat) –
maxDistance (float) –
mask (UMat | None) –
compactResult (bool) –

Return type:

_typing.Sequence[_typing.Sequence[DMatch]]

radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) → matches¶

For each query descriptor, finds the training descriptors not farther than the specified distance.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.

For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.

Parameters:

self –
queryDescriptors (cv2.typing.MatLike) –
maxDistance (float) –
masks (_typing.Sequence[cv2.typing.MatLike] | None) –
compactResult (bool) –

Return type:

_typing.Sequence[_typing.Sequence[DMatch]]

radiusMatch(queryDescriptors, trainDescriptors, maxDistance[, mask[, compactResult]]) → matches¶

For each query descriptor, finds the training descriptors not farther than the specified distance.

@param queryDescriptors Query set of descriptors.
@param trainDescriptors Train set of descriptors. This set is not added to the train descriptors
collection stored in the class object.
@param matches Found matches.
@param compactResult Parameter used when the mask (or masks) is not empty. If compactResult is
false, the matches vector has the same size as queryDescriptors rows. If compactResult is true,
the matches vector does not contain matches for fully masked-out query descriptors.
@param maxDistance Threshold for the distance between matched descriptors. Distance means here
metric distance (e.g. Hamming distance), not the distance between coordinates (which is measured
in Pixels)!
@param mask Mask specifying permissible matches between an input query and train matrices of
descriptors.

For each query descriptor, the methods find such training descriptors that the distance between the
query descriptor and the training descriptor is equal or smaller than maxDistance. Found matches are
returned in the distance increasing order.

Parameters:

self –
queryDescriptors (UMat) –
maxDistance (float) –
masks (_typing.Sequence[UMat] | None) –
compactResult (bool) –

Return type:

_typing.Sequence[_typing.Sequence[DMatch]]

write(fileName) → None¶

Parameters:

self –
fileName (str) –

Return type:

None

write(fileName) → None¶

Parameters:

self –
fs (FileStorage) –
name (str) –

Return type:

None

read(fileName) → None¶

Parameters:

self –
fileName (str) –

Return type:

None

read(fileName) → None¶

Parameters:

self –
arg1 (FileNode) –

Return type:

None

getTrainDescriptors() → retval¶

Returns a constant link to the train descriptor collection trainDescCollection .

Parameters:: self –
Return type:: _typing.Sequence[cv2.typing.MatLike]

clear() → None¶

Clears the train descriptor collections.

Parameters:: self –
Return type:: None

empty() → retval¶

Returns true if there are no train descriptors in the both collections.

Parameters:: self –
Return type:: bool

isMaskSupported() → retval¶

Returns true if the descriptor matcher supports masking permissible matches.

Parameters:: self –
Return type:: bool

train() → None¶

Trains a descriptor matcher

Trains a descriptor matcher (for example, the flann index). In all methods to match, the method
train() is run every time before matching. Some descriptor matchers (for example, BruteForceMatcher)
have an empty implementation of this method. Other matchers really train their inner structures (for
example, FlannBasedMatcher trains flann::Index ).

Parameters:: self –
Return type:: None

clone([emptyTrainData]) → retval¶

Clones the matcher.

@param emptyTrainData If emptyTrainData is false, the method creates a deep copy of the object,
that is, copies both parameters and train data. If emptyTrainData is true, the method creates an
object copy with the current parameters but with empty train data.

Parameters:

self –
emptyTrainData (bool) –

Return type:

DescriptorMatcher

class cv2.FaceDetectorYN¶

classmethod create(model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]) → retval¶

Creates an instance of face detector class with given parameters * * @param model the path to the requested model * @param config the path to the config file for compability, which is not requested for ONNX models * @param input_size the size of the input image * @param score_threshold the threshold to filter out bounding boxes of score smaller than the given value * @param nms_threshold the threshold to suppress bounding boxes of IoU bigger than the given value * @param top_k keep top K bboxes before NMS * @param backend_id the id of backend * @param target_id the id of target device @overload * * @param framework Name of origin framework * @param bufferModel A buffer with a content of binary file with weights * @param bufferConfig A buffer with a content of text file contains network configuration * @param input_size the size of the input image * @param score_threshold the threshold to filter out bounding boxes of score smaller than the given value * @param nms_threshold the threshold to suppress bounding boxes of IoU bigger than the given value * @param top_k keep top K bboxes before NMS * @param backend_id the id of backend * @param target_id the id of target device

Parameters:

cls –
model (str) –
config (str) –
input_size (cv2.typing.Size) –
score_threshold (float) –
nms_threshold (float) –
top_k (int) –
backend_id (int) –
target_id (int) –

Return type:

FaceDetectorYN

classmethod create(model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]) → retval¶

Parameters:

cls –
framework (str) –
bufferModel (numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) –
bufferConfig (numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]) –
input_size (cv2.typing.Size) –
score_threshold (float) –
nms_threshold (float) –
top_k (int) –
backend_id (int) –
target_id (int) –

Return type:

FaceDetectorYN

detect(image[, faces]) → retval, faces¶

Detects faces in the input image. Following is an example output.

 * ![image](pics/lena-face-detection.jpg)

 *  @param image an image to detect
 *  @param faces detection results stored in a 2D cv::Mat of shape [num_faces, 15]
 *  - 0-1: x, y of bbox top left corner
 *  - 2-3: width, height of bbox
 *  - 4-5: x, y of right eye (blue point in the example image)
 *  - 6-7: x, y of left eye (red point in the example image)
 *  - 8-9: x, y of nose tip (green point in the example image)
 *  - 10-11: x, y of right corner of mouth (pink point in the example image)
 *  - 12-13: x, y of left corner of mouth (yellow point in the example image)
 *  - 14: face score

Parameters:

self –
image (cv2.typing.MatLike) –
faces (cv2.typing.MatLike | None) –

Return type:

tuple[int, cv2.typing.MatLike]

detect(image[, faces]) → retval, faces¶

Detects faces in the input image. Following is an example output.

 * ![image](pics/lena-face-detection.jpg)

 *  @param image an image to detect
 *  @param faces detection results stored in a 2D cv::Mat of shape [num_faces, 15]
 *  - 0-1: x, y of bbox top left corner
 *  - 2-3: width, height of bbox
 *  - 4-5: x, y of right eye (blue point in the example image)
 *  - 6-7: x, y of left eye (red point in the example image)
 *  - 8-9: x, y of nose tip (green point in the example image)
 *  - 10-11: x, y of right corner of mouth (pink point in the example image)
 *  - 12-13: x, y of left corner of mouth (yellow point in the example image)
 *  - 14: face score

Parameters:

self –
image (UMat) –
faces (UMat | None) –

Return type:

tuple[int, UMat]

setInputSize(input_size) → None¶

Set the size for the network input, which overwrites the input size of creating model. Call this method when the size of input image does not match the input size when creating model * * @param input_size the size of the input image

Parameters:

self –
input_size (cv2.typing.Size) –

Return type:

None

getInputSize() → retval¶

Parameters:: self –
Return type:: cv2.typing.Size

setScoreThreshold(score_threshold) → None¶

Set the score threshold to filter out bounding boxes of score less than the given value * * @param score_threshold threshold for filtering out bounding boxes

Parameters:

self –
score_threshold (float) –

Return type:

None

getScoreThreshold() → retval¶

Parameters:: self –
Return type:: float

setNMSThreshold(nms_threshold) → None¶

Set the Non-maximum-suppression threshold to suppress bounding boxes that have IoU greater than the given value * * @param nms_threshold threshold for NMS operation

Parameters:

self –
nms_threshold (float) –

Return type:

None

getNMSThreshold() → retval¶

Parameters:: self –
Return type:: float

setTopK(top_k) → None¶

Set the number of bounding boxes preserved before NMS * * @param top_k the number of bounding boxes to preserve from top rank based on score

Parameters:

self –
top_k (int) –

Return type:

None

getTopK() → retval¶

Parameters:: self –
Return type:: int

class cv2.FaceRecognizerSF¶

classmethod create(model, config[, backend_id[, target_id]]) → retval¶

Creates an instance of this class with given parameters * @param model the path of the onnx model used for face recognition * @param config the path to the config file for compability, which is not requested for ONNX models * @param backend_id the id of backend * @param target_id the id of target device

Parameters:

cls –
model (str) –
config (str) –
backend_id (int) –
target_id (int) –

Return type:

FaceRecognizerSF

alignCrop(src_img, face_box[, aligned_img]) → aligned_img¶

Aligning image to put face on the standard position * @param src_img input image * @param face_box the detection result used for indicate face in input image * @param aligned_img output aligned image

Parameters:

self –
src_img (cv2.typing.MatLike) –
face_box (cv2.typing.MatLike) –
aligned_img (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

alignCrop(src_img, face_box[, aligned_img]) → aligned_img¶

Aligning image to put face on the standard position * @param src_img input image * @param face_box the detection result used for indicate face in input image * @param aligned_img output aligned image

Parameters:

self –
src_img (UMat) –
face_box (UMat) –
aligned_img (UMat | None) –

Return type:

feature(aligned_img[, face_feature]) → face_feature¶

Extracting face feature from aligned image * @param aligned_img input aligned image * @param face_feature output face feature

Parameters:

self –
aligned_img (cv2.typing.MatLike) –
face_feature (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

feature(aligned_img[, face_feature]) → face_feature¶

Extracting face feature from aligned image * @param aligned_img input aligned image * @param face_feature output face feature

Parameters:

self –
aligned_img (UMat) –
face_feature (UMat | None) –

Return type:

match(face_feature1, face_feature2[, dis_type]) → retval¶

Calculating the distance between two face features * @param face_feature1 the first input feature * @param face_feature2 the second input feature of the same size and the same type as face_feature1 * @param dis_type defining the similarity with optional values “FR_OSINE” or “FR_NORM_L2”

Parameters:

self –
face_feature1 (cv2.typing.MatLike) –
face_feature2 (cv2.typing.MatLike) –
dis_type (int) –

Return type:

match(face_feature1, face_feature2[, dis_type]) → retval¶

Parameters:

self –
face_feature1 (UMat) –
face_feature2 (UMat) –
dis_type (int) –

Return type:

class cv2.FarnebackOpticalFlow¶

classmethod create([numLevels[, pyrScale[, fastPyramids[, winSize[, numIters[, polyN[, polySigma[, flags]]]]]]]]) → retval¶

Parameters:

cls –
numLevels (int) –
pyrScale (float) –
fastPyramids (bool) –
winSize (int) –
numIters (int) –
polyN (int) –
polySigma (float) –
flags (int) –

Return type:

FarnebackOpticalFlow

getNumLevels() → retval¶

Parameters:: self –
Return type:: int

setNumLevels(numLevels) → None¶

Parameters:

self –
numLevels (int) –

Return type:

None

getPyrScale() → retval¶

Parameters:: self –
Return type:: float

setPyrScale(pyrScale) → None¶

Parameters:

self –
pyrScale (float) –

Return type:

None

getFastPyramids() → retval¶

Parameters:: self –
Return type:: bool

setFastPyramids(fastPyramids) → None¶

Parameters:

self –
fastPyramids (bool) –

Return type:

None

getWinSize() → retval¶

Parameters:: self –
Return type:: int

setWinSize(winSize) → None¶

Parameters:

self –
winSize (int) –

Return type:

None

getNumIters() → retval¶

Parameters:: self –
Return type:: int

setNumIters(numIters) → None¶

Parameters:

self –
numIters (int) –

Return type:

None

getPolyN() → retval¶

Parameters:: self –
Return type:: int

setPolyN(polyN) → None¶

Parameters:

self –
polyN (int) –

Return type:

None

getPolySigma() → retval¶

Parameters:: self –
Return type:: float

setPolySigma(polySigma) → None¶

Parameters:

self –
polySigma (float) –

Return type:

None

getFlags() → retval¶

Parameters:: self –
Return type:: int

setFlags(flags) → None¶

Parameters:

self –
flags (int) –

Return type:

None

class cv2.FastFeatureDetector¶

classmethod create([threshold[, nonmaxSuppression[, type]]]) → retval¶

Parameters:

cls –
threshold (int) –
nonmaxSuppression (bool) –
type (FastFeatureDetector_DetectorType) –

Return type:

FastFeatureDetector

setThreshold(threshold) → None¶

Parameters:

self –
threshold (int) –

Return type:

None

getThreshold() → retval¶

Parameters:: self –
Return type:: int

setNonmaxSuppression(f) → None¶

Parameters:

self –
f (bool) –

Return type:

None

getNonmaxSuppression() → retval¶

Parameters:: self –
Return type:: bool

setType(type) → None¶

Parameters:

self –
type (FastFeatureDetector_DetectorType) –

Return type:

None

getType() → retval¶

Parameters:: self –
Return type:: FastFeatureDetector_DetectorType

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

class cv2.Feature2D¶

detect(image[, mask]) → keypoints¶

Detects keypoints in an image (first variant) or image set (second variant).

@param image Image.
@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
of keypoints detected in images[i] .
@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
matrix with non-zero values in the region of interest.

@overload @param images Image set. @param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] . @param masks Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].

Parameters:

self –
image (cv2.typing.MatLike) –
mask (cv2.typing.MatLike | None) –

Return type:

_typing.Sequence[KeyPoint]

detect(image[, mask]) → keypoints¶

Detects keypoints in an image (first variant) or image set (second variant).

@param image Image.
@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
of keypoints detected in images[i] .
@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
matrix with non-zero values in the region of interest.

Parameters:

self –
image (UMat) –
mask (UMat | None) –

Return type:

_typing.Sequence[KeyPoint]

detect(image[, mask]) → keypoints¶

Detects keypoints in an image (first variant) or image set (second variant).

@param image Image.
@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
of keypoints detected in images[i] .
@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
matrix with non-zero values in the region of interest.

Parameters:

self –
images (_typing.Sequence[cv2.typing.MatLike]) –
masks (_typing.Sequence[cv2.typing.MatLike] | None) –

Return type:

_typing.Sequence[_typing.Sequence[KeyPoint]]

detect(image[, mask]) → keypoints¶

Detects keypoints in an image (first variant) or image set (second variant).

@param image Image.
@param keypoints The detected keypoints. In the second variant of the method keypoints[i] is a set
of keypoints detected in images[i] .
@param mask Mask specifying where to look for keypoints (optional). It must be a 8-bit integer
matrix with non-zero values in the region of interest.

Parameters:

self –
images (_typing.Sequence[UMat]) –
masks (_typing.Sequence[UMat] | None) –

Return type:

_typing.Sequence[_typing.Sequence[KeyPoint]]

compute(image, keypoints[, descriptors]) → keypoints, descriptors¶

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

@param image Image.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.

@overload

@param images Image set.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.

Parameters:

self –
image (cv2.typing.MatLike) –
keypoints (_typing.Sequence[KeyPoint]) –
descriptors (cv2.typing.MatLike | None) –

Return type:

tuple[_typing.Sequence[KeyPoint], cv2.typing.MatLike]

compute(image, keypoints[, descriptors]) → keypoints, descriptors¶

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

@param image Image.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.

@overload

@param images Image set.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.

Parameters:

self –
image (UMat) –
keypoints (_typing.Sequence[KeyPoint]) –
descriptors (UMat | None) –

Return type:

tuple[_typing.Sequence[KeyPoint], UMat]

compute(image, keypoints[, descriptors]) → keypoints, descriptors¶

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

@param image Image.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.

@overload

@param images Image set.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.

Parameters:

self –
images (_typing.Sequence[cv2.typing.MatLike]) –
keypoints (_typing.Sequence[_typing.Sequence[KeyPoint]]) –
descriptors (_typing.Sequence[cv2.typing.MatLike] | None) –

Return type:

tuple[_typing.Sequence[_typing.Sequence[KeyPoint]], _typing.Sequence[cv2.typing.MatLike]]

compute(image, keypoints[, descriptors]) → keypoints, descriptors¶

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

@param image Image.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.

@overload

@param images Image set.
@param keypoints Input collection of keypoints. Keypoints for which a descriptor cannot be
computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint
with several dominant orientations (for each orientation).
@param descriptors Computed descriptors. In the second variant of the method descriptors[i] are
descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the
descriptor for keypoint j-th keypoint.

Parameters:

self –
images (_typing.Sequence[UMat]) –
keypoints (_typing.Sequence[_typing.Sequence[KeyPoint]]) –
descriptors (_typing.Sequence[UMat] | None) –

Return type:

tuple[_typing.Sequence[_typing.Sequence[KeyPoint]], _typing.Sequence[UMat]]

detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) → keypoints, descriptors¶

Detects keypoints and computes the descriptors

Parameters:

self –
image (cv2.typing.MatLike) –
mask (cv2.typing.MatLike) –
descriptors (cv2.typing.MatLike | None) –
useProvidedKeypoints (bool) –

Return type:

tuple[_typing.Sequence[KeyPoint], cv2.typing.MatLike]

detectAndCompute(image, mask[, descriptors[, useProvidedKeypoints]]) → keypoints, descriptors¶

Detects keypoints and computes the descriptors

Parameters:

self –
image (UMat) –
mask (UMat) –
descriptors (UMat | None) –
useProvidedKeypoints (bool) –

Return type:

tuple[_typing.Sequence[KeyPoint], UMat]

write(fileName) → None¶

Parameters:

self –
fileName (str) –

Return type:

None

write(fileName) → None¶

Parameters:

self –
fs (FileStorage) –
name (str) –

Return type:

None

read(fileName) → None¶

Parameters:

self –
fileName (str) –

Return type:

None

read(fileName) → None¶

Parameters:

self –
arg1 (FileNode) –

Return type:

None

descriptorSize() → retval¶

Parameters:: self –
Return type:: int

descriptorType() → retval¶

Parameters:: self –
Return type:: int

defaultNorm() → retval¶

Parameters:: self –
Return type:: int

empty() → retval¶

Parameters:: self –
Return type:: bool

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

class cv2.FileNode¶

__init__(self)¶

Parameters:: self –
Return type:: None

getNode(nodename) → retval¶

@overload @param nodename Name of an element in the mapping node.

Parameters:

self –
nodename (str) –

Return type:

at(i) → retval¶

@overload @param i Index of an element in the sequence node.

Parameters:

self –
i (int) –

Return type:

keys() → retval¶

Returns keys of a mapping node. @returns Keys of a mapping node.

Parameters:: self –
Return type:: _typing.Sequence[str]

type() → retval¶

Returns type of the node. @returns Type of the node. See FileNode::Type

Parameters:: self –
Return type:: int

empty() → retval¶

Parameters:: self –
Return type:: bool

isNone() → retval¶

Parameters:: self –
Return type:: bool

isSeq() → retval¶

Parameters:: self –
Return type:: bool

isMap() → retval¶

Parameters:: self –
Return type:: bool

isInt() → retval¶

Parameters:: self –
Return type:: bool

isReal() → retval¶

Parameters:: self –
Return type:: bool

isString() → retval¶

Parameters:: self –
Return type:: bool

isNamed() → retval¶

Parameters:: self –
Return type:: bool

name() → retval¶

Parameters:: self –
Return type:: str

size() → retval¶

Parameters:: self –
Return type:: int

rawSize() → retval¶

Parameters:: self –
Return type:: int

real() → retval¶

Internal method used when reading FileStorage. Sets the type (int, real or string) and value of the previously created node.

Parameters:: self –
Return type:: float

string() → retval¶

Parameters:: self –
Return type:: str

mat() → retval¶

Parameters:: self –
Return type:: cv2.typing.MatLike

class cv2.FileStorage¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, filename: str, flags: int, encoding: str = ...)¶

Parameters:

self –
filename (str) –
flags (int) –
encoding (str) –

Return type:

None

write(name, val) → None¶

@brief Simplified writing API to use with bindings.
- @param name Name of the written object. When writing to sequences (a.k.a. “arrays”), pass an empty string.
- @param val Value of the written object.

Parameters:

self –
name (str) –
val (int) –

Return type:

None

write(name, val) → None¶

@brief Simplified writing API to use with bindings.
- @param name Name of the written object. When writing to sequences (a.k.a. “arrays”), pass an empty string.
- @param val Value of the written object.

Parameters:

self –
name (str) –
val (float) –

Return type:

None

write(name, val) → None¶

@brief Simplified writing API to use with bindings.
- @param name Name of the written object. When writing to sequences (a.k.a. “arrays”), pass an empty string.
- @param val Value of the written object.

Parameters:

self –
name (str) –
val (str) –

Return type:

None

write(name, val) → None¶

@brief Simplified writing API to use with bindings.
- @param name Name of the written object. When writing to sequences (a.k.a. “arrays”), pass an empty string.
- @param val Value of the written object.

Parameters:

self –
name (str) –
val (cv2.typing.MatLike) –

Return type:

None

write(name, val) → None¶

@brief Simplified writing API to use with bindings.
- @param name Name of the written object. When writing to sequences (a.k.a. “arrays”), pass an empty string.
- @param val Value of the written object.

Parameters:

self –
name (str) –
val (_typing.Sequence[str]) –

Return type:

None

open(filename, flags[, encoding]) → retval¶

Opens a file.

 See description of parameters in FileStorage::FileStorage. The method calls FileStorage::release
 before opening the file.
 @param filename Name of the file to open or the text string to read the data from.
 Extension of the file (.xml, .yml/.yaml or .json) determines its format (XML, YAML or JSON
 respectively). Also you can append .gz to work with compressed files, for example myHugeMatrix.xml.gz. If both
 FileStorage::WRITE and FileStorage::MEMORY flags are specified, source is used just to specify
 the output file format (e.g. mydata.xml, .yml etc.). A file name can also contain parameters.
 You can use this format, "*?base64" (e.g. "file.json?base64" (case sensitive)), as an alternative to
 FileStorage::BASE64 flag.
 @param flags Mode of operation. One of FileStorage::Mode
 @param encoding Encoding of the file. Note that UTF-16 XML encoding is not supported currently and
 you should use 8-bit encoding instead of it.

Parameters:

self –
filename (str) –
flags (int) –
encoding (str) –

Return type:

isOpened() → retval¶

Checks whether the file is opened.

 @returns true if the object is associated with the current file and false otherwise. It is a
 good practice to call this method after you tried to open a file.

Parameters:: self –
Return type:: bool

release() → None¶

Closes the file and releases all the memory buffers.

 Call this method after all I/O operations with the storage are finished.

Parameters:: self –
Return type:: None

releaseAndGetString() → retval¶

Closes the file and releases all the memory buffers.

 Call this method after all I/O operations with the storage are finished. If the storage was
 opened for writing data and FileStorage::WRITE was specified

Parameters:: self –
Return type:: str

getFirstTopLevelNode() → retval¶

Returns the first element of the top-level mapping. @returns The first element of the top-level mapping.

Parameters:: self –
Return type:: FileNode

root([streamidx]) → retval¶

Returns the top-level mapping @param streamidx Zero-based index of the stream. In most cases there is only one stream in the file. However, YAML supports multiple streams and so there can be several. @returns The top-level mapping.

Parameters:

self –
streamidx (int) –

Return type:

getNode(nodename) → retval¶

@overload

Parameters:

self –
nodename (str) –

Return type:

writeComment(comment[, append]) → None¶

Writes a comment.

 The function writes a comment into file storage. The comments are skipped when the storage is read.
 @param comment The written comment, single-line or multi-line
 @param append If true, the function tries to put the comment at the end of current line.
 Else if the comment is multi-line, or if it does not fit at the end of the current
 line, the comment starts a new line.

Parameters:

self –
comment (str) –
append (bool) –

Return type:

None

startWriteStruct(name, flags[, typeName]) → None¶

Starts to write a nested structure (sequence or a mapping). @param name name of the structure. When writing to sequences (a.k.a. “arrays”), pass an empty string. @param flags type of the structure (FileNode::MAP or FileNode::SEQ (both with optional FileNode::FLOW)). @param typeName optional name of the type you store. The effect of setting this depends on the storage format. I.e. if the format has a specification for storing type information, this parameter is used.

Parameters:

self –
name (str) –
flags (int) –
typeName (str) –

Return type:

None

endWriteStruct() → None¶

Finishes writing nested structure (should pair startWriteStruct())

Parameters:: self –
Return type:: None

getFormat() → retval¶

Returns the current format. * @returns The current format, see FileStorage::Mode

Parameters:: self –
Return type:: int

class cv2.FlannBasedMatcher¶

classmethod create() → retval¶

Parameters:: cls –
Return type:: FlannBasedMatcher

__init__(self, indexParams: cv2.typing.IndexParams = ..., searchParams: cv2.typing.SearchParams = ...)¶

Parameters:

self –
indexParams (cv2.typing.IndexParams) –
searchParams (cv2.typing.SearchParams) –

Return type:

None

class cv2.GArrayDesc¶

class cv2.GArrayT¶

__init__(self, type: cv2.gapi.ArgType)¶

Parameters:

self –
type (cv2.gapi.ArgType) –

Return type:

None

type() → retval¶

Parameters:: self –
Return type:: cv2.gapi.ArgType

class cv2.GCompileArg¶

__init__(self, arg: GKernelPackage)¶

Parameters:

self –
arg (GKernelPackage) –

Return type:

None

__init__(self, arg: cv2.gapi.GNetPackage)¶

Parameters:

self –
arg (cv2.gapi.GNetPackage) –

Return type:

None

__init__(self, arg: cv2.gapi.streaming.queue_capacity)¶

Parameters:

self –
arg (cv2.gapi.streaming.queue_capacity) –

Return type:

None

__init__(self, arg: cv2.gapi.ot.ObjectTrackerParams)¶

Parameters:

self –
arg (cv2.gapi.ot.ObjectTrackerParams) –

Return type:

None

class cv2.GComputation¶

__init__(self, ins: cv2.typing.GProtoInputArgs, outs: cv2.typing.GProtoOutputArgs)¶

Parameters:

self –
ins (cv2.typing.GProtoInputArgs) –
outs (cv2.typing.GProtoOutputArgs) –

Return type:

None

__init__(self, in_: GMat, out: GMat)¶

Parameters:

self –
in_ (GMat) –
out (GMat) –

Return type:

None

__init__(self, in_: GMat, out: GScalar)¶

Parameters:

self –
in_ (GMat) –
out (GScalar) –

Return type:

None

__init__(self, in1: GMat, in2: GMat, out: GMat)¶

Parameters:

self –
in1 (GMat) –
in2 (GMat) –
out (GMat) –

Return type:

None

compileStreaming(in_metas[, args]) → retval¶

@brief Compile the computation for streaming mode. *
- This method triggers compilation process and produces a new
- GStreamingCompiled object which then can process video stream
- data of the given format. Passing a stream in a different
- format to the compiled computation will generate a run-time
- exception.
- @param in_metas vector of input metadata configuration. Grab
- metadata from real data objects (like cv::Mat or cv::Scalar)
- using cv::descr_of(), or create it on your own.
- @param args compilation arguments for this compilation
- process. Compilation arguments directly affect what kind of
- executable object would be produced, e.g. which kernels (and
- thus, devices) would be used to execute computation.
- @return GStreamingCompiled, a streaming-oriented executable
- computation compiled specifically for the given input
- parameters.
- @sa @ref gapi_compile_args
@brief Compile the computation for streaming mode. *
- This method triggers compilation process and produces a new
- GStreamingCompiled object which then can process video stream
- data in any format. Underlying mechanisms will be adjusted to
- every new input video stream automatically, but please note that
- not all existing backends support this (see reshape()).
- @param args compilation arguments for this compilation
- process. Compilation arguments directly affect what kind of
- executable object would be produced, e.g. which kernels (and
- thus, devices) would be used to execute computation.
- @return GStreamingCompiled, a streaming-oriented executable
- computation compiled for any input image format.
- @sa @ref gapi_compile_args

Parameters:

self –
in_metas (_typing.Sequence[cv2.typing.GMetaArg]) –
args (_typing.Sequence[GCompileArg]) –

Return type:

GStreamingCompiled

compileStreaming(in_metas[, args]) → retval¶

@brief Compile the computation for streaming mode. *
- This method triggers compilation process and produces a new
- GStreamingCompiled object which then can process video stream
- data of the given format. Passing a stream in a different
- format to the compiled computation will generate a run-time
- exception.
- @param in_metas vector of input metadata configuration. Grab
- metadata from real data objects (like cv::Mat or cv::Scalar)
- using cv::descr_of(), or create it on your own.
- @param args compilation arguments for this compilation
- process. Compilation arguments directly affect what kind of
- executable object would be produced, e.g. which kernels (and
- thus, devices) would be used to execute computation.
- @return GStreamingCompiled, a streaming-oriented executable
- computation compiled specifically for the given input
- parameters.
- @sa @ref gapi_compile_args
@brief Compile the computation for streaming mode. *
- This method triggers compilation process and produces a new
- GStreamingCompiled object which then can process video stream
- data in any format. Underlying mechanisms will be adjusted to
- every new input video stream automatically, but please note that
- not all existing backends support this (see reshape()).
- @param args compilation arguments for this compilation
- process. Compilation arguments directly affect what kind of
- executable object would be produced, e.g. which kernels (and
- thus, devices) would be used to execute computation.
- @return GStreamingCompiled, a streaming-oriented executable
- computation compiled for any input image format.
- @sa @ref gapi_compile_args

Parameters:

self –
args (_typing.Sequence[GCompileArg]) –

Return type:

GStreamingCompiled

compileStreaming(in_metas[, args]) → retval¶

@brief Compile the computation for streaming mode. *
- This method triggers compilation process and produces a new
- GStreamingCompiled object which then can process video stream
- data of the given format. Passing a stream in a different
- format to the compiled computation will generate a run-time
- exception.
- @param in_metas vector of input metadata configuration. Grab
- metadata from real data objects (like cv::Mat or cv::Scalar)
- using cv::descr_of(), or create it on your own.
- @param args compilation arguments for this compilation
- process. Compilation arguments directly affect what kind of
- executable object would be produced, e.g. which kernels (and
- thus, devices) would be used to execute computation.
- @return GStreamingCompiled, a streaming-oriented executable
- computation compiled specifically for the given input
- parameters.
- @sa @ref gapi_compile_args
@brief Compile the computation for streaming mode. *
- This method triggers compilation process and produces a new
- GStreamingCompiled object which then can process video stream
- data in any format. Underlying mechanisms will be adjusted to
- every new input video stream automatically, but please note that
- not all existing backends support this (see reshape()).
- @param args compilation arguments for this compilation
- process. Compilation arguments directly affect what kind of
- executable object would be produced, e.g. which kernels (and
- thus, devices) would be used to execute computation.
- @return GStreamingCompiled, a streaming-oriented executable
- computation compiled for any input image format.
- @sa @ref gapi_compile_args

Parameters:

self –
callback (cv2.typing.ExtractMetaCallback) –
args (_typing.Sequence[GCompileArg]) –

Return type:

GStreamingCompiled

apply(callback[, args]) → retval¶

@brief Compile graph on-the-fly and immediately execute it on
- the inputs data vectors.
- Number of input/output data objects must match GComputation’s
- protocol, also types of host data objects (cv::Mat, cv::Scalar)
- must match the shapes of data objects from protocol (cv::GMat,
- cv::GScalar). If there’s a mismatch, a run-time exception will
- be generated.
- Internally, a cv::GCompiled object is created for the given
- input format configuration, which then is executed on the input
- data immediately. cv::GComputation caches compiled objects
- produced within apply() – if this method would be called next
- time with the same input parameters (image formats, image
- resolution, etc), the underlying compiled graph will be reused
- without recompilation. If new metadata doesn’t match the cached
- one, the underlying compiled graph is regenerated.
- @note compile() always triggers a compilation process and
- produces a new GCompiled object regardless if a similar one has
- been cached via apply() or not.
- @param ins vector of input data to process. Don’t create
- GRunArgs object manually, use cv::gin() wrapper instead.
- @param outs vector of output data to fill results in. cv::Mat
- objects may be empty in this vector, G-API will automatically
- initialize it with the required format & dimensions. Don’t
- create GRunArgsP object manually, use cv::gout() wrapper instead.
- @param args a list of compilation arguments to pass to the
- underlying compilation process. Don’t create GCompileArgs
- object manually, use cv::compile_args() wrapper instead.
- @sa @ref gapi_data_objects, @ref gapi_compile_args

Parameters:

self –
callback (cv2.typing.ExtractArgsCallback) –
args (_typing.Sequence[GCompileArg]) –

Return type:

_typing.Sequence[cv2.typing.GRunArg]

class cv2.GFTTDetector¶

classmethod create([maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) → retval¶

Parameters:

cls –
maxCorners (int) –
qualityLevel (float) –
minDistance (float) –
blockSize (int) –
useHarrisDetector (bool) –
k (float) –

Return type:

GFTTDetector

classmethod create([maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) → retval¶

Parameters:

cls –
maxCorners (int) –
qualityLevel (float) –
minDistance (float) –
blockSize (int) –
gradiantSize (int) –
useHarrisDetector (bool) –
k (float) –

Return type:

GFTTDetector

setMaxFeatures(maxFeatures) → None¶

Parameters:

self –
maxFeatures (int) –

Return type:

None

getMaxFeatures() → retval¶

Parameters:: self –
Return type:: int

setQualityLevel(qlevel) → None¶

Parameters:

self –
qlevel (float) –

Return type:

None

getQualityLevel() → retval¶

Parameters:: self –
Return type:: float

setMinDistance(minDistance) → None¶

Parameters:

self –
minDistance (float) –

Return type:

None

getMinDistance() → retval¶

Parameters:: self –
Return type:: float

setBlockSize(blockSize) → None¶

Parameters:

self –
blockSize (int) –

Return type:

None

getBlockSize() → retval¶

Parameters:: self –
Return type:: int

setGradientSize(gradientSize_) → None¶

Parameters:

self –
gradientSize_ (int) –

Return type:

None

getGradientSize() → retval¶

Parameters:: self –
Return type:: int

setHarrisDetector(val) → None¶

Parameters:

self –
val (bool) –

Return type:

None

getHarrisDetector() → retval¶

Parameters:: self –
Return type:: bool

setK(k) → None¶

Parameters:

self –
k (float) –

Return type:

None

getK() → retval¶

Parameters:: self –
Return type:: float

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

class cv2.GFrame¶

__init__(self)¶

Parameters:: self –
Return type:: None

class cv2.GInferInputs¶

setInput(name, value) → retval¶

Parameters:

self –
name (str) –
value (GMat) –

Return type:

GInferInputs

setInput(name, value) → retval¶

Parameters:

self –
name (str) –
value (GFrame) –

Return type:

GInferInputs

__init__(self)¶

Parameters:: self –
Return type:: None

class cv2.GInferListInputs¶

setInput(name, value) → retval¶

Parameters:

self –
name (str) –
value (GArrayT) –

Return type:

GInferListInputs

setInput(name, value) → retval¶

Parameters:

self –
name (str) –
value (GArrayT) –

Return type:

GInferListInputs

__init__(self)¶

Parameters:: self –
Return type:: None

class cv2.GInferListOutputs¶

__init__(self)¶

Parameters:: self –
Return type:: None

at(name) → retval¶

Parameters:

self –
name (str) –

Return type:

GArrayT

class cv2.GInferOutputs¶

__init__(self)¶

Parameters:: self –
Return type:: None

at(name) → retval¶

Parameters:

self –
name (str) –

Return type:

GMat

class cv2.GKernelPackage¶

size() → retval¶

@brief Returns total number of kernels * in the package (across all backends included) * * @return a number of kernels in the package

Parameters:: self –
Return type:: int

class cv2.GMat¶

__init__(self)¶

Parameters:: self –
Return type:: None

class cv2.GMatDesc¶

depth()¶

Parameters:: self –
Return type:: int

chan()¶

Parameters:: self –
Return type:: int

size()¶

Parameters:: self –
Return type:: cv2.typing.Size

planar()¶

Parameters:: self –
Return type:: bool

dims()¶

Parameters:: self –
Return type:: _typing.Sequence[int]

__init__(self, d: int, c: int, s: cv2.typing.Size, p: bool = ...)¶

Parameters:

self –
d (int) –
c (int) –
s (cv2.typing.Size) –
p (bool) –

Return type:

None

__init__(self, d: int, dd: _typing.Sequence[int])¶

Parameters:

self –
d (int) –
dd (_typing.Sequence[int]) –

Return type:

None

__init__(self, d: int, dd: _typing.Sequence[int])¶

Parameters:

self –
d (int) –
dd (_typing.Sequence[int]) –

Return type:

None

__init__(self)¶

Parameters:: self –
Return type:: None

withSizeDelta(delta) → retval¶

Parameters:

self –
delta (cv2.typing.Size) –

Return type:

withSizeDelta(delta) → retval¶

Parameters:

self –
dx (int) –
dy (int) –

Return type:

asPlanar() → retval¶

Parameters:: self –
Return type:: GMatDesc

asPlanar() → retval¶

Parameters:

self –
planes (int) –

Return type:

withSize(sz) → retval¶

Parameters:

self –
sz (cv2.typing.Size) –

Return type:

withDepth(ddepth) → retval¶

Parameters:

self –
ddepth (int) –

Return type:

withType(ddepth, dchan) → retval¶

Parameters:

self –
ddepth (int) –
dchan (int) –

Return type:

asInterleaved() → retval¶

Parameters:: self –
Return type:: GMatDesc

class cv2.GOpaqueDesc¶

class cv2.GOpaqueT¶

__init__(self, type: cv2.gapi.ArgType)¶

Parameters:

self –
type (cv2.gapi.ArgType) –

Return type:

None

type() → retval¶

Parameters:: self –
Return type:: cv2.gapi.ArgType

class cv2.GScalar¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, s: cv2.typing.Scalar)¶

Parameters:

self –
s (cv2.typing.Scalar) –

Return type:

None

class cv2.GScalarDesc¶

class cv2.GStreamingCompiled¶

__init__(self)¶

Parameters:: self –
Return type:: None

setSource(callback) → None¶

@brief Specify the input data to GStreamingCompiled for
- processing, a generic version.
- Use gin() to create an input parameter vector.
- Input vectors must have the same number of elements as defined
- in the cv::GComputation protocol (at the moment of its
- construction). Shapes of elements also must conform to protocol
- (e.g. cv::Mat needs to be passed where cv::GMat has been
- declared as input, and so on). Run-time exception is generated
- on type mismatch.
- In contrast with regular GCompiled, user can also pass an
- object of type GVideoCapture for a GMat parameter of the parent
- GComputation. The compiled pipeline will start fetching data
- from that GVideoCapture and feeding it into the
- pipeline. Pipeline stops when a GVideoCapture marks end of the
- stream (or when stop() is called).
- Passing a regular Mat for a GMat parameter makes it “infinite”
- source – pipeline may run forever feeding with this Mat until
- stopped explicitly.
- Currently only a single GVideoCapture is supported as input. If
- the parent GComputation is declared with multiple input GMat’s,
- one of those can be specified as GVideoCapture but all others
- must be regular Mat objects.
- Throws if pipeline is already running. Use stop() and then
- setSource() to run the graph on a new video stream.
- @note This method is not thread-safe (with respect to the user
- side) at the moment. Protect the access if
- start()/stop()/setSource() may be called on the same object in
- multiple threads in your application.
- @param ins vector of inputs to process.
- @sa gin

Parameters:

self –
callback (cv2.typing.ExtractArgsCallback) –

Return type:

None

start() → None¶

@brief Start the pipeline execution. *
- Use pull()/try_pull() to obtain data. Throws an exception if
- a video source was not specified.
- setSource() must be called first, even if the pipeline has been
- working already and then stopped (explicitly via stop() or due
- stream completion)
- @note This method is not thread-safe (with respect to the user
- side) at the moment. Protect the access if
- start()/stop()/setSource() may be called on the same object in
- multiple threads in your application.

Parameters:: self –
Return type:: None

pull() → retval¶

@brief Get the next processed frame from the pipeline. *
- Use gout() to create an output parameter vector.
- Output vectors must have the same number of elements as defined
- in the cv::GComputation protocol (at the moment of its
- construction). Shapes of elements also must conform to protocol
- (e.g. cv::Mat needs to be passed where cv::GMat has been
- declared as output, and so on). Run-time exception is generated
- on type mismatch.
- This method writes new data into objects passed via output
- vector. If there is no data ready yet, this method blocks. Use
- try_pull() if you need a non-blocking version.
- @param outs vector of output parameters to obtain.
- @return true if next result has been obtained,
- false marks end of the stream.

Parameters:: self –
Return type:: tuple[bool, _typing.Sequence[cv2.typing.GRunArg] | _typing.Sequence[cv2.typing.GOptRunArg]]

stop() → None¶

@brief Stop (abort) processing the pipeline. *
- Note - it is not pause but a complete stop. Calling start()
- will cause G-API to start processing the stream from the early beginning.
- Throws if the pipeline is not running.

Parameters:: self –
Return type:: None

running() → retval¶

@brief Test if the pipeline is running. *
- @note This method is not thread-safe (with respect to the user
- side) at the moment. Protect the access if
- start()/stop()/setSource() may be called on the same object in
- multiple threads in your application.
- @return true if the current stream is not over yet.

Parameters:: self –
Return type:: bool

class cv2.GeneralizedHough¶

setTemplate(templ[, templCenter]) → None¶

Parameters:

self –
templ (cv2.typing.MatLike) –
templCenter (cv2.typing.Point) –

Return type:

None

setTemplate(templ[, templCenter]) → None¶

Parameters:

self –
templ (UMat) –
templCenter (cv2.typing.Point) –

Return type:

None

setTemplate(templ[, templCenter]) → None¶

Parameters:

self –
edges (cv2.typing.MatLike) –
dx (cv2.typing.MatLike) –
dy (cv2.typing.MatLike) –
templCenter (cv2.typing.Point) –

Return type:

None

setTemplate(templ[, templCenter]) → None¶

Parameters:

self –
edges (UMat) –
dx (UMat) –
dy (UMat) –
templCenter (cv2.typing.Point) –

Return type:

None

detect(image[, positions[, votes]]) → positions, votes¶

Parameters:

self –
image (cv2.typing.MatLike) –
positions (cv2.typing.MatLike | None) –
votes (cv2.typing.MatLike | None) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

detect(image[, positions[, votes]]) → positions, votes¶

Parameters:

self –
image (UMat) –
positions (UMat | None) –
votes (UMat | None) –

Return type:

detect(image[, positions[, votes]]) → positions, votes¶

Parameters:

self –
edges (cv2.typing.MatLike) –
dx (cv2.typing.MatLike) –
dy (cv2.typing.MatLike) –
positions (cv2.typing.MatLike | None) –
votes (cv2.typing.MatLike | None) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

detect(image[, positions[, votes]]) → positions, votes¶

Parameters:

self –
edges (UMat) –
dx (UMat) –
dy (UMat) –
positions (UMat | None) –
votes (UMat | None) –

Return type:

setCannyLowThresh(cannyLowThresh) → None¶

Parameters:

self –
cannyLowThresh (int) –

Return type:

None

getCannyLowThresh() → retval¶

Parameters:: self –
Return type:: int

setCannyHighThresh(cannyHighThresh) → None¶

Parameters:

self –
cannyHighThresh (int) –

Return type:

None

getCannyHighThresh() → retval¶

Parameters:: self –
Return type:: int

setMinDist(minDist) → None¶

Parameters:

self –
minDist (float) –

Return type:

None

getMinDist() → retval¶

Parameters:: self –
Return type:: float

setDp(dp) → None¶

Parameters:

self –
dp (float) –

Return type:

None

getDp() → retval¶

Parameters:: self –
Return type:: float

setMaxBufferSize(maxBufferSize) → None¶

Parameters:

self –
maxBufferSize (int) –

Return type:

None

getMaxBufferSize() → retval¶

Parameters:: self –
Return type:: int

class cv2.GeneralizedHoughBallard¶

setLevels(levels) → None¶

Parameters:

self –
levels (int) –

Return type:

None

getLevels() → retval¶

Parameters:: self –
Return type:: int

setVotesThreshold(votesThreshold) → None¶

Parameters:

self –
votesThreshold (int) –

Return type:

None

getVotesThreshold() → retval¶

Parameters:: self –
Return type:: int

class cv2.GeneralizedHoughGuil¶

setXi(xi) → None¶

Parameters:

self –
xi (float) –

Return type:

None

getXi() → retval¶

Parameters:: self –
Return type:: float

setLevels(levels) → None¶

Parameters:

self –
levels (int) –

Return type:

None

getLevels() → retval¶

Parameters:: self –
Return type:: int

setAngleEpsilon(angleEpsilon) → None¶

Parameters:

self –
angleEpsilon (float) –

Return type:

None

getAngleEpsilon() → retval¶

Parameters:: self –
Return type:: float

setMinAngle(minAngle) → None¶

Parameters:

self –
minAngle (float) –

Return type:

None

getMinAngle() → retval¶

Parameters:: self –
Return type:: float

setMaxAngle(maxAngle) → None¶

Parameters:

self –
maxAngle (float) –

Return type:

None

getMaxAngle() → retval¶

Parameters:: self –
Return type:: float

setAngleStep(angleStep) → None¶

Parameters:

self –
angleStep (float) –

Return type:

None

getAngleStep() → retval¶

Parameters:: self –
Return type:: float

setAngleThresh(angleThresh) → None¶

Parameters:

self –
angleThresh (int) –

Return type:

None

getAngleThresh() → retval¶

Parameters:: self –
Return type:: int

setMinScale(minScale) → None¶

Parameters:

self –
minScale (float) –

Return type:

None

getMinScale() → retval¶

Parameters:: self –
Return type:: float

setMaxScale(maxScale) → None¶

Parameters:

self –
maxScale (float) –

Return type:

None

getMaxScale() → retval¶

Parameters:: self –
Return type:: float

setScaleStep(scaleStep) → None¶

Parameters:

self –
scaleStep (float) –

Return type:

None

getScaleStep() → retval¶

Parameters:: self –
Return type:: float

setScaleThresh(scaleThresh) → None¶

Parameters:

self –
scaleThresh (int) –

Return type:

None

getScaleThresh() → retval¶

Parameters:: self –
Return type:: int

setPosThresh(posThresh) → None¶

Parameters:

self –
posThresh (int) –

Return type:

None

getPosThresh() → retval¶

Parameters:: self –
Return type:: int

class cv2.GraphicalCodeDetector¶

detect(img[, points]) → retval, points¶

Detects graphical code in image and returns the quadrangle containing the code. @param img grayscale or color (BGR) image containing (or not) graphical code. @param points Output vector of vertices of the minimum-area quadrangle containing the code.

Parameters:

self –
img (cv2.typing.MatLike) –
points (cv2.typing.MatLike | None) –

Return type:

tuple[bool, cv2.typing.MatLike]

detect(img[, points]) → retval, points¶

Parameters:

self –
img (UMat) –
points (UMat | None) –

Return type:

decode(img, points[, straight_code]) → retval, straight_code¶

Decodes graphical code in image once it’s found by the detect() method.

 Returns UTF8-encoded output string or empty string if the code cannot be decoded.
 @param img grayscale or color (BGR) image containing graphical code.
 @param points Quadrangle vertices found by detect() method (or some other algorithm).
 @param straight_code The optional output image containing binarized code, will be empty if not found.

Parameters:

self –
img (cv2.typing.MatLike) –
points (cv2.typing.MatLike) –
straight_code (cv2.typing.MatLike | None) –

Return type:

tuple[str, cv2.typing.MatLike]

decode(img, points[, straight_code]) → retval, straight_code¶

Decodes graphical code in image once it’s found by the detect() method.

 Returns UTF8-encoded output string or empty string if the code cannot be decoded.
 @param img grayscale or color (BGR) image containing graphical code.
 @param points Quadrangle vertices found by detect() method (or some other algorithm).
 @param straight_code The optional output image containing binarized code, will be empty if not found.

Parameters:

self –
img (UMat) –
points (UMat) –
straight_code (UMat | None) –

Return type:

tuple[str, UMat]

detectAndDecode(img[, points[, straight_code]]) → retval, points, straight_code¶

Both detects and decodes graphical code

 @param img grayscale or color (BGR) image containing graphical code.
 @param points optional output array of vertices of the found graphical code quadrangle, will be empty if not found.
 @param straight_code The optional output image containing binarized code

Parameters:

self –
img (cv2.typing.MatLike) –
points (cv2.typing.MatLike | None) –
straight_code (cv2.typing.MatLike | None) –

Return type:

tuple[str, cv2.typing.MatLike, cv2.typing.MatLike]

detectAndDecode(img[, points[, straight_code]]) → retval, points, straight_code¶

Both detects and decodes graphical code

 @param img grayscale or color (BGR) image containing graphical code.
 @param points optional output array of vertices of the found graphical code quadrangle, will be empty if not found.
 @param straight_code The optional output image containing binarized code

Parameters:

self –
img (UMat) –
points (UMat | None) –
straight_code (UMat | None) –

Return type:

tuple[str, UMat, UMat]

detectMulti(img[, points]) → retval, points¶

Detects graphical codes in image and returns the vector of the quadrangles containing the codes. @param img grayscale or color (BGR) image containing (or not) graphical codes. @param points Output vector of vector of vertices of the minimum-area quadrangle containing the codes.

Parameters:

self –
img (cv2.typing.MatLike) –
points (cv2.typing.MatLike | None) –

Return type:

tuple[bool, cv2.typing.MatLike]

detectMulti(img[, points]) → retval, points¶

Parameters:

self –
img (UMat) –
points (UMat | None) –

Return type:

decodeMulti(img, points[, straight_code]) → retval, decoded_info, straight_code¶

Decodes graphical codes in image once it’s found by the detect() method. @param img grayscale or color (BGR) image containing graphical codes. @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded. @param points vector of Quadrangle vertices found by detect() method (or some other algorithm). @param straight_code The optional output vector of images containing binarized codes

Parameters:

self –
img (cv2.typing.MatLike) –
points (cv2.typing.MatLike) –
straight_code (_typing.Sequence[cv2.typing.MatLike] | None) –

Return type:

tuple[bool, _typing.Sequence[str], _typing.Sequence[cv2.typing.MatLike]]

decodeMulti(img, points[, straight_code]) → retval, decoded_info, straight_code¶

Parameters:

self –
img (UMat) –
points (UMat) –
straight_code (_typing.Sequence[UMat] | None) –

Return type:

tuple[bool, _typing.Sequence[str], _typing.Sequence[UMat]]

detectAndDecodeMulti(img[, points[, straight_code]]) → retval, decoded_info, points, straight_code¶

Both detects and decodes graphical codes @param img grayscale or color (BGR) image containing graphical codes. @param decoded_info UTF8-encoded output vector of string or empty vector of string if the codes cannot be decoded. @param points optional output vector of vertices of the found graphical code quadrangles. Will be empty if not found. @param straight_code The optional vector of images containing binarized codes

Parameters:

self –
img (cv2.typing.MatLike) –
points (cv2.typing.MatLike | None) –
straight_code (_typing.Sequence[cv2.typing.MatLike] | None) –

Return type:

tuple[bool, _typing.Sequence[str], cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike]]

detectAndDecodeMulti(img[, points[, straight_code]]) → retval, decoded_info, points, straight_code¶

Parameters:

self –
img (UMat) –
points (UMat | None) –
straight_code (_typing.Sequence[UMat] | None) –

Return type:

tuple[bool, _typing.Sequence[str], UMat, _typing.Sequence[UMat]]

class cv2.HOGDescriptor¶

winSize()¶

Parameters:: self –
Return type:: cv2.typing.Size

blockSize()¶

Parameters:: self –
Return type:: cv2.typing.Size

blockStride()¶

Parameters:: self –
Return type:: cv2.typing.Size

cellSize()¶

Parameters:: self –
Return type:: cv2.typing.Size

nbins()¶

Parameters:: self –
Return type:: int

derivAperture()¶

Parameters:: self –
Return type:: int

winSigma()¶

Parameters:: self –
Return type:: float

histogramNormType()¶

Parameters:: self –
Return type:: HOGDescriptor_HistogramNormType

L2HysThreshold()¶

Parameters:: self –
Return type:: float

gammaCorrection()¶

Parameters:: self –
Return type:: bool

svmDetector()¶

Parameters:: self –
Return type:: _typing.Sequence[float]

nlevels()¶

Parameters:: self –
Return type:: int

signedGradient()¶

Parameters:: self –
Return type:: bool

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, _winSize: cv2.typing.Size, _blockSize: cv2.typing.Size, _blockStride: cv2.typing.Size, _cellSize: cv2.typing.Size, _nbins: int, _derivAperture: int = ..., _winSigma: float = ..., _histogramNormType: HOGDescriptor_HistogramNormType = ..., _L2HysThreshold: float = ..., _gammaCorrection: bool = ..., _nlevels: int = ..., _signedGradient: bool = ...)¶

Parameters:

self –
_winSize (cv2.typing.Size) –
_blockSize (cv2.typing.Size) –
_blockStride (cv2.typing.Size) –
_cellSize (cv2.typing.Size) –
_nbins (int) –
_derivAperture (int) –
_winSigma (float) –
_histogramNormType (HOGDescriptor_HistogramNormType) –
_L2HysThreshold (float) –
_gammaCorrection (bool) –
_nlevels (int) –
_signedGradient (bool) –

Return type:

None

__init__(self, filename: str)¶

Parameters:

self –
filename (str) –

Return type:

None

setSVMDetector(svmdetector) → None¶

Sets coefficients for the linear SVM classifier. @param svmdetector coefficients for the linear SVM classifier.

Parameters:

self –
svmdetector (cv2.typing.MatLike) –

Return type:

None

setSVMDetector(svmdetector) → None¶

Sets coefficients for the linear SVM classifier. @param svmdetector coefficients for the linear SVM classifier.

Parameters:

self –
svmdetector (UMat) –

Return type:

None

compute(img[, winStride[, padding[, locations]]]) → descriptors¶

Computes HOG descriptors of given image. @param img Matrix of the type CV_8U containing an image where HOG features will be calculated. @param descriptors Matrix of the type CV_32F @param winStride Window stride. It must be a multiple of block stride. @param padding Padding @param locations Vector of Point

Parameters:

self –
img (cv2.typing.MatLike) –
winStride (cv2.typing.Size) –
padding (cv2.typing.Size) –
locations (_typing.Sequence[cv2.typing.Point]) –

Return type:

_typing.Sequence[float]

compute(img[, winStride[, padding[, locations]]]) → descriptors¶

Parameters:

self –
img (UMat) –
winStride (cv2.typing.Size) –
padding (cv2.typing.Size) –
locations (_typing.Sequence[cv2.typing.Point]) –

Return type:

_typing.Sequence[float]

detect(img[, hitThreshold[, winStride[, padding[, searchLocations]]]]) → foundLocations, weights¶

Performs object detection without a multi-scale window. @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. @param foundLocations Vector of point where each point contains left-top corner point of detected object boundaries. @param weights Vector that will contain confidence values for each detected object. @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. @param winStride Window stride. It must be a multiple of block stride. @param padding Padding @param searchLocations Vector of Point includes set of requested locations to be evaluated.

Parameters:

self –
img (cv2.typing.MatLike) –
hitThreshold (float) –
winStride (cv2.typing.Size) –
padding (cv2.typing.Size) –
searchLocations (_typing.Sequence[cv2.typing.Point]) –

Return type:

tuple[_typing.Sequence[cv2.typing.Point], _typing.Sequence[float]]

detect(img[, hitThreshold[, winStride[, padding[, searchLocations]]]]) → foundLocations, weights¶

Parameters:

self –
img (UMat) –
hitThreshold (float) –
winStride (cv2.typing.Size) –
padding (cv2.typing.Size) –
searchLocations (_typing.Sequence[cv2.typing.Point]) –

Return type:

tuple[_typing.Sequence[cv2.typing.Point], _typing.Sequence[float]]

detectMultiScale(img[, hitThreshold[, winStride[, padding[, scale[, groupThreshold[, useMeanshiftGrouping]]]]]]) → foundLocations, foundWeights¶

Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles. @param img Matrix of the type CV_8U or CV_8UC3 containing an image where objects are detected. @param foundLocations Vector of rectangles where each rectangle contains the detected object. @param foundWeights Vector that will contain confidence values for each detected object. @param hitThreshold Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specified in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here. @param winStride Window stride. It must be a multiple of block stride. @param padding Padding @param scale Coefficient of the detection window increase. @param groupThreshold Coefficient to regulate the similarity threshold. When detected, some objects can be covered by many rectangles. 0 means not to perform grouping. @param useMeanshiftGrouping indicates grouping algorithm

Parameters:

self –
img (cv2.typing.MatLike) –
hitThreshold (float) –
winStride (cv2.typing.Size) –
padding (cv2.typing.Size) –
scale (float) –
groupThreshold (float) –
useMeanshiftGrouping (bool) –

Return type:

tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[float]]

detectMultiScale(img[, hitThreshold[, winStride[, padding[, scale[, groupThreshold[, useMeanshiftGrouping]]]]]]) → foundLocations, foundWeights¶

Parameters:

self –
img (UMat) –
hitThreshold (float) –
winStride (cv2.typing.Size) –
padding (cv2.typing.Size) –
scale (float) –
groupThreshold (float) –
useMeanshiftGrouping (bool) –

Return type:

tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[float]]

computeGradient(img, grad, angleOfs[, paddingTL[, paddingBR]]) → grad, angleOfs¶

Computes gradients and quantized gradient orientations. @param img Matrix contains the image to be computed @param grad Matrix of type CV_32FC2 contains computed gradients @param angleOfs Matrix of type CV_8UC2 contains quantized gradient orientations @param paddingTL Padding from top-left @param paddingBR Padding from bottom-right

Parameters:

self –
img (cv2.typing.MatLike) –
grad (cv2.typing.MatLike) –
angleOfs (cv2.typing.MatLike) –
paddingTL (cv2.typing.Size) –
paddingBR (cv2.typing.Size) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

computeGradient(img, grad, angleOfs[, paddingTL[, paddingBR]]) → grad, angleOfs¶

Parameters:

self –
img (UMat) –
grad (UMat) –
angleOfs (UMat) –
paddingTL (cv2.typing.Size) –
paddingBR (cv2.typing.Size) –

Return type:

getDescriptorSize() → retval¶

Returns the number of coefficients required for the classification.

Parameters:: self –
Return type:: int

checkDetectorSize() → retval¶

Checks if detector size equal to descriptor size.

Parameters:: self –
Return type:: bool

getWinSigma() → retval¶

Returns winSigma value

Parameters:: self –
Return type:: float

load(filename[, objname]) → retval¶

loads HOGDescriptor parameters and coefficients for the linear SVM classifier from a file @param filename Name of the file to read. @param objname The optional name of the node to read (if empty, the first top-level node will be used).

Parameters:

self –
filename (str) –
objname (str) –

Return type:

save(filename[, objname]) → None¶

saves HOGDescriptor parameters and coefficients for the linear SVM classifier to a file @param filename File name @param objname Object name

Parameters:

self –
filename (str) –
objname (str) –

Return type:

None

static getDefaultPeopleDetector() → retval¶

Returns coefficients of the classifier trained for people detection (for 64x128 windows).

Return type:: _typing.Sequence[float]

static getDaimlerPeopleDetector() → retval¶

Returns coefficients of the classifier trained for people detection (for 48x96 windows).

Return type:: _typing.Sequence[float]

class cv2.KAZE¶

classmethod create([extended[, upright[, threshold[, nOctaves[, nOctaveLayers[, diffusivity]]]]]]) → retval¶

The KAZE constructor

@param extended Set to enable extraction of extended (128-byte) descriptor.
@param upright Set to enable use of upright descriptors (non rotation-invariant).
@param threshold Detector response threshold to accept point
@param nOctaves Maximum octave evolution of the image
@param nOctaveLayers Default number of sublevels per scale level
@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
DIFF_CHARBONNIER

Parameters:

cls –
extended (bool) –
upright (bool) –
threshold (float) –
nOctaves (int) –
nOctaveLayers (int) –
diffusivity (KAZE_DiffusivityType) –

Return type:

KAZE

setExtended(extended) → None¶

Parameters:

self –
extended (bool) –

Return type:

None

getExtended() → retval¶

Parameters:: self –
Return type:: bool

setUpright(upright) → None¶

Parameters:

self –
upright (bool) –

Return type:

None

getUpright() → retval¶

Parameters:: self –
Return type:: bool

setThreshold(threshold) → None¶

Parameters:

self –
threshold (float) –

Return type:

None

getThreshold() → retval¶

Parameters:: self –
Return type:: float

setNOctaves(octaves) → None¶

Parameters:

self –
octaves (int) –

Return type:

None

getNOctaves() → retval¶

Parameters:: self –
Return type:: int

setNOctaveLayers(octaveLayers) → None¶

Parameters:

self –
octaveLayers (int) –

Return type:

None

getNOctaveLayers() → retval¶

Parameters:: self –
Return type:: int

setDiffusivity(diff) → None¶

Parameters:

self –
diff (KAZE_DiffusivityType) –

Return type:

None

getDiffusivity() → retval¶

Parameters:: self –
Return type:: KAZE_DiffusivityType

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

class cv2.KalmanFilter¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, dynamParams: int, measureParams: int, controlParams: int = ..., type: int = ...)¶

Parameters:

self –
dynamParams (int) –
measureParams (int) –
controlParams (int) –
type (int) –

Return type:

None

predict([control]) → retval¶

Computes a predicted state.

@param control The optional input control

Parameters:

self –
control (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

correct(measurement) → retval¶

Updates the predicted state from the measurement.

@param measurement The measured system parameters

Parameters:

self –
measurement (cv2.typing.MatLike) –

Return type:

cv2.typing.MatLike

statePre: cv2.typing.MatLike¶

statePost: cv2.typing.MatLike¶

transitionMatrix: cv2.typing.MatLike¶

controlMatrix: cv2.typing.MatLike¶

measurementMatrix: cv2.typing.MatLike¶

processNoiseCov: cv2.typing.MatLike¶

measurementNoiseCov: cv2.typing.MatLike¶

errorCovPre: cv2.typing.MatLike¶

gain: cv2.typing.MatLike¶

errorCovPost: cv2.typing.MatLike¶

class cv2.KeyPoint¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, x: float, y: float, size: float, angle: float = ..., response: float = ..., octave: int = ..., class_id: int = ...)¶

Parameters:

self –
x (float) –
y (float) –
size (float) –
angle (float) –
response (float) –
octave (int) –
class_id (int) –

Return type:

None

static convert(keypoints[, keypointIndexes]) → points2f¶

This method converts vector of keypoints to vector of points or the reverse, where each keypoint is assigned the same size and the same orientation.

@param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
@param points2f Array of (x,y) coordinates of each keypoint
@param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to
convert only specified keypoints)

@overload @param points2f Array of (x,y) coordinates of each keypoint @param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB @param size keypoint diameter @param response keypoint detector response on the keypoint (that is, strength of the keypoint) @param octave pyramid octave in which the keypoint has been detected @param class_id object id

Parameters:

keypoints (_typing.Sequence[KeyPoint]) –
keypointIndexes (_typing.Sequence[int]) –

Return type:

_typing.Sequence[cv2.typing.Point2f]

static convert(keypoints[, keypointIndexes]) → points2f¶

This method converts vector of keypoints to vector of points or the reverse, where each keypoint is assigned the same size and the same orientation.

@param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
@param points2f Array of (x,y) coordinates of each keypoint
@param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to
convert only specified keypoints)

Parameters:

points2f (_typing.Sequence[cv2.typing.Point2f]) –
size (float) –
response (float) –
octave (int) –
class_id (int) –

Return type:

_typing.Sequence[KeyPoint]

static overlap(kp1, kp2) → retval¶

This method computes overlap for pair of keypoints. Overlap is the ratio between area of keypoint regions’ intersection and area of keypoint regions’ union (considering keypoint region as circle). If they don’t overlap, we get zero. If they coincide at same location with same size, we get 1. @param kp1 First keypoint @param kp2 Second keypoint

Parameters:

kp1 (KeyPoint) –
kp2 (KeyPoint) –

Return type:

pt: cv2.typing.Point2f¶

size: float¶

angle: float¶

response: float¶

octave: int¶

class_id: int¶

class cv2.LineSegmentDetector¶

detect(image[, lines[, width[, prec[, nfa]]]]) → lines, width, prec, nfa¶

Finds lines in the input image.

This is the output of the default parameters of the algorithm on the above shown image.

![image](pics/building_lsd.png)

@param image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use:
`lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
@param lines A vector of Vec4f elements specifying the beginning and ending point of a line. Where
Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly
oriented depending on the gradient.
@param width Vector of widths of the regions, where the lines are found. E.g. Width of line.
@param prec Vector of precisions with which the lines are found.
@param nfa Vector containing number of false alarms in the line region, with precision of 10%. The
bigger the value, logarithmically better the detection.
- -1 corresponds to 10 mean false alarms
- 0 corresponds to 1 mean false alarm
- 1 corresponds to 0.1 mean false alarms
This vector will be calculated only when the objects type is #LSD_REFINE_ADV.

Parameters:

self –
image (cv2.typing.MatLike) –
lines (cv2.typing.MatLike | None) –
width (cv2.typing.MatLike | None) –
prec (cv2.typing.MatLike | None) –
nfa (cv2.typing.MatLike | None) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

detect(image[, lines[, width[, prec[, nfa]]]]) → lines, width, prec, nfa¶

Finds lines in the input image.

This is the output of the default parameters of the algorithm on the above shown image.

![image](pics/building_lsd.png)

@param image A grayscale (CV_8UC1) input image. If only a roi needs to be selected, use:
`lsd_ptr-\>detect(image(roi), lines, ...); lines += Scalar(roi.x, roi.y, roi.x, roi.y);`
@param lines A vector of Vec4f elements specifying the beginning and ending point of a line. Where
Vec4f is (x1, y1, x2, y2), point 1 is the start, point 2 - end. Returned lines are strictly
oriented depending on the gradient.
@param width Vector of widths of the regions, where the lines are found. E.g. Width of line.
@param prec Vector of precisions with which the lines are found.
@param nfa Vector containing number of false alarms in the line region, with precision of 10%. The
bigger the value, logarithmically better the detection.
- -1 corresponds to 10 mean false alarms
- 0 corresponds to 1 mean false alarm
- 1 corresponds to 0.1 mean false alarms
This vector will be calculated only when the objects type is #LSD_REFINE_ADV.

Parameters:

self –
image (UMat) –
lines (UMat | None) –
width (UMat | None) –
prec (UMat | None) –
nfa (UMat | None) –

Return type:

tuple[UMat, UMat, UMat, UMat]

drawSegments(image, lines) → image¶

Draws the line segments on a given image. @param image The image, where the lines will be drawn. Should be bigger or equal to the image, where the lines were found. @param lines A vector of the lines that needed to be drawn.

Parameters:

self –
image (cv2.typing.MatLike) –
lines (cv2.typing.MatLike) –

Return type:

cv2.typing.MatLike

drawSegments(image, lines) → image¶

Parameters:

self –
image (UMat) –
lines (UMat) –

Return type:

compareSegments(size, lines1, lines2[, image]) → retval, image¶

Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.

@param size The size of the image, where lines1 and lines2 were found.
@param lines1 The first group of lines that needs to be drawn. It is visualized in blue color.
@param lines2 The second group of lines. They visualized in red color.
@param image Optional image, where the lines will be drawn. The image should be color(3-channel)
in order for lines1 and lines2 to be drawn in the above mentioned colors.

Parameters:

self –
size (cv2.typing.Size) –
lines1 (cv2.typing.MatLike) –
lines2 (cv2.typing.MatLike) –
image (cv2.typing.MatLike | None) –

Return type:

tuple[int, cv2.typing.MatLike]

compareSegments(size, lines1, lines2[, image]) → retval, image¶

Draws two groups of lines in blue and red, counting the non overlapping (mismatching) pixels.

@param size The size of the image, where lines1 and lines2 were found.
@param lines1 The first group of lines that needs to be drawn. It is visualized in blue color.
@param lines2 The second group of lines. They visualized in red color.
@param image Optional image, where the lines will be drawn. The image should be color(3-channel)
in order for lines1 and lines2 to be drawn in the above mentioned colors.

Parameters:

self –
size (cv2.typing.Size) –
lines1 (UMat) –
lines2 (UMat) –
image (UMat | None) –

Return type:

tuple[int, UMat]

class cv2.MSER¶

classmethod create([delta[, min_area[, max_area[, max_variation[, min_diversity[, max_evolution[, area_threshold[, min_margin[, edge_blur_size]]]]]]]]]) → retval¶

Full constructor for %MSER detector

@param delta it compares $(size_{i}-size_{i-delta})/size_{i-delta}$
@param min_area prune the area which smaller than minArea
@param max_area prune the area which bigger than maxArea
@param max_variation prune the area have similar size to its children
@param min_diversity for color image, trace back to cut off mser with diversity less than min_diversity
@param max_evolution  for color image, the evolution steps
@param area_threshold for color image, the area threshold to cause re-initialize
@param min_margin for color image, ignore too small margin
@param edge_blur_size for color image, the aperture size for edge blur

Parameters:

cls –
delta (int) –
min_area (int) –
max_area (int) –
max_variation (float) –
min_diversity (float) –
max_evolution (int) –
area_threshold (float) –
min_margin (float) –
edge_blur_size (int) –

Return type:

MSER

detectRegions(image) → msers, bboxes¶

Detect %MSER regions

@param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3)
@param msers resulting list of point sets
@param bboxes resulting bounding boxes

Parameters:

self –
image (cv2.typing.MatLike) –

Return type:

tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point]], _typing.Sequence[cv2.typing.Rect]]

detectRegions(image) → msers, bboxes¶

Detect %MSER regions

@param image input image (8UC1, 8UC3 or 8UC4, must be greater or equal than 3x3)
@param msers resulting list of point sets
@param bboxes resulting bounding boxes

Parameters:

self –
image (UMat) –

Return type:

tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point]], _typing.Sequence[cv2.typing.Rect]]

setDelta(delta) → None¶

Parameters:

self –
delta (int) –

Return type:

None

getDelta() → retval¶

Parameters:: self –
Return type:: int

setMinArea(minArea) → None¶

Parameters:

self –
minArea (int) –

Return type:

None

getMinArea() → retval¶

Parameters:: self –
Return type:: int

setMaxArea(maxArea) → None¶

Parameters:

self –
maxArea (int) –

Return type:

None

getMaxArea() → retval¶

Parameters:: self –
Return type:: int

setMaxVariation(maxVariation) → None¶

Parameters:

self –
maxVariation (float) –

Return type:

None

getMaxVariation() → retval¶

Parameters:: self –
Return type:: float

setMinDiversity(minDiversity) → None¶

Parameters:

self –
minDiversity (float) –

Return type:

None

getMinDiversity() → retval¶

Parameters:: self –
Return type:: float

setMaxEvolution(maxEvolution) → None¶

Parameters:

self –
maxEvolution (int) –

Return type:

None

getMaxEvolution() → retval¶

Parameters:: self –
Return type:: int

setAreaThreshold(areaThreshold) → None¶

Parameters:

self –
areaThreshold (float) –

Return type:

None

getAreaThreshold() → retval¶

Parameters:: self –
Return type:: float

setMinMargin(min_margin) → None¶

Parameters:

self –
min_margin (float) –

Return type:

None

getMinMargin() → retval¶

Parameters:: self –
Return type:: float

setEdgeBlurSize(edge_blur_size) → None¶

Parameters:

self –
edge_blur_size (int) –

Return type:

None

getEdgeBlurSize() → retval¶

Parameters:: self –
Return type:: int

setPass2Only(f) → None¶

Parameters:

self –
f (bool) –

Return type:

None

getPass2Only() → retval¶

Parameters:: self –
Return type:: bool

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

class cv2.MergeDebevec¶

process(src, times, response[, dst]) → dst¶

Parameters:

self –
src (_typing.Sequence[cv2.typing.MatLike]) –
times (cv2.typing.MatLike) –
response (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src, times, response[, dst]) → dst¶

Parameters:

self –
src (_typing.Sequence[UMat]) –
times (UMat) –
response (UMat) –
dst (UMat | None) –

Return type:

process(src, times, response[, dst]) → dst¶

Parameters:

self –
src (_typing.Sequence[cv2.typing.MatLike]) –
times (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src, times, response[, dst]) → dst¶

Parameters:

self –
src (_typing.Sequence[UMat]) –
times (UMat) –
dst (UMat | None) –

Return type:

class cv2.MergeExposures¶

process(src, times, response[, dst]) → dst¶

Merges images.

@param src vector of input images
@param dst result image
@param times vector of exposure time values for each image
@param response 256x1 matrix with inverse camera response function for each pixel value, it should
have the same number of channels as images.

Parameters:

self –
src (_typing.Sequence[cv2.typing.MatLike]) –
times (cv2.typing.MatLike) –
response (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src, times, response[, dst]) → dst¶

Merges images.

@param src vector of input images
@param dst result image
@param times vector of exposure time values for each image
@param response 256x1 matrix with inverse camera response function for each pixel value, it should
have the same number of channels as images.

Parameters:

self –
src (_typing.Sequence[UMat]) –
times (UMat) –
response (UMat) –
dst (UMat | None) –

Return type:

class cv2.MergeMertens¶

process(src, times, response[, dst]) → dst¶

Short version of process, that doesn’t take extra arguments.

@param src vector of input images
@param dst result image

Parameters:

self –
src (_typing.Sequence[cv2.typing.MatLike]) –
times (cv2.typing.MatLike) –
response (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src, times, response[, dst]) → dst¶

Short version of process, that doesn’t take extra arguments.

@param src vector of input images
@param dst result image

Parameters:

self –
src (_typing.Sequence[UMat]) –
times (UMat) –
response (UMat) –
dst (UMat | None) –

Return type:

process(src, times, response[, dst]) → dst¶

Short version of process, that doesn’t take extra arguments.

@param src vector of input images
@param dst result image

Parameters:

self –
src (_typing.Sequence[cv2.typing.MatLike]) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src, times, response[, dst]) → dst¶

Short version of process, that doesn’t take extra arguments.

@param src vector of input images
@param dst result image

Parameters:

self –
src (_typing.Sequence[UMat]) –
dst (UMat | None) –

Return type:

getContrastWeight() → retval¶

Parameters:: self –
Return type:: float

setContrastWeight(contrast_weiht) → None¶

Parameters:

self –
contrast_weiht (float) –

Return type:

None

getSaturationWeight() → retval¶

Parameters:: self –
Return type:: float

setSaturationWeight(saturation_weight) → None¶

Parameters:

self –
saturation_weight (float) –

Return type:

None

getExposureWeight() → retval¶

Parameters:: self –
Return type:: float

setExposureWeight(exposure_weight) → None¶

Parameters:

self –
exposure_weight (float) –

Return type:

None

class cv2.MergeRobertson¶

process(src, times, response[, dst]) → dst¶

Parameters:

self –
src (_typing.Sequence[cv2.typing.MatLike]) –
times (cv2.typing.MatLike) –
response (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src, times, response[, dst]) → dst¶

Parameters:

self –
src (_typing.Sequence[UMat]) –
times (UMat) –
response (UMat) –
dst (UMat | None) –

Return type:

process(src, times, response[, dst]) → dst¶

Parameters:

self –
src (_typing.Sequence[cv2.typing.MatLike]) –
times (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src, times, response[, dst]) → dst¶

Parameters:

self –
src (_typing.Sequence[UMat]) –
times (UMat) –
dst (UMat | None) –

Return type:

class cv2.ORB¶

classmethod create([nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize[, fastThreshold]]]]]]]]]) → retval¶

The ORB constructor

@param nfeatures The maximum number of features to retain.
@param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.
@param nlevels The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
@param edgeThreshold This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.
@param firstLevel The level of pyramid to put source image to. Previous layers are filled
with upscaled source image.
@param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
@param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
@param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
pyramid layers the perceived image area covered by a feature will be larger.
@param fastThreshold the fast threshold

Parameters:

cls –
nfeatures (int) –
scaleFactor (float) –
nlevels (int) –
edgeThreshold (int) –
firstLevel (int) –
WTA_K (int) –
scoreType (ORB_ScoreType) –
patchSize (int) –
fastThreshold (int) –

Return type:

ORB

setMaxFeatures(maxFeatures) → None¶

Parameters:

self –
maxFeatures (int) –

Return type:

None

getMaxFeatures() → retval¶

Parameters:: self –
Return type:: int

setScaleFactor(scaleFactor) → None¶

Parameters:

self –
scaleFactor (float) –

Return type:

None

getScaleFactor() → retval¶

Parameters:: self –
Return type:: float

setNLevels(nlevels) → None¶

Parameters:

self –
nlevels (int) –

Return type:

None

getNLevels() → retval¶

Parameters:: self –
Return type:: int

setEdgeThreshold(edgeThreshold) → None¶

Parameters:

self –
edgeThreshold (int) –

Return type:

None

getEdgeThreshold() → retval¶

Parameters:: self –
Return type:: int

setFirstLevel(firstLevel) → None¶

Parameters:

self –
firstLevel (int) –

Return type:

None

getFirstLevel() → retval¶

Parameters:: self –
Return type:: int

setWTA_K(wta_k) → None¶

Parameters:

self –
wta_k (int) –

Return type:

None

getWTA_K() → retval¶

Parameters:: self –
Return type:: int

setScoreType(scoreType) → None¶

Parameters:

self –
scoreType (ORB_ScoreType) –

Return type:

None

getScoreType() → retval¶

Parameters:: self –
Return type:: ORB_ScoreType

setPatchSize(patchSize) → None¶

Parameters:

self –
patchSize (int) –

Return type:

None

getPatchSize() → retval¶

Parameters:: self –
Return type:: int

setFastThreshold(fastThreshold) → None¶

Parameters:

self –
fastThreshold (int) –

Return type:

None

getFastThreshold() → retval¶

Parameters:: self –
Return type:: int

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

class cv2.PyRotationWarper¶

__init__(self, type: str, scale: float)¶

Parameters:

self –
type (str) –
scale (float) –

Return type:

None

__init__(self)¶

Parameters:: self –
Return type:: None

warpPoint(pt, K, R) → retval¶

Projects the image point.

    @param pt Source point
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @return Projected point

Parameters:

self –
pt (cv2.typing.Point2f) –
K (cv2.typing.MatLike) –
R (cv2.typing.MatLike) –

Return type:

cv2.typing.Point2f

warpPoint(pt, K, R) → retval¶

Projects the image point.

    @param pt Source point
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @return Projected point

Parameters:

self –
pt (cv2.typing.Point2f) –
K (UMat) –
R (UMat) –

Return type:

cv2.typing.Point2f

warpPointBackward(pt, K, R) → retval¶

Projects the image point backward.

    @param pt Projected point
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @return Backward-projected point

Parameters:

self –
pt (cv2.typing.Point2f) –
K (cv2.typing.MatLike) –
R (cv2.typing.MatLike) –

Return type:

cv2.typing.Point2f

warpPointBackward(pt, K, R) → retval¶

Projects the image point backward.

    @param pt Projected point
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @return Backward-projected point

Parameters:

self –
pt (cv2.typing.Point2f) –
K (UMat) –
R (UMat) –

Return type:

cv2.typing.Point2f

warpPointBackward(pt, K, R) → retval¶

Projects the image point backward.

    @param pt Projected point
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @return Backward-projected point

Parameters:

self –
pt (cv2.typing.Point2f) –
K (cv2.typing.MatLike) –
R (cv2.typing.MatLike) –

Return type:

cv2.typing.Point2f

warpPointBackward(pt, K, R) → retval¶

Projects the image point backward.

    @param pt Projected point
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @return Backward-projected point

Parameters:

self –
pt (cv2.typing.Point2f) –
K (UMat) –
R (UMat) –

Return type:

cv2.typing.Point2f

buildMaps(src_size, K, R[, xmap[, ymap]]) → retval, xmap, ymap¶

Builds the projection maps according to the given camera data.

    @param src_size Source image size
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @param xmap Projection map for the x axis
    @param ymap Projection map for the y axis
    @return Projected image minimum bounding box

Parameters:

self –
src_size (cv2.typing.Size) –
K (cv2.typing.MatLike) –
R (cv2.typing.MatLike) –
xmap (cv2.typing.MatLike | None) –
ymap (cv2.typing.MatLike | None) –

Return type:

tuple[cv2.typing.Rect, cv2.typing.MatLike, cv2.typing.MatLike]

buildMaps(src_size, K, R[, xmap[, ymap]]) → retval, xmap, ymap¶

Builds the projection maps according to the given camera data.

    @param src_size Source image size
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @param xmap Projection map for the x axis
    @param ymap Projection map for the y axis
    @return Projected image minimum bounding box

Parameters:

self –
src_size (cv2.typing.Size) –
K (UMat) –
R (UMat) –
xmap (UMat | None) –
ymap (UMat | None) –

Return type:

tuple[cv2.typing.Rect, UMat, UMat]

warp(src, K, R, interp_mode, border_mode[, dst]) → retval, dst¶

Projects the image.

    @param src Source image
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @param interp_mode Interpolation mode
    @param border_mode Border extrapolation mode
    @param dst Projected image
    @return Project image top-left corner

Parameters:

self –
src (cv2.typing.MatLike) –
K (cv2.typing.MatLike) –
R (cv2.typing.MatLike) –
interp_mode (int) –
border_mode (int) –
dst (cv2.typing.MatLike | None) –

Return type:

tuple[cv2.typing.Point, cv2.typing.MatLike]

warp(src, K, R, interp_mode, border_mode[, dst]) → retval, dst¶

Projects the image.

    @param src Source image
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @param interp_mode Interpolation mode
    @param border_mode Border extrapolation mode
    @param dst Projected image
    @return Project image top-left corner

Parameters:

self –
src (UMat) –
K (UMat) –
R (UMat) –
interp_mode (int) –
border_mode (int) –
dst (UMat | None) –

Return type:

tuple[cv2.typing.Point, UMat]

warpBackward(src, K, R, interp_mode, border_mode, dst_size[, dst]) → dst¶

Projects the image backward.

    @param src Projected image
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @param interp_mode Interpolation mode
    @param border_mode Border extrapolation mode
    @param dst_size Backward-projected image size
    @param dst Backward-projected image

Parameters:

self –
src (cv2.typing.MatLike) –
K (cv2.typing.MatLike) –
R (cv2.typing.MatLike) –
interp_mode (int) –
border_mode (int) –
dst_size (cv2.typing.Size) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

warpBackward(src, K, R, interp_mode, border_mode, dst_size[, dst]) → dst¶

Projects the image backward.

    @param src Projected image
    @param K Camera intrinsic parameters
    @param R Camera rotation matrix
    @param interp_mode Interpolation mode
    @param border_mode Border extrapolation mode
    @param dst_size Backward-projected image size
    @param dst Backward-projected image

Parameters:

self –
src (UMat) –
K (UMat) –
R (UMat) –
interp_mode (int) –
border_mode (int) –
dst_size (cv2.typing.Size) –
dst (UMat | None) –

Return type:

warpRoi(src_size, K, R) → retval¶

Parameters:

self –
src_size (cv2.typing.Size) – Source image bounding box @param K Camera intrinsic parameters @param R Camera rotation matrix @return Projected image minimum bounding box
K (cv2.typing.MatLike) –
R (cv2.typing.MatLike) –

Return type:

cv2.typing.Rect

warpRoi(src_size, K, R) → retval¶

Parameters:

self –
src_size (cv2.typing.Size) – Source image bounding box @param K Camera intrinsic parameters @param R Camera rotation matrix @return Projected image minimum bounding box
K (UMat) –
R (UMat) –

Return type:

cv2.typing.Rect

getScale() → retval¶

Parameters:: self –
Return type:: float

setScale(arg1) → None¶

Parameters:

self –
arg1 (float) –

Return type:

None

class cv2.QRCodeDetector¶

decodeCurved(img, points[, straight_qrcode]) → retval, straight_qrcode¶

Decodes QR code on a curved surface in image once it’s found by the detect() method.

 Returns UTF8-encoded output string or empty string if the code cannot be decoded.
 @param img grayscale or color (BGR) image containing QR code.
 @param points Quadrangle vertices found by detect() method (or some other algorithm).
 @param straight_qrcode The optional output image containing rectified and binarized QR code

Parameters:

self –
img (cv2.typing.MatLike) –
points (cv2.typing.MatLike) –
straight_qrcode (cv2.typing.MatLike | None) –

Return type:

tuple[str, cv2.typing.MatLike]

decodeCurved(img, points[, straight_qrcode]) → retval, straight_qrcode¶

Decodes QR code on a curved surface in image once it’s found by the detect() method.

 Returns UTF8-encoded output string or empty string if the code cannot be decoded.
 @param img grayscale or color (BGR) image containing QR code.
 @param points Quadrangle vertices found by detect() method (or some other algorithm).
 @param straight_qrcode The optional output image containing rectified and binarized QR code

Parameters:

self –
img (UMat) –
points (UMat) –
straight_qrcode (UMat | None) –

Return type:

tuple[str, UMat]

detectAndDecodeCurved(img[, points[, straight_qrcode]]) → retval, points, straight_qrcode¶

Both detects and decodes QR code on a curved surface

 @param img grayscale or color (BGR) image containing QR code.
 @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
 @param straight_qrcode The optional output image containing rectified and binarized QR code

Parameters:

self –
img (cv2.typing.MatLike) –
points (cv2.typing.MatLike | None) –
straight_qrcode (cv2.typing.MatLike | None) –

Return type:

tuple[str, cv2.typing.MatLike, cv2.typing.MatLike]

detectAndDecodeCurved(img[, points[, straight_qrcode]]) → retval, points, straight_qrcode¶

Both detects and decodes QR code on a curved surface

 @param img grayscale or color (BGR) image containing QR code.
 @param points optional output array of vertices of the found QR code quadrangle. Will be empty if not found.
 @param straight_qrcode The optional output image containing rectified and binarized QR code

Parameters:

self –
img (UMat) –
points (UMat | None) –
straight_qrcode (UMat | None) –

Return type:

tuple[str, UMat, UMat]

__init__(self)¶

Parameters:: self –
Return type:: None

setEpsX(epsX) → retval¶

sets the epsilon used during the horizontal scan of QR code stop marker detection. @param epsX Epsilon neighborhood, which allows you to determine the horizontal pattern of the scheme 1:1:3:1:1 according to QR code standard.

Parameters:

self –
epsX (float) –

Return type:

QRCodeDetector

setEpsY(epsY) → retval¶

sets the epsilon used during the vertical scan of QR code stop marker detection. @param epsY Epsilon neighborhood, which allows you to determine the vertical pattern of the scheme 1:1:3:1:1 according to QR code standard.

Parameters:

self –
epsY (float) –

Return type:

QRCodeDetector

setUseAlignmentMarkers(useAlignmentMarkers) → retval¶

use markers to improve the position of the corners of the QR code * * alignmentMarkers using by default

Parameters:

self –
useAlignmentMarkers (bool) –

Return type:

QRCodeDetector

class cv2.QRCodeDetectorAruco¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, params: QRCodeDetectorAruco.Params)¶

Parameters:

self –
params (QRCodeDetectorAruco.Params) –

Return type:

None

getDetectorParameters() → retval¶

Detector parameters getter. See cv::QRCodeDetectorAruco::Params

Parameters:: self –
Return type:: QRCodeDetectorAruco.Params

setDetectorParameters(params) → retval¶

Detector parameters setter. See cv::QRCodeDetectorAruco::Params

Parameters:

self –
params (QRCodeDetectorAruco.Params) –

Return type:

QRCodeDetectorAruco

getArucoParameters() → retval¶

Aruco detector parameters are used to search for the finder patterns.

Parameters:: self –
Return type:: cv2.aruco.DetectorParameters

setArucoParameters(params) → None¶

Aruco detector parameters are used to search for the finder patterns.

Parameters:

self –
params (cv2.aruco.DetectorParameters) –

Return type:

None

class cv2.QRCodeEncoder¶

classmethod create([parameters]) → retval¶

Constructor @param parameters QR code encoder parameters QRCodeEncoder::Params

Parameters:

cls –
parameters (QRCodeEncoder.Params) –

Return type:

QRCodeEncoder

encode(encoded_info[, qrcode]) → qrcode¶

Generates QR code from input string. @param encoded_info Input string to encode. @param qrcode Generated QR code.

Parameters:

self –
encoded_info (str) –
qrcode (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

encode(encoded_info[, qrcode]) → qrcode¶

Generates QR code from input string. @param encoded_info Input string to encode. @param qrcode Generated QR code.

Parameters:

self –
encoded_info (str) –
qrcode (UMat | None) –

Return type:

encodeStructuredAppend(encoded_info[, qrcodes]) → qrcodes¶

Generates QR code from input string in Structured Append mode. The encoded message is splitting over a number of QR codes. @param encoded_info Input string to encode. @param qrcodes Vector of generated QR codes.

Parameters:

self –
encoded_info (str) –
qrcodes (_typing.Sequence[cv2.typing.MatLike] | None) –

Return type:

_typing.Sequence[cv2.typing.MatLike]

encodeStructuredAppend(encoded_info[, qrcodes]) → qrcodes¶

Parameters:

self –
encoded_info (str) –
qrcodes (_typing.Sequence[UMat] | None) –

Return type:

_typing.Sequence[UMat]

class cv2.RotatedRect¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, center: cv2.typing.Point2f, size: cv2.typing.Size2f, angle: float)¶

Parameters:

self –
center (cv2.typing.Point2f) –
size (cv2.typing.Size2f) –
angle (float) –

Return type:

None

__init__(self, point1: cv2.typing.Point2f, point2: cv2.typing.Point2f, point3: cv2.typing.Point2f)¶

Parameters:

self –
point1 (cv2.typing.Point2f) –
point2 (cv2.typing.Point2f) –
point3 (cv2.typing.Point2f) –

Return type:

None

points() → pts¶

returns 4 vertices of the rotated rectangle @param pts The points array for storing rectangle vertices. The order is bottomLeft, topLeft, topRight, bottomRight. @note Bottom, Top, Left and Right sides refer to the original rectangle (angle is 0), so after 180 degree rotation bottomLeft point will be located at the top right corner of the rectangle.

Parameters:: self –
Return type:: _typing.Sequence[cv2.typing.Point2f]

boundingRect() → retval¶

Parameters:: self –
Return type:: cv2.typing.Rect

center: cv2.typing.Point2f¶

size: cv2.typing.Size2f¶

angle: float¶

class cv2.SIFT¶

classmethod create([nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) → retval¶

Create SIFT with specified descriptorType. @param nfeatures The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

@param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
number of octaves is computed automatically from the image resolution.

@param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
(low-contrast) regions. The larger the threshold, the less features are produced by the detector.

@note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
this argument to 0.09.

@param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
filtered out (more features are retained).

@param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
is captured with a weak camera with soft lenses, you might want to reduce the number.

@param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps
index $\texttt{x}$ to $\texttt{2x}$. This prevents localization bias. The option
is disabled by default.

@param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
number of octaves is computed automatically from the image resolution.

@param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
(low-contrast) regions. The larger the threshold, the less features are produced by the detector.

@note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
this argument to 0.09.

@param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
filtered out (more features are retained).

@param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
is captured with a weak camera with soft lenses, you might want to reduce the number.

@param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported.

@param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps
index $\texttt{x}$ to $\texttt{2x}$. This prevents localization bias. The option
is disabled by default.

Parameters:

cls –
nfeatures (int) – The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
nOctaveLayers (int) –
contrastThreshold (float) –
edgeThreshold (float) –
sigma (float) –
enable_precise_upscale (bool) –

Return type:

SIFT

classmethod create([nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) → retval¶

Create SIFT with specified descriptorType. @param nfeatures The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

@param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
number of octaves is computed automatically from the image resolution.

@param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
(low-contrast) regions. The larger the threshold, the less features are produced by the detector.

@note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
this argument to 0.09.

@param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
filtered out (more features are retained).

@param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
is captured with a weak camera with soft lenses, you might want to reduce the number.

@param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps
index $\texttt{x}$ to $\texttt{2x}$. This prevents localization bias. The option
is disabled by default.

@param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
number of octaves is computed automatically from the image resolution.

@param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
(low-contrast) regions. The larger the threshold, the less features are produced by the detector.

@note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
this argument to 0.09.

@param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
filtered out (more features are retained).

@param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
is captured with a weak camera with soft lenses, you might want to reduce the number.

@param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported.

@param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps
index $\texttt{x}$ to $\texttt{2x}$. This prevents localization bias. The option
is disabled by default.

Parameters:

cls –
nfeatures (int) – The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
nOctaveLayers (int) –
contrastThreshold (float) –
edgeThreshold (float) –
sigma (float) –
descriptorType (int) –
enable_precise_upscale (bool) –

Return type:

SIFT

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

setNFeatures(maxFeatures) → None¶

Parameters:

self –
maxFeatures (int) –

Return type:

None

getNFeatures() → retval¶

Parameters:: self –
Return type:: int

setNOctaveLayers(nOctaveLayers) → None¶

Parameters:

self –
nOctaveLayers (int) –

Return type:

None

getNOctaveLayers() → retval¶

Parameters:: self –
Return type:: int

setContrastThreshold(contrastThreshold) → None¶

Parameters:

self –
contrastThreshold (float) –

Return type:

None

getContrastThreshold() → retval¶

Parameters:: self –
Return type:: float

setEdgeThreshold(edgeThreshold) → None¶

Parameters:

self –
edgeThreshold (float) –

Return type:

None

getEdgeThreshold() → retval¶

Parameters:: self –
Return type:: float

setSigma(sigma) → None¶

Parameters:

self –
sigma (float) –

Return type:

None

getSigma() → retval¶

Parameters:: self –
Return type:: float

class cv2.SimpleBlobDetector¶

classmethod create([parameters]) → retval¶

Parameters:

cls –
parameters (SimpleBlobDetector.Params) –

Return type:

SimpleBlobDetector

setParams(params) → None¶

Parameters:

self –
params (SimpleBlobDetector.Params) –

Return type:

None

getParams() → retval¶

Parameters:: self –
Return type:: SimpleBlobDetector.Params

getDefaultName() → retval¶

Parameters:: self –
Return type:: str

getBlobContours() → retval¶

Parameters:: self –
Return type:: _typing.Sequence[_typing.Sequence[cv2.typing.Point]]

class cv2.SparseOpticalFlow¶

calc(prevImg, nextImg, prevPts, nextPts[, status[, err]]) → nextPts, status, err¶

Calculates a sparse optical flow.

@param prevImg First input image.
@param nextImg Second input image of the same size and the same type as prevImg.
@param prevPts Vector of 2D points for which the flow needs to be found.
@param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image.
@param status Output status vector. Each element of the vector is set to 1 if the
              flow for the corresponding features has been found. Otherwise, it is set to 0.
@param err Optional output vector that contains error response for each point (inverse confidence).

Parameters:

self –
prevImg (cv2.typing.MatLike) –
nextImg (cv2.typing.MatLike) –
prevPts (cv2.typing.MatLike) –
nextPts (cv2.typing.MatLike) –
status (cv2.typing.MatLike | None) –
err (cv2.typing.MatLike | None) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

calc(prevImg, nextImg, prevPts, nextPts[, status[, err]]) → nextPts, status, err¶

Calculates a sparse optical flow.

@param prevImg First input image.
@param nextImg Second input image of the same size and the same type as prevImg.
@param prevPts Vector of 2D points for which the flow needs to be found.
@param nextPts Output vector of 2D points containing the calculated new positions of input features in the second image.
@param status Output status vector. Each element of the vector is set to 1 if the
              flow for the corresponding features has been found. Otherwise, it is set to 0.
@param err Optional output vector that contains error response for each point (inverse confidence).

Parameters:

self –
prevImg (UMat) –
nextImg (UMat) –
prevPts (UMat) –
nextPts (UMat) –
status (UMat | None) –
err (UMat | None) –

Return type:

tuple[UMat, UMat, UMat]

class cv2.SparsePyrLKOpticalFlow¶

classmethod create([winSize[, maxLevel[, crit[, flags[, minEigThreshold]]]]]) → retval¶

Parameters:

cls –
winSize (cv2.typing.Size) –
maxLevel (int) –
crit (cv2.typing.TermCriteria) –
flags (int) –
minEigThreshold (float) –

Return type:

SparsePyrLKOpticalFlow

getWinSize() → retval¶

Parameters:: self –
Return type:: cv2.typing.Size

setWinSize(winSize) → None¶

Parameters:

self –
winSize (cv2.typing.Size) –

Return type:

None

getMaxLevel() → retval¶

Parameters:: self –
Return type:: int

setMaxLevel(maxLevel) → None¶

Parameters:

self –
maxLevel (int) –

Return type:

None

getTermCriteria() → retval¶

Parameters:: self –
Return type:: cv2.typing.TermCriteria

setTermCriteria(crit) → None¶

Parameters:

self –
crit (cv2.typing.TermCriteria) –

Return type:

None

getFlags() → retval¶

Parameters:: self –
Return type:: int

setFlags(flags) → None¶

Parameters:

self –
flags (int) –

Return type:

None

getMinEigThreshold() → retval¶

Parameters:: self –
Return type:: float

setMinEigThreshold(minEigThreshold) → None¶

Parameters:

self –
minEigThreshold (float) –

Return type:

None

class cv2.StereoBM¶

classmethod create([numDisparities[, blockSize]]) → retval¶

Creates StereoBM object

@param numDisparities the disparity search range. For each pixel algorithm will find the best
disparity from 0 (default minimum disparity) to numDisparities. The search range can then be
shifted by changing the minimum disparity.
@param blockSize the linear size of the blocks compared by the algorithm. The size should be odd
(as the block is centered at the current pixel). Larger block size implies smoother, though less
accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher
chance for algorithm to find a wrong correspondence.

The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for
a specific stereo pair.

Parameters:

cls –
numDisparities (int) –
blockSize (int) –

Return type:

StereoBM

getPreFilterType() → retval¶

Parameters:: self –
Return type:: int

setPreFilterType(preFilterType) → None¶

Parameters:

self –
preFilterType (int) –

Return type:

None

getPreFilterSize() → retval¶

Parameters:: self –
Return type:: int

setPreFilterSize(preFilterSize) → None¶

Parameters:

self –
preFilterSize (int) –

Return type:

None

getPreFilterCap() → retval¶

Parameters:: self –
Return type:: int

setPreFilterCap(preFilterCap) → None¶

Parameters:

self –
preFilterCap (int) –

Return type:

None

getTextureThreshold() → retval¶

Parameters:: self –
Return type:: int

setTextureThreshold(textureThreshold) → None¶

Parameters:

self –
textureThreshold (int) –

Return type:

None

getUniquenessRatio() → retval¶

Parameters:: self –
Return type:: int

setUniquenessRatio(uniquenessRatio) → None¶

Parameters:

self –
uniquenessRatio (int) –

Return type:

None

getSmallerBlockSize() → retval¶

Parameters:: self –
Return type:: int

setSmallerBlockSize(blockSize) → None¶

Parameters:

self –
blockSize (int) –

Return type:

None

getROI1() → retval¶

Parameters:: self –
Return type:: cv2.typing.Rect

setROI1(roi1) → None¶

Parameters:

self –
roi1 (cv2.typing.Rect) –

Return type:

None

getROI2() → retval¶

Parameters:: self –
Return type:: cv2.typing.Rect

setROI2(roi2) → None¶

Parameters:

self –
roi2 (cv2.typing.Rect) –

Return type:

None

class cv2.StereoMatcher¶

compute(left, right[, disparity]) → disparity¶

Computes disparity map for the specified stereo pair

@param left Left 8-bit single-channel image.
@param right Right image of the same size and the same type as the left one.
@param disparity Output disparity map. It has the same size as the input images. Some algorithms,
like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value
has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.

Parameters:

self –
left (cv2.typing.MatLike) –
right (cv2.typing.MatLike) –
disparity (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

compute(left, right[, disparity]) → disparity¶

Computes disparity map for the specified stereo pair

@param left Left 8-bit single-channel image.
@param right Right image of the same size and the same type as the left one.
@param disparity Output disparity map. It has the same size as the input images. Some algorithms,
like StereoBM or StereoSGBM compute 16-bit fixed-point disparity map (where each disparity value
has 4 fractional bits), whereas other algorithms output 32-bit floating-point disparity map.

Parameters:

self –
left (UMat) –
right (UMat) –
disparity (UMat | None) –

Return type:

getMinDisparity() → retval¶

Parameters:: self –
Return type:: int

setMinDisparity(minDisparity) → None¶

Parameters:

self –
minDisparity (int) –

Return type:

None

getNumDisparities() → retval¶

Parameters:: self –
Return type:: int

setNumDisparities(numDisparities) → None¶

Parameters:

self –
numDisparities (int) –

Return type:

None

getBlockSize() → retval¶

Parameters:: self –
Return type:: int

setBlockSize(blockSize) → None¶

Parameters:

self –
blockSize (int) –

Return type:

None

getSpeckleWindowSize() → retval¶

Parameters:: self –
Return type:: int

setSpeckleWindowSize(speckleWindowSize) → None¶

Parameters:

self –
speckleWindowSize (int) –

Return type:

None

getSpeckleRange() → retval¶

Parameters:: self –
Return type:: int

setSpeckleRange(speckleRange) → None¶

Parameters:

self –
speckleRange (int) –

Return type:

None

getDisp12MaxDiff() → retval¶

Parameters:: self –
Return type:: int

setDisp12MaxDiff(disp12MaxDiff) → None¶

Parameters:

self –
disp12MaxDiff (int) –

Return type:

None

class cv2.StereoSGBM¶

classmethod create([minDisparity[, numDisparities[, blockSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, mode]]]]]]]]]]]) → retval¶

Creates StereoSGBM object

@param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes
rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
@param numDisparities Maximum disparity minus minimum disparity. The value is always greater than
zero. In the current implementation, this parameter must be divisible by 16.
@param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
somewhere in the 3..11 range.
@param P1 The first parameter controlling the disparity smoothness. See below.
@param P2 The second parameter controlling the disparity smoothness. The larger the values are,
the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
P1 and P2 values are shown (like 8\*number_of_image_channels\*blockSize\*blockSize and
32\*number_of_image_channels\*blockSize\*blockSize , respectively).
@param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
disparity check. Set it to a non-positive value to disable the check.
@param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
The result values are passed to the Birchfield-Tomasi pixel cost function.
@param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function
value should "win" the second best value to consider the found match correct. Normally, a value
within the 5-15 range is good enough.
@param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles
and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
50-200 range.
@param speckleRange Maximum disparity variation within each connected component. If you do speckle
filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
Normally, 1 or 2 is good enough.
@param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
huge for HD-size pictures. By default, it is set to false .

The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
to a custom value.

Parameters:

cls –
minDisparity (int) –
numDisparities (int) –
blockSize (int) –
P1 (int) –
P2 (int) –
disp12MaxDiff (int) –
preFilterCap (int) –
uniquenessRatio (int) –
speckleWindowSize (int) –
speckleRange (int) –
mode (int) –

Return type:

StereoSGBM

getPreFilterCap() → retval¶

Parameters:: self –
Return type:: int

setPreFilterCap(preFilterCap) → None¶

Parameters:

self –
preFilterCap (int) –

Return type:

None

getUniquenessRatio() → retval¶

Parameters:: self –
Return type:: int

setUniquenessRatio(uniquenessRatio) → None¶

Parameters:

self –
uniquenessRatio (int) –

Return type:

None

getP1() → retval¶

Parameters:: self –
Return type:: int

setP1(P1) → None¶

Parameters:

self –
P1 (int) –

Return type:

None

getP2() → retval¶

Parameters:: self –
Return type:: int

setP2(P2) → None¶

Parameters:

self –
P2 (int) –

Return type:

None

getMode() → retval¶

Parameters:: self –
Return type:: int

setMode(mode) → None¶

Parameters:

self –
mode (int) –

Return type:

None

class cv2.Stitcher¶

classmethod create([mode]) → retval¶

Creates a Stitcher configured in one of the stitching modes.

@param mode Scenario for stitcher operation. This is usually determined by source of images
to stitch and their transformation. Default parameters will be chosen for operation in given
scenario.
@return Stitcher class instance.

Parameters:

cls –
mode (Stitcher_Mode) –

Return type:

Stitcher

estimateTransform(images[, masks]) → retval¶

These functions try to match the given images and to estimate rotations of each camera.

@note Use the functions only if you're aware of the stitching pipeline, otherwise use
Stitcher::stitch.

@param images Input images.
@param masks Masks for each input image specifying where to look for keypoints (optional).
@return Status code.

Parameters:

self –
images (_typing.Sequence[cv2.typing.MatLike]) –
masks (_typing.Sequence[cv2.typing.MatLike] | None) –

Return type:

Stitcher_Status

estimateTransform(images[, masks]) → retval¶

These functions try to match the given images and to estimate rotations of each camera.

@note Use the functions only if you're aware of the stitching pipeline, otherwise use
Stitcher::stitch.

@param images Input images.
@param masks Masks for each input image specifying where to look for keypoints (optional).
@return Status code.

Parameters:

self –
images (_typing.Sequence[UMat]) –
masks (_typing.Sequence[UMat] | None) –

Return type:

Stitcher_Status

composePanorama([pano]) → retval, pano¶

These functions try to compose the given images (or images stored internally from the other function calls) into the final pano under the assumption that the image transformations were estimated before.

@overload

@note Use the functions only if you're aware of the stitching pipeline, otherwise use
Stitcher::stitch.

@param images Input images.
@param pano Final pano.
@return Status code.

Parameters:

self –
pano (cv2.typing.MatLike | None) –

Return type:

tuple[Stitcher_Status, cv2.typing.MatLike]

composePanorama([pano]) → retval, pano¶

@overload

@note Use the functions only if you're aware of the stitching pipeline, otherwise use
Stitcher::stitch.

@param images Input images.
@param pano Final pano.
@return Status code.

Parameters:

self –
pano (UMat | None) –

Return type:

tuple[Stitcher_Status, UMat]

composePanorama([pano]) → retval, pano¶

@overload

@note Use the functions only if you're aware of the stitching pipeline, otherwise use
Stitcher::stitch.

@param images Input images.
@param pano Final pano.
@return Status code.

Parameters:

self –
images (_typing.Sequence[cv2.typing.MatLike]) –
pano (cv2.typing.MatLike | None) –

Return type:

tuple[Stitcher_Status, cv2.typing.MatLike]

composePanorama([pano]) → retval, pano¶

@overload

@note Use the functions only if you're aware of the stitching pipeline, otherwise use
Stitcher::stitch.

@param images Input images.
@param pano Final pano.
@return Status code.

Parameters:

self –
images (_typing.Sequence[UMat]) –
pano (UMat | None) –

Return type:

tuple[Stitcher_Status, UMat]

stitch(images[, pano]) → retval, pano¶

These functions try to stitch the given images.

@overload

@param images Input images.
@param masks Masks for each input image specifying where to look for keypoints (optional).
@param pano Final pano.
@return Status code.

Parameters:

self –
images (_typing.Sequence[cv2.typing.MatLike]) –
pano (cv2.typing.MatLike | None) –

Return type:

tuple[Stitcher_Status, cv2.typing.MatLike]

stitch(images[, pano]) → retval, pano¶

These functions try to stitch the given images.

@overload

@param images Input images.
@param masks Masks for each input image specifying where to look for keypoints (optional).
@param pano Final pano.
@return Status code.

Parameters:

self –
images (_typing.Sequence[UMat]) –
pano (UMat | None) –

Return type:

tuple[Stitcher_Status, UMat]

stitch(images[, pano]) → retval, pano¶

These functions try to stitch the given images.

@overload

@param images Input images.
@param masks Masks for each input image specifying where to look for keypoints (optional).
@param pano Final pano.
@return Status code.

Parameters:

self –
images (_typing.Sequence[cv2.typing.MatLike]) –
masks (_typing.Sequence[cv2.typing.MatLike]) –
pano (cv2.typing.MatLike | None) –

Return type:

tuple[Stitcher_Status, cv2.typing.MatLike]

stitch(images[, pano]) → retval, pano¶

These functions try to stitch the given images.

@overload

@param images Input images.
@param masks Masks for each input image specifying where to look for keypoints (optional).
@param pano Final pano.
@return Status code.

Parameters:

self –
images (_typing.Sequence[UMat]) –
masks (_typing.Sequence[UMat]) –
pano (UMat | None) –

Return type:

tuple[Stitcher_Status, UMat]

registrationResol() → retval¶

Parameters:: self –
Return type:: float

setRegistrationResol(resol_mpx) → None¶

Parameters:

self –
resol_mpx (float) –

Return type:

None

seamEstimationResol() → retval¶

Parameters:: self –
Return type:: float

setSeamEstimationResol(resol_mpx) → None¶

Parameters:

self –
resol_mpx (float) –

Return type:

None

compositingResol() → retval¶

Parameters:: self –
Return type:: float

setCompositingResol(resol_mpx) → None¶

Parameters:

self –
resol_mpx (float) –

Return type:

None

panoConfidenceThresh() → retval¶

Parameters:: self –
Return type:: float

setPanoConfidenceThresh(conf_thresh) → None¶

Parameters:

self –
conf_thresh (float) –

Return type:

None

waveCorrection() → retval¶

Parameters:: self –
Return type:: bool

setWaveCorrection(flag) → None¶

Parameters:

self –
flag (bool) –

Return type:

None

interpolationFlags() → retval¶

Parameters:: self –
Return type:: InterpolationFlags

setInterpolationFlags(interp_flags) → None¶

Parameters:

self –
interp_flags (InterpolationFlags) –

Return type:

None

workScale() → retval¶

Parameters:: self –
Return type:: float

class cv2.Subdiv2D¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, rect: cv2.typing.Rect)¶

Parameters:

self –
rect (cv2.typing.Rect) –

Return type:

None

insert(pt) → retval¶

Insert multiple points into a Delaunay triangulation.

@param pt Point to insert.

The function inserts a single point into a subdivision and modifies the subdivision topology
appropriately. If a point with the same coordinates exists already, no new point is added.
@returns the ID of the point.

@note If the point is outside of the triangulation specified rect a runtime error is raised.

@param ptvec Points to insert.

The function inserts a vector of points into a subdivision and modifies the subdivision topology
appropriately.

Parameters:

self –
pt (cv2.typing.Point2f) –

Return type:

insert(pt) → retval¶

Insert multiple points into a Delaunay triangulation.

@param pt Point to insert.

The function inserts a single point into a subdivision and modifies the subdivision topology
appropriately. If a point with the same coordinates exists already, no new point is added.
@returns the ID of the point.

@note If the point is outside of the triangulation specified rect a runtime error is raised.

@param ptvec Points to insert.

The function inserts a vector of points into a subdivision and modifies the subdivision topology
appropriately.

Parameters:

self –
ptvec (_typing.Sequence[cv2.typing.Point2f]) –

Return type:

None

initDelaunay(rect) → None¶

Creates a new empty Delaunay subdivision

@param rect Rectangle that includes all of the 2D points that are to be added to the subdivision.

Parameters:

self –
rect (cv2.typing.Rect) –

Return type:

None

locate(pt) → retval, edge, vertex¶

Returns the location of a point within a Delaunay triangulation.

@param pt Point to locate.
@param edge Output edge that the point belongs to or is located to the right of it.
@param vertex Optional output vertex the input point coincides with.

The function locates the input point within the subdivision and gives one of the triangle edges
or vertices.

@returns an integer which specify one of the following five cases for point location:
-  The point falls into some facet. The function returns #PTLOC_INSIDE and edge will contain one of
   edges of the facet.
-  The point falls onto the edge. The function returns #PTLOC_ON_EDGE and edge will contain this edge.
-  The point coincides with one of the subdivision vertices. The function returns #PTLOC_VERTEX and
   vertex will contain a pointer to the vertex.
-  The point is outside the subdivision reference rectangle. The function returns #PTLOC_OUTSIDE_RECT
   and no pointers are filled.
-  One of input arguments is invalid. A runtime error is raised or, if silent or "parent" error
   processing mode is selected, #PTLOC_ERROR is returned.

Parameters:

self –
pt (cv2.typing.Point2f) –

Return type:

tuple[int, int, int]

findNearest(pt) → retval, nearestPt¶

Finds the subdivision vertex closest to the given point.

@param pt Input point.
@param nearestPt Output subdivision vertex point.

The function is another function that locates the input point within the subdivision. It finds the
subdivision vertex that is the closest to the input point. It is not necessarily one of vertices
of the facet containing the input point, though the facet (located using locate() ) is used as a
starting point.

@returns vertex ID.

Parameters:

self –
pt (cv2.typing.Point2f) –

Return type:

tuple[int, cv2.typing.Point2f]

getEdgeList() → edgeList¶

Returns a list of all edges.

@param edgeList Output vector.

The function gives each edge as a 4 numbers vector, where each two are one of the edge
vertices. i.e. org_x = v[0], org_y = v[1], dst_x = v[2], dst_y = v[3].

Parameters:: self –
Return type:: _typing.Sequence[cv2.typing.Vec4f]

getLeadingEdgeList() → leadingEdgeList¶

Returns a list of the leading edge ID connected to each triangle.

@param leadingEdgeList Output vector.

The function gives one edge ID for each triangle.

Parameters:: self –
Return type:: _typing.Sequence[int]

getTriangleList() → triangleList¶

Returns a list of all triangles.

@param triangleList Output vector.

The function gives each triangle as a 6 numbers vector, where each two are one of the triangle
vertices. i.e. p1_x = v[0], p1_y = v[1], p2_x = v[2], p2_y = v[3], p3_x = v[4], p3_y = v[5].

Parameters:: self –
Return type:: _typing.Sequence[cv2.typing.Vec6f]

getVoronoiFacetList(idx) → facetList, facetCenters¶

Returns a list of all Voronoi facets.

@param idx Vector of vertices IDs to consider. For all vertices you can pass empty vector.
@param facetList Output vector of the Voronoi facets.
@param facetCenters Output vector of the Voronoi facets center points.

Parameters:

self –
idx (_typing.Sequence[int]) –

Return type:

tuple[_typing.Sequence[_typing.Sequence[cv2.typing.Point2f]], _typing.Sequence[cv2.typing.Point2f]]

getVertex(vertex) → retval, firstEdge¶

Returns vertex location from vertex ID.

@param vertex vertex ID.
@param firstEdge Optional. The first edge ID which is connected to the vertex.
@returns vertex (x,y)

Parameters:

self –
vertex (int) –

Return type:

tuple[cv2.typing.Point2f, int]

getEdge(edge, nextEdgeType) → retval¶

Returns one of the edges related to the given edge.

@param edge Subdivision edge ID.
@param nextEdgeType Parameter specifying which of the related edges to return.
The following values are possible:
-   NEXT_AROUND_ORG next around the edge origin ( eOnext on the picture below if e is the input edge)
-   NEXT_AROUND_DST next around the edge vertex ( eDnext )
-   PREV_AROUND_ORG previous around the edge origin (reversed eRnext )
-   PREV_AROUND_DST previous around the edge destination (reversed eLnext )
-   NEXT_AROUND_LEFT next around the left facet ( eLnext )
-   NEXT_AROUND_RIGHT next around the right facet ( eRnext )
-   PREV_AROUND_LEFT previous around the left facet (reversed eOnext )
-   PREV_AROUND_RIGHT previous around the right facet (reversed eDnext )

![sample output](pics/quadedge.png)

@returns edge ID related to the input edge.

Parameters:

self –
edge (int) –
nextEdgeType (int) –

Return type:

nextEdge(edge) → retval¶

Returns next edge around the edge origin.

@param edge Subdivision edge ID.

@returns an integer which is next edge ID around the edge origin: eOnext on the
picture above if e is the input edge).

Parameters:

self –
edge (int) –

Return type:

rotateEdge(edge, rotate) → retval¶

Returns another edge of the same quad-edge.

@param edge Subdivision edge ID.
@param rotate Parameter specifying which of the edges of the same quad-edge as the input
one to return. The following values are possible:
-   0 - the input edge ( e on the picture below if e is the input edge)
-   1 - the rotated edge ( eRot )
-   2 - the reversed edge (reversed e (in green))
-   3 - the reversed rotated edge (reversed eRot (in green))

@returns one of the edges ID of the same quad-edge as the input edge.

Parameters:

self –
edge (int) –
rotate (int) –

Return type:

symEdge(edge) → retval¶

Parameters:

self –
edge (int) –

Return type:

edgeOrg(edge) → retval, orgpt¶

Returns the edge origin.

@param edge Subdivision edge ID.
@param orgpt Output vertex location.

@returns vertex ID.

Parameters:

self –
edge (int) –

Return type:

tuple[int, cv2.typing.Point2f]

edgeDst(edge) → retval, dstpt¶

Returns the edge destination.

@param edge Subdivision edge ID.
@param dstpt Output vertex location.

@returns vertex ID.

Parameters:

self –
edge (int) –

Return type:

tuple[int, cv2.typing.Point2f]

class cv2.TickMeter¶

__init__(self)¶

Parameters:: self –
Return type:: None

start() → None¶

Parameters:: self –
Return type:: None

stop() → None¶

Parameters:: self –
Return type:: None

getTimeTicks() → retval¶

Parameters:: self –
Return type:: int

getTimeMicro() → retval¶

Parameters:: self –
Return type:: float

getTimeMilli() → retval¶

Parameters:: self –
Return type:: float

getTimeSec() → retval¶

Parameters:: self –
Return type:: float

getCounter() → retval¶

Parameters:: self –
Return type:: int

getFPS() → retval¶

Parameters:: self –
Return type:: float

getAvgTimeSec() → retval¶

Parameters:: self –
Return type:: float

getAvgTimeMilli() → retval¶

Parameters:: self –
Return type:: float

reset() → None¶

Parameters:: self –
Return type:: None

class cv2.Tonemap¶

process(src[, dst]) → dst¶

Tonemaps image

@param src source image - CV_32FC3 Mat (float 32 bits 3 channels)
@param dst destination image - CV_32FC3 Mat with values in [0, 1] range

Parameters:

self –
src (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

process(src[, dst]) → dst¶

Tonemaps image

@param src source image - CV_32FC3 Mat (float 32 bits 3 channels)
@param dst destination image - CV_32FC3 Mat with values in [0, 1] range

Parameters:

self –
src (UMat) –
dst (UMat | None) –

Return type:

getGamma() → retval¶

Parameters:: self –
Return type:: float

setGamma(gamma) → None¶

Parameters:

self –
gamma (float) –

Return type:

None

class cv2.TonemapDrago¶

getSaturation() → retval¶

Parameters:: self –
Return type:: float

setSaturation(saturation) → None¶

Parameters:

self –
saturation (float) –

Return type:

None

getBias() → retval¶

Parameters:: self –
Return type:: float

setBias(bias) → None¶

Parameters:

self –
bias (float) –

Return type:

None

class cv2.TonemapMantiuk¶

getScale() → retval¶

Parameters:: self –
Return type:: float

setScale(scale) → None¶

Parameters:

self –
scale (float) –

Return type:

None

getSaturation() → retval¶

Parameters:: self –
Return type:: float

setSaturation(saturation) → None¶

Parameters:

self –
saturation (float) –

Return type:

None

class cv2.TonemapReinhard¶

getIntensity() → retval¶

Parameters:: self –
Return type:: float

setIntensity(intensity) → None¶

Parameters:

self –
intensity (float) –

Return type:

None

getLightAdaptation() → retval¶

Parameters:: self –
Return type:: float

setLightAdaptation(light_adapt) → None¶

Parameters:

self –
light_adapt (float) –

Return type:

None

getColorAdaptation() → retval¶

Parameters:: self –
Return type:: float

setColorAdaptation(color_adapt) → None¶

Parameters:

self –
color_adapt (float) –

Return type:

None

class cv2.Tracker¶

init(image, boundingBox) → None¶

Initialize the tracker with a known bounding box that surrounded the target @param image The initial frame @param boundingBox The initial bounding box

Parameters:

self –
image (cv2.typing.MatLike) –
boundingBox (cv2.typing.Rect) –

Return type:

None

init(image, boundingBox) → None¶

Initialize the tracker with a known bounding box that surrounded the target @param image The initial frame @param boundingBox The initial bounding box

Parameters:

self –
image (UMat) –
boundingBox (cv2.typing.Rect) –

Return type:

None

update(image) → retval, boundingBox¶

Update the tracker, find the new most likely bounding box for the target @param image The current frame @param boundingBox The bounding box that represent the new target location, if true was returned, not modified otherwise

@return True means that target was located and false means that tracker cannot locate target in
current frame. Note, that latter *does not* imply that tracker has failed, maybe target is indeed
missing from the frame (say, out of sight)

Parameters:

self –
image (cv2.typing.MatLike) –

Return type:

tuple[bool, cv2.typing.Rect]

update(image) → retval, boundingBox¶

@return True means that target was located and false means that tracker cannot locate target in
current frame. Note, that latter *does not* imply that tracker has failed, maybe target is indeed
missing from the frame (say, out of sight)

Parameters:

self –
image (UMat) –

Return type:

tuple[bool, cv2.typing.Rect]

class cv2.TrackerDaSiamRPN¶

classmethod create([parameters]) → retval¶

Constructor @param parameters DaSiamRPN parameters TrackerDaSiamRPN::Params

Parameters:

cls –
parameters (TrackerDaSiamRPN.Params) –

Return type:

TrackerDaSiamRPN

getTrackingScore() → retval¶

Return tracking score

Parameters:: self –
Return type:: float

class cv2.TrackerGOTURN¶

classmethod create([parameters]) → retval¶

Constructor @param parameters GOTURN parameters TrackerGOTURN::Params

Parameters:

cls –
parameters (TrackerGOTURN.Params) –

Return type:

TrackerGOTURN

class cv2.TrackerMIL¶

classmethod create([parameters]) → retval¶

Create MIL tracker instance * @param parameters MIL parameters TrackerMIL::Params

Parameters:

cls –
parameters (TrackerMIL.Params) –

Return type:

TrackerMIL

class cv2.TrackerNano¶

classmethod create([parameters]) → retval¶

Constructor @param parameters NanoTrack parameters TrackerNano::Params

Parameters:

cls –
parameters (TrackerNano.Params) –

Return type:

TrackerNano

getTrackingScore() → retval¶

Return tracking score

Parameters:: self –
Return type:: float

class cv2.TrackerVit¶

classmethod create([parameters]) → retval¶

Constructor @param parameters vit tracker parameters TrackerVit::Params

Parameters:

cls –
parameters (TrackerVit.Params) –

Return type:

TrackerVit

getTrackingScore() → retval¶

Return tracking score

Parameters:: self –
Return type:: float

class cv2.UMat¶

__init__(self, usageFlags: UMatUsageFlags = ...)¶

Parameters:

self –
usageFlags (UMatUsageFlags) –

Return type:

None

__init__(self, rows: int, cols: int, type: int, usageFlags: UMatUsageFlags = ...)¶

Parameters:

self –
rows (int) –
cols (int) –
type (int) –
usageFlags (UMatUsageFlags) –

Return type:

None

__init__(self, size: cv2.typing.Size, type: int, usageFlags: UMatUsageFlags = ...)¶

Parameters:

self –
size (cv2.typing.Size) –
type (int) –
usageFlags (UMatUsageFlags) –

Return type:

None

__init__(self, rows: int, cols: int, type: int, s: cv2.typing.Scalar, usageFlags: UMatUsageFlags = ...)¶

Parameters:

self –
rows (int) –
cols (int) –
type (int) –
s (cv2.typing.Scalar) –
usageFlags (UMatUsageFlags) –

Return type:

None

__init__(self, size: cv2.typing.Size, type: int, s: cv2.typing.Scalar, usageFlags: UMatUsageFlags = ...)¶

Parameters:

self –
size (cv2.typing.Size) –
type (int) –
s (cv2.typing.Scalar) –
usageFlags (UMatUsageFlags) –

Return type:

None

__init__(self, m: UMat)¶

Parameters:

self –
m (UMat) –

Return type:

None

__init__(self, m: UMat, rowRange: cv2.typing.Range, colRange: cv2.typing.Range = ...)¶

Parameters:

self –
m (UMat) –
rowRange (cv2.typing.Range) –
colRange (cv2.typing.Range) –

Return type:

None

__init__(self, m: UMat, roi: cv2.typing.Rect)¶

Parameters:

self –
m (UMat) –
roi (cv2.typing.Rect) –

Return type:

None

__init__(self, m: UMat, ranges: _typing.Sequence[cv2.typing.Range])¶

Parameters:

self –
m (UMat) –
ranges (_typing.Sequence[cv2.typing.Range]) –

Return type:

None

static queue() → retval¶

Return type:: cv2.typing.IntPointer

static context() → retval¶

Return type:: cv2.typing.IntPointer

get() → retval¶

Parameters:: self –
Return type:: cv2.typing.MatLike

isContinuous() → retval¶

Parameters:: self –
Return type:: bool

isSubmatrix() → retval¶

Parameters:: self –
Return type:: bool

handle(accessFlags) → retval¶

Parameters:

self –
accessFlags (AccessFlag) –

Return type:

cv2.typing.IntPointer

offset: int¶

class cv2.UsacParams¶

__init__(self)¶

Parameters:: self –
Return type:: None

confidence: float¶

isParallel: bool¶

loIterations: int¶

loMethod: LocalOptimMethod¶

loSampleSize: int¶

maxIterations: int¶

neighborsSearch: NeighborSearchMethod¶

randomGeneratorState: int¶

sampler: SamplingMethod¶

score: ScoreMethod¶

threshold: float¶

final_polisher: PolishingMethod¶

final_polisher_iterations: int¶

class cv2.VariationalRefinement¶

classmethod create() → retval¶

Creates an instance of VariationalRefinement

Parameters:: cls –
Return type:: VariationalRefinement

calcUV(I0, I1, flow_u, flow_v) → flow_u, flow_v¶

@ref calc function overload to handle separate horizontal (u) and vertical (v) flow components(to avoid extra splits/merges)

Parameters:

self –
I0 (cv2.typing.MatLike) –
I1 (cv2.typing.MatLike) –
flow_u (cv2.typing.MatLike) –
flow_v (cv2.typing.MatLike) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

calcUV(I0, I1, flow_u, flow_v) → flow_u, flow_v¶

@ref calc function overload to handle separate horizontal (u) and vertical (v) flow components(to avoid extra splits/merges)

Parameters:

self –
I0 (UMat) –
I1 (UMat) –
flow_u (UMat) –
flow_v (UMat) –

Return type:

getFixedPointIterations() → retval¶

Number of outer (fixed-point) iterations in the minimization procedure.

See also: setFixedPointIterations

Parameters:: self –
Return type:: int

setFixedPointIterations(val) → None¶

@copybrief getFixedPointIterations @see getFixedPointIterations

Parameters:

self –
val (int) –

Return type:

None

getSorIterations() → retval¶

Number of inner successive over-relaxation (SOR) iterations in the minimization procedure to solve the respective linear system.

See also: setSorIterations

Parameters:: self –
Return type:: int

setSorIterations(val) → None¶

@copybrief getSorIterations @see getSorIterations

Parameters:

self –
val (int) –

Return type:

None

getOmega() → retval¶

Relaxation factor in SOR

See also: setOmega

Parameters:: self –
Return type:: float

setOmega(val) → None¶

@copybrief getOmega @see getOmega

Parameters:

self –
val (float) –

Return type:

None

getAlpha() → retval¶

Weight of the smoothness term

See also: setAlpha

Parameters:: self –
Return type:: float

setAlpha(val) → None¶

@copybrief getAlpha @see getAlpha

Parameters:

self –
val (float) –

Return type:

None

getDelta() → retval¶

Weight of the color constancy term

See also: setDelta

Parameters:: self –
Return type:: float

setDelta(val) → None¶

@copybrief getDelta @see getDelta

Parameters:

self –
val (float) –

Return type:

None

getGamma() → retval¶

Weight of the gradient constancy term

See also: setGamma

Parameters:: self –
Return type:: float

setGamma(val) → None¶

@copybrief getGamma @see getGamma

Parameters:

self –
val (float) –

Return type:

None

class cv2.VideoCapture¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, filename: str, apiPreference: int = ...)¶

Parameters:

self –
filename (str) –
apiPreference (int) –

Return type:

None

__init__(self, filename: str, apiPreference: int, params: _typing.Sequence[int])¶

Parameters:

self –
filename (str) –
apiPreference (int) –
params (_typing.Sequence[int]) –

Return type:

None

__init__(self, index: int, apiPreference: int = ...)¶

Parameters:

self –
index (int) –
apiPreference (int) –

Return type:

None

__init__(self, index: int, apiPreference: int, params: _typing.Sequence[int])¶

Parameters:

self –
index (int) –
apiPreference (int) –
params (_typing.Sequence[int]) –

Return type:

None

open(filename[, apiPreference]) → retval¶

Opens a camera for video capturing with API Preference and parameters

@overload

Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference = CAP_ANY)
@return `true` if the file has been successfully opened

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
See cv::VideoCaptureProperties

@return `true` if the file has been successfully opened

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

Parameters are same as the constructor VideoCapture(int index, int apiPreference = CAP_ANY)
@return `true` if the camera has been successfully opened.

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
See cv::VideoCaptureProperties

@return `true` if the camera has been successfully opened.

The method first calls VideoCapture::release to close the already opened file or camera.

Parameters:

self –
filename (str) –
apiPreference (int) –

Return type:

open(filename[, apiPreference]) → retval¶

Opens a camera for video capturing with API Preference and parameters

@overload

Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference = CAP_ANY)
@return `true` if the file has been successfully opened

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
See cv::VideoCaptureProperties

@return `true` if the file has been successfully opened

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

Parameters are same as the constructor VideoCapture(int index, int apiPreference = CAP_ANY)
@return `true` if the camera has been successfully opened.

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
See cv::VideoCaptureProperties

@return `true` if the camera has been successfully opened.

The method first calls VideoCapture::release to close the already opened file or camera.

Parameters:

self –
filename (str) –
apiPreference (int) –
params (_typing.Sequence[int]) –

Return type:

open(filename[, apiPreference]) → retval¶

Opens a camera for video capturing with API Preference and parameters

@overload

Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference = CAP_ANY)
@return `true` if the file has been successfully opened

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
See cv::VideoCaptureProperties

@return `true` if the file has been successfully opened

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

Parameters are same as the constructor VideoCapture(int index, int apiPreference = CAP_ANY)
@return `true` if the camera has been successfully opened.

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
See cv::VideoCaptureProperties

@return `true` if the camera has been successfully opened.

The method first calls VideoCapture::release to close the already opened file or camera.

Parameters:

self –
index (int) –
apiPreference (int) –

Return type:

open(filename[, apiPreference]) → retval¶

Opens a camera for video capturing with API Preference and parameters

@overload

Parameters are same as the constructor VideoCapture(const String& filename, int apiPreference = CAP_ANY)
@return `true` if the file has been successfully opened

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
See cv::VideoCaptureProperties

@return `true` if the file has been successfully opened

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

Parameters are same as the constructor VideoCapture(int index, int apiPreference = CAP_ANY)
@return `true` if the camera has been successfully opened.

The method first calls VideoCapture::release to close the already opened file or camera.

@overload

The `params` parameter allows to specify extra parameters encoded as pairs `(paramId_1, paramValue_1, paramId_2, paramValue_2, ...)`.
See cv::VideoCaptureProperties

@return `true` if the camera has been successfully opened.

The method first calls VideoCapture::release to close the already opened file or camera.

Parameters:

self –
index (int) –
apiPreference (int) –
params (_typing.Sequence[int]) –

Return type:

retrieve([image[, flag]]) → retval, image¶

Decodes and returns the grabbed video frame.

@param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty.
@param flag it could be a frame index or a driver specific flag
@return `false` if no frames has been grabbed

The method decodes and returns the just grabbed frame. If no frames has been grabbed
(camera has been disconnected, or there are no more frames in video file), the method returns false
and the function returns an empty image (with %cv::Mat, test it with Mat::empty()).

@sa read()

@note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video
capturing structure. It is not allowed to modify or release the image! You can copy the frame using
cvCloneImage and then do whatever you want with the copy.

Parameters:

self –
image (cv2.typing.MatLike | None) –
flag (int) –

Return type:

tuple[bool, cv2.typing.MatLike]

retrieve([image[, flag]]) → retval, image¶

Decodes and returns the grabbed video frame.

@param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty.
@param flag it could be a frame index or a driver specific flag
@return `false` if no frames has been grabbed

The method decodes and returns the just grabbed frame. If no frames has been grabbed
(camera has been disconnected, or there are no more frames in video file), the method returns false
and the function returns an empty image (with %cv::Mat, test it with Mat::empty()).

@sa read()

@note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video
capturing structure. It is not allowed to modify or release the image! You can copy the frame using
cvCloneImage and then do whatever you want with the copy.

Parameters:

self –
image (UMat | None) –
flag (int) –

Return type:

read([image]) → retval, image¶

Grabs, decodes and returns the next video frame.

@param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty.
@return `false` if no frames has been grabbed

The method/function combines VideoCapture::grab() and VideoCapture::retrieve() in one call. This is the
most convenient method for reading video files or capturing data from decode and returns the just
grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more
frames in video file), the method returns false and the function returns empty image (with %cv::Mat, test it with Mat::empty()).

@note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video
capturing structure. It is not allowed to modify or release the image! You can copy the frame using
cvCloneImage and then do whatever you want with the copy.

Parameters:

self –
image (cv2.typing.MatLike | None) –

Return type:

tuple[bool, cv2.typing.MatLike]

read([image]) → retval, image¶

Grabs, decodes and returns the next video frame.

@param [out] image the video frame is returned here. If no frames has been grabbed the image will be empty.
@return `false` if no frames has been grabbed

The method/function combines VideoCapture::grab() and VideoCapture::retrieve() in one call. This is the
most convenient method for reading video files or capturing data from decode and returns the just
grabbed frame. If no frames has been grabbed (camera has been disconnected, or there are no more
frames in video file), the method returns false and the function returns empty image (with %cv::Mat, test it with Mat::empty()).

@note In @ref videoio_c "C API", functions cvRetrieveFrame() and cv.RetrieveFrame() return image stored inside the video
capturing structure. It is not allowed to modify or release the image! You can copy the frame using
cvCloneImage and then do whatever you want with the copy.

Parameters:

self –
image (UMat | None) –

Return type:

isOpened() → retval¶

Returns true if video capturing has been initialized already.

If the previous call to VideoCapture constructor or VideoCapture::open() succeeded, the method returns
true.

Parameters:: self –
Return type:: bool

release() → None¶

Closes video file or capturing device.

The method is automatically called by subsequent VideoCapture::open and by VideoCapture
destructor.

The C function also deallocates memory and clears \*capture pointer.

Parameters:: self –
Return type:: None

grab() → retval¶

Grabs the next frame from video file or capturing device.

@return `true` (non-zero) in the case of success.

The method/function grabs the next frame from video file or camera and returns true (non-zero) in
the case of success.

The primary use of the function is in multi-camera environments, especially when the cameras do not
have hardware synchronization. That is, you call VideoCapture::grab() for each camera and after that
call the slower method VideoCapture::retrieve() to decode and get frame from each camera. This way
the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames
from different cameras will be closer in time.

Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the
correct way of retrieving data from it is to call VideoCapture::grab() first and then call
VideoCapture::retrieve() one or more times with different values of the channel parameter.

@ref tutorial_kinect_openni

Parameters:: self –
Return type:: bool

set(propId, value) → retval¶

Sets a property in the VideoCapture.

@param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...)
or one from @ref videoio_flags_others
@param value Value of the property.
@return `true` if the property is supported by backend used by the VideoCapture instance.
@note Even if it returns `true` this doesn't ensure that the property
value has been accepted by the capture device. See note in VideoCapture::get()

Parameters:

self –
propId (int) –
value (float) –

Return type:

get(propId) → retval¶

Returns the specified VideoCapture property

@param propId Property identifier from cv::VideoCaptureProperties (eg. cv::CAP_PROP_POS_MSEC, cv::CAP_PROP_POS_FRAMES, ...)
or one from @ref videoio_flags_others
@return Value for the specified property. Value 0 is returned when querying a property that is
not supported by the backend used by the VideoCapture instance.

@note Reading / writing properties involves many layers. Some unexpected result might happens
along this chain.
@code{.txt}
VideoCapture -> API Backend -> Operating System -> Device Driver -> Device Hardware
@endcode
The returned value might be different from what really used by the device or it could be encoded
using device dependent rules (eg. steps or percentage). Effective behaviour depends from device
driver and API Backend

Parameters:

self –
propId (int) –

Return type:

getBackendName() → retval¶

Returns used backend API name

 @note Stream should be opened.

Parameters:: self –
Return type:: str

setExceptionMode(enable) → None¶

Switches exceptions mode * * methods raise exceptions if not successful instead of returning an error code

Parameters:

self –
enable (bool) –

Return type:

None

getExceptionMode() → retval¶

Parameters:: self –
Return type:: bool

static waitAny(streams[, timeoutNs]) → retval, readyIndex¶

Wait for ready frames from VideoCapture.

@param streams input video streams
@param readyIndex stream indexes with grabbed frames (ready to use .retrieve() to fetch actual frame)
@param timeoutNs number of nanoseconds (0 - infinite)
@return `true` if streamReady is not empty

@throws Exception %Exception on stream errors (check .isOpened() to filter out malformed streams) or VideoCapture type is not supported

The primary use of the function is in multi-camera environments.
The method fills the ready state vector, grabs video frame, if camera is ready.

After this call use VideoCapture::retrieve() to decode and fetch frame data.

Parameters:

streams (_typing.Sequence[VideoCapture]) –
timeoutNs (int) –

Return type:

tuple[bool, _typing.Sequence[int]]

class cv2.VideoWriter¶

__init__(self)¶

Parameters:: self –
Return type:: None

__init__(self, filename: str, fourcc: int, fps: float, frameSize: cv2.typing.Size, isColor: bool = ...)¶

Parameters:

self –
filename (str) –
fourcc (int) –
fps (float) –
frameSize (cv2.typing.Size) –
isColor (bool) –

Return type:

None

__init__(self, filename: str, apiPreference: int, fourcc: int, fps: float, frameSize: cv2.typing.Size, isColor: bool = ...)¶

Parameters:

self –
filename (str) –
apiPreference (int) –
fourcc (int) –
fps (float) –
frameSize (cv2.typing.Size) –
isColor (bool) –

Return type:

None

__init__(self, filename: str, fourcc: int, fps: float, frameSize: cv2.typing.Size, params: _typing.Sequence[int])¶

Parameters:

self –
filename (str) –
fourcc (int) –
fps (float) –
frameSize (cv2.typing.Size) –
params (_typing.Sequence[int]) –

Return type:

None

__init__(self, filename: str, apiPreference: int, fourcc: int, fps: float, frameSize: cv2.typing.Size, params: _typing.Sequence[int])¶

Parameters:

self –
filename (str) –
apiPreference (int) –
fourcc (int) –
fps (float) –
frameSize (cv2.typing.Size) –
params (_typing.Sequence[int]) –

Return type:

None

open(filename, fourcc, fps, frameSize[, isColor]) → retval¶

Initializes or reinitializes video writer.

The method opens video writer. Parameters are the same as in the constructor
VideoWriter::VideoWriter.
@return `true` if video writer has been successfully initialized

The method first calls VideoWriter::release to close the already opened file.

@overload @overload @overload

Parameters:

self –
filename (str) –
fourcc (int) –
fps (float) –
frameSize (cv2.typing.Size) –
isColor (bool) –

Return type:

open(filename, fourcc, fps, frameSize[, isColor]) → retval¶

Initializes or reinitializes video writer.

The method opens video writer. Parameters are the same as in the constructor
VideoWriter::VideoWriter.
@return `true` if video writer has been successfully initialized

The method first calls VideoWriter::release to close the already opened file.

@overload @overload @overload

Parameters:

self –
filename (str) –
apiPreference (int) –
fourcc (int) –
fps (float) –
frameSize (cv2.typing.Size) –
isColor (bool) –

Return type:

open(filename, fourcc, fps, frameSize[, isColor]) → retval¶

Initializes or reinitializes video writer.

The method opens video writer. Parameters are the same as in the constructor
VideoWriter::VideoWriter.
@return `true` if video writer has been successfully initialized

The method first calls VideoWriter::release to close the already opened file.

@overload @overload @overload

Parameters:

self –
filename (str) –
fourcc (int) –
fps (float) –
frameSize (cv2.typing.Size) –
params (_typing.Sequence[int]) –

Return type:

open(filename, fourcc, fps, frameSize[, isColor]) → retval¶

Initializes or reinitializes video writer.

The method opens video writer. Parameters are the same as in the constructor
VideoWriter::VideoWriter.
@return `true` if video writer has been successfully initialized

The method first calls VideoWriter::release to close the already opened file.

@overload @overload @overload

Parameters:

self –
filename (str) –
apiPreference (int) –
fourcc (int) –
fps (float) –
frameSize (cv2.typing.Size) –
params (_typing.Sequence[int]) –

Return type:

write(image) → None¶

Writes the next video frame

@param image The written frame. In general, color images are expected in BGR format.

The function/method writes the specified image to video file. It must have the same size as has
been specified when opening the video writer.

Parameters:

self –
image (cv2.typing.MatLike) –

Return type:

None

write(image) → None¶

Writes the next video frame

@param image The written frame. In general, color images are expected in BGR format.

The function/method writes the specified image to video file. It must have the same size as has
been specified when opening the video writer.

Parameters:

self –
image (UMat) –

Return type:

None

isOpened() → retval¶

Returns true if video writer has been successfully initialized.

Parameters:: self –
Return type:: bool

release() → None¶

Closes the video writer.

The method is automatically called by subsequent VideoWriter::open and by the VideoWriter
destructor.

Parameters:: self –
Return type:: None

set(propId, value) → retval¶

Sets a property in the VideoWriter.

 @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY)
 or one of @ref videoio_flags_others

 @param value Value of the property.
 @return  `true` if the property is supported by the backend used by the VideoWriter instance.

Parameters:

self –
propId (int) –
value (float) –

Return type:

get(propId) → retval¶

Returns the specified VideoWriter property

 @param propId Property identifier from cv::VideoWriterProperties (eg. cv::VIDEOWRITER_PROP_QUALITY)
 or one of @ref videoio_flags_others

 @return Value for the specified property. Value 0 is returned when querying a property that is
 not supported by the backend used by the VideoWriter instance.

Parameters:

self –
propId (int) –

Return type:

static fourcc(c1, c2, c3, c4) → retval¶

Concatenates 4 chars to a fourcc code

@return a fourcc code

This static method constructs the fourcc code of the codec to be used in the constructor
VideoWriter::VideoWriter or VideoWriter::open.

Parameters:

c1 (str) –
c2 (str) –
c3 (str) –
c4 (str) –

Return type:

getBackendName() → retval¶

Returns used backend API name

 @note Stream should be opened.

Parameters:: self –
Return type:: str

class cv2.WarperCreator¶

class cv2.error¶

code: int¶

err: str¶

file: str¶

func: str¶

line: int¶

msg: str¶

Functions¶

cv2.AKAZE_create([descriptor_type[, descriptor_size[, descriptor_channels[, threshold[, nOctaves[, nOctaveLayers[, diffusivity[, max_points]]]]]]]]) → retval¶

The AKAZE constructor

@param descriptor_type Type of the extracted descriptor: DESCRIPTOR_KAZE,
DESCRIPTOR_KAZE_UPRIGHT, DESCRIPTOR_MLDB or DESCRIPTOR_MLDB_UPRIGHT.
@param descriptor_size Size of the descriptor in bits. 0 -\> Full size
@param descriptor_channels Number of channels in the descriptor (1, 2, 3)
@param threshold Detector response threshold to accept point
@param nOctaves Maximum octave evolution of the image
@param nOctaveLayers Default number of sublevels per scale level
@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
DIFF_CHARBONNIER
@param max_points Maximum amount of returned points. In case if image contains
more features, then the features with highest response are returned.
Negative value means no limitation.

Return type:: object

cv2.AffineFeature_create(backend[, maxTilt[, minTilt[, tiltStep[, rotateStepBase]]]]) → retval¶

Parameters:: backend – The detector/extractor you want to use as backend. @param maxTilt The highest power index of tilt factor. 5 is used in the paper as tilt sampling range n. @param minTilt The lowest power index of tilt factor. 0 is used in the paper. @param tiltStep Tilt sampling step \(\delta_t\) in Algorithm 1 in the paper. @param rotateStepBase Rotation sampling step factor b in Algorithm 1 in the paper.
Return type:: object

cv2.AgastFeatureDetector_create([threshold[, nonmaxSuppression[, type]]]) → retval¶

Return type:: object

cv2.BFMatcher_create([normType[, crossCheck]]) → retval¶

Return type:: object

cv2.BRISK_create([thresh[, octaves[, patternScale]]]) → retval¶

The BRISK constructor for a custom pattern, detection threshold and octaves

@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param patternScale apply this scale to the pattern used for sampling the neighbourhood of a
keypoint.

@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).

@param thresh AGAST detection threshold score.
@param octaves detection octaves. Use 0 to do single scale.
@param radiusList defines the radii (in pixels) where the samples around a keypoint are taken (for
keypoint scale 1).
@param numberList defines the number of sampling points on the sampling circle. Must be the same
size as radiusList..
@param dMax threshold for the short pairings used for descriptor formation (in pixels for keypoint
scale 1).
@param dMin threshold for the long pairings used for orientation determination (in pixels for
keypoint scale 1).

Parameters:: indexChange – index remapping of the bits.
Return type:: object

cv2.CV_16FC(channels) → retval¶

Parameters:: channels (int) –
Return type:: int

cv2.CV_16SC(channels) → retval¶

Parameters:: channels (int) –
Return type:: int

cv2.CV_16UC(channels) → retval¶

Parameters:: channels (int) –
Return type:: int

cv2.CV_32FC(channels) → retval¶

Parameters:: channels (int) –
Return type:: int

cv2.CV_32SC(channels) → retval¶

Parameters:: channels (int) –
Return type:: int

cv2.CV_64FC(channels) → retval¶

Parameters:: channels (int) –
Return type:: int

cv2.CV_8SC(channels) → retval¶

Parameters:: channels (int) –
Return type:: int

cv2.CV_8UC(channels) → retval¶

Parameters:: channels (int) –
Return type:: int

cv2.CV_MAKETYPE(depth, channels) → retval¶

Parameters:

depth (int) –
channels (int) –

Return type:

cv2.CamShift(probImage, window, criteria) → retval, window¶

Finds an object center, size, and orientation.

See the OpenCV sample camshiftdemo.c that tracks colored objects.

@note

(Python) A sample explaining the camshift tracking algorithm can be found at opencv_source_code/samples/python/camshift.py

Parameters:

probImage (cv2.typing.MatLike) – Back projection of the object histogram. See calcBackProject.
window (cv2.typing.Rect) – Initial search window.
criteria (cv2.typing.TermCriteria) – Stop criteria for the underlying meanShift.returns (in old interfaces) Number of iterations CAMSHIFT took to converge The function implements the CAMSHIFT object tracking algorithm @cite Bradski98 . First, it finds an object center using meanShift and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()

Return type:

tuple[cv2.typing.RotatedRect, cv2.typing.Rect]

cv2.Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient]]]) → edges¶

Finds edges in an image using the Canny algorithm @cite Canny86 .

The function finds edges in the input image and marks them in the output map edges using the Canny algorithm. The smallest value between threshold1 and threshold2 is used for edge linking. The largest value is used to find initial segments of strong edges. See http://en.wikipedia.org/wiki/Canny_edge_detector

Finds edges in an image using the Canny algorithm with custom image gradient.

Parameters:

image (cv2.typing.MatLike) – 8-bit input image.
edges (cv2.typing.MatLike | None) – output edge map; single channels 8-bit image, which has the same size as image .
threshold1 (float) – first threshold for the hysteresis procedure.
threshold2 (float) – second threshold for the hysteresis procedure.
apertureSize (int) – aperture size for the Sobel operator.
L2gradient (bool) – a flag, indicating whether a more accurate \(L_2\) norm\(=\sqrt{(dI/dx)^2 + (dI/dy)^2}\) should be used to calculate the image gradient magnitude ( L2gradient=true ), or whether the default \(L_1\) norm \(=|dI/dx|+|dI/dy|\) is enough ( L2gradient=false ).
dx – 16-bit x derivative of input image (CV_16SC1 or CV_16SC3).
dy – 16-bit y derivative of input image (same type as dx).

Return type:

cv2.typing.MatLike

cv2.CascadeClassifier_convert(oldcascade, newcascade) → retval¶

Return type:: object

cv2.DISOpticalFlow_create([preset]) → retval¶

Creates an instance of DISOpticalFlow

@param preset one of PRESET_ULTRAFAST, PRESET_FAST and PRESET_MEDIUM

Return type:: object

cv2.DescriptorMatcher_create(descriptorMatcherType) → retval¶

Creates a descriptor matcher of a given type with the default parameters (using default constructor).

@param descriptorMatcherType Descriptor matcher type. Now the following matcher types are
supported:
-   `BruteForce` (it uses L2 )
-   `BruteForce-L1`
-   `BruteForce-Hamming`
-   `BruteForce-Hamming(2)`
-   `FlannBased`

Return type:: object

cv2.EMD(signature1, signature2, distType[, cost[, lowerBound[, flow]]]) → retval, lowerBound, flow¶

Computes the “minimal work” distance between two weighted point configurations.

The function computes the earth mover distance and/or a lower boundary of the distance between the two weighted point configurations. One of the applications described in @cite RubnerSept98, @cite Rubner2000 is multi-dimensional histogram comparison for image retrieval. EMD is a transportation problem that is solved using some modification of a simplex algorithm, thus the complexity is exponential in the worst case, though, on average it is much faster. In the case of a real metric the lower boundary can be calculated even faster (using linear-time algorithm) and it can be used to determine roughly whether the two signatures are far enough so that they cannot relate to the same object.

Parameters:

signature1 (cv2.typing.MatLike) – First signature, a \(\texttt{size1}\times \texttt{dims}+1\) floating-point matrix.Each row stores the point weight followed by the point coordinates. The matrix is allowed to have a single column (weights only) if the user-defined cost matrix is used. The weights must be non-negative and have at least one non-zero value.
signature2 (cv2.typing.MatLike) – Second signature of the same format as signature1 , though the number of rowsmay be different. The total weights may be different. In this case an extra “dummy” point is added to either signature1 or signature2. The weights must be non-negative and have at least one non-zero value.
distType (int) – Used metric. See #DistanceTypes.
cost (cv2.typing.MatLike | None) – User-defined \(\texttt{size1}\times \texttt{size2}\) cost matrix. Also, if a cost matrixis used, lower boundary lowerBound cannot be calculated because it needs a metric function.
lowerBound (float | None) – Optional input/output parameter: lower boundary of a distance between the twosignatures that is a distance between mass centers. The lower boundary may not be calculated if the user-defined cost matrix is used, the total weights of point configurations are not equal, or if the signatures consist of weights only (the signature matrices have a single column). You must initialize *lowerBound . If the calculated distance between mass centers is greater or equal to *lowerBound (it means that the signatures are far enough), the function does not calculate EMD. In any case *lowerBound is set to the calculated distance between mass centers on return. Thus, if you want to calculate both distance between mass centers and EMD, *lowerBound should be set to 0.
flow (cv2.typing.MatLike | None) – Resultant \(\texttt{size1} \times \texttt{size2}\) flow matrix: \(\texttt{flow}_{i,j}\) isa flow from \(i\) -th point of signature1 to \(j\) -th point of signature2 .

Return type:

tuple[float, float, cv2.typing.MatLike]

cv2.FaceDetectorYN_create(model, config, input_size[, score_threshold[, nms_threshold[, top_k[, backend_id[, target_id]]]]]) → retval¶

Return type:: object

cv2.FaceRecognizerSF_create(model, config[, backend_id[, target_id]]) → retval¶

Return type:: object

cv2.FarnebackOpticalFlow_create([numLevels[, pyrScale[, fastPyramids[, winSize[, numIters[, polyN[, polySigma[, flags]]]]]]]]) → retval¶

Return type:: object

cv2.FastFeatureDetector_create([threshold[, nonmaxSuppression[, type]]]) → retval¶

Return type:: object

cv2.FlannBasedMatcher_create() → retval¶

Return type:: object

cv2.GFTTDetector_create([maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) → retval¶

Return type:: object

cv2.GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]) → dst¶

Blurs an image using a Gaussian filter.

The function convolves the source image with the specified Gaussian kernel. In-place filtering is supported.

See also: sepFilter2D, filter2D, blur, boxFilter, bilateralFilter, medianBlur

Parameters:

src (cv2.typing.MatLike) – input image; the image can have any number of channels, which are processedindependently, but the depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst (cv2.typing.MatLike | None) – output image of the same size and type as src.
ksize (cv2.typing.Size) – Gaussian kernel size. ksize.width and ksize.height can differ but they both must bepositive and odd. Or, they can be zero’s and then they are computed from sigma.
sigmaX (float) – Gaussian kernel standard deviation in X direction.
sigmaY (float) – Gaussian kernel standard deviation in Y direction; if sigmaY is zero, it is set to beequal to sigmaX, if both sigmas are zeros, they are computed from ksize.width and ksize.height, respectively (see #getGaussianKernel for details); to fully control the result regardless of possible future modifications of all this semantics, it is recommended to specify all of ksize, sigmaX, and sigmaY.
borderType (int) – pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.HOGDescriptor_getDaimlerPeopleDetector() → retval¶

Returns coefficients of the classifier trained for people detection (for 48x96 windows).

Return type:: object

cv2.HOGDescriptor_getDefaultPeopleDetector() → retval¶

Returns coefficients of the classifier trained for people detection (for 64x128 windows).

Return type:: object

cv2.HoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) → circles¶

Finds circles in a grayscale image using the Hough transform.

The function finds circles in a grayscale image using a modification of the Hough transform.

Example: : @include snippets/imgproc_HoughLinesCircles.cpp

It also helps to smooth image a bit unless it’s already soft. For example, GaussianBlur() with 7x7 kernel and 1.5x1.5 sigma or similar blurring may help.

Note

Usually the function detects the centers of circles well. However, it may fail to find correctradii. You can assist to the function by specifying the radius range ( minRadius and maxRadius ) if you know it. Or, in the case of #HOUGH_GRADIENT method you may set maxRadius to a negative number to return centers only without radius search, and find the correct radius using an additional procedure.

See also: fitEllipse, minEnclosingCircle

Parameters:

image (cv2.typing.MatLike) – 8-bit, single-channel, grayscale input image.
circles (cv2.typing.MatLike | None) – Output vector of found circles. Each vector is encoded as 3 or 4 elementfloating-point vector \((x, y, radius)\) or \((x, y, radius, votes)\) .
method (int) – Detection method, see #HoughModes. The available methods are #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT.
dp (float) – Inverse ratio of the accumulator resolution to the image resolution. For example, ifdp=1 , the accumulator has the same resolution as the input image. If dp=2 , the accumulator has half as big width and height. For #HOUGH_GRADIENT_ALT the recommended value is dp=1.5, unless some small very circles need to be detected.
minDist (float) – Minimum distance between the centers of the detected circles. If the parameter istoo small, multiple neighbor circles may be falsely detected in addition to a true one. If it is too large, some circles may be missed.
param1 (float) – First method-specific parameter. In case of #HOUGH_GRADIENT and #HOUGH_GRADIENT_ALT,it is the higher threshold of the two passed to the Canny edge detector (the lower one is twice smaller). Note that #HOUGH_GRADIENT_ALT uses #Scharr algorithm to compute image derivatives, so the threshold value should normally be higher, such as 300 or normally exposed and contrasty images.
param2 (float) – Second method-specific parameter. In case of #HOUGH_GRADIENT, it is theaccumulator threshold for the circle centers at the detection stage. The smaller it is, the more false circles may be detected. Circles, corresponding to the larger accumulator values, will be returned first. In the case of #HOUGH_GRADIENT_ALT algorithm, this is the circle “perfectness” measure. The closer it to 1, the better shaped circles algorithm selects. In most cases 0.9 should be fine. If you want get better detection of small circles, you may decrease it to 0.85, 0.8 or even less. But then also try to limit the search range [minRadius, maxRadius] to avoid many false circles.
minRadius (int) – Minimum circle radius.
maxRadius (int) – Maximum circle radius. If <= 0, uses the maximum image dimension. If < 0, #HOUGH_GRADIENT returnscenters without finding the radius. #HOUGH_GRADIENT_ALT always computes circle radiuses.

Return type:

cv2.typing.MatLike

cv2.HoughLines(image, rho, theta, threshold[, lines[, srn[, stn[, min_theta[, max_theta]]]]]) → lines¶

Finds lines in a binary image using the standard Hough transform.

The function implements the standard or standard multi-scale Hough transform algorithm for line detection. See http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm for a good explanation of Hough transform.

Parameters:

image (cv2.typing.MatLike) – 8-bit, single-channel binary source image. The image may be modified by the function.
lines (cv2.typing.MatLike | None) – Output vector of lines. Each line is represented by a 2 or 3 element vector\((\rho, \theta)\) or \((\rho, \theta, \textrm{votes})\), where \(\rho\) is the distance from the coordinate origin \((0,0)\) (top-left corner of the image), \(\theta\) is the line rotation angle in radians ( \(0 \sim \textrm{vertical line}, \pi/2 \sim \textrm{horizontal line}\) ), and \(\textrm{votes}\) is the value of accumulator.
rho (float) – Distance resolution of the accumulator in pixels.
theta (float) – Angle resolution of the accumulator in radians.
threshold (int) –
srn (float) – For the multi-scale Hough transform, it is a divisor for the distance resolution rho.The coarse accumulator distance resolution is rho and the accurate accumulator resolution is rho/srn. If both srn=0 and stn=0, the classical Hough transform is used. Otherwise, both these parameters should be positive.
stn (float) – For the multi-scale Hough transform, it is a divisor for the distance resolution theta.
min_theta (float) – For standard and multi-scale Hough transform, minimum angle to check for lines.Must fall between 0 and max_theta.
max_theta (float) – For standard and multi-scale Hough transform, an upper bound for the angle.Must fall between min_theta and CV_PI. The actual maximum angle in the accumulator may be slightly less than max_theta, depending on the parameters min_theta and theta.

Return type:

cv2.typing.MatLike

cv2.HoughLinesP(image, rho, theta, threshold[, lines[, minLineLength[, maxLineGap]]]) → lines¶

Finds line segments in a binary image using the probabilistic Hough transform.

The function implements the probabilistic Hough transform algorithm for line detection, described in @cite Matas00

See the line detection example below: @include snippets/imgproc_HoughLinesP.cpp This is a sample picture the function parameters have been tuned for:

And this is the output of the above program in case of the probabilistic Hough transform:

See also: LineSegmentDetector

Parameters:

image (cv2.typing.MatLike) – 8-bit, single-channel binary source image. The image may be modified by the function.
lines (cv2.typing.MatLike | None) – Output vector of lines. Each line is represented by a 4-element vector\((x_1, y_1, x_2, y_2)\) , where \((x_1,y_1)\) and \((x_2, y_2)\) are the ending points of each detected line segment.
rho (float) – Distance resolution of the accumulator in pixels.
theta (float) – Angle resolution of the accumulator in radians.
threshold (int) –
minLineLength (float) – Minimum line length. Line segments shorter than that are rejected.
maxLineGap (float) – Maximum allowed gap between points on the same line to link them.

Return type:

cv2.typing.MatLike

cv2.HoughLinesPointSet(point, lines_max, threshold, min_rho, max_rho, rho_step, min_theta, max_theta, theta_step[, lines]) → lines¶

Finds lines in a set of points using the standard Hough transform.

The function finds lines in a set of points using a modification of the Hough transform. @include snippets/imgproc_HoughLinesPointSet.cpp

Parameters:

point (cv2.typing.MatLike) – Input vector of points. Each vector must be encoded as a Point vector \((x,y)\). Type must be CV_32FC2 or CV_32SC2.
lines (cv2.typing.MatLike | None) – Output vector of found lines. Each vector is encoded as a vector \((votes, rho, theta)\).The larger the value of ‘votes’, the higher the reliability of the Hough line.
lines_max (int) – Max count of Hough lines.
threshold (int) –
min_rho (float) – Minimum value for \(\rho\) for the accumulator (Note: \(\rho\) can be negative. The absolute value \(|\rho|\) is the distance of a line to the origin.).
max_rho (float) – Maximum value for \(\rho\) for the accumulator.
rho_step (float) – Distance resolution of the accumulator.
min_theta (float) – Minimum angle value of the accumulator in radians.
max_theta (float) – Upper bound for the angle value of the accumulator in radians. The actual maximumangle may be slightly less than max_theta, depending on the parameters min_theta and theta_step.
theta_step (float) – Angle resolution of the accumulator in radians.

Return type:

cv2.typing.MatLike

cv2.HoughLinesWithAccumulator(image, rho, theta, threshold[, lines[, srn[, stn[, min_theta[, max_theta]]]]]) → lines¶

Finds lines in a binary image using the standard Hough transform and get accumulator. *

@note This function is for bindings use only. Use original function in C++ code
@sa HoughLines

Parameters:

image (cv2.typing.MatLike) –
rho (float) –
theta (float) –
threshold (int) –
lines (cv2.typing.MatLike | None) –
srn (float) –
stn (float) –
min_theta (float) –
max_theta (float) –

Return type:

cv2.typing.MatLike

cv2.HuMoments(m[, hu]) → hu¶

@overload

Parameters:

m (cv2.typing.Moments) –
hu (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.KAZE_create([extended[, upright[, threshold[, nOctaves[, nOctaveLayers[, diffusivity]]]]]]) → retval¶

The KAZE constructor

@param extended Set to enable extraction of extended (128-byte) descriptor.
@param upright Set to enable use of upright descriptors (non rotation-invariant).
@param threshold Detector response threshold to accept point
@param nOctaves Maximum octave evolution of the image
@param nOctaveLayers Default number of sublevels per scale level
@param diffusivity Diffusivity type. DIFF_PM_G1, DIFF_PM_G2, DIFF_WEICKERT or
DIFF_CHARBONNIER

Return type:: object

cv2.KeyPoint_convert(keypoints[, keypointIndexes]) → points2f¶

This method converts vector of keypoints to vector of points or the reverse, where each keypoint is assigned the same size and the same orientation.

@param keypoints Keypoints obtained from any feature detection algorithm like SIFT/SURF/ORB
@param points2f Array of (x,y) coordinates of each keypoint
@param keypointIndexes Array of indexes of keypoints to be converted to points. (Acts like a mask to
convert only specified keypoints)

Return type:: object

cv2.KeyPoint_overlap(kp1, kp2) → retval¶

Return type:: object

cv2.LUT(src, lut[, dst]) → dst¶

Performs a look-up table transform of an array.

The function LUT fills the output array with values from the look-up table. Indices of the entries are taken from the input array. That is, the function processes each element of src as follows:

\[\begin{equation*}\texttt{dst} (I) \leftarrow \texttt{lut(src(I) + d)}\end{equation*}\]

where

\[\begin{equation*}d = \fork{0}{if \(\texttt{src}\) has depth \(\texttt{CV_8U}\)}{128}{if \(\texttt{src}\) has depth \(\texttt{CV_8S}\)}\end{equation*}\]

See also: convertScaleAbs, Mat::convertTo

Parameters:

src (cv2.typing.MatLike) – input array of 8-bit elements.
lut (cv2.typing.MatLike) – look-up table of 256 elements; in case of multi-channel input array, the table shouldeither have a single channel (in this case the same table is used for all channels) or the same number of channels as in the input array.
dst (cv2.typing.MatLike | None) – output array of the same size and number of channels as src, and the same depth as lut.

Return type:

cv2.typing.MatLike

cv2.Laplacian(src, ddepth[, dst[, ksize[, scale[, delta[, borderType]]]]]) → dst¶

Calculates the Laplacian of an image.

The function calculates the Laplacian of the source image by adding up the second x and y derivatives calculated using the Sobel operator:

\[\begin{equation*}\texttt{dst} = \Delta \texttt{src} = \frac{\partial^2 \texttt{src}}{\partial x^2} + \frac{\partial^2 \texttt{src}}{\partial y^2}\end{equation*}\]

This is done when ksize > 1. When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture:

\[\begin{equation*}\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\end{equation*}\]

See also: Sobel, Scharr

Parameters:

src (cv2.typing.MatLike) – Source image.
dst (cv2.typing.MatLike | None) – Destination image of the same size and the same number of channels as src .
ddepth (int) – Desired depth of the destination image, see @ref filter_depths “combinations”.
ksize (int) – Aperture size used to compute the second-derivative filters. See #getDerivKernels fordetails. The size must be positive and odd.
scale (float) – Optional scale factor for the computed Laplacian values. By default, no scaling isapplied. See #getDerivKernels for details.
delta (float) – Optional delta value that is added to the results prior to storing them in dst .
borderType (int) – Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.MSER_create([delta[, min_area[, max_area[, max_variation[, min_diversity[, max_evolution[, area_threshold[, min_margin[, edge_blur_size]]]]]]]]]) → retval¶

Full constructor for %MSER detector

@param delta it compares $(size_{i}-size_{i-delta})/size_{i-delta}$
@param min_area prune the area which smaller than minArea
@param max_area prune the area which bigger than maxArea
@param max_variation prune the area have similar size to its children
@param min_diversity for color image, trace back to cut off mser with diversity less than min_diversity
@param max_evolution  for color image, the evolution steps
@param area_threshold for color image, the area threshold to cause re-initialize
@param min_margin for color image, ignore too small margin
@param edge_blur_size for color image, the aperture size for edge blur

Return type:: object

cv2.Mahalanobis(v1, v2, icovar) → retval¶

Calculates the Mahalanobis distance between two vectors.

The function cv::Mahalanobis calculates and returns the weighted distance between two vectors:

\[\begin{equation*}d( \texttt{vec1} , \texttt{vec2} )= \sqrt{\sum_{i,j}{\texttt{icovar(i,j)}\cdot(\texttt{vec1}(I)-\texttt{vec2}(I))\cdot(\texttt{vec1(j)}-\texttt{vec2(j)})} }\end{equation*}\]

The covariance matrix may be calculated using the #calcCovarMatrix function and then inverted using the invert function (preferably using the #DECOMP_SVD method, as the most accurate).

Parameters:

v1 (cv2.typing.MatLike) – first 1D input vector.
v2 (cv2.typing.MatLike) – second 1D input vector.
icovar (cv2.typing.MatLike) – inverse covariance matrix.

Return type:

cv2.ORB_create([nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize[, fastThreshold]]]]]]]]]) → retval¶

The ORB constructor

@param nfeatures The maximum number of features to retain.
@param scaleFactor Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical
pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor
will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor
will mean that to cover certain scale range you will need more pyramid levels and so the speed
will suffer.
@param nlevels The number of pyramid levels. The smallest level will have linear size equal to
input_image_linear_size/pow(scaleFactor, nlevels - firstLevel).
@param edgeThreshold This is size of the border where the features are not detected. It should
roughly match the patchSize parameter.
@param firstLevel The level of pyramid to put source image to. Previous layers are filled
with upscaled source image.
@param WTA_K The number of points that produce each element of the oriented BRIEF descriptor. The
default value 2 means the BRIEF where we take a random point pair and compare their brightnesses,
so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3
random points (of course, those point coordinates are random, but they are generated from the
pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel
rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such
output will occupy 2 bits, and therefore it will need a special variant of Hamming distance,
denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each
bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
@param scoreType The default HARRIS_SCORE means that Harris algorithm is used to rank features
(the score is written to KeyPoint::score and is used to retain best nfeatures features);
FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints,
but it is a little faster to compute.
@param patchSize size of the patch used by the oriented BRIEF descriptor. Of course, on smaller
pyramid layers the perceived image area covered by a feature will be larger.
@param fastThreshold the fast threshold

Return type:: object

cv2.PCABackProject(data, mean, eigenvectors[, result]) → result¶

wrap PCA::backProject

Parameters:

data (cv2.typing.MatLike) –
mean (cv2.typing.MatLike) –
eigenvectors (cv2.typing.MatLike) –
result (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.PCACompute(data, mean[, eigenvectors[, maxComponents]]) → mean, eigenvectors¶

wrap PCA::operator() wrap PCA::operator()

Parameters:

data (cv2.typing.MatLike) –
mean (cv2.typing.MatLike) –
eigenvectors (cv2.typing.MatLike | None) –
maxComponents (int) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.PCACompute2(data, mean[, eigenvectors[, eigenvalues[, maxComponents]]]) → mean, eigenvectors, eigenvalues¶

wrap PCA::operator() and add eigenvalues output parameter wrap PCA::operator() and add eigenvalues output parameter

Parameters:

data (cv2.typing.MatLike) –
mean (cv2.typing.MatLike) –
eigenvectors (cv2.typing.MatLike | None) –
eigenvalues (cv2.typing.MatLike | None) –
maxComponents (int) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.PCAProject(data, mean, eigenvectors[, result]) → result¶

wrap PCA::project

Parameters:

data (cv2.typing.MatLike) –
mean (cv2.typing.MatLike) –
eigenvectors (cv2.typing.MatLike) –
result (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.PSNR(src1, src2[, R]) → retval¶

Computes the Peak Signal-to-Noise Ratio (PSNR) image quality metric.

This function calculates the Peak Signal-to-Noise Ratio (PSNR) image quality metric in decibels (dB), between two input arrays src1 and src2. The arrays must have the same type.

The PSNR is calculated as follows:

\[\begin{equation*} \texttt{PSNR} = 10 \cdot \log_{10}{\left( \frac{R^2}{MSE} \right) } \end{equation*}\]

where R is the maximum integer value of depth (e.g. 255 in the case of CV_8U data) and MSE is the mean squared error between the two arrays.

Parameters:

src1 (cv2.typing.MatLike) – first input array.
src2 (cv2.typing.MatLike) – second input array of the same size as src1.
R (float) – the maximum pixel value (255 by default)

Return type:

cv2.QRCodeEncoder_create([parameters]) → retval¶

Constructor @param parameters QR code encoder parameters QRCodeEncoder::Params

Return type:: object

cv2.RQDecomp3x3(src[, mtxR[, mtxQ[, Qx[, Qy[, Qz]]]]]) → retval, mtxR, mtxQ, Qx, Qy, Qz¶

Computes an RQ decomposition of 3x3 matrices.

The function computes a RQ decomposition using the given rotations. This function is used in #decomposeProjectionMatrix to decompose the left 3x3 submatrix of a projection matrix into a camera and a rotation matrix.

It optionally returns three rotation matrices, one for each axis, and the three Euler angles in degrees (as the return value) that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see @cite Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.

Parameters:

src (cv2.typing.MatLike) – 3x3 input matrix.
mtxR (cv2.typing.MatLike | None) – Output 3x3 upper-triangular matrix.
mtxQ (cv2.typing.MatLike | None) – Output 3x3 orthogonal matrix.
Qx (cv2.typing.MatLike | None) – Optional output 3x3 rotation matrix around x-axis.
Qy (cv2.typing.MatLike | None) – Optional output 3x3 rotation matrix around y-axis.
Qz (cv2.typing.MatLike | None) – Optional output 3x3 rotation matrix around z-axis.

Return type:

tuple[cv2.typing.Vec3d, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.Rodrigues(src[, dst[, jacobian]]) → dst, jacobian¶

Converts a rotation matrix to a rotation vector or vice versa.

\[\begin{equation*}\begin{array}{l} \theta \leftarrow norm(r) \\ r \leftarrow r/ \theta \\ R = \cos(\theta) I + (1- \cos{\theta} ) r r^T + \sin(\theta) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} \end{array}\end{equation*}\]

Inverse transformation can be also done easily, since

\[\begin{equation*}\sin ( \theta ) \vecthreethree{0}{-r_z}{r_y}{r_z}{0}{-r_x}{-r_y}{r_x}{0} = \frac{R - R^T}{2}\end{equation*}\]

A rotation vector is a convenient and most compact representation of a rotation matrix (since any rotation matrix has just 3 degrees of freedom). The representation is used in the global 3D geometry optimization procedures like @ref calibrateCamera, @ref stereoCalibrate, or @ref solvePnP .

Note

More information about the computation of the derivative of a 3D rotation matrix with respect to its exponential coordinatecan be found in: - A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates, Guillermo Gallego, Anthony J. Yezzi @cite Gallego2014ACF

Note

Useful information on SE(3) and Lie Groups can be found in: - A tutorial on SE(3) transformation parameterizations and on-manifold optimization, Jose-Luis Blanco @cite blanco2010tutorial - Lie Groups for 2D and 3D Transformation, Ethan Eade @cite Eade17 - A micro Lie theory for state estimation in robotics, Joan Solà, Jérémie Deray, Dinesh Atchuthan @cite Sol2018AML

Parameters:

src (cv2.typing.MatLike) – Input rotation vector (3x1 or 1x3) or rotation matrix (3x3).
dst (cv2.typing.MatLike | None) – Output rotation matrix (3x3) or rotation vector (3x1 or 1x3), respectively.
jacobian (cv2.typing.MatLike | None) – Optional output Jacobian matrix, 3x9 or 9x3, which is a matrix of partialderivatives of the output array components with respect to the input array components.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.SIFT_create([nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) → retval¶

Create SIFT with specified descriptorType. @param nfeatures The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)

@param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
number of octaves is computed automatically from the image resolution.

@param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
(low-contrast) regions. The larger the threshold, the less features are produced by the detector.

@note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
this argument to 0.09.

@param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
filtered out (more features are retained).

@param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
is captured with a weak camera with soft lenses, you might want to reduce the number.

@param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps
index $\texttt{x}$ to $\texttt{2x}$. This prevents localization bias. The option
is disabled by default.

@param nOctaveLayers The number of layers in each octave. 3 is the value used in D. Lowe paper. The
number of octaves is computed automatically from the image resolution.

@param contrastThreshold The contrast threshold used to filter out weak features in semi-uniform
(low-contrast) regions. The larger the threshold, the less features are produced by the detector.

@note The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When
nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set
this argument to 0.09.

@param edgeThreshold The threshold used to filter out edge-like features. Note that the its meaning
is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are
filtered out (more features are retained).

@param sigma The sigma of the Gaussian applied to the input image at the octave \#0. If your image
is captured with a weak camera with soft lenses, you might want to reduce the number.

@param descriptorType The type of descriptors. Only CV_32F and CV_8U are supported.

@param enable_precise_upscale Whether to enable precise upscaling in the scale pyramid, which maps
index $\texttt{x}$ to $\texttt{2x}$. This prevents localization bias. The option
is disabled by default.

Parameters:: nfeatures – The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
Return type:: object

cv2.SVBackSubst(w, u, vt, rhs[, dst]) → dst¶

wrap SVD::backSubst

Parameters:

w (cv2.typing.MatLike) –
u (cv2.typing.MatLike) –
vt (cv2.typing.MatLike) –
rhs (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.SVDecomp(src[, w[, u[, vt[, flags]]]]) → w, u, vt¶

wrap SVD::compute

Parameters:

src (cv2.typing.MatLike) –
w (cv2.typing.MatLike | None) –
u (cv2.typing.MatLike | None) –
vt (cv2.typing.MatLike | None) –
flags (int) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.Scharr(src, ddepth, dx, dy[, dst[, scale[, delta[, borderType]]]]) → dst¶

Calculates the first x- or y- image derivative using Scharr operator.

The function computes the first x- or y- spatial image derivative using the Scharr operator. The call

\[\begin{equation*}\texttt{Scharr(src, dst, ddepth, dx, dy, scale, delta, borderType)}\end{equation*}\]

is equivalent to

\[\begin{equation*}\texttt{Sobel(src, dst, ddepth, dx, dy, FILTER_SCHARR, scale, delta, borderType)} .\end{equation*}\]

See also: cartToPolar

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image of the same size and the same number of channels as src.
ddepth (int) – output image depth, see @ref filter_depths “combinations”
dx (int) – order of the derivative x.
dy (int) – order of the derivative y.
scale (float) – optional scale factor for the computed derivative values; by default, no scaling isapplied (see #getDerivKernels for details).
delta (float) – optional delta value that is added to the results prior to storing them in dst.
borderType (int) – pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.SimpleBlobDetector_create([parameters]) → retval¶

Return type:: object

cv2.Sobel(src, ddepth, dx, dy[, dst[, ksize[, scale[, delta[, borderType]]]]]) → dst¶

Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator.

In all cases except one, the \(\texttt{ksize} \times \texttt{ksize}\) separable kernel is used to calculate the derivative. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). ksize = 1 can only be used for the first or the second x- or y- derivatives.

There is also the special value ksize = #FILTER_SCHARR (-1) that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. The Scharr aperture is

\[\begin{equation*}\vecthreethree{-3}{0}{3}{-10}{0}{10}{-3}{0}{3}\end{equation*}\]

for the x-derivative, or transposed for the y-derivative.

The function calculates an image derivative by convolving the image with the appropriate kernel:

\[\begin{equation*}\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\end{equation*}\]

The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. Most often, the function is called with ( xorder = 1, yorder = 0, ksize = 3) or ( xorder = 0, yorder = 1, ksize = 3) to calculate the first x- or y- image derivative. The first case corresponds to a kernel of:

\[\begin{equation*}\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\end{equation*}\]

The second case corresponds to a kernel of:

\[\begin{equation*}\vecthreethree{-1}{-2}{-1}{0}{0}{0}{1}{2}{1}\end{equation*}\]

See also: Scharr, Laplacian, sepFilter2D, filter2D, GaussianBlur, cartToPolar

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image of the same size and the same number of channels as src .
ddepth (int) – output image depth, see @ref filter_depths “combinations”; in the case of 8-bit input images it will result in truncated derivatives.
dx (int) – order of the derivative x.
dy (int) – order of the derivative y.
ksize (int) – size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
scale (float) – optional scale factor for the computed derivative values; by default, no scaling isapplied (see #getDerivKernels for details).
delta (float) – optional delta value that is added to the results prior to storing them in dst.
borderType (int) – pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.SparsePyrLKOpticalFlow_create([winSize[, maxLevel[, crit[, flags[, minEigThreshold]]]]]) → retval¶

Return type:: object

cv2.StereoBM_create([numDisparities[, blockSize]]) → retval¶

Creates StereoBM object

@param numDisparities the disparity search range. For each pixel algorithm will find the best
disparity from 0 (default minimum disparity) to numDisparities. The search range can then be
shifted by changing the minimum disparity.
@param blockSize the linear size of the blocks compared by the algorithm. The size should be odd
(as the block is centered at the current pixel). Larger block size implies smoother, though less
accurate disparity map. Smaller block size gives more detailed disparity map, but there is higher
chance for algorithm to find a wrong correspondence.

The function create StereoBM object. You can then call StereoBM::compute() to compute disparity for
a specific stereo pair.

Return type:: object

cv2.StereoSGBM_create([minDisparity[, numDisparities[, blockSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, mode]]]]]]]]]]]) → retval¶

Creates StereoSGBM object

@param minDisparity Minimum possible disparity value. Normally, it is zero but sometimes
rectification algorithms can shift images, so this parameter needs to be adjusted accordingly.
@param numDisparities Maximum disparity minus minimum disparity. The value is always greater than
zero. In the current implementation, this parameter must be divisible by 16.
@param blockSize Matched block size. It must be an odd number \>=1 . Normally, it should be
somewhere in the 3..11 range.
@param P1 The first parameter controlling the disparity smoothness. See below.
@param P2 The second parameter controlling the disparity smoothness. The larger the values are,
the smoother the disparity is. P1 is the penalty on the disparity change by plus or minus 1
between neighbor pixels. P2 is the penalty on the disparity change by more than 1 between neighbor
pixels. The algorithm requires P2 \> P1 . See stereo_match.cpp sample where some reasonably good
P1 and P2 values are shown (like 8\*number_of_image_channels\*blockSize\*blockSize and
32\*number_of_image_channels\*blockSize\*blockSize , respectively).
@param disp12MaxDiff Maximum allowed difference (in integer pixel units) in the left-right
disparity check. Set it to a non-positive value to disable the check.
@param preFilterCap Truncation value for the prefiltered image pixels. The algorithm first
computes x-derivative at each pixel and clips its value by [-preFilterCap, preFilterCap] interval.
The result values are passed to the Birchfield-Tomasi pixel cost function.
@param uniquenessRatio Margin in percentage by which the best (minimum) computed cost function
value should "win" the second best value to consider the found match correct. Normally, a value
within the 5-15 range is good enough.
@param speckleWindowSize Maximum size of smooth disparity regions to consider their noise speckles
and invalidate. Set it to 0 to disable speckle filtering. Otherwise, set it somewhere in the
50-200 range.
@param speckleRange Maximum disparity variation within each connected component. If you do speckle
filtering, set the parameter to a positive value, it will be implicitly multiplied by 16.
Normally, 1 or 2 is good enough.
@param mode Set it to StereoSGBM::MODE_HH to run the full-scale two-pass dynamic programming
algorithm. It will consume O(W\*H\*numDisparities) bytes, which is large for 640x480 stereo and
huge for HD-size pictures. By default, it is set to false .

The first constructor initializes StereoSGBM with all the default parameters. So, you only have to
set StereoSGBM::numDisparities at minimum. The second constructor enables you to set each parameter
to a custom value.

Return type:: object

cv2.Stitcher_create([mode]) → retval¶

Creates a Stitcher configured in one of the stitching modes.

@param mode Scenario for stitcher operation. This is usually determined by source of images
to stitch and their transformation. Default parameters will be chosen for operation in given
scenario.
@return Stitcher class instance.

Return type:: object

cv2.TrackerDaSiamRPN_create([parameters]) → retval¶

Constructor @param parameters DaSiamRPN parameters TrackerDaSiamRPN::Params

Return type:: object

cv2.TrackerGOTURN_create([parameters]) → retval¶

Constructor @param parameters GOTURN parameters TrackerGOTURN::Params

Return type:: object

cv2.TrackerMIL_create([parameters]) → retval¶

Create MIL tracker instance * @param parameters MIL parameters TrackerMIL::Params

Return type:: object

cv2.TrackerNano_create([parameters]) → retval¶

Constructor @param parameters NanoTrack parameters TrackerNano::Params

Return type:: object

cv2.TrackerVit_create([parameters]) → retval¶

Constructor @param parameters vit tracker parameters TrackerVit::Params

Return type:: object

cv2.UMat_context() → retval¶

Return type:: object

cv2.UMat_queue() → retval¶

Return type:: object

cv2.VariationalRefinement_create() → retval¶

Creates an instance of VariationalRefinement

Return type:: object

cv2.VideoCapture_waitAny(streams[, timeoutNs]) → retval, readyIndex¶

Wait for ready frames from VideoCapture.

@param streams input video streams
@param readyIndex stream indexes with grabbed frames (ready to use .retrieve() to fetch actual frame)
@param timeoutNs number of nanoseconds (0 - infinite)
@return `true` if streamReady is not empty

@throws Exception %Exception on stream errors (check .isOpened() to filter out malformed streams) or VideoCapture type is not supported

The primary use of the function is in multi-camera environments.
The method fills the ready state vector, grabs video frame, if camera is ready.

After this call use VideoCapture::retrieve() to decode and fetch frame data.

Return type:: object

cv2.VideoWriter_fourcc(c1, c2, c3, c4) → retval¶

Concatenates 4 chars to a fourcc code

@return a fourcc code

This static method constructs the fourcc code of the codec to be used in the constructor
VideoWriter::VideoWriter or VideoWriter::open.

Return type:: object

cv2._registerMatType(cv.Mat) -> None (Internal)¶

Return type:: object

cv2.absdiff(src1, src2[, dst]) → dst¶

Calculates the per-element absolute difference between two arrays or between an array and a scalar.

The function cv::absdiff calculates:

Absolute difference between two arrays when they have the same size and type:

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2}(I)|)\end{equation*}\]
Absolute difference between an array and a scalar when the second array is constructed from Scalar or has as many elements as the number of channels in src1:

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1}(I) - \texttt{src2} |)\end{equation*}\]
Absolute difference between a scalar and an array when the first array is constructed from Scalar or has as many elements as the number of channels in src2:

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} (| \texttt{src1} - \texttt{src2}(I) |)\end{equation*}\]

where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.

Note

Saturation is not applied when the arrays have the depth CV_32S.You may even get a negative value in the case of overflow.

Note

(Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.absdiff(src,X) means absdiff(src,(X,X,X,X)). absdiff(src,(X,)) means absdiff(src,(X,0,0,0)).

See also: cv::abs(const Mat&)

Parameters:

src1 (cv2.typing.MatLike) – first input array or a scalar.
src2 (cv2.typing.MatLike) – second input array or a scalar.
dst (cv2.typing.MatLike | None) – output array that has the same size and type as input arrays.

Return type:

cv2.typing.MatLike

cv2.accumulate(src, dst[, mask]) → dst¶

Adds an image to the accumulator image.

The function adds src or some of its elements to dst :

\[\begin{equation*}\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\end{equation*}\]

The function supports multi-channel images. Each channel is processed independently.

The function cv::accumulate can be used, for example, to collect statistics of a scene background viewed by a still camera and for the further foreground-background segmentation.

See also: accumulateSquare, accumulateProduct, accumulateWeighted

Parameters:

src (cv2.typing.MatLike) – Input image of type CV_8UC(n), CV_16UC(n), CV_32FC(n) or CV_64FC(n), where n is a positive integer.
dst (cv2.typing.MatLike) –
mask (cv2.typing.MatLike | None) – Optional operation mask.

Return type:

cv2.typing.MatLike

cv2.accumulateProduct(src1, src2, dst[, mask]) → dst¶

Adds the per-element product of two input images to the accumulator image.

The function adds the product of two images or their selected regions to the accumulator dst :

\[\begin{equation*}\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src1} (x,y) \cdot \texttt{src2} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\end{equation*}\]

The function supports multi-channel images. Each channel is processed independently.

See also: accumulate, accumulateSquare, accumulateWeighted

Parameters:

src1 (cv2.typing.MatLike) – First input image, 1- or 3-channel, 8-bit or 32-bit floating point.
src2 (cv2.typing.MatLike) – Second input image of the same type and the same size as src1 .
dst (cv2.typing.MatLike) –
mask (cv2.typing.MatLike | None) – Optional operation mask.

Return type:

cv2.typing.MatLike

cv2.accumulateSquare(src, dst[, mask]) → dst¶

Adds the square of a source image to the accumulator image.

The function adds the input image src or its selected region, raised to a power of 2, to the accumulator dst :

\[\begin{equation*}\texttt{dst} (x,y) \leftarrow \texttt{dst} (x,y) + \texttt{src} (x,y)^2 \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\end{equation*}\]

The function supports multi-channel images. Each channel is processed independently.

See also: accumulateSquare, accumulateProduct, accumulateWeighted

Parameters:

src (cv2.typing.MatLike) – Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
dst (cv2.typing.MatLike) –
mask (cv2.typing.MatLike | None) – Optional operation mask.

Return type:

cv2.typing.MatLike

cv2.accumulateWeighted(src, dst, alpha[, mask]) → dst¶

Updates a running average.

The function calculates the weighted sum of the input image src and the accumulator dst so that dst becomes a running average of a frame sequence:

\[\begin{equation*}\texttt{dst} (x,y) \leftarrow (1- \texttt{alpha} ) \cdot \texttt{dst} (x,y) + \texttt{alpha} \cdot \texttt{src} (x,y) \quad \text{if} \quad \texttt{mask} (x,y) \ne 0\end{equation*}\]

That is, alpha regulates the update speed (how fast the accumulator “forgets” about earlier images). The function supports multi-channel images. Each channel is processed independently.

See also: accumulate, accumulateSquare, accumulateProduct

Parameters:

src (cv2.typing.MatLike) – Input image as 1- or 3-channel, 8-bit or 32-bit floating point.
dst (cv2.typing.MatLike) –
alpha (float) – Weight of the input image.
mask (cv2.typing.MatLike | None) – Optional operation mask.

Return type:

cv2.typing.MatLike

cv2.adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]) → dst¶

Applies an adaptive threshold to an array.

The function transforms a grayscale image to a binary image according to the formulae:

THRESH_BINARY

\[\begin{equation*}dst(x,y) = \fork{\texttt{maxValue}}{if \(src(x,y) > T(x,y)\)}{0}{otherwise}\end{equation*}\]
THRESH_BINARY_INV

\[\begin{equation*}dst(x,y) = \fork{0}{if \(src(x,y) > T(x,y)\)}{\texttt{maxValue}}{otherwise}\end{equation*}\]

where \(T(x,y)\) is a threshold calculated individually for each pixel (see adaptiveMethod parameter).

The function can process the image in-place.

See also: threshold, blur, GaussianBlur

Parameters:

src (cv2.typing.MatLike) – Source 8-bit single-channel image.
dst (cv2.typing.MatLike | None) – Destination image of the same size and the same type as src.
maxValue (float) – Non-zero value assigned to the pixels for which the condition is satisfied
adaptiveMethod (int) – Adaptive thresholding algorithm to use, see #AdaptiveThresholdTypes.The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries.
thresholdType (int) – Thresholding type that must be either #THRESH_BINARY or #THRESH_BINARY_INV,see #ThresholdTypes.
blockSize (int) – Size of a pixel neighborhood that is used to calculate a threshold value for thepixel: 3, 5, 7, and so on.
C (float) – Constant subtracted from the mean or weighted mean (see the details below). Normally, itis positive but may be zero or negative as well.

Return type:

cv2.typing.MatLike

cv2.add(src1, src2[, dst[, mask[, dtype]]]) → dst¶

Calculates the per-element sum of two arrays or an array and a scalar.

The function add calculates:

Sum of two arrays when both input arrays have the same size and the same number of channels:

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\end{equation*}\]

Sum of an array and a scalar when src2 is constructed from Scalar or has the same number of elements as src1.channels():

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) + \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\end{equation*}\]

Sum of a scalar and an array when src1 is constructed from Scalar or has the same number of elements as src2.channels():

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} + \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\end{equation*}\]

where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.

The first function in the list above can be replaced with matrix expressions:

    dst = src1 + src2;
    dst += src1; // equivalent to add(dst, src1, dst);

The input arrays and the output array can all have the same or different depths. For example, you can add a 16-bit unsigned array to a 8-bit signed array and store the sum as a 32-bit floating-point array. Depth of the output array is determined by the dtype parameter. In the second and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this case, the output array will have the same depth as the input array, be it src1, src2 or both.

Note

Saturation is not applied when the output array has the depth CV_32S. You may even getresult of an incorrect sign in the case of overflow.

Note

(Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.add(src,X) means add(src,(X,X,X,X)). add(src,(X,)) means add(src,(X,0,0,0)).

See also: subtract, addWeighted, scaleAdd, Mat::convertTo

Parameters:

src1 (cv2.typing.MatLike) – first input array or a scalar.
src2 (cv2.typing.MatLike) – second input array or a scalar.
dst (cv2.typing.MatLike | None) – output array that has the same size and number of channels as the input array(s); thedepth is defined by dtype or src1/src2.
mask (cv2.typing.MatLike | None) – optional operation mask - 8-bit single channel array, that specifies elements of theoutput array to be changed.
dtype (int) – optional depth of the output array (see the discussion below).

Return type:

cv2.typing.MatLike

cv2.addText(img, text, org, nameFont[, pointSize[, color[, weight[, style[, spacing]]]]]) → None¶

Draws a text on the image.

Parameters:

img (cv2.typing.MatLike) – 8-bit 3-channel image where the text should be drawn.
text (str) – Text to write on an image.
org (cv2.typing.Point) – Point(x,y) where the text should start on an image.
nameFont (str) – Name of the font. The name should match the name of a system font (such asTimes). If the font is not found, a default one is used.
pointSize (int) – Size of the font. If not specified, equal zero or negative, the point size of thefont is set to a system-dependent default value. Generally, this is 12 points.
color (cv2.typing.Scalar) – Color of the font in BGRA where A = 255 is fully transparent.
weight (int) – Font weight. Available operation flags are : cv::QtFontWeights You can also specify a positive integer for better control.
style (int) – Font style. Available operation flags are : cv::QtFontStyles
spacing (int) – Spacing between characters. It can be negative or positive.

Return type:

None

cv2.addWeighted(src1, alpha, src2, beta, gamma[, dst[, dtype]]) → dst¶

Calculates the weighted sum of two arrays.

The function addWeighted calculates the weighted sum of two arrays as follows:

\[\begin{equation*}\texttt{dst} (I)= \texttt{saturate} ( \texttt{src1} (I)* \texttt{alpha} + \texttt{src2} (I)* \texttt{beta} + \texttt{gamma} )\end{equation*}\]

where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently. The function can be replaced with a matrix expression:

    dst = src1*alpha + src2*beta + gamma;

Note

Saturation is not applied when the output array has the depth CV_32S. You may even getresult of an incorrect sign in the case of overflow.

See also: add, subtract, scaleAdd, Mat::convertTo

Parameters:

src1 (cv2.typing.MatLike) – first input array.
alpha (float) – weight of the first array elements.
src2 (cv2.typing.MatLike) – second input array of the same size and channel number as src1.
beta (float) – weight of the second array elements.
gamma (float) – scalar added to each sum.
dst (cv2.typing.MatLike | None) – output array that has the same size and number of channels as the input arrays.
dtype (int) – optional depth of the output array; when both input arrays have the same depth, dtypecan be set to -1, which will be equivalent to src1.depth().

Return type:

cv2.typing.MatLike

cv2.applyColorMap(src, colormap[, dst]) → dst¶

Applies a user colormap on a given image.

Parameters:

src (cv2.typing.MatLike) – The source image, grayscale or colored of type CV_8UC1 or CV_8UC3.
dst (cv2.typing.MatLike | None) – The result is the colormapped source image. Note: Mat::create is called on dst.
colormap (int) – The colormap to apply, see #ColormapTypes
userColor – The colormap to apply of type CV_8UC1 or CV_8UC3 and size 256

Return type:

cv2.typing.MatLike

cv2.approxPolyDP(curve, epsilon, closed[, approxCurve]) → approxCurve¶

Approximates a polygonal curve(s) with the specified precision.

The function cv::approxPolyDP approximates a curve or a polygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm

Parameters:

curve (cv2.typing.MatLike) – Input vector of a 2D point stored in std::vector or Mat
approxCurve (cv2.typing.MatLike | None) – Result of the approximation. The type should match the type of the input curve.
epsilon (float) – Parameter specifying the approximation accuracy. This is the maximum distancebetween the original curve and its approximation.
closed (bool) – If true, the approximated curve is closed (its first and last vertices areconnected). Otherwise, it is not closed.

Return type:

cv2.typing.MatLike

cv2.arcLength(curve, closed) → retval¶

Calculates a contour perimeter or a curve length.

The function computes a curve length or a closed contour perimeter.

Parameters:

curve (cv2.typing.MatLike) – Input vector of 2D points, stored in std::vector or Mat.
closed (bool) – Flag indicating whether the curve is closed or not.

Return type:

cv2.arrowedLine(img, pt1, pt2, color[, thickness[, line_type[, shift[, tipLength]]]]) → img¶

Draws an arrow segment pointing from the first point to the second one.

The function cv::arrowedLine draws an arrow between pt1 and pt2 points in the image. See also #line.

Parameters:

img (cv2.typing.MatLike) – Image.
pt1 (cv2.typing.Point) – The point the arrow starts from.
pt2 (cv2.typing.Point) – The point the arrow points to.
color (cv2.typing.Scalar) – Line color.
thickness (int) – Line thickness.
line_type (int) – Type of the line. See #LineTypes
shift (int) – Number of fractional bits in the point coordinates.
tipLength (float) – The length of the arrow tip in relation to the arrow length

Return type:

cv2.typing.MatLike

cv2.batchDistance(src1, src2, dtype[, dist[, nidx[, normType[, K[, mask[, update[, crosscheck]]]]]]]) → dist, nidx¶

naive nearest neighbor finder

see http://en.wikipedia.org/wiki/Nearest_neighbor_search @todo document

Parameters:

src1 (cv2.typing.MatLike) –
src2 (cv2.typing.MatLike) –
dtype (int) –
dist (cv2.typing.MatLike | None) –
nidx (cv2.typing.MatLike | None) –
normType (int) –
K (int) –
mask (cv2.typing.MatLike | None) –
update (int) –
crosscheck (bool) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.bilateralFilter(src, d, sigmaColor, sigmaSpace[, dst[, borderType]]) → dst¶

Applies the bilateral filter to an image.

The function applies bilateral filtering to the input image, as described in http://www.dai.ed.ac.uk/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html bilateralFilter can reduce unwanted noise very well while keeping edges fairly sharp. However, it is very slow compared to most filters.

Sigma values: For simplicity, you can set the 2 sigma values to be the same. If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look “cartoonish”.

Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering.

This filter does not work inplace.

Parameters:

src (cv2.typing.MatLike) – Source 8-bit or floating-point, 1-channel or 3-channel image.
dst (cv2.typing.MatLike | None) – Destination image of the same size and type as src .
d (int) – Diameter of each pixel neighborhood that is used during filtering. If it is non-positive,it is computed from sigmaSpace.
sigmaColor (float) – Filter sigma in the color space. A larger value of the parameter means thatfarther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color.
sigmaSpace (float) – Filter sigma in the coordinate space. A larger value of the parameter means thatfarther pixels will influence each other as long as their colors are close enough (see sigmaColor ). When d>0, it specifies the neighborhood size regardless of sigmaSpace. Otherwise, d is proportional to sigmaSpace.
borderType (int) – border mode used to extrapolate pixels outside of the image, see #BorderTypes

Return type:

cv2.typing.MatLike

cv2.bitwise_and(src1, src2[, dst[, mask]]) → dst¶

computes bitwise conjunction of the two arrays (dst = src1 & src2)Calculates the per-element bit-wise conjunction of two arrays or an array and a scalar.

The function cv::bitwise_and calculates the per-element bit-wise logical conjunction for:

Two arrays when src1 and src2 have the same size:

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\end{equation*}\]
An array and a scalar when src2 is constructed from Scalar or has the same number of elements as src1.channels():

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} (I) \wedge \texttt{src2} \quad \texttt{if mask} (I) \ne0\end{equation*}\]
A scalar and an array when src1 is constructed from Scalar or has the same number of elements as src2.channels():

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} \wedge \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\end{equation*}\]

In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the second and third cases above, the scalar is first converted to the array type.

Parameters:

src1 (cv2.typing.MatLike) – first input array or a scalar.
src2 (cv2.typing.MatLike) – second input array or a scalar.
dst (cv2.typing.MatLike | None) – output array that has the same size and type as the inputarrays.
mask (cv2.typing.MatLike | None) – optional operation mask, 8-bit single channel array, thatspecifies elements of the output array to be changed.

Return type:

cv2.typing.MatLike

cv2.bitwise_not(src[, dst[, mask]]) → dst¶

Inverts every bit of an array.

The function cv::bitwise_not calculates per-element bit-wise inversion of the input array:

\[\begin{equation*}\texttt{dst} (I) = \neg \texttt{src} (I)\end{equation*}\]

In case of a floating-point input array, its machine-specific bit representation (usually IEEE754-compliant) is used for the operation. In case of multi-channel arrays, each channel is processed independently.

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike | None) – output array that has the same size and type as the inputarray.
mask (cv2.typing.MatLike | None) – optional operation mask, 8-bit single channel array, thatspecifies elements of the output array to be changed.

Return type:

cv2.typing.MatLike

cv2.bitwise_or(src1, src2[, dst[, mask]]) → dst¶

Calculates the per-element bit-wise disjunction of two arrays or anarray and a scalar.

The function cv::bitwise_or calculates the per-element bit-wise logical disjunction for:

Two arrays when src1 and src2 have the same size:

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\end{equation*}\]
An array and a scalar when src2 is constructed from Scalar or has the same number of elements as src1.channels():

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} (I) \vee \texttt{src2} \quad \texttt{if mask} (I) \ne0\end{equation*}\]
A scalar and an array when src1 is constructed from Scalar or has the same number of elements as src2.channels():

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} \vee \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\end{equation*}\]

Parameters:

src1 (cv2.typing.MatLike) – first input array or a scalar.
src2 (cv2.typing.MatLike) – second input array or a scalar.
dst (cv2.typing.MatLike | None) – output array that has the same size and type as the inputarrays.
mask (cv2.typing.MatLike | None) – optional operation mask, 8-bit single channel array, thatspecifies elements of the output array to be changed.

Return type:

cv2.typing.MatLike

cv2.bitwise_xor(src1, src2[, dst[, mask]]) → dst¶

Calculates the per-element bit-wise “exclusive or” operation on twoarrays or an array and a scalar.

The function cv::bitwise_xor calculates the per-element bit-wise logical “exclusive-or” operation for:

Two arrays when src1 and src2 have the same size:

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\end{equation*}\]
An array and a scalar when src2 is constructed from Scalar or has the same number of elements as src1.channels():

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} (I) \oplus \texttt{src2} \quad \texttt{if mask} (I) \ne0\end{equation*}\]
A scalar and an array when src1 is constructed from Scalar or has the same number of elements as src2.channels():

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} \oplus \texttt{src2} (I) \quad \texttt{if mask} (I) \ne0\end{equation*}\]

In case of floating-point arrays, their machine-specific bit representations (usually IEEE754-compliant) are used for the operation. In case of multi-channel arrays, each channel is processed independently. In the 2nd and 3rd cases above, the scalar is first converted to the array type.

Parameters:

src1 (cv2.typing.MatLike) – first input array or a scalar.
src2 (cv2.typing.MatLike) – second input array or a scalar.
dst (cv2.typing.MatLike | None) – output array that has the same size and type as the inputarrays.
mask (cv2.typing.MatLike | None) – optional operation mask, 8-bit single channel array, thatspecifies elements of the output array to be changed.

Return type:

cv2.typing.MatLike

cv2.blendLinear(src1, src2, weights1, weights2[, dst]) → dst¶

@overload

variant without mask parameter

Parameters:

src1 (cv2.typing.MatLike) –
src2 (cv2.typing.MatLike) –
weights1 (cv2.typing.MatLike) –
weights2 (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.blur(src, ksize[, dst[, anchor[, borderType]]]) → dst¶

Blurs an image using the normalized box filter.

The function smooths an image using the kernel:

\[\begin{equation*}\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\end{equation*}\]

The call blur(src, dst, ksize, anchor, borderType) is equivalent to boxFilter(src, dst, src.type(), ksize, anchor, true, borderType).

See also: boxFilter, bilateralFilter, GaussianBlur, medianBlur

Parameters:

src (cv2.typing.MatLike) – input image; it can have any number of channels, which are processed independently, butthe depth should be CV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst (cv2.typing.MatLike | None) – output image of the same size and type as src.
ksize (cv2.typing.Size) – blurring kernel size.
anchor (cv2.typing.Point) – anchor point; default value Point(-1,-1) means that the anchor is at the kernelcenter.
borderType (int) – border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.borderInterpolate(p, len, borderType) → retval¶

Computes the source location of an extrapolated pixel.

The function computes and returns the coordinate of a donor pixel corresponding to the specified extrapolated pixel when using the specified extrapolation border mode. For example, if you use cv::BORDER_WRAP mode in the horizontal direction, cv::BORDER_REFLECT_101 in the vertical direction and want to compute value of the “virtual” pixel Point(-5, 100) in a floating-point image img , it looks like:

    float val = img.at<float>(borderInterpolate(100, img.rows, cv::BORDER_REFLECT_101),
                              borderInterpolate(-5, img.cols, cv::BORDER_WRAP));

Normally, the function is not called directly. It is used inside filtering functions and also in copyMakeBorder.

See also: copyMakeBorder

Parameters:

p (int) – 0-based coordinate of the extrapolated pixel along one of the axes, likely <0 or >= len
len (int) – Length of the array along the corresponding axis.
borderType (int) – Border type, one of the #BorderTypes, except for #BORDER_TRANSPARENT and#BORDER_ISOLATED . When borderType==#BORDER_CONSTANT , the function always returns -1, regardless of p and len.

Return type:

cv2.boundingRect(array) → retval¶

Calculates the up-right bounding rectangle of a point set or non-zero pixels of gray-scale image.

The function calculates and returns the minimal up-right bounding rectangle for the specified point set or non-zero pixels of gray-scale image.

Parameters:: array (cv2.typing.MatLike) – Input gray-scale image or 2D point set, stored in std::vector or Mat.
Return type:: cv2.typing.Rect

cv2.boxFilter(src, ddepth, ksize[, dst[, anchor[, normalize[, borderType]]]]) → dst¶

Blurs an image using the box filter.

The function smooths an image using the kernel:

\[\begin{equation*}\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\end{equation*}\]

where

\[\begin{equation*}\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\end{equation*}\]

Unnormalized box filter is useful for computing various integral characteristics over each pixel neighborhood, such as covariance matrices of image derivatives (used in dense optical flow algorithms, and so on). If you need to compute pixel sums over variable-size windows, use #integral.

See also: blur, bilateralFilter, GaussianBlur, medianBlur, integral

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image of the same size and type as src.
ddepth (int) – the output image depth (-1 to use src.depth()).
ksize (cv2.typing.Size) – blurring kernel size.
anchor (cv2.typing.Point) – anchor point; default value Point(-1,-1) means that the anchor is at the kernelcenter.
normalize (bool) – flag, specifying whether the kernel is normalized by its area or not.
borderType (int) – border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.boxPoints(box[, points]) → points¶

Finds the four vertices of a rotated rect. Useful to draw the rotated rectangle.

The function finds the four vertices of a rotated rectangle. This function is useful to draw the rectangle. In C++, instead of using this function, you can directly use RotatedRect::points method. Please visit the @ref tutorial_bounding_rotated_ellipses “tutorial on Creating Bounding rotated boxes and ellipses for contours” for more information.

Parameters:

box (cv2.typing.RotatedRect) – The input rotated rectangle. It may be the output of @ref minAreaRect.
points (cv2.typing.MatLike | None) – The output array of four vertices of rectangles.

Return type:

cv2.typing.MatLike

cv2.broadcast(src, shape[, dst]) → dst¶

Broadcast the given Mat to the given shape. * @param src input array

@param shape target shape. Should be a list of CV_32S numbers. Note that negative values are not supported.
@param dst output array that has the given shape

Parameters:

src (cv2.typing.MatLike) –
shape (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.buildOpticalFlowPyramid(img, winSize, maxLevel[, pyramid[, withDerivatives[, pyrBorder[, derivBorder[, tryReuseInputImage]]]]]) → retval, pyramid¶

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Parameters:

img (cv2.typing.MatLike) – 8-bit input image.
pyramid (_typing.Sequence[cv2.typing.MatLike] | None) – output pyramid.
winSize (cv2.typing.Size) – window size of optical flow algorithm. Must be not less than winSize argument ofcalcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
maxLevel (int) – 0-based maximal pyramid level number.
withDerivatives (bool) – set to precompute gradients for the every pyramid level. If pyramid isconstructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
pyrBorder (int) – the border mode for pyramid layers.
derivBorder (int) – the border mode for gradients.
tryReuseInputImage (bool) – put ROI of input image into the pyramid if possible. You can pass falseto force data copying.

Returns:

number of levels in constructed pyramid. Can be less than maxLevel.

Return type:

tuple[int, _typing.Sequence[cv2.typing.MatLike]]

cv2.calcBackProject(images, channels, hist, ranges, scale[, dst]) → dst¶

@overload

Parameters:

images (_typing.Sequence[cv2.typing.MatLike]) –
channels (_typing.Sequence[int]) –
hist (cv2.typing.MatLike) –
ranges (_typing.Sequence[float]) –
scale (float) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.calcCovarMatrix(samples, mean, flags[, covar[, ctype]]) → covar, mean¶

@overload

Note

use #COVAR_ROWS or #COVAR_COLS flag

Parameters:

samples (cv2.typing.MatLike) – samples stored as rows/columns of a single matrix.
covar (cv2.typing.MatLike | None) – output covariance matrix of the type ctype and square size.
mean (cv2.typing.MatLike) – input or output (depending on the flags) array as the average value of the input vectors.
flags (int) – operation flags as a combination of #CovarFlags
ctype (int) – type of the matrixl; it equals ‘CV_64F’ by default.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]]) → hist¶

@overload

this variant supports only uniform histograms.

ranges argument is either empty vector or a flattened vector of histSize.size()*2 elements (histSize.size() element pairs). The first and second elements of each pair specify the lower and upper boundaries.

Parameters:

images (_typing.Sequence[cv2.typing.MatLike]) –
channels (_typing.Sequence[int]) –
mask (cv2.typing.MatLike | None) –
histSize (_typing.Sequence[int]) –
ranges (_typing.Sequence[float]) –
hist (cv2.typing.MatLike | None) –
accumulate (bool) –

Return type:

cv2.typing.MatLike

cv2.calcOpticalFlowFarneback(prev, next, flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags) → flow¶

Computes a dense optical flow using the Gunnar Farneback’s algorithm.

The function finds an optical flow for each prev pixel using the @cite Farneback2003 algorithm so that

\[\begin{equation*}\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\end{equation*}\]

An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/cpp/fback.cpp
(Python) An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/python/opt_flow.py

Note

Some examples:

Parameters:

prev (cv2.typing.MatLike) – first 8-bit single-channel input image.
next (cv2.typing.MatLike) – second input image of the same size and the same type as prev.
flow (cv2.typing.MatLike) – computed flow image that has the same size as prev and type CV_32FC2.
pyr_scale (float) – parameter, specifying the image scale (<1) to build pyramids for each image;pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous one.
levels (int) – number of pyramid layers including the initial image; levels=1 means that no extralayers are created and only the original images are used.
winsize (int) – averaging window size; larger values increase the algorithm robustness to imagenoise and give more chances for fast motion detection, but yield more blurred motion field.
iterations (int) – number of iterations the algorithm does at each pyramid level.
poly_n (int) – size of the pixel neighborhood used to find polynomial expansion in each pixel;larger values mean that the image will be approximated with smoother surfaces, yielding more robust algorithm and more blurred motion field, typically poly_n =5 or 7.
poly_sigma (float) – standard deviation of the Gaussian that is used to smooth derivatives used as abasis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a good value would be poly_sigma=1.5.
flags (int) –
operation flags that can be a combination of the following: - OPTFLOW_USE_INITIAL_FLOW uses the input flow as an initial flow approximation.
- OPTFLOW_FARNEBACK_GAUSSIAN uses the Gaussian \(\texttt{winsize}\times\texttt{winsize}\) filter instead of a box filter of the same size for optical flow estimation; usually, this option gives z more accurate flow than with a box filter, at the cost of lower speed; normally, winsize for a Gaussian window should be set to a larger value to achieve the same level of robustness.

Return type:

cv2.typing.MatLike

cv2.calcOpticalFlowPyrLK(prevImg, nextImg, prevPts, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, flags[, minEigThreshold]]]]]]]) → nextPts, status, err¶

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method withpyramids.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See @cite Bouguet00 . The function is parallelized with the TBB library.

An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
(Python) An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py
(Python) An example using the Lucas-Kanade tracker for homography matching can be found at opencv_source_code/samples/python/lk_homography.py

Note

Some examples:

Parameters:

prevImg (cv2.typing.MatLike) – first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
nextImg (cv2.typing.MatLike) – second input image or pyramid of the same size and the same type as prevImg.
prevPts (cv2.typing.MatLike) – vector of 2D points for which the flow needs to be found; point coordinates must besingle-precision floating-point numbers.
nextPts (cv2.typing.MatLike) – output vector of 2D points (with single-precision floating-point coordinates)containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
status (cv2.typing.MatLike | None) – output status vector (of unsigned chars); each element of the vector is set to 1 ifthe flow for the corresponding features has been found, otherwise, it is set to 0.
err (cv2.typing.MatLike | None) – output vector of errors; each element of the vector is set to an error for thecorresponding feature, type of the error measure can be set in flags parameter; if the flow wasn’t found then the error is not defined (use the status parameter to find such cases).
winSize (cv2.typing.Size) – size of the search window at each pyramid level.
maxLevel (int) – 0-based maximal pyramid level number; if set to 0, pyramids are not used (singlelevel), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel.
criteria (cv2.typing.TermCriteria) – parameter, specifying the termination criteria of the iterative search algorithm(after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon.
flags (int) –
operation flags: - OPTFLOW_USE_INITIAL_FLOW uses initial estimations, stored in nextPts; if the flag is not set, then prevPts is copied to nextPts and is considered the initial estimate.
- OPTFLOW_LK_GET_MIN_EIGENVALS use minimum eigen values as an error measure (see minEigThreshold description); if the flag is not set, then L1 distance between patches around the original and a moved point, divided by number of pixels in a window, is used as a error measure.
minEigThreshold (float) – the algorithm calculates the minimum eigen value of a 2x2 normal matrix ofoptical flow equations (this matrix is called a spatial gradient matrix in @cite Bouguet00), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.calibrateCamera(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]) → retval, cameraMatrix, distCoeffs, rvecs, tvecs¶

@overload

Parameters:

objectPoints (_typing.Sequence[cv2.typing.MatLike]) –
imagePoints (_typing.Sequence[cv2.typing.MatLike]) –
imageSize (cv2.typing.Size) –
cameraMatrix (cv2.typing.MatLike) –
distCoeffs (cv2.typing.MatLike) –
rvecs (_typing.Sequence[cv2.typing.MatLike] | None) –
tvecs (_typing.Sequence[cv2.typing.MatLike] | None) –
flags (int) –
criteria (cv2.typing.TermCriteria) –

Return type:

tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]

cv2.calibrateCameraExtended(objectPoints, imagePoints, imageSize, cameraMatrix, distCoeffs[, rvecs[, tvecs[, stdDeviationsIntrinsics[, stdDeviationsExtrinsics[, perViewErrors[, flags[, criteria]]]]]]]) → retval, cameraMatrix, distCoeffs, rvecs, tvecs, stdDeviationsIntrinsics, stdDeviationsExtrinsics, perViewErrors¶

Finds the camera intrinsic and extrinsic parameters from several views of a calibrationpattern.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on @cite Zhang2000 and @cite BouguetMCT . The coordinates of 3D object points and their corresponding 2D projections in each view must be specified. That may be achieved by using an object with known geometry and easily detectable feature points. Such an object is called a calibration rig or calibration pattern, and OpenCV has built-in support for a chessboard as a calibration rig (see @ref findChessboardCorners). Currently, initialization of intrinsic parameters (when @ref CALIB_USE_INTRINSIC_GUESS is not set) is only implemented for planar calibration patterns (where Z-coordinates of the object points must be all zeros). 3D calibration rigs can also be used as long as initial cameraMatrix is provided.

The algorithm performs the following steps:

Compute the initial intrinsic parameters (the option only available for planar calibration patterns) or read them from the input parameters. The distortion coefficients are all set to zeros initially unless some of CALIB_FIX_K? are specified.
Estimate the initial camera pose as if the intrinsic parameters have been already known. This is done using @ref solvePnP .
Run the global Levenberg-Marquardt optimization algorithm to minimize the reprojection error, that is, the total sum of squared distances between the observed feature points imagePoints and the projected (using the current estimates for camera parameters and the poses) object points objectPoints. See @ref projectPoints for details.

@note If you use a non-square (i.e. non-N-by-N) grid and @ref findChessboardCorners for calibration, and @ref calibrateCamera returns bad values (zero distortion coefficients, \(c_x\) and \(c_y\) very far from the image center, and/or large differences between \(f_x\) and \(f_y\) (ratios of 10:1 or more)), then you are probably using patternSize=cvSize(rows,cols) instead of using patternSize=cvSize(cols,rows) in @ref findChessboardCorners.

@note The function may throw exceptions, if unsupported combination of parameters is provided or the system is underconstrained.

@sa calibrateCameraRO, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

Parameters:

objectPoints (_typing.Sequence[cv2.typing.MatLike]) – In the new interface it is a vector of vectors of calibration pattern points inthe calibration pattern coordinate space (e.g. std::vector<std::vectorcv::Vec3f>). The outer vector contains as many elements as the number of pattern views. If the same calibration pattern is shown in each view and it is fully visible, all the vectors will be the same. Although, it is possible to use partially occluded patterns or even different patterns in different views. Then, the vectors will be different. Although the points are 3D, they all lie in the calibration pattern’s XY coordinate plane (thus 0 in the Z-coordinate), if the used calibration pattern is a planar rig. In the old interface all the vectors of object points from different views are concatenated together.
imagePoints (_typing.Sequence[cv2.typing.MatLike]) – In the new interface it is a vector of vectors of the projections of calibrationpattern points (e.g. std::vector<std::vectorcv::Vec2f>). imagePoints.size() and objectPoints.size(), and imagePoints[i].size() and objectPoints[i].size() for each i, must be equal, respectively. In the old interface all the vectors of object points from different views are concatenated together.
imageSize (cv2.typing.Size) – Size of the image used only to initialize the camera intrinsic matrix.
cameraMatrix (cv2.typing.MatLike) – Input/output 3x3 floating-point camera intrinsic matrix\(\cameramatrix{A}\) . If @ref CALIB_USE_INTRINSIC_GUESS and/or @ref CALIB_FIX_ASPECT_RATIO, @ref CALIB_FIX_PRINCIPAL_POINT or @ref CALIB_FIX_FOCAL_LENGTH are specified, some or all of fx, fy, cx, cy must be initialized before calling the function.
distCoeffs (cv2.typing.MatLike) – Input/output vector of distortion coefficients\(\distcoeffs\).
rvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Output vector of rotation vectors (@ref Rodrigues ) estimated for each pattern view(e.g. std::vectorcv::Mat>). That is, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space. Due to its duality, this tuple is equivalent to the position of the calibration pattern with respect to the camera coordinate space.
tvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Output vector of translation vectors estimated for each pattern view, see parameterdescribtion above.
stdDeviationsIntrinsics (cv2.typing.MatLike | None) – Output vector of standard deviations estimated for intrinsicparameters. Order of deviations values: \((f_x, f_y, c_x, c_y, k_1, k_2, p_1, p_2, k_3, k_4, k_5, k_6 , s_1, s_2, s_3, s_4, \tau_x, \tau_y)\) If one of parameters is not estimated, it’s deviation is equals to zero.
stdDeviationsExtrinsics (cv2.typing.MatLike | None) – Output vector of standard deviations estimated for extrinsicparameters. Order of deviations values: \((R_0, T_0, \dotsc , R_{M - 1}, T_{M - 1})\) where M is the number of pattern views. \(R_i, T_i\) are concatenated 1x3 vectors. @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
flags – Different flags that may be zero or a combination of the following values:- @ref CALIB_USE_INTRINSIC_GUESS cameraMatrix contains valid initial values of fx, fy, cx, cy that are optimized further. Otherwise, (cx, cy) is initially set to the image center ( imageSize is used), and focal distances are computed in a least-squares fashion. Note, that if intrinsic parameters are known, there is no need to use this function just to estimate extrinsic parameters. Use @ref solvePnP instead.

@ref CALIB_FIX_PRINCIPAL_POINT The principal point is not changed during the global optimization. It stays at the center or at a different location specified when @ref CALIB_USE_INTRINSIC_GUESS is set too.
@ref CALIB_FIX_ASPECT_RATIO The functions consider only fy as a free parameter. The ratio fx/fy stays the same as in the input cameraMatrix . When @ref CALIB_USE_INTRINSIC_GUESS is not set, the actual input values of fx and fy are ignored, only their ratio is computed and used further.
@ref CALIB_ZERO_TANGENT_DIST Tangential distortion coefficients \((p_1, p_2)\) are set to zeros and stay zero.
@ref CALIB_FIX_FOCAL_LENGTH The focal length is not changed during the global optimization if @ref CALIB_USE_INTRINSIC_GUESS is set.
@ref CALIB_FIX_K1,…, @ref CALIB_FIX_K6 The corresponding radial distortion coefficient is not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
@ref CALIB_RATIONAL_MODEL Coefficients k4, k5, and k6 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients or more.
@ref CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients or more.
@ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
@ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients.
@ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.

Parameters:

criteria (cv2.typing.TermCriteria) – Termination criteria for the iterative optimization algorithm.
perViewErrors (cv2.typing.MatLike | None) –

Returns:

the overall RMS re-projection error.

Return type:

tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.calibrateCameraRO(objectPoints, imagePoints, imageSize, iFixedPoint, cameraMatrix, distCoeffs[, rvecs[, tvecs[, newObjPoints[, flags[, criteria]]]]]) → retval, cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints¶

@overload

Parameters:

objectPoints (_typing.Sequence[cv2.typing.MatLike]) –
imagePoints (_typing.Sequence[cv2.typing.MatLike]) –
imageSize (cv2.typing.Size) –
iFixedPoint (int) –
cameraMatrix (cv2.typing.MatLike) –
distCoeffs (cv2.typing.MatLike) –
rvecs (_typing.Sequence[cv2.typing.MatLike] | None) –
tvecs (_typing.Sequence[cv2.typing.MatLike] | None) –
newObjPoints (cv2.typing.MatLike | None) –
flags (int) –
criteria (cv2.typing.TermCriteria) –

Return type:

tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]

cv2.calibrateCameraROExtended(objectPoints, imagePoints, imageSize, iFixedPoint, cameraMatrix, distCoeffs[, rvecs[, tvecs[, newObjPoints[, stdDeviationsIntrinsics[, stdDeviationsExtrinsics[, stdDeviationsObjPoints[, perViewErrors[, flags[, criteria]]]]]]]]]) → retval, cameraMatrix, distCoeffs, rvecs, tvecs, newObjPoints, stdDeviationsIntrinsics, stdDeviationsExtrinsics, stdDeviationsObjPoints, perViewErrors¶

Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern.

This function is an extension of #calibrateCamera with the method of releasing object which was proposed in @cite strobl2011iccv. In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can dramatically improve the precision of the estimated camera parameters. Both the object-releasing method and standard method are supported by this function. Use the parameter iFixedPoint for method selection. In the internal implementation, #calibrateCamera is a wrapper for this function.

The function estimates the intrinsic camera parameters and extrinsic parameters for each of the views. The algorithm is based on @cite Zhang2000, @cite BouguetMCT and @cite strobl2011iccv. See #calibrateCamera for other detailed explanations. @sa calibrateCamera, findChessboardCorners, solvePnP, initCameraMatrix2D, stereoCalibrate, undistort

Parameters:

objectPoints (_typing.Sequence[cv2.typing.MatLike]) – Vector of vectors of calibration pattern points in the calibration patterncoordinate space. See #calibrateCamera for details. If the method of releasing object to be used, the identical calibration board must be used in each view and it must be fully visible, and all objectPoints[i] must be the same and all points should be roughly close to a plane. The calibration target has to be rigid, or at least static if the camera (rather than the calibration target) is shifted for grabbing images.
imagePoints (_typing.Sequence[cv2.typing.MatLike]) – Vector of vectors of the projections of calibration pattern points. See#calibrateCamera for details.
imageSize (cv2.typing.Size) – Size of the image used only to initialize the intrinsic camera matrix.
iFixedPoint (int) – The index of the 3D object point in objectPoints[0] to be fixed. It also acts asa switch for calibration method selection. If object-releasing method to be used, pass in the parameter in the range of [1, objectPoints[0].size()-2], otherwise a value out of this range will make standard calibration method selected. Usually the top-right corner point of the calibration board grid is recommended to be fixed when object-releasing method being utilized. According to \cite strobl2011iccv, two other points are also fixed. In this implementation, objectPoints[0].front and objectPoints[0].back.z are used. With object-releasing method, accurate rvecs, tvecs and newObjPoints are only possible if coordinates of these three fixed points are accurate enough.
cameraMatrix (cv2.typing.MatLike) – Output 3x3 floating-point camera matrix. See #calibrateCamera for details.
distCoeffs (cv2.typing.MatLike) – Output vector of distortion coefficients. See #calibrateCamera for details.
rvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Output vector of rotation vectors estimated for each pattern view. See #calibrateCamerafor details.
tvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Output vector of translation vectors estimated for each pattern view.
newObjPoints (cv2.typing.MatLike | None) – The updated output vector of calibration pattern points. The coordinates mightbe scaled based on three fixed points. The returned coordinates are accurate only if the above mentioned three fixed points are accurate. If not needed, noArray() can be passed in. This parameter is ignored with standard calibration method.
stdDeviationsIntrinsics (cv2.typing.MatLike | None) – Output vector of standard deviations estimated for intrinsic parameters.See #calibrateCamera for details.
stdDeviationsExtrinsics (cv2.typing.MatLike | None) – Output vector of standard deviations estimated for extrinsic parameters.See #calibrateCamera for details.
stdDeviationsObjPoints (cv2.typing.MatLike | None) – Output vector of standard deviations estimated for refined coordinatesof calibration pattern points. It has the same size and order as objectPoints[0] vector. This parameter is ignored with standard calibration method. @param perViewErrors Output vector of the RMS re-projection error estimated for each pattern view.
flags (int) – Different flags that may be zero or a combination of some predefined values. See#calibrateCamera for details. If the method of releasing object is used, the calibration time may be much longer. CALIB_USE_QR or CALIB_USE_LU could be used for faster calibration with potentially less precise and less stable in some rare cases.
criteria (cv2.typing.TermCriteria) – Termination criteria for the iterative optimization algorithm.
perViewErrors (cv2.typing.MatLike | None) –

Returns:

the overall RMS re-projection error.

Return type:

tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.calibrateHandEye(R_gripper2base, t_gripper2base, R_target2cam, t_target2cam[, R_cam2gripper[, t_cam2gripper[, method]]]) → R_cam2gripper, t_cam2gripper¶

Computes Hand-Eye calibration: \(_{}^{g}\textrm{T}_c\)

The function performs the Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions) and the following methods are implemented:

R. Tsai, R. Lenz A New Technique for Fully Autonomous and Efficient 3D Robotics Hand/EyeCalibration \cite Tsai89
F. Park, B. Martin Robot Sensor Calibration: Solving AX = XB on the Euclidean Group \cite Park94
R. Horaud, F. Dornaika Hand-Eye Calibration \cite Horaud95

Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented methods:

N. Andreff, R. Horaud, B. Espiau On-line Hand-Eye Calibration \cite Andreff99
K. Daniilidis Hand-Eye Calibration Using Dual Quaternions \cite Daniilidis98

The following picture describes the Hand-Eye calibration problem where the transformation between a camera (“eye”) mounted on a robot gripper (“hand”) has to be estimated. This configuration is called eye-in-hand.

The eye-to-hand configuration consists in a static camera observing a calibration pattern mounted on the robot end-effector. The transformation from the camera to the robot base frame can then be estimated by inputting the suitable transformations to the function, see below.

The calibration procedure is the following:

a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
the robot gripper is moved in order to acquire several poses
for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics

\[\begin{equation*} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{b}\textrm{R}_g & _{}^{b}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \end{equation*}\]

for each pose, the homogeneous transformation between the calibration target frame and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences

\[\begin{equation*} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_t & _{}^{c}\textrm{t}_t \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_t\\ Y_t\\ Z_t\\ 1 \end{bmatrix} \end{equation*}\]

The Hand-Eye calibration procedure returns the following homogeneous transformation

\[\begin{equation*} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_c & _{}^{g}\textrm{t}_c \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} \end{equation*}\]

This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{X}\mathbf{B}\) equation:

for an eye-in-hand configuration

\[\begin{equation*} \begin{align*} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{b}{\textrm{T}_g}^{(2)} \hspace{0.2em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{b}{\textrm{T}_g}^{(2)})^{-1} \hspace{0.2em} ^{b}{\textrm{T}_g}^{(1)} \hspace{0.2em} ^{g}\textrm{T}_c &= \hspace{0.1em} ^{g}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \end{equation*}\]

for an eye-to-hand configuration

\[\begin{equation*} \begin{align*} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(1)} &= \hspace{0.1em} ^{g}{\textrm{T}_b}^{(2)} \hspace{0.2em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} \\ (^{g}{\textrm{T}_b}^{(2)})^{-1} \hspace{0.2em} ^{g}{\textrm{T}_b}^{(1)} \hspace{0.2em} ^{b}\textrm{T}_c &= \hspace{0.1em} ^{b}\textrm{T}_c \hspace{0.2em} ^{c}{\textrm{T}_t}^{(2)} (^{c}{\textrm{T}_t}^{(1)})^{-1} \\ \textrm{A}_i \textrm{X} &= \textrm{X} \textrm{B}_i \\ \end{align*} \end{equation*}\]

\note Additional information can be found on this website. \note A minimum of 2 motions with non parallel rotation axes are necessary to determine the hand-eye transformation. So at least 3 different poses are required, but it is strongly recommended to use many more poses.

Parameters:

R_gripper2base (_typing.Sequence[cv2.typing.MatLike]) – [in] Rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the gripper frame to the robot base frame (\(_{}^{b}\textrm{T}_g\)). This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors, for all the transformations from gripper frame to robot base frame.
t_gripper2base (_typing.Sequence[cv2.typing.MatLike]) – [in] Translation part extracted from the homogeneous matrix that transforms a pointexpressed in the gripper frame to the robot base frame (\(_{}^{b}\textrm{T}_g\)). This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations from gripper frame to robot base frame.
R_target2cam (_typing.Sequence[cv2.typing.MatLike]) – [in] Rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the target frame to the camera frame (\(_{}^{c}\textrm{T}_t\)). This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors, for all the transformations from calibration target frame to camera frame.
t_target2cam (_typing.Sequence[cv2.typing.MatLike]) – [in] Rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the target frame to the camera frame (\(_{}^{c}\textrm{T}_t\)). This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations from calibration target frame to camera frame.
R_cam2gripper (cv2.typing.MatLike | None) – [out] Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the camera frame to the gripper frame (\(_{}^{g}\textrm{T}_c\)).
t_cam2gripper (cv2.typing.MatLike | None) – [out] Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a pointexpressed in the camera frame to the gripper frame (\(_{}^{g}\textrm{T}_c\)).
method (HandEyeCalibrationMethod) – [in] One of the implemented Hand-Eye calibration method, see cv::HandEyeCalibrationMethod

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.calibrateRobotWorldHandEye(R_world2cam, t_world2cam, R_base2gripper, t_base2gripper[, R_base2world[, t_base2world[, R_gripper2cam[, t_gripper2cam[, method]]]]]) → R_base2world, t_base2world, R_gripper2cam, t_gripper2cam¶

Computes Robot-World/Hand-Eye calibration: \(_{}^{w}\textrm{T}_b\) and \(_{}^{c}\textrm{T}_g\)

The function performs the Robot-World/Hand-Eye calibration using various methods. One approach consists in estimating the rotation then the translation (separable solutions):

M. Shah, Solving the robot-world/hand-eye calibration problem using the kronecker product \cite Shah2013SolvingTR

Another approach consists in estimating simultaneously the rotation and the translation (simultaneous solutions), with the following implemented method:

A. Li, L. Wang, and D. Wu, Simultaneous robot-world and hand-eye calibration using dual-quaternions and kronecker product \cite Li2010SimultaneousRA

The following picture describes the Robot-World/Hand-Eye calibration problem where the transformations between a robot and a world frame and between a robot gripper (“hand”) and a camera (“eye”) mounted at the robot end-effector have to be estimated.

The calibration procedure is the following:

a static calibration pattern is used to estimate the transformation between the target frame and the camera frame
the robot gripper is moved in order to acquire several poses
for each pose, the homogeneous transformation between the gripper frame and the robot base frame is recorded using for instance the robot kinematics

\[\begin{equation*} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{g}\textrm{R}_b & _{}^{g}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \end{equation*}\]

for each pose, the homogeneous transformation between the calibration target frame (the world frame) and the camera frame is recorded using for instance a pose estimation method (PnP) from 2D-3D point correspondences

\[\begin{equation*} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_w & _{}^{c}\textrm{t}_w \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} \end{equation*}\]

The Robot-World/Hand-Eye calibration procedure returns the following homogeneous transformations

\[\begin{equation*} \begin{bmatrix} X_w\\ Y_w\\ Z_w\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{w}\textrm{R}_b & _{}^{w}\textrm{t}_b \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_b\\ Y_b\\ Z_b\\ 1 \end{bmatrix} \end{equation*}\]

\[\begin{equation*} \begin{bmatrix} X_c\\ Y_c\\ Z_c\\ 1 \end{bmatrix} = \begin{bmatrix} _{}^{c}\textrm{R}_g & _{}^{c}\textrm{t}_g \\ 0_{1 \times 3} & 1 \end{bmatrix} \begin{bmatrix} X_g\\ Y_g\\ Z_g\\ 1 \end{bmatrix} \end{equation*}\]

This problem is also known as solving the \(\mathbf{A}\mathbf{X}=\mathbf{Z}\mathbf{B}\) equation, with:

\(\mathbf{A} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_w\)
\(\mathbf{X} \Leftrightarrow \hspace{0.1em} _{}^{w}\textrm{T}_b\)
\(\mathbf{Z} \Leftrightarrow \hspace{0.1em} _{}^{c}\textrm{T}_g\)
\(\mathbf{B} \Leftrightarrow \hspace{0.1em} _{}^{g}\textrm{T}_b\)

\note At least 3 measurements are required (input vectors size must be greater or equal to 3).

Parameters:

R_world2cam (_typing.Sequence[cv2.typing.MatLike]) – [in] Rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the world frame to the camera frame (\(_{}^{c}\textrm{T}_w\)). This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors, for all the transformations from world frame to the camera frame.
t_world2cam (_typing.Sequence[cv2.typing.MatLike]) – [in] Translation part extracted from the homogeneous matrix that transforms a pointexpressed in the world frame to the camera frame (\(_{}^{c}\textrm{T}_w\)). This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations from world frame to the camera frame.
R_base2gripper (_typing.Sequence[cv2.typing.MatLike]) – [in] Rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the robot base frame to the gripper frame (\(_{}^{g}\textrm{T}_b\)). This is a vector (vector<Mat>) that contains the rotation, (3x3) rotation matrices or (3x1) rotation vectors, for all the transformations from robot base frame to the gripper frame.
t_base2gripper (_typing.Sequence[cv2.typing.MatLike]) – [in] Rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the robot base frame to the gripper frame (\(_{}^{g}\textrm{T}_b\)). This is a vector (vector<Mat>) that contains the (3x1) translation vectors for all the transformations from robot base frame to the gripper frame.
R_base2world (cv2.typing.MatLike | None) – [out] Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the robot base frame to the world frame (\(_{}^{w}\textrm{T}_b\)).
t_base2world (cv2.typing.MatLike | None) – [out] Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a pointexpressed in the robot base frame to the world frame (\(_{}^{w}\textrm{T}_b\)).
R_gripper2cam (cv2.typing.MatLike | None) – [out] Estimated (3x3) rotation part extracted from the homogeneous matrix that transforms a pointexpressed in the gripper frame to the camera frame (\(_{}^{c}\textrm{T}_g\)).
t_gripper2cam (cv2.typing.MatLike | None) – [out] Estimated (3x1) translation part extracted from the homogeneous matrix that transforms a pointexpressed in the gripper frame to the camera frame (\(_{}^{c}\textrm{T}_g\)).
method (RobotWorldHandEyeCalibrationMethod) – [in] One of the implemented Robot-World/Hand-Eye calibration method, see cv::RobotWorldHandEyeCalibrationMethod

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.calibrationMatrixValues(cameraMatrix, imageSize, apertureWidth, apertureHeight) → fovx, fovy, focalLength, principalPoint, aspectRatio¶

Computes useful camera characteristics from the camera intrinsic matrix.

The function computes various useful camera characteristics from the previously estimated camera matrix.

@note Do keep in mind that the unity measure ‘mm’ stands for whatever unit of measure one chooses for the chessboard pitch (it can thus be any value).

Parameters:

cameraMatrix (cv2.typing.MatLike) – Input camera intrinsic matrix that can be estimated by #calibrateCamera or#stereoCalibrate .
imageSize (cv2.typing.Size) – Input image size in pixels.
apertureWidth (float) – Physical width in mm of the sensor.
apertureHeight (float) – Physical height in mm of the sensor.
fovx – Output field of view in degrees along the horizontal sensor axis.
fovy – Output field of view in degrees along the vertical sensor axis.
focalLength – Focal length of the lens in mm.
principalPoint – Principal point in mm.
aspectRatio – \(f_y/f_x\)

Return type:

tuple[float, float, float, cv2.typing.Point2d, float]

cv2.cartToPolar(x, y[, magnitude[, angle[, angleInDegrees]]]) → magnitude, angle¶

Calculates the magnitude and angle of 2D vectors.

The function cv::cartToPolar calculates either the magnitude, angle, or both for every 2D vector (x(I),y(I)):

\[\begin{equation*}\begin{array}{l} \texttt{magnitude} (I)= \sqrt{\texttt{x}(I)^2+\texttt{y}(I)^2} , \\ \texttt{angle} (I)= \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))[ \cdot180 / \pi ] \end{array}\end{equation*}\]

The angles are calculated with accuracy about 0.3 degrees. For the point (0,0), the angle is set to 0.

See also: Sobel, Scharr

Parameters:

x (cv2.typing.MatLike) – array of x-coordinates; this must be a single-precision ordouble-precision floating-point array.
y (cv2.typing.MatLike) – array of y-coordinates, that must have the same size and same type as x.
magnitude (cv2.typing.MatLike | None) – output array of magnitudes of the same size and type as x.
angle (cv2.typing.MatLike | None) – output array of angles that has the same size and type asx; the angles are measured in radians (from 0 to 2*Pi) or in degrees (0 to 360 degrees).
angleInDegrees (bool) – a flag, indicating whether the angles are measuredin radians (which is by default), or in degrees.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.checkChessboard(img, size) → retval¶

Parameters:

img (cv2.typing.MatLike) –
size (cv2.typing.Size) –

Return type:

cv2.checkHardwareSupport(feature) → retval¶

Returns true if the specified feature is supported by the host hardware.

The function returns true if the host hardware supports the specified feature. When user calls setUseOptimized(false), the subsequent calls to checkHardwareSupport() will return false until setUseOptimized(true) is called. This way user can dynamically switch on and off the optimized code in OpenCV.

Parameters:: feature (int) – The feature of interest, one of cv::CpuFeatures
Return type:: bool

cv2.checkRange(a[, quiet[, minVal[, maxVal]]]) → retval, pos¶

Checks every element of an input array for invalid values.

The function cv::checkRange checks that every array element is neither NaN nor infinite. When minVal > -DBL_MAX and maxVal < DBL_MAX, the function also checks that each value is between minVal and maxVal. In case of multi-channel arrays, each channel is processed independently. If some values are out of range, position of the first outlier is stored in pos (when pos != NULL). Then, the function either returns false (when quiet=true) or throws an exception.

Parameters:

a (cv2.typing.MatLike) – input array.
quiet (bool) – a flag, indicating whether the functions quietly return false when the array elementsare out of range or they throw an exception.
pos – optional output parameter, when not NULL, must be a pointer to array of src.dimselements.
minVal (float) – inclusive lower boundary of valid values range.
maxVal (float) – exclusive upper boundary of valid values range.

Return type:

tuple[bool, cv2.typing.Point]

cv2.circle(img, center, radius, color[, thickness[, lineType[, shift]]]) → img¶

Draws a circle.

The function cv::circle draws a simple or filled circle with a given center and radius.

Parameters:

img (cv2.typing.MatLike) – Image where the circle is drawn.
center (cv2.typing.Point) – Center of the circle.
radius (int) – Radius of the circle.
color (cv2.typing.Scalar) – Circle color.
thickness (int) – Thickness of the circle outline, if positive. Negative values, like #FILLED,mean that a filled circle is to be drawn.
lineType (int) – Type of the circle boundary. See #LineTypes
shift (int) – Number of fractional bits in the coordinates of the center and in the radius value.

Return type:

cv2.typing.MatLike

cv2.clipLine(imgRect, pt1, pt2) → retval, pt1, pt2¶

@overload

Parameters:

imgRect (cv2.typing.Rect) – Image rectangle.
pt1 (cv2.typing.Point) – First line point.
pt2 (cv2.typing.Point) – Second line point.

Return type:

tuple[bool, cv2.typing.Point, cv2.typing.Point]

cv2.colorChange(src, mask[, dst[, red_mul[, green_mul[, blue_mul]]]]) → dst¶

Given an original color image, two differently colored versions of this image can be mixedseamlessly.

Multiplication factor is between .5 to 2.5.

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
mask (cv2.typing.MatLike) – Input 8-bit 1 or 3-channel image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as src .
red_mul (float) – R-channel multiply factor.
green_mul (float) – G-channel multiply factor.
blue_mul (float) – B-channel multiply factor.

Return type:

cv2.typing.MatLike

cv2.compare(src1, src2, cmpop[, dst]) → dst¶

Performs the per-element comparison of two arrays or an array and scalar value.

The function compares:

Elements of two arrays when src1 and src2 have the same size:

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} (I) \,\texttt{cmpop}\, \texttt{src2} (I)\end{equation*}\]
Elements of src1 with a scalar src2 when src2 is constructed from Scalar or has a single element:

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1}(I) \,\texttt{cmpop}\, \texttt{src2}\end{equation*}\]
src1 with elements of src2 when src1 is constructed from Scalar or has a single element:

\[\begin{equation*}\texttt{dst} (I) = \texttt{src1} \,\texttt{cmpop}\, \texttt{src2} (I)\end{equation*}\]

When the comparison result is true, the corresponding element of output array is set to 255. The comparison operations can be replaced with the equivalent matrix expressions:

    Mat dst1 = src1 >= src2;
    Mat dst2 = src1 < 8;
    ...

See also: checkRange, min, max, threshold

Parameters:

src1 (cv2.typing.MatLike) – first input array or a scalar; when it is an array, it must have a single channel.
src2 (cv2.typing.MatLike) – second input array or a scalar; when it is an array, it must have a single channel.
dst (cv2.typing.MatLike | None) – output array of type ref CV_8U that has the same size and the same number of channels as the input arrays.
cmpop (int) – a flag, that specifies correspondence between the arrays (cv::CmpTypes)

Return type:

cv2.typing.MatLike

cv2.compareHist(H1, H2, method) → retval¶

Compares two histograms.

The function cv::compareHist compares two dense or two sparse histograms using the specified method.

The function returns \(d(H_1, H_2)\) .

While the function works well with 1-, 2-, 3-dimensional dense histograms, it may not be suitable for high-dimensional sparse histograms. In such histograms, because of aliasing and sampling problems, the coordinates of non-zero histogram bins can slightly shift. To compare such histograms or more general sparse configurations of weighted points, consider using the #EMD function.

Parameters:

H1 (cv2.typing.MatLike) – First compared histogram.
H2 (cv2.typing.MatLike) – Second compared histogram of the same size as H1 .
method (int) – Comparison method, see #HistCompMethods

Return type:

cv2.completeSymm(m[, lowerToUpper]) → m¶

Copies the lower or the upper half of a square matrix to its another half.

The function cv::completeSymm copies the lower or the upper half of a square matrix to its another half. The matrix diagonal remains unchanged:

\(\texttt{m}_{ij}=\texttt{m}_{ji}\) for \(i > j\) if lowerToUpper=false
\(\texttt{m}_{ij}=\texttt{m}_{ji}\) for \(i < j\) if lowerToUpper=true

See also: flip, transpose

Parameters:

m (cv2.typing.MatLike) – input-output floating-point square matrix.
lowerToUpper (bool) – operation flag; if true, the lower half is copied tothe upper half. Otherwise, the upper half is copied to the lower half.

Return type:

cv2.typing.MatLike

cv2.composeRT(rvec1, tvec1, rvec2, tvec2[, rvec3[, tvec3[, dr3dr1[, dr3dt1[, dr3dr2[, dr3dt2[, dt3dr1[, dt3dt1[, dt3dr2[, dt3dt2]]]]]]]]]]) → rvec3, tvec3, dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2¶

Combines two rotation-and-shift transformations.

The functions compute:

\[\begin{equation*}\begin{array}{l} \texttt{rvec3} = \mathrm{rodrigues} ^{-1} \left ( \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \mathrm{rodrigues} ( \texttt{rvec1} ) \right ) \\ \texttt{tvec3} = \mathrm{rodrigues} ( \texttt{rvec2} ) \cdot \texttt{tvec1} + \texttt{tvec2} \end{array} ,\end{equation*}\]

where \(\mathrm{rodrigues}\) denotes a rotation vector to a rotation matrix transformation, and \(\mathrm{rodrigues}^{-1}\) denotes the inverse transformation. See #Rodrigues for details.

Also, the functions can compute the derivatives of the output vectors with regards to the input vectors (see #matMulDeriv ). The functions are used inside #stereoCalibrate but can also be used in your own code where Levenberg-Marquardt or another gradient-based solver is used to optimize a function that contains a matrix multiplication.

Parameters:

rvec1 (cv2.typing.MatLike) – First rotation vector.
tvec1 (cv2.typing.MatLike) – First translation vector.
rvec2 (cv2.typing.MatLike) – Second rotation vector.
tvec2 (cv2.typing.MatLike) – Second translation vector.
rvec3 (cv2.typing.MatLike | None) – Output rotation vector of the superposition.
tvec3 (cv2.typing.MatLike | None) – Output translation vector of the superposition.
dr3dr1 (cv2.typing.MatLike | None) – Optional output derivative of rvec3 with regard to rvec1
dr3dt1 (cv2.typing.MatLike | None) – Optional output derivative of rvec3 with regard to tvec1
dr3dr2 (cv2.typing.MatLike | None) – Optional output derivative of rvec3 with regard to rvec2
dr3dt2 (cv2.typing.MatLike | None) – Optional output derivative of rvec3 with regard to tvec2
dt3dr1 (cv2.typing.MatLike | None) – Optional output derivative of tvec3 with regard to rvec1
dt3dt1 (cv2.typing.MatLike | None) – Optional output derivative of tvec3 with regard to tvec1
dt3dr2 (cv2.typing.MatLike | None) – Optional output derivative of tvec3 with regard to rvec2
dt3dt2 (cv2.typing.MatLike | None) – Optional output derivative of tvec3 with regard to tvec2

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.computeCorrespondEpilines(points, whichImage, F[, lines]) → lines¶

For points in an image of a stereo pair, computes the corresponding epilines in the other image.

For every point in one of the two images of a stereo pair, the function finds the equation of the corresponding epipolar line in the other image.

From the fundamental matrix definition (see #findFundamentalMat ), line \(l^{(2)}_i\) in the second image for the point \(p^{(1)}_i\) in the first image (when whichImage=1 ) is computed as:

\[\begin{equation*}l^{(2)}_i = F p^{(1)}_i\end{equation*}\]

And vice versa, when whichImage=2, \(l^{(1)}_i\) is computed from \(p^{(2)}_i\) as:

\[\begin{equation*}l^{(1)}_i = F^T p^{(2)}_i\end{equation*}\]

Line coefficients are defined up to a scale. They are normalized so that \(a_i^2+b_i^2=1\) .

Parameters:

points (cv2.typing.MatLike) – Input points. \(N \times 1\) or \(1 \times N\) matrix of type CV_32FC2 orvector<Point2f> .
whichImage (int) – Index of the image (1 or 2) that contains the points .
F (cv2.typing.MatLike) – Fundamental matrix that can be estimated using #findFundamentalMat or #stereoRectify .
lines (cv2.typing.MatLike | None) – Output vector of the epipolar lines corresponding to the points in the other image.Each line \(ax + by + c=0\) is encoded by 3 numbers \((a, b, c)\) .

Return type:

cv2.typing.MatLike

cv2.computeECC(templateImage, inputImage[, inputMask]) → retval¶

Computes the Enhanced Correlation Coefficient value between two images @cite EP08 .

@sa findTransformECC

Parameters:

templateImage (cv2.typing.MatLike) – single-channel template image; CV_8U or CV_32F array.
inputImage (cv2.typing.MatLike) – single-channel input image to be warped to provide an image similar to templateImage, same type as templateImage.
inputMask (cv2.typing.MatLike | None) – An optional mask to indicate valid values of inputImage.

Return type:

cv2.connectedComponents(image[, labels[, connectivity[, ltype]]]) → retval, labels¶

@overload

Parameters:

image (cv2.typing.MatLike) – the 8-bit single-channel image to be labeled
labels (cv2.typing.MatLike | None) – destination labeled image
connectivity (int) – 8 or 4 for 8-way or 4-way connectivity respectively
ltype (int) – output image label type. Currently CV_32S and CV_16U are supported.

Return type:

tuple[int, cv2.typing.MatLike]

cv2.connectedComponentsWithAlgorithm(image, connectivity, ltype, ccltype[, labels]) → retval, labels¶

computes the connected components labeled image of boolean image

image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu’s (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.

Parameters:

image (cv2.typing.MatLike) – the 8-bit single-channel image to be labeled
labels (cv2.typing.MatLike | None) – destination labeled image
connectivity (int) – 8 or 4 for 8-way or 4-way connectivity respectively
ltype (int) – output image label type. Currently CV_32S and CV_16U are supported.
ccltype (int) – connected components algorithm type (see the #ConnectedComponentsAlgorithmsTypes).

Return type:

tuple[int, cv2.typing.MatLike]

cv2.connectedComponentsWithStats(image[, labels[, stats[, centroids[, connectivity[, ltype]]]]]) → retval, labels, stats, centroids¶

@overload

Parameters:

image (cv2.typing.MatLike) – the 8-bit single-channel image to be labeled
labels (cv2.typing.MatLike | None) – destination labeled image
stats (cv2.typing.MatLike | None) – statistics output for each label, including the background label.Statistics are accessed via stats(label, COLUMN) where COLUMN is one of #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S.
centroids (cv2.typing.MatLike | None) – centroid output for each label, including the background label. Centroids areaccessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
connectivity (int) – 8 or 4 for 8-way or 4-way connectivity respectively
ltype (int) – output image label type. Currently CV_32S and CV_16U are supported.

Return type:

tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.connectedComponentsWithStatsWithAlgorithm(image, connectivity, ltype, ccltype[, labels[, stats[, centroids]]]) → retval, labels, stats, centroids¶

computes the connected components labeled image of boolean image and also produces a statistics output for each label

image with 4 or 8 way connectivity - returns N, the total number of labels [0, N-1] where 0 represents the background label. ltype specifies the output label image type, an important consideration based on the total number of labels or alternatively the total number of pixels in the source image. ccltype specifies the connected components labeling algorithm to use, currently Bolelli (Spaghetti) @cite Bolelli2019, Grana (BBDT) @cite Grana2010 and Wu’s (SAUF) @cite Wu2009 algorithms are supported, see the #ConnectedComponentsAlgorithmsTypes for details. Note that SAUF algorithm forces a row major ordering of labels while Spaghetti and BBDT do not. This function uses parallel version of the algorithms (statistics included) if at least one allowed parallel framework is enabled and if the rows of the image are at least twice the number returned by #getNumberOfCPUs.

Parameters:

image (cv2.typing.MatLike) – the 8-bit single-channel image to be labeled
labels (cv2.typing.MatLike | None) – destination labeled image
stats (cv2.typing.MatLike | None) – statistics output for each label, including the background label.Statistics are accessed via stats(label, COLUMN) where COLUMN is one of #ConnectedComponentsTypes, selecting the statistic. The data type is CV_32S.
centroids (cv2.typing.MatLike | None) – centroid output for each label, including the background label. Centroids areaccessed via centroids(label, 0) for x and centroids(label, 1) for y. The data type CV_64F.
connectivity (int) – 8 or 4 for 8-way or 4-way connectivity respectively
ltype (int) – output image label type. Currently CV_32S and CV_16U are supported.
ccltype (int) – connected components algorithm type (see #ConnectedComponentsAlgorithmsTypes).

Return type:

tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.contourArea(contour[, oriented]) → retval¶

Calculates a contour area.

The function computes a contour area. Similarly to moments , the area is computed using the Green formula. Thus, the returned area and the number of non-zero pixels, if you draw the contour using #drawContours or #fillPoly , can be different. Also, the function will most certainly give a wrong results for contours with self-intersections.

Example:

    vector<Point> contour;
    contour.push_back(Point2f(0, 0));
    contour.push_back(Point2f(10, 0));
    contour.push_back(Point2f(10, 10));
    contour.push_back(Point2f(5, 4));

    double area0 = contourArea(contour);
    vector<Point> approx;
    approxPolyDP(contour, approx, 5, true);
    double area1 = contourArea(approx);

    cout << "area0 =" << area0 << endl <<
            "area1 =" << area1 << endl <<
            "approx poly vertices" << approx.size() << endl;

Parameters:

contour (cv2.typing.MatLike) – Input vector of 2D points (contour vertices), stored in std::vector or Mat.
oriented (bool) – Oriented area flag. If it is true, the function returns a signed area value,depending on the contour orientation (clockwise or counter-clockwise). Using this feature you can determine orientation of a contour by taking the sign of an area. By default, the parameter is false, which means that the absolute value is returned.

Return type:

cv2.convertFp16(src[, dst]) → dst¶

Converts an array to half precision floating number.

This function converts FP32 (single precision floating point) from/to FP16 (half precision floating point). CV_16S format is used to represent FP16 data. There are two use modes (src -> dst): CV_32F -> CV_16S and CV_16S -> CV_32F. The input array has to have type of CV_32F or CV_16S to represent the bit depth. If the input array is neither of them, the function will raise an error. The format of half precision floating point is defined in IEEE 754-2008.

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike | None) – output array.

Return type:

cv2.typing.MatLike

cv2.convertMaps(map1, map2, dstmap1type[, dstmap1[, dstmap2[, nninterpolation]]]) → dstmap1, dstmap2¶

Converts image transformation maps from one representation to another.

The function converts a pair of maps for remap from one representation to another. The following options ( (map1.type(), map2.type()) \(\rightarrow\) (dstmap1.type(), dstmap2.type()) ) are supported:

\(\texttt{(CV_32FC1, CV_32FC1)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). This is the most frequently used conversion operation, in which the original floating-point maps (see #remap) are converted to a more compact and much faster fixed-point representation. The first output array contains the rounded coordinates and the second array (created only when nninterpolation=false ) contains indices in the interpolation tables.
\(\texttt{(CV_32FC2)} \rightarrow \texttt{(CV_16SC2, CV_16UC1)}\). The same as above but the original maps are stored in one 2-channel matrix.
Reverse conversion. Obviously, the reconstructed floating-point maps will not be exactly the same as the originals.

See also: remap, undistort, initUndistortRectifyMap

Parameters:

map1 (cv2.typing.MatLike) – The first input map of type CV_16SC2, CV_32FC1, or CV_32FC2 .
map2 (cv2.typing.MatLike) – The second input map of type CV_16UC1, CV_32FC1, or none (empty matrix),respectively.
dstmap1 (cv2.typing.MatLike | None) – The first output map that has the type dstmap1type and the same size as src .
dstmap2 (cv2.typing.MatLike | None) – The second output map.
dstmap1type (int) – Type of the first output map that should be CV_16SC2, CV_32FC1, orCV_32FC2 .
nninterpolation (bool) – Flag indicating whether the fixed-point maps are used for thenearest-neighbor or for a more complex interpolation.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.convertPointsFromHomogeneous(src[, dst]) → dst¶

Converts points from homogeneous to Euclidean space.

The function converts points homogeneous to Euclidean space using perspective projection. That is, each point (x1, x2, … x(n-1), xn) is converted to (x1/xn, x2/xn, …, x(n-1)/xn). When xn=0, the output point coordinates will be (0,0,0,…).

Parameters:

src (cv2.typing.MatLike) – Input vector of N-dimensional points.
dst (cv2.typing.MatLike | None) – Output vector of N-1-dimensional points.

Return type:

cv2.typing.MatLike

cv2.convertPointsToHomogeneous(src[, dst]) → dst¶

Converts points from Euclidean to homogeneous space.

The function converts points from Euclidean to homogeneous space by appending 1’s to the tuple of point coordinates. That is, each point (x1, x2, …, xn) is converted to (x1, x2, …, xn, 1).

Parameters:

src (cv2.typing.MatLike) – Input vector of N-dimensional points.
dst (cv2.typing.MatLike | None) – Output vector of N+1-dimensional points.

Return type:

cv2.typing.MatLike

cv2.convertScaleAbs(src[, dst[, alpha[, beta]]]) → dst¶

Scales, calculates absolute values, and converts the result to 8-bit.

On each element of the input array, the function convertScaleAbs performs three operations sequentially: scaling, taking an absolute value, conversion to an unsigned 8-bit type:

\[\begin{equation*}\texttt{dst} (I)= \texttt{saturate\_cast<uchar>} (| \texttt{src} (I)* \texttt{alpha} + \texttt{beta} |)\end{equation*}\]

In case of multi-channel arrays, the function processes each channel independently. When the output is not 8-bit, the operation can be emulated by calling the Mat::convertTo method (or by using matrix expressions) and then by calculating an absolute value of the result. For example:

    Mat_<float> A(30,30);
    randu(A, Scalar(-100), Scalar(100));
    Mat_<float> B = A*5 + 3;
    B = abs(B);
    // Mat_<float> B = abs(A*5+3) will also do the job,
    // but it will allocate a temporary matrix

See also: Mat::convertTo, cv::abs(const Mat&)

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike | None) – output array.
alpha (float) – optional scale factor.
beta (float) – optional delta added to the scaled values.

Return type:

cv2.typing.MatLike

cv2.convexHull(points[, hull[, clockwise[, returnPoints]]]) → hull¶

Finds the convex hull of a point set.

The function cv::convexHull finds the convex hull of a 2D point set using the Sklansky’s algorithm @cite Sklansky82 that has O(N logN) complexity in the current implementation.

Check @ref tutorial_hull “the corresponding tutorial” for more details.

useful links:

https://www.learnopencv.com/convex-hull-using-opencv-in-python-and-c/

Note

points and hull should be different arrays, inplace processing isn’t supported.

Parameters:

points (cv2.typing.MatLike) – Input 2D point set, stored in std::vector or Mat.
hull (cv2.typing.MatLike | None) – Output convex hull. It is either an integer vector of indices or vector of points. Inthe first case, the hull elements are 0-based indices of the convex hull points in the original array (since the set of convex hull points is a subset of the original point set). In the second case, hull elements are the convex hull points themselves.
clockwise (bool) – Orientation flag. If it is true, the output convex hull is oriented clockwise.Otherwise, it is oriented counter-clockwise. The assumed coordinate system has its X axis pointing to the right, and its Y axis pointing upwards.
returnPoints (bool) – Operation flag. In case of a matrix, when the flag is true, the functionreturns convex hull points. Otherwise, it returns indices of the convex hull points. When the output array is std::vector, the flag is ignored, and the output depends on the type of the vector: std::vector<int> implies returnPoints=false, std::vector<Point> implies returnPoints=true.

Return type:

cv2.typing.MatLike

cv2.convexityDefects(contour, convexhull[, convexityDefects]) → convexityDefects¶

Finds the convexity defects of a contour.

The figure below displays convexity defects of a hand contour:

Parameters:

contour (cv2.typing.MatLike) – Input contour.
convexhull (cv2.typing.MatLike) – Convex hull obtained using convexHull that should contain indices of the contourpoints that make the hull.
convexityDefects (cv2.typing.MatLike | None) – The output vector of convexity defects. In C++ and the new Python/Javainterface each convexity defect is represented as 4-element integer vector (a.k.a. #Vec4i): (start_index, end_index, farthest_pt_index, fixpt_depth), where indices are 0-based indices in the original contour of the convexity defect beginning, end and the farthest point, and fixpt_depth is fixed-point approximation (with 8 fractional bits) of the distance between the farthest contour point and the hull. That is, to get the floating-point value of the depth will be fixpt_depth/256.0.

Return type:

cv2.typing.MatLike

cv2.copyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value]]) → dst¶

Forms a border around an image.

The function copies the source image into the middle of the destination image. The areas to the left, to the right, above and below the copied source image will be filled with extrapolated pixels. This is not what filtering functions based on it do (they extrapolate pixels on-fly), but what other more complex functions, including your own, may do to simplify image boundary handling.

The function supports the mode when src is already in the middle of dst . In this case, the function does not copy src itself but simply constructs the border, for example:

    // let border be the same in all directions
    int border=2;
    // constructs a larger image to fit both the image and the border
    Mat gray_buf(rgb.rows + border*2, rgb.cols + border*2, rgb.depth());
    // select the middle part of it w/o copying data
    Mat gray(gray_canvas, Rect(border, border, rgb.cols, rgb.rows));
    // convert image from RGB to grayscale
    cvtColor(rgb, gray, COLOR_RGB2GRAY);
    // form a border in-place
    copyMakeBorder(gray, gray_buf, border, border,
                   border, border, BORDER_REPLICATE);
    // now do some custom filtering ...
    ...

Note

When the source image is a part (ROI) of a bigger image, the function will try to use thepixels outside of the ROI to form a border. To disable this feature and always do extrapolation, as if src was not a ROI, use borderType | #BORDER_ISOLATED.

See also: borderInterpolate

Parameters:

src (cv2.typing.MatLike) – Source image.
dst (cv2.typing.MatLike | None) – Destination image of the same type as src and the size Size(src.cols+left+right,src.rows+top+bottom) .
top (int) – the top pixels
bottom (int) – the bottom pixels
left (int) – the left pixels
right (int) – Parameter specifying how many pixels in each direction from the source image rectangleto extrapolate. For example, top=1, bottom=1, left=1, right=1 mean that 1 pixel-wide border needs to be built.
borderType (int) – Border type. See borderInterpolate for details.
value (cv2.typing.Scalar) – Border value if borderType==BORDER_CONSTANT .

Return type:

cv2.typing.MatLike

cv2.copyTo(src, mask[, dst]) → dst¶

This is an overloaded member function, provided for convenience (python)Copies the matrix to another one. When the operation mask is specified, if the Mat::create call shown above reallocates the matrix, the newly allocated matrix is initialized with all zeros before copying the data.

Parameters:

src (cv2.typing.MatLike) – source matrix.
dst (cv2.typing.MatLike | None) – Destination matrix. If it does not have a proper size or type before the operation, it isreallocated.
mask (cv2.typing.MatLike) – Operation mask of the same size as *this. Its non-zero elements indicate which matrixelements need to be copied. The mask has to be of type CV_8U and can have 1 or multiple channels.

Return type:

cv2.typing.MatLike

cv2.cornerEigenValsAndVecs(src, blockSize, ksize[, dst[, borderType]]) → dst¶

Calculates eigenvalues and eigenvectors of image blocks for corner detection.

For every pixel \(p\) , the function cornerEigenValsAndVecs considers a blockSize \(\times\) blockSize neighborhood \(S(p)\) . It calculates the covariation matrix of derivatives over the neighborhood as:

\[\begin{equation*}M = \begin{bmatrix} \sum _{S(p)}(dI/dx)^2 & \sum _{S(p)}dI/dx dI/dy \\ \sum _{S(p)}dI/dx dI/dy & \sum _{S(p)}(dI/dy)^2 \end{bmatrix}\end{equation*}\]

where the derivatives are computed using the Sobel operator.

After that, it finds eigenvectors and eigenvalues of \(M\) and stores them in the destination image as \((\lambda_1, \lambda_2, x_1, y_1, x_2, y_2)\) where

\(\lambda_1, \lambda_2\) are the non-sorted eigenvalues of \(M\)
\(x_1, y_1\) are the eigenvectors corresponding to \(\lambda_1\)
\(x_2, y_2\) are the eigenvectors corresponding to \(\lambda_2\)

The output of the function can be used for robust edge or corner detection.

See also: cornerMinEigenVal, cornerHarris, preCornerDetect

Parameters:

src (cv2.typing.MatLike) – Input single-channel 8-bit or floating-point image.
dst (cv2.typing.MatLike | None) – Image to store the results. It has the same size as src and the type CV_32FC(6) .
blockSize (int) – Neighborhood size (see details below).
ksize (int) – Aperture parameter for the Sobel operator.
borderType (int) – Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.cornerHarris(src, blockSize, ksize, k[, dst[, borderType]]) → dst¶

Harris corner detector.

The function runs the Harris corner detector on the image. Similarly to cornerMinEigenVal and cornerEigenValsAndVecs , for each pixel \((x, y)\) it calculates a \(2\times2\) gradient covariance matrix \(M^{(x,y)}\) over a \(\texttt{blockSize} \times \texttt{blockSize}\) neighborhood. Then, it computes the following characteristic:

\[\begin{equation*}\texttt{dst} (x,y) = \mathrm{det} M^{(x,y)} - k \cdot \left ( \mathrm{tr} M^{(x,y)} \right )^2\end{equation*}\]

Corners in the image can be found as the local maxima of this response map.

Parameters:

src (cv2.typing.MatLike) – Input single-channel 8-bit or floating-point image.
dst (cv2.typing.MatLike | None) – Image to store the Harris detector responses. It has the type CV_32FC1 and the samesize as src .
blockSize (int) – Neighborhood size (see the details on #cornerEigenValsAndVecs ).
ksize (int) – Aperture parameter for the Sobel operator.
k (float) – Harris detector free parameter. See the formula above.
borderType (int) – Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.cornerMinEigenVal(src, blockSize[, dst[, ksize[, borderType]]]) → dst¶

Calculates the minimal eigenvalue of gradient matrices for corner detection.

The function is similar to cornerEigenValsAndVecs but it calculates and stores only the minimal eigenvalue of the covariance matrix of derivatives, that is, \(\min(\lambda_1, \lambda_2)\) in terms of the formulae in the cornerEigenValsAndVecs description.

Parameters:

src (cv2.typing.MatLike) – Input single-channel 8-bit or floating-point image.
dst (cv2.typing.MatLike | None) – Image to store the minimal eigenvalues. It has the type CV_32FC1 and the same size assrc .
blockSize (int) – Neighborhood size (see the details on #cornerEigenValsAndVecs ).
ksize (int) – Aperture parameter for the Sobel operator.
borderType (int) – Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.cornerSubPix(image, corners, winSize, zeroZone, criteria) → corners¶

Refines the corner locations.

The function iterates to find the sub-pixel accurate location of corners or radial saddle points as described in @cite forstner1987fast, and as shown on the figure below.

Sub-pixel accurate corner locator is based on the observation that every vector from the center \(q\) to a point \(p\) located within a neighborhood of \(q\) is orthogonal to the image gradient at \(p\) subject to image and measurement noise. Consider the expression:

\[\begin{equation*}\epsilon _i = {DI_{p_i}}^T \cdot (q - p_i)\end{equation*}\]

where \({DI_{p_i}}\) is an image gradient at one of the points \(p_i\) in a neighborhood of \(q\) . The value of \(q\) is to be found so that \(\epsilon_i\) is minimized. A system of equations may be set up with \(\epsilon_i\) set to zero:

\[\begin{equation*}\sum _i(DI_{p_i} \cdot {DI_{p_i}}^T) \cdot q - \sum _i(DI_{p_i} \cdot {DI_{p_i}}^T \cdot p_i)\end{equation*}\]

where the gradients are summed within a neighborhood (“search window”) of \(q\) . Calling the first gradient term \(G\) and the second gradient term \(b\) gives:

\[\begin{equation*}q = G^{-1} \cdot b\end{equation*}\]

The algorithm sets the center of the neighborhood window at this new center \(q\) and then iterates until the center stays within a set threshold.

Parameters:

image (cv2.typing.MatLike) – Input single-channel, 8-bit or float image.
corners (cv2.typing.MatLike) – Initial coordinates of the input corners and refined coordinates provided foroutput.
winSize (cv2.typing.Size) – Half of the side length of the search window. For example, if winSize=Size(5,5) ,then a \((5*2+1) \times (5*2+1) = 11 \times 11\) search window is used.
zeroZone (cv2.typing.Size) – Half of the size of the dead region in the middle of the search zone over whichthe summation in the formula below is not done. It is used sometimes to avoid possible singularities of the autocorrelation matrix. The value of (-1,-1) indicates that there is no such a size.
criteria (cv2.typing.TermCriteria) – Criteria for termination of the iterative process of corner refinement. That is,the process of corner position refinement stops either after criteria.maxCount iterations or when the corner position moves by less than criteria.epsilon on some iteration.

Return type:

cv2.typing.MatLike

cv2.correctMatches(F, points1, points2[, newPoints1[, newPoints2]]) → newPoints1, newPoints2¶

Refines coordinates of corresponding points.

The function implements the Optimal Triangulation Method (see Multiple View Geometry @cite HartleyZ00 for details). For each given point correspondence points1[i] <-> points2[i], and a fundamental matrix F, it computes the corrected correspondences newPoints1[i] <-> newPoints2[i] that minimize the geometric error \(d(points1[i], newPoints1[i])^2 + d(points2[i],newPoints2[i])^2\) (where \(d(a,b)\) is the geometric distance between points \(a\) and \(b\) ) subject to the epipolar constraint \(newPoints2^T \cdot F \cdot newPoints1 = 0\) .

Parameters:

F (cv2.typing.MatLike) – 3x3 fundamental matrix.
points1 (cv2.typing.MatLike) – 1xN array containing the first set of points.
points2 (cv2.typing.MatLike) – 1xN array containing the second set of points.
newPoints1 (cv2.typing.MatLike | None) – The optimized points1.
newPoints2 (cv2.typing.MatLike | None) – The optimized points2.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.countNonZero(src) → retval¶

Counts non-zero array elements.

The function returns the number of non-zero elements in src :

\[\begin{equation*}\sum _{I: \; \texttt{src} (I) \ne0 } 1\end{equation*}\]

See also: mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix

Parameters:: src (cv2.typing.MatLike) – single-channel array.
Return type:: int

cv2.createAlignMTB([max_bits[, exclude_range[, cut]]]) → retval¶

Creates AlignMTB object

Parameters:

max_bits (int) – logarithm to the base 2 of maximal shift in each dimension. Values of 5 and 6 areusually good enough (31 and 63 pixels shift respectively).
exclude_range (int) – range for exclusion bitmap that is constructed to suppress noise around themedian value.
cut (bool) – if true cuts images, otherwise fills the new regions with zeros.

Return type:

AlignMTB

cv2.createBackgroundSubtractorKNN([history[, dist2Threshold[, detectShadows]]]) → retval¶

Creates KNN Background Subtractor

Parameters:

history (int) – Length of the history.
dist2Threshold (float) – Threshold on the squared distance between the pixel and the sample to decidewhether a pixel is close to that sample. This parameter does not affect the background update.
detectShadows (bool) – If true, the algorithm will detect shadows and mark them. It decreases thespeed a bit, so if you do not need this feature, set the parameter to false.

Return type:

BackgroundSubtractorKNN

cv2.createBackgroundSubtractorMOG2([history[, varThreshold[, detectShadows]]]) → retval¶

Creates MOG2 Background Subtractor

Parameters:

history (int) – Length of the history.
varThreshold (float) – Threshold on the squared Mahalanobis distance between the pixel and the modelto decide whether a pixel is well described by the background model. This parameter does not affect the background update.
detectShadows (bool) – If true, the algorithm will detect shadows and mark them. It decreases thespeed a bit, so if you do not need this feature, set the parameter to false.

Return type:

BackgroundSubtractorMOG2

cv2.createButton(buttonName, onChange[, userData, buttonType, initialButtonState]) → None¶

Parameters:

buttonName (str) –
onChange (_typing.Callable[[tuple[int] | tuple[int, _typing.Any]], None]) –
userData (_typing.Any | None) –
buttonType (int) –
initialButtonState (int) –

Return type:

None

cv2.createCLAHE([clipLimit[, tileGridSize]]) → retval¶

Creates a smart pointer to a cv::CLAHE class and initializes it.

Parameters:

clipLimit (float) – Threshold for contrast limiting.
tileGridSize (cv2.typing.Size) – Size of grid for histogram equalization. Input image will be divided intoequally sized rectangular tiles. tileGridSize defines the number of tiles in row and column.

Return type:

CLAHE

cv2.createCalibrateDebevec([samples[, lambda_[, random]]]) → retval¶

Creates CalibrateDebevec object

Parameters:

samples (int) – number of pixel locations to use
lambda – smoothness term weight. Greater values produce smoother results, but can alter theresponse.
random (bool) – if true sample pixel locations are chosen at random, otherwise they form arectangular grid.
lambda_ (float) –

Return type:

CalibrateDebevec

cv2.createCalibrateRobertson([max_iter[, threshold]]) → retval¶

Creates CalibrateRobertson object

Parameters:

max_iter (int) – maximal number of Gauss-Seidel solver iterations.
threshold (float) – target difference between results of two successive steps of the minimization.

Return type:

CalibrateRobertson

cv2.createGeneralizedHoughBallard() → retval¶

Creates a smart pointer to a cv::GeneralizedHoughBallard class and initializes it.

Return type:: GeneralizedHoughBallard

cv2.createGeneralizedHoughGuil() → retval¶

Creates a smart pointer to a cv::GeneralizedHoughGuil class and initializes it.

Return type:: GeneralizedHoughGuil

cv2.createHanningWindow(winSize, type[, dst]) → dst¶

This function computes a Hanning window coefficients in two dimensions.

See (http://en.wikipedia.org/wiki/Hann_function) and (http://en.wikipedia.org/wiki/Window_function) for more information.

An example is shown below:

    // create hanning window of size 100x100 and type CV_32F
    Mat hann;
    createHanningWindow(hann, Size(100, 100), CV_32F);

Parameters:

dst (cv2.typing.MatLike | None) – Destination array to place Hann coefficients in
winSize (cv2.typing.Size) – The window size specifications (both width and height must be > 1)
type (int) – Created array type

Return type:

cv2.typing.MatLike

cv2.createLineSegmentDetector([refine[, scale[, sigma_scale[, quant[, ang_th[, log_eps[, density_th[, n_bins]]]]]]]]) → retval¶

Creates a smart pointer to a LineSegmentDetector object and initializes it.

The LineSegmentDetector algorithm is defined using the standard values. Only advanced users may want to edit those, as to tailor it for their own application.

Parameters:

refine (int) – The way found lines will be refined, see #LineSegmentDetectorModes
scale (float) – The scale of the image that will be used to find the lines. Range (0..1].
sigma_scale (float) – Sigma for Gaussian filter. It is computed as sigma = sigma_scale/scale.
quant (float) – Bound to the quantization error on the gradient norm.
ang_th (float) – Gradient angle tolerance in degrees.
log_eps (float) – Detection threshold: -log10(NFA) > log_eps. Used only when advance refinement is chosen.
density_th (float) – Minimal density of aligned region points in the enclosing rectangle.
n_bins (int) – Number of bins in pseudo-ordering of gradient modulus.

Return type:

LineSegmentDetector

cv2.createMergeDebevec() → retval¶

Creates MergeDebevec object

Return type:: MergeDebevec

cv2.createMergeMertens([contrast_weight[, saturation_weight[, exposure_weight]]]) → retval¶

Creates MergeMertens object

Parameters:

contrast_weight (float) – contrast measure weight. See MergeMertens.
saturation_weight (float) – saturation measure weight
exposure_weight (float) – well-exposedness measure weight

Return type:

MergeMertens

cv2.createMergeRobertson() → retval¶

Creates MergeRobertson object

Return type:: MergeRobertson

cv2.createTonemap([gamma]) → retval¶

Creates simple linear mapper with gamma correction

Parameters:: gamma (float) – positive value for gamma correction. Gamma value of 1.0 implies no correction, gammaequal to 2.2f is suitable for most displays. Generally gamma > 1 brightens the image and gamma < 1 darkens it.
Return type:: Tonemap

cv2.createTonemapDrago([gamma[, saturation[, bias]]]) → retval¶

Creates TonemapDrago object

Parameters:

gamma (float) – gamma value for gamma correction. See createTonemap
saturation (float) – positive saturation enhancement value. 1.0 preserves saturation, values greaterthan 1 increase saturation and values less than 1 decrease it.
bias (float) – value for bias function in [0, 1] range. Values from 0.7 to 0.9 usually give bestresults, default value is 0.85.

Return type:

TonemapDrago

cv2.createTonemapMantiuk([gamma[, scale[, saturation]]]) → retval¶

Creates TonemapMantiuk object

Parameters:

gamma (float) – gamma value for gamma correction. See createTonemap
scale (float) – contrast scale factor. HVS response is multiplied by this parameter, thus compressingdynamic range. Values from 0.6 to 0.9 produce best results.
saturation (float) – saturation enhancement value. See createTonemapDrago

Return type:

TonemapMantiuk

cv2.createTonemapReinhard([gamma[, intensity[, light_adapt[, color_adapt]]]]) → retval¶

Creates TonemapReinhard object

Parameters:

gamma (float) – gamma value for gamma correction. See createTonemap
intensity (float) – result intensity in [-8, 8] range. Greater intensity produces brighter results.
light_adapt (float) – light adaptation in [0, 1] range. If 1 adaptation is based only on pixelvalue, if 0 it’s global, otherwise it’s a weighted mean of this two cases.
color_adapt (float) – chromatic adaptation in [0, 1] range. If 1 channels are treated independently,if 0 adaptation level is the same for each channel.

Return type:

TonemapReinhard

cv2.createTrackbar(trackbarName, windowName, value, count, onChange) → None¶

Parameters:

trackbarName (str) –
windowName (str) –
value (int) –
count (int) –
onChange (_typing.Callable[[int], None]) –

Return type:

None

cv2.cubeRoot(val) → retval¶

Computes the cube root of an argument.

The function cubeRoot computes \(\sqrt[3]{\texttt{val}}\). Negative arguments are handled correctly. NaN and Inf are not handled. The accuracy approaches the maximum possible accuracy for single-precision data. @param val A function argument.

Parameters:: val (float) –
Return type:: float

cv2.cvtColor(src, code[, dst[, dstCn]]) → dst¶

Converts an image from one color space to another.

The function converts an input image from one color space to another. In case of a transformation to-from RGB color space, the order of the channels should be specified explicitly (RGB or BGR). Note that the default color format in OpenCV is often referred to as RGB but it is actually BGR (the bytes are reversed). So the first byte in a standard (24-bit) color image will be an 8-bit Blue component, the second byte will be Green, and the third byte will be Red. The fourth, fifth, and sixth bytes would then be the second pixel (Blue, then Green, then Red), and so on.

The conventional ranges for R, G, and B channel values are:

0 to 255 for CV_8U images
0 to 65535 for CV_16U images
0 to 1 for CV_32F images

In case of linear transformations, the range does not matter. But in case of a non-linear transformation, an input RGB image should be normalized to the proper value range to get the correct results, for example, for RGB \(\rightarrow\) L*u*v* transformation. For example, if you have a 32-bit floating-point image directly converted from an 8-bit image without any scaling, then it will have the 0..255 value range instead of 0..1 assumed by the function. So, before calling #cvtColor , you need first to scale the image down:

    img *= 1./255;
    cvtColor(img, img, COLOR_BGR2Luv);

If you use #cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.

If conversion adds the alpha channel, its value will set to the maximum of corresponding channel range: 255 for CV_8U, 65535 for CV_16U, 1 for CV_32F.

See also: @ref imgproc_color_conversions

Parameters:

src (cv2.typing.MatLike) – input image: 8-bit unsigned, 16-bit unsigned ( CV_16UC… ), or single-precisionfloating-point.
dst (cv2.typing.MatLike | None) – output image of the same size and depth as src.
code (int) – color space conversion code (see #ColorConversionCodes).
dstCn (int) – number of channels in the destination image; if the parameter is 0, the number of thechannels is derived automatically from src and code.

Return type:

cv2.typing.MatLike

cv2.cvtColorTwoPlane(src1, src2, code[, dst]) → dst¶

Converts an image from one color space to another where the source image isstored in two planes.

This function only supports YUV420 to RGB conversion as of now.

Parameters:

src1 (cv2.typing.MatLike) – 8-bit image (#CV_8U) of the Y plane.
src2 (cv2.typing.MatLike) – image containing interleaved U/V plane.
dst (cv2.typing.MatLike | None) – output image.
code – Specifies the type of conversion. It can take any of the following values:- #COLOR_YUV2BGR_NV12

#COLOR_YUV2RGB_NV12
#COLOR_YUV2BGRA_NV12
#COLOR_YUV2RGBA_NV12
#COLOR_YUV2BGR_NV21
#COLOR_YUV2RGB_NV21
#COLOR_YUV2BGRA_NV21
#COLOR_YUV2RGBA_NV21

Return type:: cv2.typing.MatLike

cv2.dct(src[, dst[, flags]]) → dst¶

Performs a forward or inverse discrete Cosine transform of 1D or 2D array.

The function cv::dct performs a forward or inverse discrete Cosine transform (DCT) of a 1D or 2D floating-point array:

Forward Cosine transform of a 1D vector of N elements:

\[\begin{equation*}Y = C^{(N)} \cdot X\end{equation*}\]

where

\[\begin{equation*}C^{(N)}_{jk}= \sqrt{\alpha_j/N} \cos \left ( \frac{\pi(2k+1)j}{2N} \right )\end{equation*}\]

and \(\alpha_0=1\), \(\alpha_j=2\) for j > 0.
Inverse Cosine transform of a 1D vector of N elements:

\[\begin{equation*}X = \left (C^{(N)} \right )^{-1} \cdot Y = \left (C^{(N)} \right )^T \cdot Y\end{equation*}\]

(since \(C^{(N)}\) is an orthogonal matrix, \(C^{(N)} \cdot \left(C^{(N)}\right)^T = I\) )
Forward 2D Cosine transform of M x N matrix:

\[\begin{equation*}Y = C^{(N)} \cdot X \cdot \left (C^{(N)} \right )^T\end{equation*}\]
Inverse 2D Cosine transform of M x N matrix:

\[\begin{equation*}X = \left (C^{(N)} \right )^T \cdot X \cdot C^{(N)}\end{equation*}\]

The function chooses the mode of operation by looking at the flags and size of the input array:

If (flags & #DCT_INVERSE) == 0 , the function does a forward 1D or 2D transform. Otherwise, it is an inverse 1D or 2D transform.
If (flags & #DCT_ROWS) != 0 , the function performs a 1D transform of each row.
If the array is a single column or a single row, the function performs a 1D transform.
If none of the above is true, the function performs a 2D transform.

Note

Currently dct supports even-size arrays (2, 4, 6 …). For data analysis and approximation, youcan pad the array when necessary. Also, the function performance depends very much, and not monotonically, on the array size (see getOptimalDFTSize ). In the current implementation DCT of a vector of size N is calculated via DFT of a vector of size N/2 . Thus, the optimal DCT size N1 >= N can be calculated as:

    size_t getOptimalDCTSize(size_t N) { return 2*getOptimalDFTSize((N+1)/2); }
    N1 = getOptimalDCTSize(N);

**See also:** dft , getOptimalDFTSize , idct


:param src: input floating-point array.
:type src: cv2.typing.MatLike
:param dst: output array of the same size and type as src .
:type dst: cv2.typing.MatLike | None
:param flags: transformation flags as a combination of cv::DftFlags (DCT_*)
:type flags: int
:rtype: cv2.typing.MatLike

cv2.decolor(src[, grayscale[, color_boost]]) → grayscale, color_boost¶

Transforms a color image to a grayscale image. It is a basic tool in digital printing, stylizedblack-and-white photograph rendering, and in many single channel image processing applications @cite CL12 .

This function is to be applied on color images.

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
grayscale (cv2.typing.MatLike | None) – Output 8-bit 1-channel image.
color_boost (cv2.typing.MatLike | None) – Output 8-bit 3-channel image.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.decomposeEssentialMat(E[, R1[, R2[, t]]]) → R1, R2, t¶

Decompose an essential matrix to possible rotations and translation.

This function decomposes the essential matrix E using svd decomposition @cite HartleyZ00. In general, four possible poses exist for the decomposition of E. They are \([R_1, t]\), \([R_1, -t]\), \([R_2, t]\), \([R_2, -t]\).

If E gives the epipolar constraint \([p_2; 1]^T A^{-T} E A^{-1} [p_1; 1] = 0\) between the image points \(p_1\) in the first image and \(p_2\) in second image, then any of the tuples \([R_1, t]\), \([R_1, -t]\), \([R_2, t]\), \([R_2, -t]\) is a change of basis from the first camera’s coordinate system to the second camera’s coordinate system. However, by decomposing E, one can only get the direction of the translation. For this reason, the translation t is returned with unit length.

Parameters:

E (cv2.typing.MatLike) – The input essential matrix.
R1 (cv2.typing.MatLike | None) – One possible rotation matrix.
R2 (cv2.typing.MatLike | None) – Another possible rotation matrix.
t (cv2.typing.MatLike | None) – One possible translation.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.decomposeHomographyMat(H, K[, rotations[, translations[, normals]]]) → retval, rotations, translations, normals¶

Decompose a homography matrix to rotation(s), translation(s) and plane normal(s).

This function extracts relative camera motion between two views of a planar object and returns up to four mathematical solution tuples of rotation, translation, and plane normal. The decomposition of the homography matrix H is described in detail in @cite Malis2007.

If the homography H, induced by the plane, gives the constraint

\[\begin{equation*}s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\end{equation*}\]

\(p_i\) and the destination image points \(p'_i\), then the tuple of rotations[k] and translations[k] is a change of basis from the source camera’s coordinate system to the destination camera’s coordinate system. However, by decomposing H, one can only get the translation normalized by the (typically unknown) depth of the scene, i.e. its direction but with normalized length.

If point correspondences are available, at least two solutions may further be invalidated, by applying positive depth constraint, i.e. all points must be in front of the camera.

Parameters:

H (cv2.typing.MatLike) – The input homography matrix between two images.
K (cv2.typing.MatLike) – The input camera intrinsic matrix.
rotations (_typing.Sequence[cv2.typing.MatLike] | None) – Array of rotation matrices.
translations (_typing.Sequence[cv2.typing.MatLike] | None) – Array of translation matrices.
normals (_typing.Sequence[cv2.typing.MatLike] | None) – Array of plane normal matrices.

Return type:

tuple[int, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]

cv2.decomposeProjectionMatrix(projMatrix[, cameraMatrix[, rotMatrix[, transVect[, rotMatrixX[, rotMatrixY[, rotMatrixZ[, eulerAngles]]]]]]]) → cameraMatrix, rotMatrix, transVect, rotMatrixX, rotMatrixY, rotMatrixZ, eulerAngles¶

Decomposes a projection matrix into a rotation matrix and a camera intrinsic matrix.

The function computes a decomposition of a projection matrix into a calibration and a rotation matrix and the position of a camera.

It optionally returns three rotation matrices, one for each axis, and three Euler angles that could be used in OpenGL. Note, there is always more than one sequence of rotations about the three principal axes that results in the same orientation of an object, e.g. see @cite Slabaugh . Returned three rotation matrices and corresponding three Euler angles are only one of the possible solutions.

The function is based on #RQDecomp3x3 .

Parameters:

projMatrix (cv2.typing.MatLike) – 3x4 input projection matrix P.
cameraMatrix (cv2.typing.MatLike | None) – Output 3x3 camera intrinsic matrix \(\cameramatrix{A}\).
rotMatrix (cv2.typing.MatLike | None) – Output 3x3 external rotation matrix R.
transVect (cv2.typing.MatLike | None) – Output 4x1 translation vector T.
rotMatrixX (cv2.typing.MatLike | None) – Optional 3x3 rotation matrix around x-axis.
rotMatrixY (cv2.typing.MatLike | None) – Optional 3x3 rotation matrix around y-axis.
rotMatrixZ (cv2.typing.MatLike | None) – Optional 3x3 rotation matrix around z-axis.
eulerAngles (cv2.typing.MatLike | None) – Optional three-element vector containing three Euler angles of rotation indegrees.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.demosaicing(src, code[, dst[, dstCn]]) → dst¶

main function for all demosaicing processes

The function can do the following transformations:

Demosaicing using bilinear interpolation

#COLOR_BayerBG2BGR , #COLOR_BayerGB2BGR , #COLOR_BayerRG2BGR , #COLOR_BayerGR2BGR

#COLOR_BayerBG2GRAY , #COLOR_BayerGB2GRAY , #COLOR_BayerRG2GRAY , #COLOR_BayerGR2GRAY
Demosaicing using Variable Number of Gradients.

#COLOR_BayerBG2BGR_VNG , #COLOR_BayerGB2BGR_VNG , #COLOR_BayerRG2BGR_VNG , #COLOR_BayerGR2BGR_VNG
Edge-Aware Demosaicing.

#COLOR_BayerBG2BGR_EA , #COLOR_BayerGB2BGR_EA , #COLOR_BayerRG2BGR_EA , #COLOR_BayerGR2BGR_EA
Demosaicing with alpha channel

#COLOR_BayerBG2BGRA , #COLOR_BayerGB2BGRA , #COLOR_BayerRG2BGRA , #COLOR_BayerGR2BGRA

See also: cvtColor

Parameters:

src (cv2.typing.MatLike) – input image: 8-bit unsigned or 16-bit unsigned.
dst (cv2.typing.MatLike | None) – output image of the same size and depth as src.
code (int) – Color space conversion code (see the description below).
dstCn (int) – number of channels in the destination image; if the parameter is 0, the number of thechannels is derived automatically from src and code.

Return type:

cv2.typing.MatLike

cv2.denoise_TVL1(observations, result[, lambda_[, niters]]) → None¶

Primal-dual algorithm is an algorithm for solving special types of variational problems (that is,finding a function to minimize some functional). As the image denoising, in particular, may be seen as the variational problem, primal-dual algorithm then can be used to perform denoising and this is exactly what is implemented.

It should be noted, that this implementation was taken from the July 2013 blog entry @cite MA13 , which also contained (slightly more general) ready-to-use source code on Python. Subsequently, that code was rewritten on C++ with the usage of openCV by Vadim Pisarevsky at the end of July 2013 and finally it was slightly adapted by later authors.

Although the thorough discussion and justification of the algorithm involved may be found in @cite ChambolleEtAl, it might make sense to skim over it here, following @cite MA13 . To begin with, we consider the 1-byte gray-level images as the functions from the rectangular domain of pixels (it may be seen as set \(\left\{(x,y)\in\mathbb{N}\times\mathbb{N}\mid 1\leq x\leq n,\;1\leq y\leq m\right\}\) for some \(m,\;n\in\mathbb{N}\)) into \(\{0,1,\dots,255\}\). We shall denote the noised images as \(f_i\) and with this view, given some image \(x\) of the same size, we may measure how bad it is by the formula

\[\begin{equation*}\left\|\left\|\nabla x\right\|\right\| + \lambda\sum_i\left\|\left\|x-f_i\right\|\right\|\end{equation*}\]

\(\|\|\cdot\|\|\) here denotes \(L_2\)-norm and as you see, the first addend states that we want our image to be smooth (ideally, having zero gradient, thus being constant) and the second states that we want our result to be close to the observations we’ve got. If we treat \(x\) as a function, this is exactly the functional what we seek to minimize and here the Primal-Dual algorithm comes into play.

Parameters:

observations (_typing.Sequence[cv2.typing.MatLike]) – This array should contain one or more noised versions of the image that is tobe restored.
result (cv2.typing.MatLike) – Here the denoised image will be stored. There is no need to do pre-allocation ofstorage space, as it will be automatically allocated, if necessary.
lambda – Corresponds to \(\lambda\) in the formulas above. As it is enlarged, the smooth(blurred) images are treated more favorably than detailed (but maybe more noised) ones. Roughly speaking, as it becomes smaller, the result will be more blur but more sever outliers will be removed.
niters (int) – Number of iterations that the algorithm will run. Of course, as more iterations asbetter, but it is hard to quantitatively refine this statement, so just use the default and increase it if the results are poor.
lambda_ (float) –

Return type:

None

cv2.destroyAllWindows() → None¶

Destroys all of the HighGUI windows.

The function destroyAllWindows destroys all of the opened HighGUI windows.

Return type:: None

cv2.destroyWindow(winname) → None¶

Destroys the specified window.

The function destroyWindow destroys the window with the given name.

Parameters:: winname (str) – Name of the window to be destroyed.
Return type:: None

cv2.detailEnhance(src[, dst[, sigma_s[, sigma_r]]]) → dst¶

This filter enhances the details of a particular image.

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as src.
sigma_s (float) –
sigma_r (float) –

Return type:

cv2.typing.MatLike

cv2.determinant(mtx) → retval¶

Returns the determinant of a square floating-point matrix.

The function cv::determinant calculates and returns the determinant of the specified matrix. For small matrices ( mtx.cols=mtx.rows<=3 ), the direct method is used. For larger matrices, the function uses LU factorization with partial pivoting.

For symmetric positively-determined matrices, it is also possible to use eigen decomposition to calculate the determinant.

See also: trace, invert, solve, eigen, @ref MatrixExpressions

Parameters:: mtx (cv2.typing.MatLike) – input matrix that must have CV_32FC1 or CV_64FC1 type andsquare size.
Return type:: float

cv2.dft(src[, dst[, flags[, nonzeroRows]]]) → dst¶

Performs a forward or inverse Discrete Fourier transform of a 1D or 2D floating-point array.

The function cv::dft performs one of the following:

Forward the Fourier transform of a 1D vector of N elements:

\[\begin{equation*}Y = F^{(N)} \cdot X,\end{equation*}\]

where \(F^{(N)}_{jk}=\exp(-2\pi i j k/N)\) and \(i=\sqrt{-1}\)
Inverse the Fourier transform of a 1D vector of N elements:

\[\begin{equation*}\begin{array}{l} X'= \left (F^{(N)} \right )^{-1} \cdot Y = \left (F^{(N)} \right )^* \cdot y \\ X = (1/N) \cdot X, \end{array}\end{equation*}\]

where \(F^*=\left(\textrm{Re}(F^{(N)})-\textrm{Im}(F^{(N)})\right)^T\)
Forward the 2D Fourier transform of a M x N matrix:

\[\begin{equation*}Y = F^{(M)} \cdot X \cdot F^{(N)}\end{equation*}\]
Inverse the 2D Fourier transform of a M x N matrix:

\[\begin{equation*}\begin{array}{l} X'= \left (F^{(M)} \right )^* \cdot Y \cdot \left (F^{(N)} \right )^* \\ X = \frac{1}{M \cdot N} \cdot X' \end{array}\end{equation*}\]

In case of real (single-channel) data, the output spectrum of the forward Fourier transform or input spectrum of the inverse Fourier transform can be represented in a packed format called CCS (complex-conjugate-symmetrical). It was borrowed from IPL (Intel* Image Processing Library). Here is how 2D CCS spectrum looks:

\[\begin{equation*}\begin{bmatrix} Re Y_{0,0} & Re Y_{0,1} & Im Y_{0,1} & Re Y_{0,2} & Im Y_{0,2} & \cdots & Re Y_{0,N/2-1} & Im Y_{0,N/2-1} & Re Y_{0,N/2} \\ Re Y_{1,0} & Re Y_{1,1} & Im Y_{1,1} & Re Y_{1,2} & Im Y_{1,2} & \cdots & Re Y_{1,N/2-1} & Im Y_{1,N/2-1} & Re Y_{1,N/2} \\ Im Y_{1,0} & Re Y_{2,1} & Im Y_{2,1} & Re Y_{2,2} & Im Y_{2,2} & \cdots & Re Y_{2,N/2-1} & Im Y_{2,N/2-1} & Im Y_{1,N/2} \\ \hdotsfor{9} \\ Re Y_{M/2-1,0} & Re Y_{M-3,1} & Im Y_{M-3,1} & \hdotsfor{3} & Re Y_{M-3,N/2-1} & Im Y_{M-3,N/2-1}& Re Y_{M/2-1,N/2} \\ Im Y_{M/2-1,0} & Re Y_{M-2,1} & Im Y_{M-2,1} & \hdotsfor{3} & Re Y_{M-2,N/2-1} & Im Y_{M-2,N/2-1}& Im Y_{M/2-1,N/2} \\ Re Y_{M/2,0} & Re Y_{M-1,1} & Im Y_{M-1,1} & \hdotsfor{3} & Re Y_{M-1,N/2-1} & Im Y_{M-1,N/2-1}& Re Y_{M/2,N/2} \end{bmatrix}\end{equation*}\]

In case of 1D transform of a real vector, the output looks like the first row of the matrix above.

So, the function chooses an operation mode depending on the flags and size of the input array:

If #DFT_ROWS is set or the input array has a single row or single column, the function performs a 1D forward or inverse transform of each row of a matrix when #DFT_ROWS is set. Otherwise, it performs a 2D transform.
If the input array is real and #DFT_INVERSE is not set, the function performs a forward 1D or 2D transform:
- When #DFT_COMPLEX_OUTPUT is set, the output is a complex matrix of the same size as input.
- When #DFT_COMPLEX_OUTPUT is not set, the output is a real matrix of the same size as input. In case of 2D transform, it uses the packed format as shown above. In case of a single 1D transform, it looks like the first row of the matrix above. In case of multiple 1D transforms (when using the #DFT_ROWS flag), each row of the output matrix looks like the first row of the matrix above.
If the input array is complex and either #DFT_INVERSE or #DFT_REAL_OUTPUT are not set, the output is a complex array of the same size as input. The function performs a forward or inverse 1D or 2D transform of the whole input array or each row of the input array independently, depending on the flags DFT_INVERSE and DFT_ROWS.
When #DFT_INVERSE is set and the input array is real, or it is complex but #DFT_REAL_OUTPUT is set, the output is a real array of the same size as input. The function performs a 1D or 2D inverse transformation of the whole input array or each individual row, depending on the flags #DFT_INVERSE and #DFT_ROWS.

If #DFT_SCALE is set, the scaling is done after the transformation.

Unlike dct , the function supports arrays of arbitrary size. But only those arrays are processed efficiently, whose sizes can be factorized in a product of small prime numbers (2, 3, and 5 in the current implementation). Such an efficient DFT size can be calculated using the getOptimalDFTSize method.

The sample below illustrates how to calculate a DFT-based convolution of two 2D real arrays:

    void convolveDFT(InputArray A, InputArray B, OutputArray C)
    {
        // reallocate the output array if needed
        C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type());
        Size dftSize;
        // calculate the size of DFT transform
        dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1);
        dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1);

        // allocate temporary buffers and initialize them with 0's
        Mat tempA(dftSize, A.type(), Scalar::all(0));
        Mat tempB(dftSize, B.type(), Scalar::all(0));

        // copy A and B to the top-left corners of tempA and tempB, respectively
        Mat roiA(tempA, Rect(0,0,A.cols,A.rows));
        A.copyTo(roiA);
        Mat roiB(tempB, Rect(0,0,B.cols,B.rows));
        B.copyTo(roiB);

        // now transform the padded A & B in-place;
        // use "nonzeroRows" hint for faster processing
        dft(tempA, tempA, 0, A.rows);
        dft(tempB, tempB, 0, B.rows);

        // multiply the spectrums;
        // the function handles packed spectrum representations well
        mulSpectrums(tempA, tempB, tempA);

        // transform the product back from the frequency domain.
        // Even though all the result rows will be non-zero,
        // you need only the first C.rows of them, and thus you
        // pass nonzeroRows == C.rows
        dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows);

        // now copy the result back to C.
        tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C);

        // all the temporary buffers will be deallocated automatically
    }

To optimize this sample, consider the following approaches:

Since nonzeroRows != 0 is passed to the forward transform calls and since A and B are copied to the top-left corners of tempA and tempB, respectively, it is not necessary to clear the whole tempA and tempB. It is only necessary to clear the tempA.cols - A.cols ( tempB.cols - B.cols) rightmost columns of the matrices.
This DFT-based convolution does not have to be applied to the whole big arrays, especially if B is significantly smaller than A or vice versa. Instead, you can calculate convolution by parts. To do this, you need to split the output array C into multiple tiles. For each tile, estimate which parts of A and B are required to calculate convolution in this tile. If the tiles in C are too small, the speed will decrease a lot because of repeated work. In the ultimate case, when each tile in C is a single pixel, the algorithm becomes equivalent to the naive convolution algorithm. If the tiles are too big, the temporary arrays tempA and tempB become too big and there is also a slowdown because of bad cache locality. So, there is an optimal tile size somewhere in the middle.
If different tiles in C can be calculated in parallel and, thus, the convolution is done by parts, the loop can be threaded.

All of the above improvements have been implemented in #matchTemplate and #filter2D . Therefore, by using them, you can get the performance even better than with the above theoretically optimal implementation. Though, those two functions actually calculate cross-correlation, not convolution, so you need to “flip” the second convolution operand B vertically and horizontally using flip . @note

An example using the discrete fourier transform can be found at opencv_source_code/samples/cpp/dft.cpp
(Python) An example using the dft functionality to perform Wiener deconvolution can be found at opencv_source/samples/python/deconvolution.py
(Python) An example rearranging the quadrants of a Fourier image can be found at opencv_source/samples/python/dft.py

See also: dct , getOptimalDFTSize , mulSpectrums, filter2D , matchTemplate , flip , cartToPolar ,magnitude , phase

Parameters:

src (cv2.typing.MatLike) – input array that could be real or complex.
dst (cv2.typing.MatLike | None) – output array whose size and type depends on the flags .
flags (int) – transformation flags, representing a combination of the #DftFlags
nonzeroRows (int) – when the parameter is not zero, the function assumes that only the firstnonzeroRows rows of the input array (#DFT_INVERSE is not set) or only the first nonzeroRows of the output array (#DFT_INVERSE is set) contain non-zeros, thus, the function can handle the rest of the rows more efficiently and save some time; this technique is very useful for calculating array cross-correlation or convolution using DFT.

Return type:

cv2.typing.MatLike

cv2.dilate(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) → dst¶

Dilates an image by using a specific structuring element.

The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken:

\[\begin{equation*}\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\end{equation*}\]

The function supports the in-place mode. Dilation can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.

See also: erode, morphologyEx, getStructuringElement

Parameters:

src (cv2.typing.MatLike) – input image; the number of channels can be arbitrary, but the depth should be one ofCV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst (cv2.typing.MatLike | None) – output image of the same size and type as src.
kernel (cv2.typing.MatLike) – structuring element used for dilation; if element=Mat(), a 3 x 3 rectangularstructuring element is used. Kernel can be created using #getStructuringElement
anchor (cv2.typing.Point) – position of the anchor within the element; default value (-1, -1) means that theanchor is at the element center.
iterations (int) – number of times dilation is applied.
borderType (int) – pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not suported.
borderValue (cv2.typing.Scalar) – border value in case of a constant border

Return type:

cv2.typing.MatLike

cv2.displayOverlay(winname, text[, delayms]) → None¶

Displays a text on a window image as an overlay for a specified duration.

The function displayOverlay displays useful information/tips on top of the window for a certain amount of time delayms. The function does not modify the image, displayed in the window, that is, after the specified delay the original content of the window is restored.

Parameters:

winname (str) – Name of the window.
text (str) – Overlay text to write on a window image.
delayms (int) – The period (in milliseconds), during which the overlay text is displayed. If thisfunction is called before the previous overlay text timed out, the timer is restarted and the text is updated. If this value is zero, the text never disappears.

Return type:

None

cv2.displayStatusBar(winname, text[, delayms]) → None¶

Displays a text on the window statusbar during the specified period of time.

The function displayStatusBar displays useful information/tips on top of the window for a certain amount of time delayms . This information is displayed on the window statusbar (the window must be created with the CV_GUI_EXPANDED flags).

Parameters:

winname (str) – Name of the window.
text (str) – Text to write on the window statusbar.
delayms (int) – Duration (in milliseconds) to display the text. If this function is called beforethe previous text timed out, the timer is restarted and the text is updated. If this value is zero, the text never disappears.

Return type:

None

cv2.distanceTransform(src, distanceType, maskSize[, dst[, dstType]]) → dst¶

@overload

Parameters:

src (cv2.typing.MatLike) – 8-bit, single-channel (binary) source image.
dst (cv2.typing.MatLike | None) – Output image with calculated distances. It is a 8-bit or 32-bit floating-point,single-channel image of the same size as src .
distanceType (int) – Type of distance, see #DistanceTypes
maskSize (int) – Size of the distance transform mask, see #DistanceTransformMasks. In case of the#DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times 5\) or any larger aperture.
dstType (int) – Type of output image. It can be CV_8U or CV_32F. Type CV_8U can be used only forthe first variant of the function and distanceType == #DIST_L1.

Return type:

cv2.typing.MatLike

cv2.distanceTransformWithLabels(src, distanceType, maskSize[, dst[, labels[, labelType]]]) → dst, labels¶

Calculates the distance to the closest zero pixel for each pixel of the source image.

The function cv::distanceTransform calculates the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.

When maskSize == #DIST_MASK_PRECISE and distanceType == #DIST_L2 , the function runs the algorithm described in @cite Felzenszwalb04 . This algorithm is parallelized with the TBB library.

In other cases, the algorithm @cite Borgefors86 is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight’s move (the latest is available for a \(5\times 5\) mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted as b), and all knight’s moves must have the same cost (denoted as c). For the #DIST_C and #DIST_L1 types, the distance is calculated precisely, whereas for #DIST_L2 (Euclidean distance) the distance can be calculated only with a relative error (a \(5\times 5\) mask gives more accurate results). For a,b, and c, OpenCV uses the values suggested in the original paper:

DIST_L1: a = 1, b = 2
DIST_L2:
- 3 x 3: a=0.955, b=1.3693
- 5 x 5: a=1, b=1.4, c=2.1969
DIST_C: a = 1, b = 1

Typically, for a fast, coarse distance estimation #DIST_L2, a \(3\times 3\) mask is used. For a more accurate distance estimation #DIST_L2, a \(5\times 5\) mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.

This variant of the function does not only compute the minimum distance for each pixel \((x, y)\) but also identifies the nearest connected component consisting of zero pixels (labelType==#DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==#DIST_LABEL_PIXEL). Index of the component/pixel is stored in labels(x, y). When labelType==#DIST_LABEL_CCOMP, the function automatically finds connected components of zero pixels in the input image and marks them with distinct labels. When labelType==#DIST_LABEL_PIXEL, the function scans through the input image and marks all the zero pixels with distinct labels.

In this mode, the complexity is still linear. That is, the function provides a very fast way to compute the Voronoi diagram for a binary image. Currently, the second variant can use only the approximate distance transform algorithm, i.e. maskSize=#DIST_MASK_PRECISE is not supported yet.

Parameters:

src (cv2.typing.MatLike) – 8-bit, single-channel (binary) source image.
dst (cv2.typing.MatLike | None) – Output image with calculated distances. It is a 8-bit or 32-bit floating-point,single-channel image of the same size as src.
labels (cv2.typing.MatLike | None) – Output 2D array of labels (the discrete Voronoi diagram). It has the typeCV_32SC1 and the same size as src.
distanceType (int) – Type of distance, see #DistanceTypes
maskSize (int) – Size of the distance transform mask, see #DistanceTransformMasks.#DIST_MASK_PRECISE is not supported by this variant. In case of the #DIST_L1 or #DIST_C distance type, the parameter is forced to 3 because a \(3\times 3\) mask gives the same result as \(5\times 5\) or any larger aperture.
labelType (int) – Type of the label array to build, see #DistanceTransformLabelTypes.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.divSpectrums(a, b, flags[, c[, conjB]]) → c¶

Performs the per-element division of the first Fourier spectrum by the second Fourier spectrum.

The function cv::divSpectrums performs the per-element division of the first array by the second array. The arrays are CCS-packed or complex matrices that are results of a real or complex Fourier transform.

Parameters:

a (cv2.typing.MatLike) – first input array.
b (cv2.typing.MatLike) – second input array of the same size and type as src1 .
c (cv2.typing.MatLike | None) – output array of the same size and type as src1 .
flags (int) – operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates thateach row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a 0 as value.
conjB (bool) – optional flag that conjugates the second input array before the multiplication (true)or not (false).

Return type:

cv2.typing.MatLike

cv2.divide(src1, src2[, dst[, scale[, dtype]]]) → dst¶

Performs per-element division of two arrays or a scalar by an array.

The function cv::divide divides one array by another:

\[\begin{equation*}\texttt{dst(I) = saturate(src1(I)*scale/src2(I))}\end{equation*}\]

or a scalar by an array when there is no src1 :

\[\begin{equation*}\texttt{dst(I) = saturate(scale/src2(I))}\end{equation*}\]

Different channels of multi-channel arrays are processed independently.

For integer types when src2(I) is zero, dst(I) will also be zero.

Note

In case of floating point data there is no special defined behavior for zero src2(I) values.Regular floating-point division is used. Expect correct IEEE-754 behaviour for floating-point data (with NaN, Inf result values).

Note

Saturation is not applied when the output array has the depth CV_32S. You may even getresult of an incorrect sign in the case of overflow.

Note

(Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.divide(src,X) means divide(src,(X,X,X,X)). divide(src,(X,)) means divide(src,(X,0,0,0)).

See also: multiply, add, subtract@overload

Parameters:

src1 (cv2.typing.MatLike) – first input array.
src2 (cv2.typing.MatLike) – second input array of the same size and type as src1.
scale (float) – scalar factor.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src2.
dtype (int) – optional depth of the output array; if -1, dst will have depth src2.depth(), but incase of an array-by-array division, you can only pass -1 when src1.depth()==src2.depth().

Return type:

cv2.typing.MatLike

cv2.registerLayer(type, class) → None¶

Parameters:

layerTypeName (str) –
layerClass (_typing.Type[cv2.dnn.LayerProtocol]) –

Return type:

None

cv2.unregisterLayer(type) → None¶

Parameters:: layerTypeName (str) –
Return type:: None

cv2.drawChessboardCorners(image, patternSize, corners, patternWasFound) → image¶

Renders the detected chessboard corners.

The function draws individual chessboard corners detected either as red circles if the board was not found, or as colored corners connected with lines if the board was found.

Parameters:

image (cv2.typing.MatLike) – Destination image. It must be an 8-bit color image.
patternSize (cv2.typing.Size) – Number of inner corners per a chessboard row and column(patternSize = cv::Size(points_per_row,points_per_column)).
corners (cv2.typing.MatLike) – Array of detected corners, the output of #findChessboardCorners.
patternWasFound (bool) – Parameter indicating whether the complete board was found or not. Thereturn value of #findChessboardCorners should be passed here.

Return type:

cv2.typing.MatLike

cv2.drawContours(image, contours, contourIdx, color[, thickness[, lineType[, hierarchy[, maxLevel[, offset]]]]]) → image¶

Draws contours outlines or filled contours.

The function draws contour outlines in the image if \(\texttt{thickness} \ge 0\) or fills the area bounded by the contours if \(\texttt{thickness}<0\) . The example below shows how to retrieve connected components from the binary image and label them: : @include snippets/imgproc_drawContours.cpp

Note

When thickness=#FILLED, the function is designed to handle connected components with holes correctlyeven when no hierarchy data is provided. This is done by analyzing all the outlines together using even-odd rule. This may give incorrect results if you have a joint collection of separately retrieved contours. In order to solve this problem, you need to call #drawContours separately for each sub-group of contours, or iterate over the collection using contourIdx parameter.

Parameters:

image (cv2.typing.MatLike) – Destination image.
contours (_typing.Sequence[cv2.typing.MatLike]) – All the input contours. Each contour is stored as a point vector.
contourIdx (int) – Parameter indicating a contour to draw. If it is negative, all the contours are drawn.
color (cv2.typing.Scalar) – Color of the contours.
thickness (int) – Thickness of lines the contours are drawn with. If it is negative (for example,thickness=#FILLED ), the contour interiors are drawn.
lineType (int) – Line connectivity. See #LineTypes
hierarchy (cv2.typing.MatLike | None) – Optional information about hierarchy. It is only needed if you want to draw onlysome of the contours (see maxLevel ).
maxLevel (int) – Maximal level for drawn contours. If it is 0, only the specified contour is drawn.If it is 1, the function draws the contour(s) and all the nested contours. If it is 2, the function draws the contours, all the nested contours, all the nested-to-nested contours, and so on. This parameter is only taken into account when there is hierarchy available.
offset (cv2.typing.Point) – Optional contour shift parameter. Shift all the drawn contours by the specified\(\texttt{offset}=(dx,dy)\) .

Return type:

cv2.typing.MatLike

cv2.drawFrameAxes(image, cameraMatrix, distCoeffs, rvec, tvec, length[, thickness]) → image¶

Draw axes of the world/object coordinate system from pose estimation. @sa solvePnP

This function draws the axes of the world/object coordinate system w.r.t. to the camera frame. OX is drawn in red, OY in green and OZ in blue.

Parameters:

image (cv2.typing.MatLike) – Input/output image. It must have 1 or 3 channels. The number of channels is not altered.
cameraMatrix (cv2.typing.MatLike) – Input 3x3 floating-point matrix of camera intrinsic parameters.\(\cameramatrix{A}\)
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\). If the vector is empty, the zero distortion coefficients are assumed.
rvec (cv2.typing.MatLike) – Rotation vector (see @ref Rodrigues ) that, together with tvec, brings points fromthe model coordinate system to the camera coordinate system.
tvec (cv2.typing.MatLike) – Translation vector.
length (float) – Length of the painted axes in the same unit than tvec (usually in meters).
thickness (int) – Line thickness of the painted axes.

Return type:

cv2.typing.MatLike

cv2.drawKeypoints(image, keypoints, outImage[, color[, flags]]) → outImage¶

Draws keypoints.

@note For Python API, flags are modified as cv.DRAW_MATCHES_FLAGS_DEFAULT, cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS, cv.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG, cv.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS

Parameters:

image (cv2.typing.MatLike) – Source image.
keypoints (_typing.Sequence[KeyPoint]) – Keypoints from the source image.
outImage (cv2.typing.MatLike) – Output image. Its content depends on the flags value defining what is drawn in theoutput image. See possible flags bit values below.
color (cv2.typing.Scalar) – Color of keypoints.
flags (DrawMatchesFlags) – Flags setting drawing features. Possible flags bit values are defined byDrawMatchesFlags. See details above in drawMatches .

Return type:

cv2.typing.MatLike

cv2.drawMarker(img, position, color[, markerType[, markerSize[, thickness[, line_type]]]]) → img¶

Draws a marker on a predefined position in an image.

The function cv::drawMarker draws a marker on a given position in the image. For the moment several marker types are supported, see #MarkerTypes for more information.

Parameters:

img (cv2.typing.MatLike) – Image.
position (cv2.typing.Point) – The point where the crosshair is positioned.
color (cv2.typing.Scalar) – Line color.
markerType (int) – The specific type of marker you want to use, see #MarkerTypes
thickness (int) – Line thickness.
line_type (int) – Type of the line, See #LineTypes
markerSize (int) – The length of the marker axis [default = 20 pixels]

Return type:

cv2.typing.MatLike

cv2.drawMatches(img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[, flags]]]]) → outImg¶

Draws the found matches of keypoints from two images.

This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles). See cv::DrawMatchesFlags. @overload

Parameters:

img1 (cv2.typing.MatLike) – First source image.
keypoints1 (_typing.Sequence[KeyPoint]) – Keypoints from the first source image.
img2 (cv2.typing.MatLike) – Second source image.
keypoints2 (_typing.Sequence[KeyPoint]) – Keypoints from the second source image.
matches1to2 (_typing.Sequence[DMatch]) – Matches from the first image to the second one, which means that keypoints1[i]has a corresponding point in keypoints2[matches[i]] .
outImg (cv2.typing.MatLike) – Output image. Its content depends on the flags value defining what is drawn in theoutput image. See possible flags bit values below.
matchColor (cv2.typing.Scalar) – Color of matches (lines and connected keypoints). If matchColor==Scalar::all(-1), the color is generated randomly.
singlePointColor (cv2.typing.Scalar) – Color of single keypoints (circles), which means that keypoints do nothave the matches. If singlePointColor==Scalar::all(-1) , the color is generated randomly.
matchesMask (_typing.Sequence[str]) – Mask determining which matches are drawn. If the mask is empty, all matches aredrawn.
flags (DrawMatchesFlags) – Flags setting drawing features. Possible flags bit values are defined byDrawMatchesFlags.

Return type:

cv2.typing.MatLike

cv2.drawMatchesKnn(img1, keypoints1, img2, keypoints2, matches1to2, outImg[, matchColor[, singlePointColor[, matchesMask[, flags]]]]) → outImg¶

Parameters:

img1 (cv2.typing.MatLike) –
keypoints1 (_typing.Sequence[KeyPoint]) –
img2 (cv2.typing.MatLike) –
keypoints2 (_typing.Sequence[KeyPoint]) –
matches1to2 (_typing.Sequence[_typing.Sequence[DMatch]]) –
outImg (cv2.typing.MatLike) –
matchColor (cv2.typing.Scalar) –
singlePointColor (cv2.typing.Scalar) –
matchesMask (_typing.Sequence[_typing.Sequence[str]]) –
flags (DrawMatchesFlags) –

Return type:

cv2.typing.MatLike

cv2.edgePreservingFilter(src[, dst[, flags[, sigma_s[, sigma_r]]]]) → dst¶

Filtering is the fundamental operation in image and video processing. Edge-preserving smoothingfilters are used in many different applications @cite EM11 .

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
dst (cv2.typing.MatLike | None) – Output 8-bit 3-channel image.
flags (int) – Edge preserving filters: cv::RECURS_FILTER or cv::NORMCONV_FILTER
sigma_s (float) –
sigma_r (float) –

Return type:

cv2.typing.MatLike

cv2.eigen(src[, eigenvalues[, eigenvectors]]) → retval, eigenvalues, eigenvectors¶

Calculates eigenvalues and eigenvectors of a symmetric matrix.

The function cv::eigen calculates just eigenvalues, or eigenvalues and eigenvectors of the symmetric matrix src:

    src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()

Note

Use cv::eigenNonSymmetric for calculation of real eigenvalues and eigenvectors of non-symmetric matrix.

See also: eigenNonSymmetric, completeSymm , PCA

Parameters:

src (cv2.typing.MatLike) – input matrix that must have CV_32FC1 or CV_64FC1 type, square size and be symmetrical(src ^T^ == src).
eigenvalues (cv2.typing.MatLike | None) – output vector of eigenvalues of the same type as src; the eigenvalues are storedin the descending order.
eigenvectors (cv2.typing.MatLike | None) – output matrix of eigenvectors; it has the same size and type as src; theeigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues.

Return type:

tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.eigenNonSymmetric(src[, eigenvalues[, eigenvectors]]) → eigenvalues, eigenvectors¶

Calculates eigenvalues and eigenvectors of a non-symmetric matrix (real eigenvalues only).

The function calculates eigenvalues and eigenvectors (optional) of the square matrix src:

    src*eigenvectors.row(i).t() = eigenvalues.at<srcType>(i)*eigenvectors.row(i).t()

Note

Assumes real eigenvalues.

See also: eigen

Parameters:

src (cv2.typing.MatLike) – input matrix (CV_32FC1 or CV_64FC1 type).
eigenvalues (cv2.typing.MatLike | None) – output vector of eigenvalues (type is the same type as src).
eigenvectors (cv2.typing.MatLike | None) – output matrix of eigenvectors (type is the same type as src). The eigenvectors are stored as subsequent matrix rows, in the same order as the corresponding eigenvalues.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.ellipse(img, center, axes, angle, startAngle, endAngle, color[, thickness[, lineType[, shift]]]) → img¶

Draws a simple or thick elliptic arc or fills an ellipse sector.

The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. The drawing code uses general parametric form. A piecewise-linear curve is used to approximate the elliptic arc boundary. If you need more control of the ellipse rendering, you can retrieve the curve using #ellipse2Poly and then render it with #polylines or fill it with #fillPoly. If you use the first variant of the function and want to draw the whole ellipse, not an arc, pass startAngle=0 and endAngle=360. If startAngle is greater than endAngle, they are swapped. The figure below explains the meaning of the parameters to draw the blue arc.

Parameters of Elliptic Arc

Parameters:

img (cv2.typing.MatLike) – Image.
center (cv2.typing.Point) – Center of the ellipse.
axes (cv2.typing.Size) – Half of the size of the ellipse main axes.
angle (float) – Ellipse rotation angle in degrees.
startAngle (float) – Starting angle of the elliptic arc in degrees.
endAngle (float) – Ending angle of the elliptic arc in degrees.
color (cv2.typing.Scalar) – Ellipse color.
thickness (int) – Thickness of the ellipse arc outline, if positive. Otherwise, this indicates thata filled ellipse sector is to be drawn.
lineType (int) – Type of the ellipse boundary. See #LineTypes
shift (int) – Number of fractional bits in the coordinates of the center and values of axes.@overload
box – Alternative ellipse representation via RotatedRect. This means that the function drawsan ellipse inscribed in the rotated rectangle.

Return type:

cv2.typing.MatLike

cv2.ellipse2Poly(center, axes, angle, arcStart, arcEnd, delta) → pts¶

Approximates an elliptic arc with a polyline.

The function ellipse2Poly computes the vertices of a polyline that approximates the specified elliptic arc. It is used by #ellipse. If arcStart is greater than arcEnd, they are swapped.

Parameters:

center (cv2.typing.Point) – Center of the arc.
axes (cv2.typing.Size) – Half of the size of the ellipse main axes. See #ellipse for details.
angle (int) – Rotation angle of the ellipse in degrees. See #ellipse for details.
arcStart (int) – Starting angle of the elliptic arc in degrees.
arcEnd (int) – Ending angle of the elliptic arc in degrees.
delta (int) – Angle between the subsequent polyline vertices. It defines the approximationaccuracy.
pts – Output vector of polyline vertices.

Return type:

_typing.Sequence[cv2.typing.Point]

cv2.empty_array_desc() → retval¶

Return type:: GArrayDesc

cv2.empty_gopaque_desc() → retval¶

Return type:: GOpaqueDesc

cv2.empty_scalar_desc() → retval¶

Return type:: GScalarDesc

cv2.equalizeHist(src[, dst]) → dst¶

Equalizes the histogram of a grayscale image.

The function equalizes the histogram of the input image using the following algorithm:

Calculate the histogram \(H\) for src .
Normalize the histogram so that the sum of histogram bins is 255.
Compute the integral of the histogram:

\[\begin{equation*}H'_i = \sum _{0 \le j < i} H(j)\end{equation*}\]

Transform the image using \(H'\) as a look-up table: \(\texttt{dst}(x,y) = H'(\texttt{src}(x,y))\)

The algorithm normalizes the brightness and increases the contrast of the image.

Parameters:

src (cv2.typing.MatLike) – Source 8-bit single channel image.
dst (cv2.typing.MatLike | None) – Destination image of the same size and type as src .

Return type:

cv2.typing.MatLike

cv2.erode(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) → dst¶

Erodes an image by using a specific structuring element.

The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken:

\[\begin{equation*}\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\end{equation*}\]

The function supports the in-place mode. Erosion can be applied several ( iterations ) times. In case of multi-channel images, each channel is processed independently.

See also: dilate, morphologyEx, getStructuringElement

Parameters:

src (cv2.typing.MatLike) – input image; the number of channels can be arbitrary, but the depth should be one ofCV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst (cv2.typing.MatLike | None) – output image of the same size and type as src.
kernel (cv2.typing.MatLike) – structuring element used for erosion; if element=Mat(), a 3 x 3 rectangularstructuring element is used. Kernel can be created using #getStructuringElement.
anchor (cv2.typing.Point) – position of the anchor within the element; default value (-1, -1) means that theanchor is at the element center.
iterations (int) – number of times erosion is applied.
borderType (int) – pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue (cv2.typing.Scalar) – border value in case of a constant border

Return type:

cv2.typing.MatLike

cv2.estimateAffine2D(from_, to[, inliers[, method[, ransacReprojThreshold[, maxIters[, confidence[, refineIters]]]]]]) → retval, inliers¶

Computes an optimal affine transformation between two 2D point sets.

It computes

\[\begin{equation*} \begin{bmatrix} x\\ y\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12}\\ a_{21} & a_{22}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ \end{bmatrix} \end{equation*}\]

The function estimates an optimal 2D affine transformation between two 2D point sets using the selected robust algorithm.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

@note The RANSAC method can handle practically any ratio of outliers but needs a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also: estimateAffinePartial2D, getAffineTransform

Parameters:

from – First input 2D point set containing \((X,Y)\).
to (cv2.typing.MatLike) – Second input 2D point set containing \((x,y)\).
inliers (cv2.typing.MatLike | None) – Output vector indicating which points are inliers (1-inlier, 0-outlier).
method – Robust method used to compute transformation. The following methods are possible:- @ref RANSAC - RANSAC-based robust method

@ref LMEDS - Least-Median robust method RANSAC is the default method.

Parameters:

ransacReprojThreshold (float) – Maximum reprojection error in the RANSAC algorithm to considera point as an inlier. Applies only to RANSAC.
maxIters (int) – The maximum number of robust method iterations.
confidence (float) – Confidence level, between 0 and 1, for the estimated transformation. Anythingbetween 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters (int) – Maximum number of iterations of refining algorithm (Levenberg-Marquardt).Passing 0 will disable refining, so the output matrix will be output of robust method.
from_ (cv2.typing.MatLike) –

Returns:

Output 2D affine transformation matrix \(2 \times 3\) or empty matrix if transformationcould not be estimated. The returned matrix has the following form:

\[\begin{equation*} \begin{bmatrix} a_{11} & a_{12} & b_1\\ a_{21} & a_{22} & b_2\\ \end{bmatrix} \end{equation*}\]

Return type:: tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.estimateAffine3D(src, dst[, out[, inliers[, ransacThreshold[, confidence]]]]) → retval, out, inliers¶

Computes an optimal affine transformation between two 3D point sets.

It computes

\[\begin{equation*} \begin{bmatrix} x\\ y\\ z\\ \end{bmatrix} = \begin{bmatrix} a_{11} & a_{12} & a_{13}\\ a_{21} & a_{22} & a_{23}\\ a_{31} & a_{32} & a_{33}\\ \end{bmatrix} \begin{bmatrix} X\\ Y\\ Z\\ \end{bmatrix} + \begin{bmatrix} b_1\\ b_2\\ b_3\\ \end{bmatrix} \end{equation*}\]

The function estimates an optimal 3D affine transformation between two 3D point sets using the RANSAC algorithm.

It computes \(R,s,t\) minimizing \(\sum{i} dst_i - c \cdot R \cdot src_i \) where \(R\) is a 3x3 rotation matrix, \(t\) is a 3x1 translation vector and \(s\) is a scalar size value. This is an implementation of the algorithm by Umeyama \cite umeyama1991least . The estimated affine transform has a homogeneous scale which is a subclass of affine transformations with 7 degrees of freedom. The paired point sets need to comprise at least 3 points each.

Parameters:

src (cv2.typing.MatLike) – First input 3D point set.
dst (cv2.typing.MatLike) – Second input 3D point set.
out – Output 3D affine transformation matrix \(3 \times 4\) of the form\begin{equation*}

(1)¶\[\begin{bmatrix} a_{11} & a_{12} & a_{13} & b_1\\ a_{21} & a_{22} & a_{23} & b_2\\ a_{31} & a_{32} & a_{33} & b_3\\ \end{bmatrix}\]

\end{equation*}

Parameters:

inliers (cv2.typing.MatLike | None) – Output vector indicating which points are inliers (1-inlier, 0-outlier).
ransacThreshold (float) – Maximum reprojection error in the RANSAC algorithm to consider a point asan inlier.
confidence (float) – Confidence level, between 0 and 1, for the estimated transformation. Anythingbetween 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
scale – If null is passed, the scale parameter c will be assumed to be 1.0.Else the pointed-to variable will be set to the optimal scale.
force_rotation – If true, the returned rotation will never be a reflection.This might be unwanted, e.g. when optimizing a transform between a right- and a left-handed coordinate system.

Returns:

3D affine transformation matrix \(3 \times 4\) of the form\begin{equation*}T =

(2)¶\[\begin{bmatrix} R & t\\ \end{bmatrix}\]

\end{equation*}

Return type:: tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.estimateAffinePartial2D(from_, to[, inliers[, method[, ransacReprojThreshold[, maxIters[, confidence[, refineIters]]]]]]) → retval, inliers¶

Computes an optimal limited affine transformation with 4 degrees of freedom betweentwo 2D point sets.

The function estimates an optimal 2D affine transformation with 4 degrees of freedom limited to combinations of translation, rotation, and uniform scaling. Uses the selected algorithm for robust estimation.

The computed transformation is then refined further (using only inliers) with the Levenberg-Marquardt method to reduce the re-projection error even more.

Estimated transformation matrix is:

\[\begin{equation*} \begin{bmatrix} \cos(\theta) \cdot s & -\sin(\theta) \cdot s & t_x \\ \sin(\theta) \cdot s & \cos(\theta) \cdot s & t_y \end{bmatrix} \end{equation*}\]

Where \( \theta \) is the rotation angle, \( s \) the scaling factor and \( t_x, t_y \) are translations in \( x, y \) axes respectively.

@note The RANSAC method can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers.

See also: estimateAffine2D, getAffineTransform

Parameters:

from – First input 2D point set.
to (cv2.typing.MatLike) – Second input 2D point set.
inliers (cv2.typing.MatLike | None) – Output vector indicating which points are inliers.
method – Robust method used to compute transformation. The following methods are possible:- @ref RANSAC - RANSAC-based robust method

@ref LMEDS - Least-Median robust method RANSAC is the default method.

Parameters:

ransacReprojThreshold (float) – Maximum reprojection error in the RANSAC algorithm to considera point as an inlier. Applies only to RANSAC.
maxIters (int) – The maximum number of robust method iterations.
confidence (float) – Confidence level, between 0 and 1, for the estimated transformation. Anythingbetween 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
refineIters (int) – Maximum number of iterations of refining algorithm (Levenberg-Marquardt).Passing 0 will disable refining, so the output matrix will be output of robust method.
from_ (cv2.typing.MatLike) –

Returns:

Output 2D affine transformation (4 degrees of freedom) matrix \(2 \times 3\) orempty matrix if transformation could not be estimated.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.estimateChessboardSharpness(image, patternSize, corners[, rise_distance[, vertical[, sharpness]]]) → retval, sharpness¶

Estimates the sharpness of a detected chessboard.

Image sharpness, as well as brightness, are a critical parameter for accuracte camera calibration. For accessing these parameters for filtering out problematic calibraiton images, this method calculates edge profiles by traveling from black to white chessboard cell centers. Based on this, the number of pixels is calculated required to transit from black to white. This width of the transition area is a good indication of how sharp the chessboard is imaged and should be below ~3.0 pixels.

The optional sharpness array is of type CV_32FC1 and has for each calculated profile one row with the following five entries:

0 = x coordinate of the underlying edge in the image
1 = y coordinate of the underlying edge in the image
2 = width of the transition area (sharpness)
3 = signal strength in the black cell (min brightness)
4 = signal strength in the white cell (max brightness)

Parameters:

image (cv2.typing.MatLike) – Gray image used to find chessboard corners
patternSize (cv2.typing.Size) – Size of a found chessboard pattern
corners (cv2.typing.MatLike) – Corners found by #findChessboardCornersSB
rise_distance (float) – Rise distance 0.8 means 10% … 90% of the final signal strength
vertical (bool) – By default edge responses for horizontal lines are calculated
sharpness (cv2.typing.MatLike | None) – Optional output array with a sharpness value for calculated edge responses (see description)

Returns:

Scalar(average sharpness, average min brightness, average max brightness,0)

Return type:

tuple[cv2.typing.Scalar, cv2.typing.MatLike]

cv2.estimateTranslation3D(src, dst[, out[, inliers[, ransacThreshold[, confidence]]]]) → retval, out, inliers¶

Computes an optimal translation between two 3D point sets. *

It computes
\[\begin{equation*} * \begin{bmatrix} * x\\ * y\\ * z\\ * \end{bmatrix} * = * \begin{bmatrix} * X\\ * Y\\ * Z\\ * \end{bmatrix} * + * \begin{bmatrix} * b_1\\ * b_2\\ * b_3\\ * \end{bmatrix} * \end{equation*}\]
@param src First input 3D point set containing \((X,Y,Z)\).
@param dst Second input 3D point set containing \((x,y,z)\).
@param out Output 3D translation vector \(3 \times 1\) of the form
\[\begin{equation*} * \begin{bmatrix} * b_1 \\ * b_2 \\ * b_3 \\ * \end{bmatrix} * \end{equation*}\]
@param inliers Output vector indicating which points are inliers (1-inlier, 0-outlier).
@param ransacThreshold Maximum reprojection error in the RANSAC algorithm to consider a point as
an inlier.
@param confidence Confidence level, between 0 and 1, for the estimated transformation. Anything
between 0.95 and 0.99 is usually good enough. Values too close to 1 can slow down the estimation
significantly. Values lower than 0.8-0.9 can result in an incorrectly estimated transformation.
The function estimates an optimal 3D translation between two 3D point sets using the
RANSAC algorithm.

Parameters:

src (cv2.typing.MatLike) –
dst (cv2.typing.MatLike) –
out (cv2.typing.MatLike | None) –
inliers (cv2.typing.MatLike | None) –
ransacThreshold (float) –
confidence (float) –

Return type:

tuple[int, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.exp(src[, dst]) → dst¶

Calculates the exponent of every array element.

The function cv::exp calculates the exponent of every element of the input array:

\[\begin{equation*}\texttt{dst} [I] = e^{ src(I) }\end{equation*}\]

The maximum relative error is about 7e-6 for single-precision input and less than 1e-10 for double-precision input. Currently, the function converts denormalized values to zeros on output. Special values (NaN, Inf) are not handled.

See also: log , cartToPolar , polarToCart , phase , pow , sqrt , magnitude

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src.

Return type:

cv2.typing.MatLike

cv2.extractChannel(src, coi[, dst]) → dst¶

Extracts a single channel from src (coi is 0-based index)

See also: mixChannels, split

Parameters:

src (cv2.typing.MatLike) – input array
dst (cv2.typing.MatLike | None) – output array
coi (int) – index of channel to extract

Return type:

cv2.typing.MatLike

cv2.fastAtan2(y, x) → retval¶

Calculates the angle of a 2D vector in degrees.

The function fastAtan2 calculates the full-range angle of an input 2D vector. The angle is measured in degrees and varies from 0 to 360 degrees. The accuracy is about 0.3 degrees. @param x x-coordinate of the vector. @param y y-coordinate of the vector.

Parameters:

y (float) –
x (float) –

Return type:

cv2.fastNlMeansDenoising(src[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) → dst¶

Perform image denoising using Non-local Means Denoising algorithmhttp://www.ipol.im/pub/algo/bcm_non_local_means_denoising/ with several computational optimizations. Noise expected to be a gaussian white noise

This function expected to be applied to grayscale images. For colored images look at fastNlMeansDenoisingColored. Advanced usage of this functions can be manual denoising of colored image in different colorspaces. Such approach is used in fastNlMeansDenoisingColored by converting image to CIELAB colorspace and then separately denoise L and AB components with different h parameter.

Parameters:

src (cv2.typing.MatLike) – Input 8-bit or 16-bit (only with NORM_L1) 1-channel,2-channel, 3-channel or 4-channel image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as src .
templateWindowSize (int) – Size in pixels of the template patch that is used to compute weights.Should be odd. Recommended value 7 pixels
searchWindowSize (int) – Size in pixels of the window that is used to compute weighted average forgiven pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
h (float) – Array of parameters regulating filter strength, either oneparameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
normType – Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1

Return type:

cv2.typing.MatLike

cv2.fastNlMeansDenoisingColored(src[, dst[, h[, hColor[, templateWindowSize[, searchWindowSize]]]]]) → dst¶

Modification of fastNlMeansDenoising function for colored images

The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoising function.

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as src .
templateWindowSize (int) – Size in pixels of the template patch that is used to compute weights.Should be odd. Recommended value 7 pixels
searchWindowSize (int) – Size in pixels of the window that is used to compute weighted average forgiven pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
h (float) – Parameter regulating filter strength for luminance component. Bigger h value perfectlyremoves noise but also removes image details, smaller h value preserves details but also preserves some noise
hColor (float) – The same as h but for color components. For most images value equals 10will be enough to remove colored noise and do not distort colors

Return type:

cv2.typing.MatLike

cv2.fastNlMeansDenoisingColoredMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize[, dst[, h[, hColor[, templateWindowSize[, searchWindowSize]]]]]) → dst¶

Modification of fastNlMeansDenoisingMulti function for colored images sequences

The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using fastNlMeansDenoisingMulti function.

Parameters:

srcImgs (_typing.Sequence[cv2.typing.MatLike]) – Input 8-bit 3-channel images sequence. All images should have the same type andsize.
imgToDenoiseIndex (int) – Target image to denoise index in srcImgs sequence
temporalWindowSize (int) – Number of surrounding images to use for target image denoising. Shouldbe odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as srcImgs images.
templateWindowSize (int) – Size in pixels of the template patch that is used to compute weights.Should be odd. Recommended value 7 pixels
searchWindowSize (int) – Size in pixels of the window that is used to compute weighted average forgiven pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
h (float) – Parameter regulating filter strength for luminance component. Bigger h value perfectlyremoves noise but also removes image details, smaller h value preserves details but also preserves some noise.
hColor (float) – The same as h but for color components.

Return type:

cv2.typing.MatLike

cv2.fastNlMeansDenoisingMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) → dst¶

Modification of fastNlMeansDenoising function for images sequence where consecutive images have beencaptured in small period of time. For example video. This version of the function is for grayscale images or for manual manipulation with colorspaces. See @cite Buades2005DenoisingIS for more details (open access here).

Parameters:

srcImgs (_typing.Sequence[cv2.typing.MatLike]) – Input 8-bit or 16-bit (only with NORM_L1) 1-channel,2-channel, 3-channel or 4-channel images sequence. All images should have the same type and size.
imgToDenoiseIndex (int) – Target image to denoise index in srcImgs sequence
temporalWindowSize (int) – Number of surrounding images to use for target image denoising. Shouldbe odd. Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as srcImgs images.
templateWindowSize (int) – Size in pixels of the template patch that is used to compute weights.Should be odd. Recommended value 7 pixels
searchWindowSize (int) – Size in pixels of the window that is used to compute weighted average forgiven pixel. Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels
h (float) – Array of parameters regulating filter strength, either oneparameter applied to all channels or one per channel in dst. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
normType – Type of norm used for weight calculation. Can be either NORM_L2 or NORM_L1

Return type:

cv2.typing.MatLike

cv2.fillConvexPoly(img, points, color[, lineType[, shift]]) → img¶

Fills a convex polygon.

The function cv::fillConvexPoly draws a filled convex polygon. This function is much faster than the function #fillPoly . It can fill not only convex polygons but any monotonic polygon without self-intersections, that is, a polygon whose contour intersects every horizontal line (scan line) twice at the most (though, its top-most and/or the bottom edge could be horizontal).

Parameters:

img (cv2.typing.MatLike) – Image.
points (cv2.typing.MatLike) – Polygon vertices.
color (cv2.typing.Scalar) – Polygon color.
lineType (int) – Type of the polygon boundaries. See #LineTypes
shift (int) – Number of fractional bits in the vertex coordinates.

Return type:

cv2.typing.MatLike

cv2.fillPoly(img, pts, color[, lineType[, shift[, offset]]]) → img¶

Fills the area bounded by one or more polygons.

The function cv::fillPoly fills an area bounded by several polygonal contours. The function can fill complex areas, for example, areas with holes, contours with self-intersections (some of their parts), and so forth.

Parameters:

img (cv2.typing.MatLike) – Image.
pts (_typing.Sequence[cv2.typing.MatLike]) – Array of polygons where each polygon is represented as an array of points.
color (cv2.typing.Scalar) – Polygon color.
lineType (int) – Type of the polygon boundaries. See #LineTypes
shift (int) – Number of fractional bits in the vertex coordinates.
offset (cv2.typing.Point) – Optional offset of all points of the contours.

Return type:

cv2.typing.MatLike

cv2.filter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]]) → dst¶

Convolves an image with the kernel.

The function applies an arbitrary linear filter to an image. In-place operation is supported. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode.

The function does actually compute correlation, not the convolution:

\[\begin{equation*}\texttt{dst} (x,y) = \sum _{ \substack{0\leq x' < \texttt{kernel.cols}\\{0\leq y' < \texttt{kernel.rows}}}} \texttt{kernel} (x',y')* \texttt{src} (x+x'- \texttt{anchor.x} ,y+y'- \texttt{anchor.y} )\end{equation*}\]

That is, the kernel is not mirrored around the anchor point. If you need a real convolution, flip the kernel using #flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1).

The function uses the DFT-based algorithm in case of sufficiently large kernels (~11 x 11 or larger) and the direct algorithm for small kernels.

See also: sepFilter2D, dft, matchTemplate

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image of the same size and the same number of channels as src.
ddepth (int) – desired depth of the destination image, see @ref filter_depths “combinations”
kernel (cv2.typing.MatLike) – convolution kernel (or rather a correlation kernel), a single-channel floating pointmatrix; if you want to apply different kernels to different channels, split the image into separate color planes using split and process them individually.
anchor (cv2.typing.Point) – anchor of the kernel that indicates the relative position of a filtered point withinthe kernel; the anchor should lie within the kernel; default value (-1,-1) means that the anchor is at the kernel center.
delta (float) – optional value added to the filtered pixels before storing them in dst.
borderType (int) – pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.filterHomographyDecompByVisibleRefpoints(rotations, normals, beforePoints, afterPoints[, possibleSolutions[, pointsMask]]) → possibleSolutions¶

Filters homography decompositions based on additional information.

This function is intended to filter the output of the #decomposeHomographyMat based on additional information as described in @cite Malis2007 . The summary of the method: the #decomposeHomographyMat function returns 2 unique solutions and their “opposites” for a total of 4 solutions. If we have access to the sets of points visible in the camera frame before and after the homography transformation is applied, we can determine which are the true potential solutions and which are the opposites by verifying which homographies are consistent with all visible reference points being in front of the camera. The inputs are left unchanged; the filtered solution set is returned as indices into the existing one.

Parameters:

rotations (_typing.Sequence[cv2.typing.MatLike]) – Vector of rotation matrices.
normals (_typing.Sequence[cv2.typing.MatLike]) – Vector of plane normal matrices.
beforePoints (cv2.typing.MatLike) – Vector of (rectified) visible reference points before the homography is applied
afterPoints (cv2.typing.MatLike) – Vector of (rectified) visible reference points after the homography is applied
possibleSolutions (cv2.typing.MatLike | None) – Vector of int indices representing the viable solution set after filtering
pointsMask (cv2.typing.MatLike | None) – optional Mat/Vector of 8u type representing the mask for the inliers as given by the #findHomography function

Return type:

cv2.typing.MatLike

cv2.filterSpeckles(img, newVal, maxSpeckleSize, maxDiff[, buf]) → img, buf¶

Filters off small noise blobs (speckles) in the disparity map

Parameters:

img (cv2.typing.MatLike) – The input 16-bit signed disparity image
newVal (float) – The disparity value used to paint-off the speckles
maxSpeckleSize (int) – The maximum speckle size to consider it a speckle. Larger blobs are notaffected by the algorithm
maxDiff (float) – Maximum difference between neighbor disparity pixels to put them into the sameblob. Note that since StereoBM, StereoSGBM and may be other algorithms return a fixed-point disparity map, where disparity values are multiplied by 16, this scale factor should be taken into account when specifying this parameter value.
buf (cv2.typing.MatLike | None) – The optional temporary buffer to avoid memory allocation within the function.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.find4QuadCornerSubpix(img, corners, region_size) → retval, corners¶

Parameters:

img (cv2.typing.MatLike) –
corners (cv2.typing.MatLike) –
region_size (cv2.typing.Size) –

Return type:

tuple[bool, cv2.typing.MatLike]

cv2.findChessboardCorners(image, patternSize[, corners[, flags]]) → retval, corners¶

Finds the positions of internal corners of the chessboard.

The function attempts to determine whether the input image is a view of the chessboard pattern and locate the internal chessboard corners. The function returns a non-zero value if all of the corners are found and they are placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0. For example, a regular chessboard has 8 x 8 squares and 7 x 7 internal corners, that is, points where the black squares touch each other. The detected coordinates are approximate, and to determine their positions more accurately, the function calls #cornerSubPix. You also may use the function #cornerSubPix with different parameters if returned coordinates are not accurate enough.

Sample usage of detecting and drawing chessboard corners: :

    Size patternsize(8,6); //interior number of corners
    Mat gray = ....; //source image
    vector<Point2f> corners; //this will be filled by the detected corners

    //CALIB_CB_FAST_CHECK saves a lot of time on images
    //that do not contain any chessboard corners
    bool patternfound = findChessboardCorners(gray, patternsize, corners,
            CALIB_CB_ADAPTIVE_THRESH + CALIB_CB_NORMALIZE_IMAGE
            + CALIB_CB_FAST_CHECK);

    if(patternfound)
      cornerSubPix(gray, corners, Size(11, 11), Size(-1, -1),
        TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 30, 0.1));

    drawChessboardCorners(img, patternsize, Mat(corners), patternfound);

Use gen_pattern.py (@ref tutorial_camera_calibration_pattern) to create checkerboard.

Note

The function requires white space (like a square-thick border, the wider the better) aroundthe board to make the detection more robust in various environments. Otherwise, if there is no border and the background is dark, the outer black squares cannot be segmented properly and so the square grouping and ordering algorithm fails.

Parameters:

image (cv2.typing.MatLike) – Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize (cv2.typing.Size) – Number of inner corners per a chessboard row and column( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners (cv2.typing.MatLike | None) – Output array of detected corners.
flags – Various operation flags that can be zero or a combination of the following values:- @ref CALIB_CB_ADAPTIVE_THRESH Use adaptive thresholding to convert the image to black and white, rather than a fixed threshold level (computed from the average image brightness).

@ref CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with #equalizeHist before applying fixed or adaptive thresholding.
@ref CALIB_CB_FILTER_QUADS Use additional criteria (like contour area, perimeter, square-like shape) to filter out false quads extracted at the contour retrieval stage.
@ref CALIB_CB_FAST_CHECK Run a fast check on the image that looks for chessboard corners, and shortcut the call if none is found. This can drastically speed up the call in the degenerate condition when no chessboard is observed.
@ref CALIB_CB_PLAIN All other flags are ignored. The input image is taken as is. No image processing is done to improve to find the checkerboard. This has the effect of speeding up the execution of the function but could lead to not recognizing the checkerboard if the image is not previously binarized in the appropriate manner.

Return type:: tuple[bool, cv2.typing.MatLike]

cv2.findChessboardCornersSB(image, patternSize[, corners[, flags]]) → retval, corners¶

@overload

Parameters:

image (cv2.typing.MatLike) –
patternSize (cv2.typing.Size) –
corners (cv2.typing.MatLike | None) –
flags (int) –

Return type:

tuple[bool, cv2.typing.MatLike]

cv2.findChessboardCornersSBWithMeta(image, patternSize, flags[, corners[, meta]]) → retval, corners, meta¶

Finds the positions of internal corners of the chessboard using a sector based approach.

The function is analog to #findChessboardCorners but uses a localized radon transformation approximated by box filters being more robust to all sort of noise, faster on larger images and is able to directly return the sub-pixel position of the internal chessboard corners. The Method is based on the paper @cite duda2018 “Accurate Detection and Localization of Checkerboard Corners for Calibration” demonstrating that the returned sub-pixel positions are more accurate than the one returned by cornerSubPix allowing a precise camera calibration for demanding applications.

In the case, the flags @ref CALIB_CB_LARGER or @ref CALIB_CB_MARKER are given, the result can be recovered from the optional meta array. Both flags are helpful to use calibration patterns exceeding the field of view of the camera. These oversized patterns allow more accurate calibrations as corners can be utilized, which are as close as possible to the image borders. For a consistent coordinate system across all images, the optional marker (see image below) can be used to move the origin of the board to the location where the black circle is located.

Use gen_pattern.py (@ref tutorial_camera_calibration_pattern) to create checkerboard.

Note

The function requires a white boarder with roughly the same width as oneof the checkerboard fields around the whole board to improve the detection in various environments. In addition, because of the localized radon transformation it is beneficial to use round corners for the field corners which are located on the outside of the board. The following figure illustrates a sample checkerboard optimized for the detection. However, any other checkerboard can be used as well.

Parameters:

image (cv2.typing.MatLike) – Source chessboard view. It must be an 8-bit grayscale or color image.
patternSize (cv2.typing.Size) – Number of inner corners per a chessboard row and column( patternSize = cv::Size(points_per_row,points_per_colum) = cv::Size(columns,rows) ).
corners (cv2.typing.MatLike | None) – Output array of detected corners.
flags – Various operation flags that can be zero or a combination of the following values:- @ref CALIB_CB_NORMALIZE_IMAGE Normalize the image gamma with equalizeHist before detection.

@ref CALIB_CB_EXHAUSTIVE Run an exhaustive search to improve detection rate.
@ref CALIB_CB_ACCURACY Up sample input image to improve sub-pixel accuracy due to aliasing effects.
@ref CALIB_CB_LARGER The detected pattern is allowed to be larger than patternSize (see description).
@ref CALIB_CB_MARKER The detected pattern must have a marker (see description). This should be used if an accurate camera calibration is required.

Parameters:: meta – Optional output arrray of detected corners (CV_8UC1 and size = cv::Size(columns,rows)).Each entry stands for one corner of the pattern and can have one of the following values:

0 = no meta data attached
1 = left-top corner of a black cell
2 = left-top corner of a white cell
3 = left-top corner of a black cell with a white marker dot
4 = left-top corner of a white cell with a black marker dot (pattern origin in case of markers otherwise first corner)

Return type:: tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.findCirclesGrid(image, patternSize, flags, blobDetector, parameters[, centers]) → retval, centers¶

Finds centers in the grid of circles.

The function attempts to determine whether the input image contains a grid of circles. If it is, the function locates centers of the circles. The function returns a non-zero value if all of the centers have been found and they have been placed in a certain order (row by row, left to right in every row). Otherwise, if the function fails to find all the corners or reorder them, it returns 0.

Sample usage of detecting and drawing the centers of circles: :

    Size patternsize(7,7); //number of centers
    Mat gray = ...; //source image
    vector<Point2f> centers; //this will be filled by the detected centers

    bool patternfound = findCirclesGrid(gray, patternsize, centers);

    drawChessboardCorners(img, patternsize, Mat(centers), patternfound);

Note

The function requires white space (like a square-thick border, the wider the better) aroundthe board to make the detection more robust in various environments. @overload

Parameters:

image (cv2.typing.MatLike) – grid view of input circles; it must be an 8-bit grayscale or color image.
patternSize (cv2.typing.Size) – number of circles per row and column( patternSize = Size(points_per_row, points_per_colum) ).
centers (cv2.typing.MatLike | None) – output array of detected centers.
flags – various operation flags that can be one of the following values:- @ref CALIB_CB_SYMMETRIC_GRID uses symmetric pattern of circles.

@ref CALIB_CB_ASYMMETRIC_GRID uses asymmetric pattern of circles.
@ref CALIB_CB_CLUSTERING uses a special algorithm for grid detection. It is more robust to perspective distortions but much more sensitive to background clutter.

Parameters:

blobDetector (cv2.typing.FeatureDetector) – feature detector that finds blobs like dark circles on light background. If blobDetector is NULL then image represents Point2f array of candidates.
parameters (CirclesGridFinderParameters) – struct for finding circles in a grid pattern.

Return type:

tuple[bool, cv2.typing.MatLike]

cv2.findContours(image, mode, method[, contours[, hierarchy[, offset]]]) → contours, hierarchy¶

Finds contours in a binary image.

The function retrieves contours from the binary image using the algorithm @cite Suzuki85 . The contours are a useful tool for shape analysis and object detection and recognition. See squares.cpp in the OpenCV sample directory.

Note

Since opencv 3.2 source image is not modified by this function.

Note

In Python, hierarchy is nested inside a top level array. Use hierarchy[0][i] to access hierarchical elements of i-th contour.

Parameters:

image (cv2.typing.MatLike) – Source, an 8-bit single-channel image. Non-zero pixels are treated as 1’s. Zeropixels remain 0’s, so the image is treated as binary . You can use #compare, #inRange, #threshold , #adaptiveThreshold, #Canny, and others to create a binary image out of a grayscale or color one. If mode equals to #RETR_CCOMP or #RETR_FLOODFILL, the input can also be a 32-bit integer image of labels (CV_32SC1).
contours (_typing.Sequence[cv2.typing.MatLike] | None) – Detected contours. Each contour is stored as a vector of points (e.g.std::vector<std::vectorcv::Point >).
hierarchy (cv2.typing.MatLike | None) – Optional output vector (e.g. std::vectorcv::Vec4i), containing information about the image topology. It hasas many elements as the number of contours. For each i-th contour contours[i], the elements hierarchy[i][0] , hierarchy[i][1] , hierarchy[i][2] , and hierarchy[i][3] are set to 0-based indices in contours of the next and previous contours at the same hierarchical level, the first child contour and the parent contour, respectively. If for the contour i there are no next, previous, parent, or nested contours, the corresponding elements of hierarchy[i] will be negative.
mode (int) – Contour retrieval mode, see #RetrievalModes
method (int) – Contour approximation method, see #ContourApproximationModes
offset (cv2.typing.Point) – Optional offset by which every contour point is shifted. This is useful if thecontours are extracted from the image ROI and then they should be analyzed in the whole image context.

Return type:

tuple[_typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]

cv2.findEssentialMat(points1, points2, cameraMatrix[, method[, prob[, threshold[, maxIters[, mask]]]]]) → retval, mask¶

Calculates an essential matrix from the corresponding points in two images from potentially two different cameras.

This function estimates essential matrix based on the five-point algorithm solver in @cite Nister03 . @cite SteweniusCFS is also a related. The epipolar geometry is described by the following equation:

\[\begin{equation*}[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\end{equation*}\]

where \(E\) is an essential matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively. The result of this function may be passed further to #decomposeEssentialMat or #recoverPose to recover the relative pose between cameras. @overload

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[\begin{equation*}A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\end{equation*}\]

\[\begin{equation*}[p_2; 1]^T K^{-T} E K^{-1} [p_1; 1] = 0\end{equation*}\]

Parameters:

points1 (cv2.typing.MatLike) – Array of N (N >= 5) 2D points from the first image. The point coordinates shouldbe floating-point (single or double precision).
points2 (cv2.typing.MatLike) – Array of the second image points of the same size and format as points1 .
cameraMatrix (cv2.typing.MatLike) – Camera intrinsic matrix \(\cameramatrix{A}\) .Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix. If this assumption does not hold for your use case, use #undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera intrinsic matrix. When passing these coordinates, pass the identity matrix for this parameter.
method – Method for computing an essential matrix.- @ref RANSAC for the RANSAC algorithm.

@ref LMEDS for the LMedS algorithm.

Parameters:

prob (float) – Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level ofconfidence (probability) that the estimated matrix is correct.
threshold (float) – Parameter used for RANSAC. It is the maximum distance from a point to an epipolarline in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask (cv2.typing.MatLike | None) – Output array of N elements, every element of which is set to 0 for outliers and to 1for the other points. The array is computed only in the RANSAC and LMedS methods.
maxIters (int) – The maximum number of robust method iterations.
focal – focal length of the camera. Note that this function assumes that points1 and points2are feature points from cameras with same focal length and principal point.
pp – principal point of the camera.
cameraMatrix1 – Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use #undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter.
cameraMatrix2 – Camera matrix \(K = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .Note that this function assumes that points1 and points2 are feature points from cameras with the same camera matrix. If this assumption does not hold for your use case, use #undistortPoints with P = cv::NoArray() for both cameras to transform image points to normalized image coordinates, which are valid for the identity camera matrix. When passing these coordinates, pass the identity matrix for this parameter.
distCoeffs1 – Input vector of distortion coefficients\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
distCoeffs2 – Input vector of distortion coefficients\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.findFundamentalMat(points1, points2, method, ransacReprojThreshold, confidence, maxIters[, mask]) → retval, mask¶

Calculates a fundamental matrix from the corresponding points in two images.

The epipolar geometry is described by the following equation:

\[\begin{equation*}[p_2; 1]^T F [p_1; 1] = 0\end{equation*}\]

where \(F\) is a fundamental matrix, \(p_1\) and \(p_2\) are corresponding points in the first and the second images, respectively.

The function calculates the fundamental matrix using one of four methods listed above and returns the found fundamental matrix. Normally just one matrix is found. But in case of the 7-point algorithm, the function may return up to 3 solutions ( \(9 \times 3\) matrix that stores all 3 matrices sequentially).

The calculated fundamental matrix may be passed further to #computeCorrespondEpilines that finds the epipolar lines corresponding to the specified points. It can also be passed to #stereoRectifyUncalibrated to compute the rectification transformation. :

    // Example. Estimation of fundamental matrix using the RANSAC algorithm
    int point_count = 100;
    vector<Point2f> points1(point_count);
    vector<Point2f> points2(point_count);

    // initialize the points here ...
    for( int i = 0; i < point_count; i++ )
    {
        points1[i] = ...;
        points2[i] = ...;
    }

    Mat fundamental_matrix =
     findFundamentalMat(points1, points2, FM_RANSAC, 3, 0.99);

@overload @overload

Parameters:

points1 (cv2.typing.MatLike) – Array of N points from the first image. The point coordinates should befloating-point (single or double precision).
points2 (cv2.typing.MatLike) – Array of the second image points of the same size and format as points1 .
method – Method for computing a fundamental matrix.- @ref FM_7POINT for a 7-point algorithm. \(N = 7\)

@ref FM_8POINT for an 8-point algorithm. \(N \ge 8\)
@ref FM_RANSAC for the RANSAC algorithm. \(N \ge 8\)
@ref FM_LMEDS for the LMedS algorithm. \(N \ge 8\)

Parameters:

ransacReprojThreshold (float) – Parameter used only for RANSAC. It is the maximum distance from a point to an epipolarline in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
confidence (float) – Parameter used for the RANSAC and LMedS methods only. It specifies a desirable levelof confidence (probability) that the estimated matrix is correct.
mask (cv2.typing.MatLike | None) – [out] optional output mask
maxIters (int) – The maximum number of robust method iterations.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.findHomography(srcPoints, dstPoints[, method[, ransacReprojThreshold[, mask[, maxIters[, confidence]]]]]) → retval, mask¶

Finds a perspective transformation between two planes.

The function finds and returns the perspective transformation \(H\) between the source and the destination planes:

\[\begin{equation*}s_i \vecthree{x'_i}{y'_i}{1} \sim H \vecthree{x_i}{y_i}{1}\end{equation*}\]

so that the back-projection error

\[\begin{equation*}\sum _i \left ( x'_i- \frac{h_{11} x_i + h_{12} y_i + h_{13}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2+ \left ( y'_i- \frac{h_{21} x_i + h_{22} y_i + h_{23}}{h_{31} x_i + h_{32} y_i + h_{33}} \right )^2\end{equation*}\]

is minimized. If the parameter method is set to the default value 0, the function uses all the point pairs to compute an initial homography estimate with a simple least-squares scheme.

However, if not all of the point pairs ( \(srcPoints_i\), \(dstPoints_i\) ) fit the rigid perspective transformation (that is, there are some outliers), this initial estimate will be poor. In this case, you can use one of the three robust methods. The methods RANSAC, LMeDS and RHO try many different random subsets of the corresponding point pairs (of four pairs each, collinear pairs are discarded), estimate the homography matrix using this subset and a simple least-squares algorithm, and then compute the quality/goodness of the computed homography (which is the number of inliers for RANSAC or the least median re-projection error for LMeDS). The best subset is then used to produce the initial estimate of the homography matrix and the mask of inliers/outliers.

Regardless of the method, robust or not, the computed homography matrix is refined further (using inliers only in case of a robust method) with the Levenberg-Marquardt method to reduce the re-projection error even more.

The methods RANSAC and RHO can handle practically any ratio of outliers but need a threshold to distinguish inliers from outliers. The method LMeDS does not need any threshold but it works correctly only when there are more than 50% of inliers. Finally, if there are no outliers and the noise is rather small, use the default method (method=0).

The function is used to find initial intrinsic and extrinsic matrices. Homography matrix is determined up to a scale. Thus, it is normalized so that \(h_{33}=1\). Note that whenever an \(H\) matrix cannot be estimated, an empty one will be returned.

@sa getAffineTransform, estimateAffine2D, estimateAffinePartial2D, getPerspectiveTransform, warpPerspective, perspectiveTransform @overload

Parameters:

srcPoints (cv2.typing.MatLike) – Coordinates of the points in the original plane, a matrix of the type CV_32FC2or vector<Point2f> .
dstPoints (cv2.typing.MatLike) – Coordinates of the points in the target plane, a matrix of the type CV_32FC2 ora vector<Point2f> .
method – Method used to compute a homography matrix. The following methods are possible:- 0 - a regular method using all the points, i.e., the least squares method

@ref RANSAC - RANSAC-based robust method
@ref LMEDS - Least-Median robust method
@ref RHO - PROSAC-based robust method

Parameters:: ransacReprojThreshold – Maximum allowed reprojection error to treat a point pair as an inlier(used in the RANSAC and RHO methods only). That is, if

\[\begin{equation*}\| \texttt{dstPoints} _i - \texttt{convertPointsHomogeneous} ( \texttt{H} \cdot \texttt{srcPoints} _i) \|_2 > \texttt{ransacReprojThreshold}\end{equation*}\]

then the point \(i\) is considered as an outlier. If srcPoints and dstPoints are measured in pixels, it usually makes sense to set this parameter somewhere in the range of 1 to 10.

Parameters:

mask (cv2.typing.MatLike | None) – Optional output mask set by a robust method ( RANSAC or LMeDS ). Note that the inputmask values are ignored.
maxIters (int) – The maximum number of RANSAC iterations.
confidence (float) – Confidence level, between 0 and 1.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.findNonZero(src[, idx]) → idx¶

Returns the list of locations of non-zero pixels

Given a binary matrix (likely returned from an operation such as threshold(), compare(), >, ==, etc, return all of the non-zero indices as a cv::Mat or std::vectorcv::Point (x,y) For example:

    cv::Mat binaryImage; // input, binary image
    cv::Mat locations;   // output, locations of non-zero pixels
    cv::findNonZero(binaryImage, locations);

    // access pixel coordinates
    Point pnt = locations.at<Point>(i);

    cv::Mat binaryImage; // input, binary image
    vector<Point> locations;   // output, locations of non-zero pixels
    cv::findNonZero(binaryImage, locations);

    // access pixel coordinates
    Point pnt = locations[i];

Parameters:

src (cv2.typing.MatLike) – single-channel array
idx (cv2.typing.MatLike | None) – the output array, type of cv::Mat or std::vector, corresponding to non-zero indices in the input

Return type:

cv2.typing.MatLike

cv2.findTransformECC(templateImage, inputImage, warpMatrix, motionType, criteria, inputMask, gaussFiltSize) → retval, warpMatrix¶

Finds the geometric transform (warp) between two images in terms of the ECC criterion @cite EP08 .

The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion (@cite EP08), that is

\[\begin{equation*}\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\end{equation*}\]

where

\[\begin{equation*}\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\end{equation*}\]

(the equation holds with homogeneous coordinates for homography). It returns the final enhanced correlation coefficient, that is the correlation coefficient between the template image and the final warped input image. When a \(3\times 3\) matrix is given with motionType =0, 1 or 2, the third row is ignored.

Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an area-based alignment that builds on intensity similarities. In essence, the function updates the initial transformation that roughly aligns the images. If this information is missing, the identity warp (unity matrix) is used as an initialization. Note that if images undergo strong displacements/rotations, an initial transformation that roughly aligns the images is necessary (e.g., a simple euclidean/similarity transform that allows for the images showing the same image content approximately). Use inverse warping in the second image to take an image close to the first one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws an exception if algorithm does not converges.

@sa computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography @overload

Parameters:

templateImage (cv2.typing.MatLike) – single-channel template image; CV_8U or CV_32F array.
inputImage (cv2.typing.MatLike) – single-channel input image which should be warped with the final warpMatrix inorder to provide an image similar to templateImage, same type as templateImage.
warpMatrix (cv2.typing.MatLike) – floating-point \(2\times 3\) or \(3\times 3\) mapping matrix (warp).
motionType (int) –
parameter, specifying the type of motion: - MOTION_TRANSLATION sets a translational motion model; warpMatrix is \(2\times 3\) with the first \(2\times 2\) part being the unity matrix and the rest two parameters being estimated.
- MOTION_EUCLIDEAN sets a Euclidean (rigid) transformation as motion model; three parameters are estimated; warpMatrix is \(2\times 3\).
- MOTION_AFFINE sets an affine motion model (DEFAULT); six parameters are estimated; warpMatrix is \(2\times 3\).
- MOTION_HOMOGRAPHY sets a homography as a motion model; eight parameters are estimated;`warpMatrix` is \(3\times 3\).
criteria (cv2.typing.TermCriteria) – parameter, specifying the termination criteria of the ECC algorithm;criteria.epsilon defines the threshold of the increment in the correlation coefficient between two iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion). Default values are shown in the declaration above.
inputMask (cv2.typing.MatLike) – An optional mask to indicate valid values of inputImage.
gaussFiltSize (int) – An optional value indicating size of gaussian blur filter; (DEFAULT: 5)

Return type:

tuple[float, cv2.typing.MatLike]

cv2.fitEllipse(points) → retval¶

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits (in a least-squares sense) a set of 2D points best of all. It returns the rotated rectangle in which the ellipse is inscribed. The first algorithm described by @cite Fitzgibbon95 is used. Developer should keep in mind that it is possible that the returned ellipse/rotatedRect data contains negative indices, due to the data points being close to the border of the containing Mat element.

Parameters:: points (cv2.typing.MatLike) – Input 2D point set, stored in std::vector<> or Mat
Return type:: cv2.typing.RotatedRect

cv2.fitEllipseAMS(points) → retval¶

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Approximate Mean Square (AMS) proposed by @cite Taubin1991 is used.

For an ellipse, this basis set is \( \chi= \left(x^2, x y, y^2, x, y, 1\right) \), which is a set of six free coefficients \( A^T=\left\{A_{\text{xx}},A_{\text{xy}},A_{\text{yy}},A_x,A_y,A_0\right\} \). However, to specify an ellipse, all that is needed is five numbers; the major and minor axes lengths \( (a,b) \), the position \( (x_0,y_0) \), and the orientation \( \theta \). This is because the basis set includes lines, quadratics, parabolic and hyperbolic functions as well as elliptical functions as possible fits. If the fit is found to be a parabolic or hyperbolic function then the standard #fitEllipse method is used. The AMS method restricts the fit to parabolic, hyperbolic and elliptical curves by imposing the condition that \( A^T ( D_x^T D_x + D_y^T D_y) A = 1 \) where the matrices \( Dx \) and \( Dy \) are the partial derivatives of the design matrix \( D \) with respect to x and y. The matrices are formed row by row applying the following to each of the points in the set: \f{align*}{ D(i,:)&=\left{x_i^2, x_i y_i, y_i^2, x_i, y_i, 1\right} & D_x(i,:)&=\left{2 x_i,y_i,0,1,0,0\right} & D_y(i,:)&=\left{0,x_i,2 y_i,0,1,0\right} \f} The AMS method minimizes the cost function \f{equation*}{ \epsilon ^2=\frac{ A^T D^T D A }{ A^T (D_x^T D_x + D_y^T D_y) A^T } \f}

The minimum cost is found by solving the generalized eigenvalue problem.

\f{equation*}{ D^T D A = \lambda \left( D_x^T D_x + D_y^T D_y\right) A \f}

@param points Input 2D point set, stored in std::vector<> or Mat

Parameters:: points (cv2.typing.MatLike) –
Return type:: cv2.typing.RotatedRect

cv2.fitEllipseDirect(points) → retval¶

Fits an ellipse around a set of 2D points.

The function calculates the ellipse that fits a set of 2D points. It returns the rotated rectangle in which the ellipse is inscribed. The Direct least square (Direct) method by @cite Fitzgibbon1999 is used.

\f{equation*}{ \epsilon ^2= A^T D^T D A \quad \text{with} \quad A^T C A =1 \quad \text{and} \quad C=\left(\begin{matrix} 0 & 0 & 2 & 0 & 0 & 0 \ 0 & -1 & 0 & 0 & 0 & 0 \ 2 & 0 & 0 & 0 & 0 & 0 \ 0 & 0 & 0 & 0 & 0 & 0 \ 0 & 0 & 0 & 0 & 0 & 0 \ 0 & 0 & 0 & 0 & 0 & 0 \end{matrix} \right) \f}

The minimum cost is found by solving the generalized eigenvalue problem.

\f{equation*}{ D^T D A = \lambda \left( C\right) A \f}

The system produces only one positive eigenvalue \( \lambda\) which is chosen as the solution with its eigenvector \(\mathbf{u}\). These are used to find the coefficients

\f{equation*}{ A = \sqrt{\frac{1}{\mathbf{u}^T C \mathbf{u}}} \mathbf{u} \f} The scaling factor guarantees that \(A^T C A =1\).

@param points Input 2D point set, stored in std::vector<> or Mat

Parameters:: points (cv2.typing.MatLike) –
Return type:: cv2.typing.RotatedRect

cv2.fitLine(points, distType, param, reps, aeps[, line]) → line¶

Fits a line to a 2D or 3D point set.

The function fitLine fits a line to a 2D or 3D point set by minimizing \(\sum_i \rho(r_i)\) where \(r_i\) is a distance between the \(i^{th}\) point, the line and \(\rho(r)\) is a distance function, one of the following:

DIST_L2

\[\begin{equation*}\rho (r) = r^2/2 \quad \text{(the simplest and the fastest least-squares method)}\end{equation*}\]

DIST_L1

\[\begin{equation*}\rho (r) = r\end{equation*}\]

DIST_L12

\[\begin{equation*}\rho (r) = 2 \cdot ( \sqrt{1 + \frac{r^2}{2}} - 1)\end{equation*}\]

DIST_FAIR

\[\begin{equation*}\rho \left (r \right ) = C^2 \cdot \left ( \frac{r}{C} - \log{\left(1 + \frac{r}{C}\right)} \right ) \quad \text{where} \quad C=1.3998\end{equation*}\]

DIST_WELSCH

\[\begin{equation*}\rho \left (r \right ) = \frac{C^2}{2} \cdot \left ( 1 - \exp{\left(-\left(\frac{r}{C}\right)^2\right)} \right ) \quad \text{where} \quad C=2.9846\end{equation*}\]

DIST_HUBER

\[\begin{equation*}\rho (r) = \fork{r^2/2}{if \(r < C\)}{C \cdot (r-C/2)}{otherwise} \quad \text{where} \quad C=1.345\end{equation*}\]

The algorithm is based on the M-estimator ( http://en.wikipedia.org/wiki/M-estimator ) technique that iteratively fits the line using the weighted least-squares algorithm. After each iteration the weights \(w_i\) are adjusted to be inversely proportional to \(\rho(r_i)\) .

Parameters:

points (cv2.typing.MatLike) – Input vector of 2D or 3D points, stored in std::vector<> or Mat.
line (cv2.typing.MatLike | None) – Output line parameters. In case of 2D fitting, it should be a vector of 4 elements(like Vec4f) - (vx, vy, x0, y0), where (vx, vy) is a normalized vector collinear to the line and (x0, y0) is a point on the line. In case of 3D fitting, it should be a vector of 6 elements (like Vec6f) - (vx, vy, vz, x0, y0, z0), where (vx, vy, vz) is a normalized vector collinear to the line and (x0, y0, z0) is a point on the line.
distType (int) – Distance used by the M-estimator, see #DistanceTypes
param (float) – Numerical parameter ( C ) for some types of distances. If it is 0, an optimal valueis chosen.
reps (float) – Sufficient accuracy for the radius (distance between the coordinate origin and the line).
aeps (float) – Sufficient accuracy for the angle. 0.01 would be a good default value for reps and aeps.

Return type:

cv2.typing.MatLike

cv2.flip(src, flipCode[, dst]) → dst¶

Flips a 2D array around vertical, horizontal, or both axes.

The function cv::flip flips the array in one of three different ways (row and column indices are 0-based):

\[\begin{equation*}\texttt{dst} _{ij} = \left\{ \begin{array}{l l} \texttt{src} _{\texttt{src.rows}-i-1,j} & if\; \texttt{flipCode} = 0 \\ \texttt{src} _{i, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} > 0 \\ \texttt{src} _{ \texttt{src.rows} -i-1, \texttt{src.cols} -j-1} & if\; \texttt{flipCode} < 0 \\ \end{array} \right.\end{equation*}\]

The example scenarios of using the function are the following:

Vertical flipping of the image (flipCode == 0) to switch between top-left and bottom-left image origin. This is a typical operation in video processing on Microsoft Windows* OS.
Horizontal flipping of the image with the subsequent horizontal shift and absolute difference calculation to check for a vertical-axis symmetry (flipCode > 0).
Simultaneous horizontal and vertical flipping of the image with the subsequent shift and absolute difference calculation to check for a central symmetry (flipCode < 0).
Reversing the order of point arrays (flipCode > 0 or flipCode == 0).

See also: transpose , repeat , completeSymm

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src.
flipCode (int) – a flag to specify how to flip the array; 0 meansflipping around the x-axis and positive value (for example, 1) means flipping around y-axis. Negative value (for example, -1) means flipping around both axes.

Return type:

cv2.typing.MatLike

cv2.flipND(src, axis[, dst]) → dst¶

Flips a n-dimensional at given axis * @param src input array

@param dst output array that has the same shape of src
@param axis axis that performs a flip on. 0 <= axis < src.dims.

Parameters:

src (cv2.typing.MatLike) –
axis (int) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.floodFill(image, mask, seedPoint, newVal[, loDiff[, upDiff[, flags]]]) → retval, image, mask, rect¶

Fills a connected component with the given color.

The function cv::floodFill fills a connected component starting from the seed point with the specified color. The connectivity is determined by the color/brightness closeness of the neighbor pixels. The pixel at \((x,y)\) is considered to belong to the repainted domain if:

in case of a grayscale image and floating range

\[\begin{equation*}\texttt{src} (x',y')- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} (x',y')+ \texttt{upDiff}\end{equation*}\]

in case of a grayscale image and fixed range

\[\begin{equation*}\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)- \texttt{loDiff} \leq \texttt{src} (x,y) \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)+ \texttt{upDiff}\end{equation*}\]

in case of a color image and floating range

\[\begin{equation*}\texttt{src} (x',y')_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} (x',y')_r+ \texttt{upDiff} _r,\end{equation*}\]

\[\begin{equation*}\texttt{src} (x',y')_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} (x',y')_g+ \texttt{upDiff} _g\end{equation*}\]

and

\[\begin{equation*}\texttt{src} (x',y')_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} (x',y')_b+ \texttt{upDiff} _b\end{equation*}\]

in case of a color image and fixed range

\[\begin{equation*}\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r- \texttt{loDiff} _r \leq \texttt{src} (x,y)_r \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_r+ \texttt{upDiff} _r,\end{equation*}\]

\[\begin{equation*}\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g- \texttt{loDiff} _g \leq \texttt{src} (x,y)_g \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_g+ \texttt{upDiff} _g\end{equation*}\]

and

\[\begin{equation*}\texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b- \texttt{loDiff} _b \leq \texttt{src} (x,y)_b \leq \texttt{src} ( \texttt{seedPoint} .x, \texttt{seedPoint} .y)_b+ \texttt{upDiff} _b\end{equation*}\]

where \(src(x',y')\) is the value of one of pixel neighbors that is already known to belong to the component. That is, to be added to the connected component, a color/brightness of the pixel should be close enough to:

Color/brightness of one of its neighbors that already belong to the connected component in case of a floating range.
Color/brightness of the seed point in case of a fixed range.

Use these functions to either mark a connected component with the specified color in-place, or build a mask and then extract the contour, or copy the region to another image, and so on.

Note

Since the mask is larger than the filled image, a pixel \((x, y)\) in image corresponds to thepixel \((x+1, y+1)\) in the mask .

See also: findContours

Parameters:

image (cv2.typing.MatLike) – Input/output 1- or 3-channel, 8-bit, or floating-point image. It is modified by thefunction unless the #FLOODFILL_MASK_ONLY flag is set in the second variant of the function. See the details below.
mask (cv2.typing.MatLike) – Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixelstaller than image. If an empty Mat is passed it will be created automatically. Since this is both an input and output parameter, you must take responsibility of initializing it. Flood-filling cannot go across non-zero pixels in the input mask. For example, an edge detector output can be used as a mask to stop filling at edges. On output, pixels in the mask corresponding to filled pixels in the image are set to 1 or to the specified value in flags as described below. Additionally, the function fills the border of the mask with ones to simplify internal processing. It is therefore possible to use the same mask in multiple calls to the function to make sure the filled areas do not overlap.
seedPoint (cv2.typing.Point) – Starting point.
newVal (cv2.typing.Scalar) – New value of the repainted domain pixels.
loDiff (cv2.typing.Scalar) – Maximal lower brightness/color difference between the currently observed pixel andone of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff (cv2.typing.Scalar) – Maximal upper brightness/color difference between the currently observed pixel andone of its neighbors belonging to the component, or a seed pixel being added to the component.
rect – Optional output parameter set by the function to the minimum bounding rectangle of therepainted domain.
flags (int) – Operation flags. The first 8 bits contain a connectivity value. The default value of4 means that only the four nearest neighbor pixels (those that share an edge) are considered. A connectivity value of 8 means that the eight nearest neighbor pixels (those that share a corner) will be considered. The next 8 bits (8-16) contain a value between 1 and 255 with which to fill the mask (the default value is 1). For example, 4 | ( 255 << 8 ) will consider 4 nearest neighbours and fill the mask with a value of 255. The following additional options occupy higher bits and therefore may be further combined with the connectivity and mask fill values using bit-wise or (|), see #FloodFillFlags.

Return type:

tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.Rect]

cv2.gemm(src1, src2, alpha, src3, beta[, dst[, flags]]) → dst¶

Performs generalized matrix multiplication.

The function cv::gemm performs generalized matrix multiplication similar to the gemm functions in BLAS level 3. For example, gemm(src1, src2, alpha, src3, beta, dst, GEMM_1_T + GEMM_3_T) corresponds to

\[\begin{equation*}\texttt{dst} = \texttt{alpha} \cdot \texttt{src1} ^T \cdot \texttt{src2} + \texttt{beta} \cdot \texttt{src3} ^T\end{equation*}\]

In case of complex (two-channel) data, performed a complex matrix multiplication.

The function can be replaced with a matrix expression. For example, the above call can be replaced with:

    dst = alpha*src1.t()*src2 + beta*src3.t();

See also: mulTransposed , transform

Parameters:

src1 (cv2.typing.MatLike) – first multiplied input matrix that could be real(CV_32FC1,CV_64FC1) or complex(CV_32FC2, CV_64FC2).
src2 (cv2.typing.MatLike) – second multiplied input matrix of the same type as src1.
alpha (float) – weight of the matrix product.
src3 (cv2.typing.MatLike) – third optional delta matrix added to the matrix product; itshould have the same type as src1 and src2.
beta (float) – weight of src3.
dst (cv2.typing.MatLike | None) – output matrix; it has the proper size and the same type asinput matrices.
flags (int) – operation flags (cv::GemmFlags)

Return type:

cv2.typing.MatLike

cv2.getAffineTransform(src, dst) → retval¶

@overload

Parameters:

src (cv2.typing.MatLike) –
dst (cv2.typing.MatLike) –

Return type:

cv2.typing.MatLike

cv2.getBuildInformation() → retval¶

Returns full configuration time cmake output.

Returned value is raw cmake output including version control system revision, compiler version, compiler flags, enabled modules and third party libraries, etc. Output format depends on target architecture.

Return type:: str

cv2.getCPUFeaturesLine() → retval¶

Returns list of CPU features enabled during compilation.

Returned value is a string containing space separated list of CPU features with following markers:

no markers - baseline features
prefix * - features enabled in dispatcher
suffix ? - features enabled but not available in HW

Example: SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2 *AVX512-SKX?

Return type:: str

cv2.getCPUTickCount() → retval¶

Returns the number of CPU ticks.

The function returns the current number of CPU ticks on some architectures (such as x86, x64, PowerPC). On other platforms the function is equivalent to getTickCount. It can also be used for very accurate time measurements, as well as for RNG initialization. Note that in case of multi-CPU systems a thread, from which getCPUTickCount is called, can be suspended and resumed at another CPU with its own counter. So, theoretically (and practically) the subsequent calls to the function do not necessary return the monotonously increasing values. Also, since a modern CPU varies the CPU frequency depending on the load, the number of CPU clocks spent in some code cannot be directly converted to time units. Therefore, getTickCount is generally a preferable solution for measuring execution time.

Return type:: int

cv2.getDefaultNewCameraMatrix(cameraMatrix[, imgsize[, centerPrincipalPoint]]) → retval¶

Returns the default new camera matrix.

The function returns the camera matrix that is either an exact copy of the input cameraMatrix (when centerPrinicipalPoint=false ), or the modified one (when centerPrincipalPoint=true).

In the latter case, the new camera matrix will be:

\[\begin{equation*}\begin{bmatrix} f_x && 0 && ( \texttt{imgSize.width} -1)*0.5 \\ 0 && f_y && ( \texttt{imgSize.height} -1)*0.5 \\ 0 && 0 && 1 \end{bmatrix} ,\end{equation*}\]

where \(f_x\) and \(f_y\) are \((0,0)\) and \((1,1)\) elements of cameraMatrix, respectively.

By default, the undistortion functions in OpenCV (see #initUndistortRectifyMap, #undistort) do not move the principal point. However, when you work with stereo, it is important to move the principal points in both views to the same y-coordinate (which is required by most of stereo correspondence algorithms), and may be to the same x-coordinate too. So, you can form the new camera matrix for each view where the principal points are located at the center.

Parameters:

cameraMatrix (cv2.typing.MatLike) – Input camera matrix.
imgsize (cv2.typing.Size) – Camera view image size in pixels.
centerPrincipalPoint (bool) – Location of the principal point in the new camera matrix. Theparameter indicates whether this location should be at the image center or not.

Return type:

cv2.typing.MatLike

cv2.getDerivKernels(dx, dy, ksize[, kx[, ky[, normalize[, ktype]]]]) → kx, ky¶

Returns filter coefficients for computing spatial image derivatives.

The function computes and returns the filter coefficients for spatial image derivatives. When ksize=FILTER_SCHARR, the Scharr \(3 \times 3\) kernels are generated (see #Scharr). Otherwise, Sobel kernels are generated (see #Sobel). The filters are normally passed to #sepFilter2D or to

Parameters:

kx (cv2.typing.MatLike | None) – Output matrix of row filter coefficients. It has the type ktype .
ky (cv2.typing.MatLike | None) – Output matrix of column filter coefficients. It has the type ktype .
dx (int) – Derivative order in respect of x.
dy (int) – Derivative order in respect of y.
ksize (int) – Aperture size. It can be FILTER_SCHARR, 1, 3, 5, or 7.
normalize (bool) – Flag indicating whether to normalize (scale down) the filter coefficients or not.Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). If you are going to filter floating-point images, you are likely to use the normalized kernels. But if you compute derivatives of an 8-bit image, store the results in a 16-bit image, and wish to preserve all the fractional bits, you may want to set normalize=false .
ktype (int) – Type of filter coefficients. It can be CV_32f or CV_64F .

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.getFontScaleFromHeight(fontFace, pixelHeight[, thickness]) → retval¶

Calculates the font-specific size to use to achieve a given height in pixels.

See also: cv::putText

Parameters:

fontFace (int) – Font to use, see cv::HersheyFonts.
pixelHeight (int) – Pixel height to compute the fontScale for
thickness (int) – Thickness of lines used to render the text.See putText for details.

Returns:

The fontSize to use for cv::putText

Return type:

cv2.getGaborKernel(ksize, sigma, theta, lambd, gamma[, psi[, ktype]]) → retval¶

Returns Gabor filter coefficients.

For more details about gabor filter equations and parameters, see: Gabor Filter.

Parameters:

ksize (cv2.typing.Size) – Size of the filter returned.
sigma (float) – Standard deviation of the gaussian envelope.
theta (float) – Orientation of the normal to the parallel stripes of a Gabor function.
lambd (float) – Wavelength of the sinusoidal factor.
gamma (float) – Spatial aspect ratio.
psi (float) – Phase offset.
ktype (int) – Type of filter coefficients. It can be CV_32F or CV_64F .

Return type:

cv2.typing.MatLike

cv2.getGaussianKernel(ksize, sigma[, ktype]) → retval¶

Returns Gaussian filter coefficients.

The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter coefficients:

\[\begin{equation*}G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\end{equation*}\]

where \(i=0..\texttt{ksize}-1\) and \(\alpha\) is the scale factor chosen so that \(\sum_i G_i=1\).

Two of such generated kernels can be passed to sepFilter2D. Those functions automatically recognize smoothing kernels (a symmetrical kernel with sum of weights equal to 1) and handle them accordingly. You may also use the higher-level GaussianBlur.

See also: sepFilter2D, getDerivKernels, getStructuringElement, GaussianBlur

Parameters:

ksize (int) – Aperture size. It should be odd ( \(\texttt{ksize} \mod 2 = 1\) ) and positive.
sigma (float) – Gaussian standard deviation. If it is non-positive, it is computed from ksize assigma = 0.3*((ksize-1)*0.5 - 1) + 0.8.
ktype (int) – Type of filter coefficients. It can be CV_32F or CV_64F .

Return type:

cv2.typing.MatLike

cv2.getHardwareFeatureName(feature) → retval¶

Returns feature name by ID

Returns empty string if feature is not defined

Parameters:: feature (int) –
Return type:: str

cv2.getLogLevel() → retval¶

Return type:: int

cv2.getNumThreads() → retval¶

Returns the number of threads used by OpenCV for parallel regions.

Always returns 1 if OpenCV is built without threading support.

The exact meaning of return value depends on the threading framework used by OpenCV library:

TBB - The number of threads, that OpenCV will try to use for parallel regions. If there is any tbb::thread_scheduler_init in user code conflicting with OpenCV, then function returns default number of threads used by TBB library.
OpenMP - An upper bound on the number of threads that could be used to form a new team.
Concurrency - The number of threads, that OpenCV will try to use for parallel regions.
GCD - Unsupported; returns the GCD thread pool limit (512) for compatibility.
C= - The number of threads, that OpenCV will try to use for parallel regions, if before called setNumThreads with threads > 0, otherwise returns the number of logical CPUs, available for the process.

See also: setNumThreads, getThreadNum

Return type:: int

cv2.getNumberOfCPUs() → retval¶

Returns the number of logical CPUs available for the process.

Return type:: int

cv2.getOptimalDFTSize(vecsize) → retval¶

Returns the optimal DFT size for a given vector size.

DFT performance is not a monotonic function of a vector size. Therefore, when you calculate convolution of two arrays or perform the spectral analysis of an array, it usually makes sense to pad the input data with zeros to get a bit larger array that can be transformed much faster than the original one. Arrays whose size is a power-of-two (2, 4, 8, 16, 32, …) are the fastest to process. Though, the arrays whose size is a product of 2’s, 3’s, and 5’s (for example, 300 = 5*5*3*2*2) are also processed quite efficiently.

The function cv::getOptimalDFTSize returns the minimum number N that is greater than or equal to vecsize so that the DFT of a vector of size N can be processed efficiently. In the current implementation N = 2 ^p^ * 3 ^q^ * 5 ^r^ for some integer p, q, r.

The function returns a negative number if vecsize is too large (very close to INT_MAX ).

While the function cannot be used directly to estimate the optimal vector size for DCT transform (since the current DCT implementation supports only even-size vectors), it can be easily processed as getOptimalDFTSize((vecsize+1)/2)*2.

See also: dft , dct , idft , idct , mulSpectrums

Parameters:: vecsize (int) – vector size.
Return type:: int

cv2.getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]) → retval, validPixROI¶

Returns the new camera intrinsic matrix based on the free scaling parameter.

The function computes and returns the optimal new camera intrinsic matrix based on the free scaling parameter. By varying this parameter, you may retrieve only sensible pixels alpha=0 , keep all the original image pixels if there is valuable information in the corners alpha=1 , or get something in between. When alpha>0 , the undistorted result is likely to have some black pixels corresponding to “virtual” pixels outside of the captured distorted image. The original camera intrinsic matrix, distortion coefficients, the computed new camera intrinsic matrix, and newImageSize should be passed to #initUndistortRectifyMap to produce the maps for #remap .

Parameters:

cameraMatrix (cv2.typing.MatLike) – Input camera intrinsic matrix.
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
imageSize (cv2.typing.Size) – Original image size.
alpha (float) – Free scaling parameter between 0 (when all the pixels in the undistorted image arevalid) and 1 (when all the source image pixels are retained in the undistorted image). See #stereoRectify for details.
newImgSize (cv2.typing.Size) – Image size after rectification. By default, it is set to imageSize .
validPixROI – Optional output rectangle that outlines all-good-pixels region in theundistorted image. See roi1, roi2 description in #stereoRectify .
centerPrincipalPoint (bool) – Optional flag that indicates whether in the new camera intrinsic matrix theprincipal point should be at the image center or not. By default, the principal point is chosen to best fit a subset of the source image (determined by alpha) to the corrected image.

Returns:

new_camera_matrix Output new camera intrinsic matrix.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.Rect]

cv2.getPerspectiveTransform(src, dst[, solveMethod]) → retval¶

Calculates a perspective transform from four pairs of the corresponding points.

The function calculates the \(3 \times 3\) matrix of a perspective transform so that:

\[\begin{equation*}\begin{bmatrix} t_i x'_i \\ t_i y'_i \\ t_i \end{bmatrix} = \texttt{map_matrix} \cdot \begin{bmatrix} x_i \\ y_i \\ 1 \end{bmatrix}\end{equation*}\]

where

\[\begin{equation*}dst(i)=(x'_i,y'_i), src(i)=(x_i, y_i), i=0,1,2,3\end{equation*}\]

See also: findHomography, warpPerspective, perspectiveTransform

Parameters:

src (cv2.typing.MatLike) – Coordinates of quadrangle vertices in the source image.
dst (cv2.typing.MatLike) – Coordinates of the corresponding quadrangle vertices in the destination image.
solveMethod (int) – method passed to cv::solve (#DecompTypes)

Return type:

cv2.typing.MatLike

cv2.getRectSubPix(image, patchSize, center[, patch[, patchType]]) → patch¶

Retrieves a pixel rectangle from an image with sub-pixel accuracy.

The function getRectSubPix extracts pixels from src:

\[\begin{equation*}patch(x, y) = src(x + \texttt{center.x} - ( \texttt{dst.cols} -1)*0.5, y + \texttt{center.y} - ( \texttt{dst.rows} -1)*0.5)\end{equation*}\]

where the values of the pixels at non-integer coordinates are retrieved using bilinear interpolation. Every channel of multi-channel images is processed independently. Also the image should be a single channel or three channel image. While the center of the rectangle must be inside the image, parts of the rectangle may be outside.

See also: warpAffine, warpPerspective

Parameters:

image (cv2.typing.MatLike) – Source image.
patchSize (cv2.typing.Size) – Size of the extracted patch.
center (cv2.typing.Point2f) – Floating point coordinates of the center of the extracted rectangle within thesource image. The center must be inside the image.
patch (cv2.typing.MatLike | None) – Extracted patch that has the size patchSize and the same number of channels as src .
patchType (int) – Depth of the extracted pixels. By default, they have the same depth as src .

Return type:

cv2.typing.MatLike

cv2.getRotationMatrix2D(center, angle, scale) → retval¶

Calculates an affine matrix of 2D rotation.

The function calculates the following matrix:

\[\begin{equation*}\begin{bmatrix} \alpha & \beta & (1- \alpha ) \cdot \texttt{center.x} - \beta \cdot \texttt{center.y} \\ - \beta & \alpha & \beta \cdot \texttt{center.x} + (1- \alpha ) \cdot \texttt{center.y} \end{bmatrix}\end{equation*}\]

where

\[\begin{equation*}\begin{array}{l} \alpha = \texttt{scale} \cdot \cos \texttt{angle} , \\ \beta = \texttt{scale} \cdot \sin \texttt{angle} \end{array}\end{equation*}\]

The transformation maps the rotation center to itself. If this is not the target, adjust the shift.

See also: getAffineTransform, warpAffine, transform

Parameters:

center (cv2.typing.Point2f) – Center of the rotation in the source image.
angle (float) – Rotation angle in degrees. Positive values mean counter-clockwise rotation (thecoordinate origin is assumed to be the top-left corner).
scale (float) – Isotropic scale factor.

Return type:

cv2.typing.MatLike

cv2.getStructuringElement(shape, ksize[, anchor]) → retval¶

Returns a structuring element of the specified size and shape for morphological operations.

The function constructs and returns the structuring element that can be further passed to #erode, #dilate or #morphologyEx. But you can also construct an arbitrary binary mask yourself and use it as the structuring element.

Parameters:

shape (int) – Element shape that could be one of #MorphShapes
ksize (cv2.typing.Size) – Size of the structuring element.
anchor (cv2.typing.Point) – Anchor position within the element. The default value \((-1, -1)\) means that theanchor is at the center. Note that only the shape of a cross-shaped element depends on the anchor position. In other cases the anchor just regulates how much the result of the morphological operation is shifted.

Return type:

cv2.typing.MatLike

cv2.getTextSize(text, fontFace, fontScale, thickness) → retval, baseLine¶

Calculates the width and height of a text string.

The function cv::getTextSize calculates and returns the size of a box that contains the specified text. That is, the following code renders some text, the tight box surrounding it, and the baseline: :

    String text = "Funny text inside the box";
    int fontFace = FONT_HERSHEY_SCRIPT_SIMPLEX;
    double fontScale = 2;
    int thickness = 3;

    Mat img(600, 800, CV_8UC3, Scalar::all(0));

    int baseline=0;
    Size textSize = getTextSize(text, fontFace,
                                fontScale, thickness, &baseline);
    baseline += thickness;

    // center the text
    Point textOrg((img.cols - textSize.width)/2,
                  (img.rows + textSize.height)/2);

    // draw the box
    rectangle(img, textOrg + Point(0, baseline),
              textOrg + Point(textSize.width, -textSize.height),
              Scalar(0,0,255));
    // ... and the baseline first
    line(img, textOrg + Point(0, thickness),
         textOrg + Point(textSize.width, thickness),
         Scalar(0, 0, 255));

    // then put the text itself
    putText(img, text, textOrg, fontFace, fontScale,
            Scalar::all(255), thickness, 8);

See also: putText

Parameters:

text (str) – Input text string.
fontFace (int) – Font to use, see #HersheyFonts.
fontScale (float) – Font scale factor that is multiplied by the font-specific base size.
thickness (int) – Thickness of lines used to render the text. See #putText for details.
baseLine – [out] y-coordinate of the baseline relative to the bottom-most textpoint.

Returns:

The size of a box that contains the specified text.

Return type:

tuple[cv2.typing.Size, int]

cv2.getThreadNum() → retval¶

Returns the index of the currently executed thread within the current parallel region. Alwaysreturns 0 if called outside of parallel region.

The exact meaning of the return value depends on the threading framework used by OpenCV library:

TBB - Unsupported with current 4.1 TBB release. Maybe will be supported in future.
OpenMP - The thread number, within the current team, of the calling thread.
Concurrency - An ID for the virtual processor that the current context is executing on (0 for master thread and unique number for others, but not necessary 1,2,3,…).
GCD - System calling thread’s ID. Never returns 0 inside parallel region.
C= - The index of the current parallel task.

Deprecated since version unknown: Current implementation doesn’t corresponding to this documentation.

See also: setNumThreads, getNumThreads

Return type:: int

cv2.getTickCount() → retval¶

Returns the number of ticks.

The function returns the number of ticks after the certain event (for example, when the machine was turned on). It can be used to initialize RNG or to measure a function execution time by reading the tick count before and after the function call.

See also: getTickFrequency, TickMeter

Return type:: int

cv2.getTickFrequency() → retval¶

Returns the number of ticks per second.

The function returns the number of ticks per second. That is, the following code computes the execution time in seconds:

    double t = (double)getTickCount();
    // do something ...
    t = ((double)getTickCount() - t)/getTickFrequency();

See also: getTickCount, TickMeter

Return type:: float

cv2.getTrackbarPos(trackbarname, winname) → retval¶

Returns the trackbar position.

The function returns the current position of the specified trackbar.

Note

[Qt Backend Only] winname can be empty if the trackbar is attached to the controlpanel.

Parameters:

trackbarname (str) – Name of the trackbar.
winname (str) – Name of the window that is the parent of the trackbar.

Return type:

cv2.getValidDisparityROI(roi1, roi2, minDisparity, numberOfDisparities, blockSize) → retval¶

Parameters:

roi1 (cv2.typing.Rect) –
roi2 (cv2.typing.Rect) –
minDisparity (int) –
numberOfDisparities (int) –
blockSize (int) –

Return type:

cv2.typing.Rect

cv2.getVersionMajor() → retval¶

Returns major library version

Return type:: int

cv2.getVersionMinor() → retval¶

Returns minor library version

Return type:: int

cv2.getVersionRevision() → retval¶

Returns revision field of the library version

Return type:: int

cv2.getVersionString() → retval¶

Returns library version string

For example “3.4.1-dev”.

See also: getMajorVersion, getMinorVersion, getRevisionVersion

Return type:: str

cv2.getWindowImageRect(winname) → retval¶

Provides rectangle of image in the window.

The function getWindowImageRect returns the client screen coordinates, width and height of the image rendering area.

See also: resizeWindow moveWindow

Parameters:: winname (str) – Name of the window.
Return type:: cv2.typing.Rect

cv2.getWindowProperty(winname, prop_id) → retval¶

Provides parameters of a window.

The function getWindowProperty returns properties of a window.

See also: setWindowProperty

Parameters:

winname (str) – Name of the window.
prop_id (int) – Window property to retrieve. The following operation flags are available: (cv::WindowPropertyFlags)

Return type:

cv2.goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) → corners¶

Determines strong corners on an image.

The function finds the most prominent corners in the image or in the specified image region, as described in @cite Shi94

Function calculates the corner quality measure at every source image pixel using the #cornerMinEigenVal or #cornerHarris .
Function performs a non-maximum suppression (the local maximums in 3 x 3 neighborhood are retained).
The corners with the minimal eigenvalue less than \(\texttt{qualityLevel} \cdot \max_{x,y} qualityMeasureMap(x,y)\) are rejected.
The remaining corners are sorted by the quality measure in the descending order.
Function throws away each corner for which there is a stronger corner at a distance less than maxDistance.

The function can be used to initialize a point-based tracker of an object.

Note

If the function is called with different values A and B of the parameter qualityLevel , andA > B, the vector of returned corners with qualityLevel=A will be the prefix of the output vector with qualityLevel=B .

See also: cornerMinEigenVal, cornerHarris, calcOpticalFlowPyrLK, estimateRigidTransform,

Parameters:

image (cv2.typing.MatLike) – Input 8-bit or floating-point 32-bit, single-channel image.
corners (cv2.typing.MatLike | None) – Output vector of detected corners.
maxCorners (int) – Maximum number of corners to return. If there are more corners than are found,the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel (float) – Parameter characterizing the minimal accepted quality of image corners. Theparameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance (float) – Minimum possible Euclidean distance between the returned corners.
mask (cv2.typing.MatLike | None) – Optional region of interest. If the image is not empty (it needs to have the typeCV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
blockSize (int) – Size of an average block for computing a derivative covariation matrix over eachpixel neighborhood. See cornerEigenValsAndVecs .
useHarrisDetector (bool) – Parameter indicating whether to use a Harris detector (see #cornerHarris)or #cornerMinEigenVal.
k (float) – Free parameter of the Harris detector.

Return type:

cv2.typing.MatLike

cv2.goodFeaturesToTrackWithQuality(image, maxCorners, qualityLevel, minDistance, mask[, corners[, cornersQuality[, blockSize[, gradientSize[, useHarrisDetector[, k]]]]]]) → corners, cornersQuality¶

Same as above, but returns also quality measure of the detected corners.

Parameters:

image (cv2.typing.MatLike) – Input 8-bit or floating-point 32-bit, single-channel image.
corners (cv2.typing.MatLike | None) – Output vector of detected corners.
maxCorners (int) – Maximum number of corners to return. If there are more corners than are found,the strongest of them is returned. maxCorners <= 0 implies that no limit on the maximum is set and all detected corners are returned.
qualityLevel (float) – Parameter characterizing the minimal accepted quality of image corners. Theparameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see #cornerMinEigenVal ) or the Harris function response (see #cornerHarris ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected.
minDistance (float) – Minimum possible Euclidean distance between the returned corners.
mask (cv2.typing.MatLike) – Region of interest. If the image is not empty (it needs to have the typeCV_8UC1 and the same size as image ), it specifies the region in which the corners are detected.
cornersQuality (cv2.typing.MatLike | None) – Output vector of quality measure of the detected corners.
blockSize (int) – Size of an average block for computing a derivative covariation matrix over eachpixel neighborhood. See cornerEigenValsAndVecs .
gradientSize (int) – Aperture parameter for the Sobel operator used for derivatives computation.See cornerEigenValsAndVecs .
useHarrisDetector (bool) – Parameter indicating whether to use a Harris detector (see #cornerHarris)or #cornerMinEigenVal.
k (float) – Free parameter of the Harris detector.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.grabCut(img, mask, rect, bgdModel, fgdModel, iterCount[, mode]) → mask, bgdModel, fgdModel¶

Runs the GrabCut algorithm.

The function implements the GrabCut image segmentation algorithm.

Parameters:

img (cv2.typing.MatLike) – Input 8-bit 3-channel image.
mask (cv2.typing.MatLike) – Input/output 8-bit single-channel mask. The mask is initialized by the function whenmode is set to #GC_INIT_WITH_RECT. Its elements may have one of the #GrabCutClasses.
rect (cv2.typing.Rect) – ROI containing a segmented object. The pixels outside of the ROI are marked as”obvious background”. The parameter is only used when mode==#GC_INIT_WITH_RECT .
bgdModel (cv2.typing.MatLike) – Temporary array for the background model. Do not modify it while you areprocessing the same image.
fgdModel (cv2.typing.MatLike) – Temporary arrays for the foreground model. Do not modify it while you areprocessing the same image.
iterCount (int) – Number of iterations the algorithm should make before returning the result. Notethat the result can be refined with further calls with mode==#GC_INIT_WITH_MASK or mode==GC_EVAL .
mode (int) – Operation mode that could be one of the #GrabCutModes

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.groupRectangles(rectList, groupThreshold[, eps]) → rectList, weights¶

@overload

Parameters:

rectList (_typing.Sequence[cv2.typing.Rect]) –
groupThreshold (int) –
eps (float) –

Return type:

tuple[_typing.Sequence[cv2.typing.Rect], _typing.Sequence[int]]

cv2.hasNonZero(src) → retval¶

Checks for the presence of at least one non-zero array element.

The function returns whether there are non-zero elements in src

See also: mean, meanStdDev, norm, minMaxLoc, calcCovarMatrix

Parameters:: src (cv2.typing.MatLike) – single-channel array.
Return type:: bool

cv2.haveImageReader(filename) → retval¶

Returns true if the specified image can be decoded by OpenCV

Parameters:: filename (str) – File name of the image
Return type:: bool

cv2.haveImageWriter(filename) → retval¶

Returns true if an image with the specified filename can be encoded by OpenCV

@param filename File name of the image

Parameters:: filename (str) –
Return type:: bool

cv2.haveOpenVX() → retval¶

Return type:: bool

cv2.hconcat(src[, dst]) → dst¶

@overload @code{.cpp} std::vectorcv::Mat matrices = { cv::Mat(4, 1, CV_8UC1, cv::Scalar(1)), cv::Mat(4, 1, CV_8UC1, cv::Scalar(2)), cv::Mat(4, 1, CV_8UC1, cv::Scalar(3)),};

cv::Mat out;
cv::hconcat( matrices, out );
//out:
//[1, 2, 3;
// 1, 2, 3;
// 1, 2, 3;
// 1, 2, 3]

@endcode @param src input array or vector of matrices. all of the matrices must have the same number of rows and the same depth. @param dst output array. It has the same number of rows and depth as the src, and the sum of cols of the src. same depth.

Parameters:

src (_typing.Sequence[cv2.typing.MatLike]) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.idct(src[, dst[, flags]]) → dst¶

Calculates the inverse Discrete Cosine Transform of a 1D or 2D array.

idct(src, dst, flags) is equivalent to dct(src, dst, flags | DCT_INVERSE).

See also: dct, dft, idft, getOptimalDFTSize

Parameters:

src (cv2.typing.MatLike) – input floating-point single-channel array.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src.
flags (int) – operation flags.

Return type:

cv2.typing.MatLike

cv2.idft(src[, dst[, flags[, nonzeroRows]]]) → dst¶

Calculates the inverse Discrete Fourier Transform of a 1D or 2D array.

idft(src, dst, flags) is equivalent to dft(src, dst, flags | #DFT_INVERSE) .

Note

None of dft and idft scales the result by default. So, you should pass #DFT_SCALE to one ofdft or idft explicitly to make these transforms mutually inverse.

See also: dft, dct, idct, mulSpectrums, getOptimalDFTSize

Parameters:

src (cv2.typing.MatLike) – input floating-point real or complex array.
dst (cv2.typing.MatLike | None) – output array whose size and type depend on the flags.
flags (int) – operation flags (see dft and #DftFlags).
nonzeroRows (int) – number of dst rows to process; the rest of the rows have undefined content (seethe convolution sample in dft description.

Return type:

cv2.typing.MatLike

cv2.illuminationChange(src, mask[, dst[, alpha[, beta]]]) → dst¶

Applying an appropriate non-linear transformation to the gradient field inside the selection andthen integrating back with a Poisson solver, modifies locally the apparent illumination of an image.

This is useful to highlight under-exposed foreground objects or to reduce specular reflections.

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
mask (cv2.typing.MatLike) – Input 8-bit 1 or 3-channel image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as src.
alpha (float) – Value ranges between 0-2.
beta (float) – Value ranges between 0-2.

Return type:

cv2.typing.MatLike

cv2.imcount(filename[, flags]) → retval¶

Returns the number of images inside the give file

The function imcount will return the number of pages in a multi-page image, or 1 for single-page images

Parameters:

filename (str) – Name of file to be loaded.
flags (int) – Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR.

Return type:

cv2.imdecode(buf, flags) → retval¶

Reads an image from a buffer in memory.

The function imdecode reads an image from the specified buffer in the memory. If the buffer is too short or contains invalid data, the function returns an empty matrix ( Mat::data==NULL ).

See cv::imread for the list of supported formats and flags description.

Note

In the case of color images, the decoded images will have the channels stored in B G R order.

Parameters:

buf (cv2.typing.MatLike) – Input array or vector of bytes.
flags (int) – The same flags as in cv::imread, see cv::ImreadModes.

Return type:

cv2.typing.MatLike

cv2.imdecodemulti(buf, flags[, mats[, range]]) → retval, mats¶

Reads a multi-page image from a buffer in memory.

The function imdecodemulti reads a multi-page image from the specified buffer in the memory. If the buffer is too short or contains invalid data, the function returns false.

See cv::imreadmulti for the list of supported formats and flags description.

Note

In the case of color images, the decoded images will have the channels stored in B G R order.

Parameters:

buf (cv2.typing.MatLike) – Input array or vector of bytes.
flags (int) – The same flags as in cv::imread, see cv::ImreadModes.
mats (_typing.Sequence[cv2.typing.MatLike] | None) – A vector of Mat objects holding each page, if more than one.
range (cv2.typing.Range) – A continuous selection of pages.

Return type:

tuple[bool, _typing.Sequence[cv2.typing.MatLike]]

cv2.imencode(ext, img[, params]) → retval, buf¶

Encodes an image into a memory buffer.

The function imencode compresses the image and stores it in the memory buffer that is resized to fit the result. See cv::imwrite for the list of supported formats and flags description.

Parameters:

ext (str) – File extension that defines the output format. Must include a leading period.
img (cv2.typing.MatLike) – Image to be written.
buf – Output buffer resized to fit the compressed image.
params (_typing.Sequence[int]) – Format-specific parameters. See cv::imwrite and cv::ImwriteFlags.

Return type:

tuple[bool, numpy.ndarray[_typing.Any, numpy.dtype[numpy.uint8]]]

cv2.imread(filename[, flags]) → retval¶

Loads an image from a file.

@anchor imread

The function imread loads an image from the specified file and returns it. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format), the function returns an empty matrix ( Mat::data==NULL ).

Currently, the following file formats are supported:

Windows bitmaps - *.bmp, *.dib (always supported)
JPEG files - *.jpeg, *.jpg, *.jpe (see the Note section)
JPEG 2000 files - *.jp2 (see the Note section)
Portable Network Graphics - *.png (see the Note section)
WebP - *.webp (see the Note section)
AVIF - *.avif (see the Note section)
Portable image format - *.pbm, *.pgm, *.ppm *.pxm, *.pnm (always supported)
PFM files - *.pfm (see the Note section)
Sun rasters - *.sr, *.ras (always supported)
TIFF files - *.tiff, *.tif (see the Note section)
OpenEXR Image files - *.exr (see the Note section)
Radiance HDR - *.hdr, *.pic (always supported)
Raster and Vector geospatial data supported by GDAL (see the Note section)

@note

The function determines the type of an image by the content, not by the file extension.
In the case of color images, the decoded images will have the channels stored in B G R order.
When using IMREAD_GRAYSCALE, the codec’s internal grayscale conversion will be used, if available. Results may differ to the output of cvtColor()
On Microsoft Windows* OS and MacOSX*, the codecs shipped with an OpenCV image (libjpeg, libpng, libtiff, and libjasper) are used by default. So, OpenCV can always read JPEGs, PNGs, and TIFFs. On MacOSX, there is also an option to use native MacOSX image readers. But beware that currently these native image loaders give images with different pixel values because of the color management embedded into MacOSX.
On Linux*, BSD flavors and other Unix-like open-source operating systems, OpenCV looks for codecs supplied with an OS image. Install the relevant packages (do not forget the development files, for example, “libjpeg-dev”, in Debian* and Ubuntu*) to get the codec support or turn on the OPENCV_BUILD_3RDPARTY_LIBS flag in CMake.
In the case you set WITH_GDAL flag to true in CMake and @ref IMREAD_LOAD_GDAL to load the image, then the GDAL driver will be used in order to decode the image, supporting the following formats: Raster, Vector.
If EXIF information is embedded in the image file, the EXIF orientation will be taken into account and thus the image will be rotated accordingly except if the flags @ref IMREAD_IGNORE_ORIENTATION or @ref IMREAD_UNCHANGED are passed.
Use the IMREAD_UNCHANGED flag to keep the floating point values from PFM image.
By default number of pixels must be less than 2^30. Limit can be set using system variable OPENCV_IO_MAX_IMAGE_PIXELS

Parameters:

filename (str) – Name of file to be loaded.
flags (int) – Flag that can take values of cv::ImreadModes

Return type:

cv2.typing.MatLike

cv2.imreadmulti(filename[, mats[, flags]]) → retval, mats¶

Loads a of images of a multi-page image from a file.

The function imreadmulti loads a multi-page image from the specified file into a vector of Mat objects.

The function imreadmulti loads a specified range from a multi-page image from the specified file into a vector of Mat objects.

See also: cv::imread See also: cv::imread

Parameters:

filename (str) – Name of file to be loaded.
mats (_typing.Sequence[cv2.typing.MatLike] | None) – A vector of Mat objects holding each page.
flags (int) – Flag that can take values of cv::ImreadModes, default with cv::IMREAD_ANYCOLOR.
start – Start index of the image to load
count – Count number of images to load

Return type:

tuple[bool, _typing.Sequence[cv2.typing.MatLike]]

cv2.imshow(winname, mat) → None¶

Displays an image in the specified window.

The function imshow displays an image in the specified window. If the window was created with the cv::WINDOW_AUTOSIZE flag, the image is shown with its original size, however it is still limited by the screen resolution. Otherwise, the image is scaled to fit the window. The function may scale the image, depending on its depth:

If the image is 8-bit unsigned, it is displayed as is.
If the image is 16-bit unsigned, the pixels are divided by 256. That is, the value range [0,255*256] is mapped to [0,255].
If the image is 32-bit or 64-bit floating-point, the pixel values are multiplied by 255. That is, the value range [0,1] is mapped to [0,255].
32-bit integer images are not processed anymore due to ambiguouty of required transform. Convert to 8-bit unsigned matrix using a custom preprocessing specific to image’s context.

If window was created with OpenGL support, cv::imshow also support ogl::Buffer , ogl::Texture2D and cuda::GpuMat as input.

If the window was not created before this function, it is assumed creating a window with cv::WINDOW_AUTOSIZE.

If you need to show an image that is bigger than the screen resolution, you will need to call namedWindow(“”, WINDOW_NORMAL) before the imshow.

Note

This function should be followed by a call to cv::waitKey or cv::pollKey to perform GUIhousekeeping tasks that are necessary to actually show the given image and make the window respond to mouse and keyboard events. Otherwise, it won’t display the image and the window might lock up. For example, waitKey(0) will display the window infinitely until any keypress (it is suitable for image display). waitKey(25) will display a frame and wait approximately 25 ms for a key press (suitable for displaying a video frame-by-frame). To remove the window, use cv::destroyWindow.

Note

[Windows Backend Only] Pressing Ctrl+C will copy the image to the clipboard. Pressing Ctrl+S will show a dialog to save the image.

Parameters:

winname (str) – Name of the window.
mat (cv2.typing.MatLike) – Image to be shown.

Return type:

None

cv2.imwrite(filename, img[, params]) → retval¶

Saves an image to a specified file.

The function imwrite saves the image to the specified file. The image format is chosen based on the filename extension (see cv::imread for the list of extensions). In general, only 8-bit unsigned (CV_8U) single-channel or 3-channel (with ‘BGR’ channel order) images can be saved using this function, with these exceptions:

With OpenEXR encoder, only 32-bit float (CV_32F) images can be saved.
- 8-bit unsigned (CV_8U) images are not supported.
With Radiance HDR encoder, non 64-bit float (CV_64F) images can be saved.
- All images will be converted to 32-bit float (CV_32F).
With JPEG 2000 encoder, 8-bit unsigned (CV_8U) and 16-bit unsigned (CV_16U) images can be saved.
With PAM encoder, 8-bit unsigned (CV_8U) and 16-bit unsigned (CV_16U) images can be saved.
With PNG encoder, 8-bit unsigned (CV_8U) and 16-bit unsigned (CV_16U) images can be saved.
- PNG images with an alpha channel can be saved using this function. To do this, create 8-bit (or 16-bit) 4-channel image BGRA, where the alpha channel goes last. Fully transparent pixels should have alpha set to 0, fully opaque pixels should have alpha set to 255/65535 (see the code sample below).
With PGM/PPM encoder, 8-bit unsigned (CV_8U) and 16-bit unsigned (CV_16U) images can be saved.
With TIFF encoder, 8-bit unsigned (CV_8U), 16-bit unsigned (CV_16U), 32-bit float (CV_32F) and 64-bit float (CV_64F) images can be saved.
- Multiple images (vector of Mat) can be saved in TIFF format (see the code sample below).
- 32-bit float 3-channel (CV_32FC3) TIFF images will be saved using the LogLuv high dynamic range encoding (4 bytes per pixel)

If the image format is not supported, the image will be converted to 8-bit unsigned (CV_8U) and saved that way.

If the format, depth or channel order is different, use Mat::convertTo and cv::cvtColor to convert it before saving. Or, use the universal FileStorage I/O functions to save the image to XML or YAML format.

The sample below shows how to create a BGRA image, how to set custom compression parameters and save it to a PNG file. It also demonstrates how to save multiple images in a TIFF file: @include snippets/imgcodecs_imwrite.cpp

Parameters:

filename (str) – Name of the file.
img (cv2.typing.MatLike) – (Mat or vector of Mat) Image or Images to be saved.
params (_typing.Sequence[int]) – Format-specific parameters encoded as pairs (paramId_1, paramValue_1, paramId_2, paramValue_2, … .) see cv::ImwriteFlags

Return type:

cv2.imwritemulti(filename, img[, params]) → retval¶

Parameters:

filename (str) –
img (_typing.Sequence[cv2.typing.MatLike]) –
params (_typing.Sequence[int]) –

Return type:

cv2.inRange(src, lowerb, upperb[, dst]) → dst¶

Checks if array elements lie between the elements of two other arrays.

The function checks the range as follows:

For every element of a single-channel input array:

\[\begin{equation*}\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0\end{equation*}\]
For two-channel arrays:

\[\begin{equation*}\texttt{dst} (I)= \texttt{lowerb} (I)_0 \leq \texttt{src} (I)_0 \leq \texttt{upperb} (I)_0 \land \texttt{lowerb} (I)_1 \leq \texttt{src} (I)_1 \leq \texttt{upperb} (I)_1\end{equation*}\]
and so forth.

That is, dst (I) is set to 255 (all 1 -bits) if src (I) is within the specified 1D, 2D, 3D, … box and 0 otherwise.

When the lower and/or upper boundary parameters are scalars, the indexes (I) at lowerb and upperb in the above formulas should be omitted.

Parameters:

src (cv2.typing.MatLike) – first input array.
lowerb (cv2.typing.MatLike) – inclusive lower boundary array or a scalar.
upperb (cv2.typing.MatLike) – inclusive upper boundary array or a scalar.
dst (cv2.typing.MatLike | None) – output array of the same size as src and CV_8U type.

Return type:

cv2.typing.MatLike

cv2.initCameraMatrix2D(objectPoints, imagePoints, imageSize[, aspectRatio]) → retval¶

Finds an initial camera intrinsic matrix from 3D-2D point correspondences.

The function estimates and returns an initial camera intrinsic matrix for the camera calibration process. Currently, the function only supports planar calibration patterns, which are patterns where each object point has z-coordinate =0.

Parameters:

objectPoints (_typing.Sequence[cv2.typing.MatLike]) – Vector of vectors of the calibration pattern points in the calibration patterncoordinate space. In the old interface all the per-view vectors are concatenated. See #calibrateCamera for details.
imagePoints (_typing.Sequence[cv2.typing.MatLike]) – Vector of vectors of the projections of the calibration pattern points. In theold interface all the per-view vectors are concatenated.
imageSize (cv2.typing.Size) – Image size in pixels used to initialize the principal point.
aspectRatio (float) – If it is zero or negative, both \(f_x\) and \(f_y\) are estimated independently.Otherwise, \(f_x = f_y \cdot \texttt{aspectRatio}\) .

Return type:

cv2.typing.MatLike

cv2.initInverseRectificationMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]) → map1, map2¶

Computes the projection and inverse-rectification transformation map. In essense, this is the inverse of#initUndistortRectifyMap to accomodate stereo-rectification of projectors (‘inverse-cameras’) in projector-camera pairs.

The function computes the joint projection and inverse rectification transformation and represents the result in the form of maps for #remap. The projected image looks like a distorted version of the original which, once projected by a projector, should visually match the original. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a projector-camera pair, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .

The projector is oriented differently in the coordinate space, according to R. In case of projector-camera pairs, this helps align the projector (in the same manner as #initUndistortRectifyMap for the camera) to create a stereo-rectified pair. This allows epipolar lines on both images to become horizontal and have the same y-coordinate (in case of a horizontally aligned projector-camera pair).

The function builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (projected and inverse-rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original digital image). The following process is applied:

\[\begin{equation*} \begin{array}{l} \text{newCameraMatrix}\\ x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ \\\text{Undistortion} \\\scriptsize{\textit{though equation shown is for radial undistortion, function implements cv::undistortPoints()}}\\ r^2 \leftarrow x^2 + y^2 \\ \theta \leftarrow \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6}\\ x' \leftarrow \frac{x}{\theta} \\ y' \leftarrow \frac{y}{\theta} \\ \\\text{Rectification}\\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x'' \leftarrow X/W \\ y'' \leftarrow Y/W \\ \\\text{cameraMatrix}\\ map_x(u,v) \leftarrow x'' f_x + c_x \\ map_y(u,v) \leftarrow y'' f_y + c_y \end{array} \end{equation*}\]

where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients vector distCoeffs.

In case of a stereo-rectified projector-camera pair, this function is called for the projector while #initUndistortRectifyMap is called for the camera head. This is done after #stereoRectify, which in turn is called after #stereoCalibrate. If the projector-camera pair is not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For the projector and camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as

\[\begin{equation*}\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\end{equation*}\]

where cameraMatrix can be chosen arbitrarily.

Parameters:

cameraMatrix (cv2.typing.MatLike) – Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
R (cv2.typing.MatLike) – Optional rectification transformation in the object space (3x3 matrix). R1 or R2,computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed.
newCameraMatrix (cv2.typing.MatLike) – New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
size (cv2.typing.Size) – Distorted image size.
m1type (int) – Type of the first output map. Can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
map1 (cv2.typing.MatLike | None) – The first output map for #remap.
map2 (cv2.typing.MatLike | None) – The second output map for #remap.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]) → map1, map2¶

Computes the undistortion and rectification transformation map.

The function computes the joint undistortion and rectification transformation and represents the result in the form of maps for #remap. The undistorted image looks like original, as if it is captured with a camera using the camera matrix =newCameraMatrix and zero distortion. In case of a monocular camera, newCameraMatrix is usually equal to cameraMatrix, or it can be computed by #getOptimalNewCameraMatrix for a better control over scaling. In case of a stereo camera, newCameraMatrix is normally set to P1 or P2 computed by #stereoRectify .

Also, this new camera is oriented differently in the coordinate space, according to R. That, for example, helps to align two heads of a stereo camera so that the epipolar lines on both images become horizontal and have the same y- coordinate (in case of a horizontally aligned stereo camera).

The function actually builds the maps for the inverse mapping algorithm that is used by #remap. That is, for each pixel \((u, v)\) in the destination (corrected and rectified) image, the function computes the corresponding coordinates in the source image (that is, in the original image from camera). The following process is applied:

\[\begin{equation*} \begin{array}{l} x \leftarrow (u - {c'}_x)/{f'}_x \\ y \leftarrow (v - {c'}_y)/{f'}_y \\ {[X\,Y\,W]} ^T \leftarrow R^{-1}*[x \, y \, 1]^T \\ x' \leftarrow X/W \\ y' \leftarrow Y/W \\ r^2 \leftarrow x'^2 + y'^2 \\ x'' \leftarrow x' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + 2p_1 x' y' + p_2(r^2 + 2 x'^2) + s_1 r^2 + s_2 r^4\\ y'' \leftarrow y' \frac{1 + k_1 r^2 + k_2 r^4 + k_3 r^6}{1 + k_4 r^2 + k_5 r^4 + k_6 r^6} + p_1 (r^2 + 2 y'^2) + 2 p_2 x' y' + s_3 r^2 + s_4 r^4 \\ s\vecthree{x'''}{y'''}{1} = \vecthreethree{R_{33}(\tau_x, \tau_y)}{0}{-R_{13}((\tau_x, \tau_y)} {0}{R_{33}(\tau_x, \tau_y)}{-R_{23}(\tau_x, \tau_y)} {0}{0}{1} R(\tau_x, \tau_y) \vecthree{x''}{y''}{1}\\ map_x(u,v) \leftarrow x''' f_x + c_x \\ map_y(u,v) \leftarrow y''' f_y + c_y \end{array} \end{equation*}\]

where \((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) are the distortion coefficients.

In case of a stereo camera, this function is called twice: once for each camera head, after #stereoRectify, which in its turn is called after #stereoCalibrate. But if the stereo camera was not calibrated, it is still possible to compute the rectification transformations directly from the fundamental matrix using #stereoRectifyUncalibrated. For each camera, the function computes homography H as the rectification transformation in a pixel domain, not a rotation matrix R in 3D space. R can be computed from H as

\[\begin{equation*}\texttt{R} = \texttt{cameraMatrix} ^{-1} \cdot \texttt{H} \cdot \texttt{cameraMatrix}\end{equation*}\]

where cameraMatrix can be chosen arbitrarily.

Parameters:

cameraMatrix (cv2.typing.MatLike) – Input camera matrix \(A=\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
R (cv2.typing.MatLike) – Optional rectification transformation in the object space (3x3 matrix). R1 or R2 ,computed by #stereoRectify can be passed here. If the matrix is empty, the identity transformation is assumed. In #initUndistortRectifyMap R assumed to be an identity matrix.
newCameraMatrix (cv2.typing.MatLike) – New camera matrix \(A'=\vecthreethree{f_x'}{0}{c_x'}{0}{f_y'}{c_y'}{0}{0}{1}\).
size (cv2.typing.Size) – Undistorted image size.
m1type (int) – Type of the first output map that can be CV_32FC1, CV_32FC2 or CV_16SC2, see #convertMaps
map1 (cv2.typing.MatLike | None) – The first output map.
map2 (cv2.typing.MatLike | None) – The second output map.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.inpaint(src, inpaintMask, inpaintRadius, flags[, dst]) → dst¶

Restores the selected region in an image using the region neighborhood.

The function reconstructs the selected image area from the pixel near the area boundary. The function may be used to remove dust and scratches from a scanned photo, or to remove undesirable objects from still images or video. See http://en.wikipedia.org/wiki/Inpainting for more details.

@note

An example using the inpainting technique can be found at opencv_source_code/samples/cpp/inpaint.cpp
(Python) An example using the inpainting technique can be found at opencv_source_code/samples/python/inpaint.py

Parameters:

src (cv2.typing.MatLike) – Input 8-bit, 16-bit unsigned or 32-bit float 1-channel or 8-bit 3-channel image.
inpaintMask (cv2.typing.MatLike) – Inpainting mask, 8-bit 1-channel image. Non-zero pixels indicate the area thatneeds to be inpainted.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as src .
inpaintRadius (float) – Radius of a circular neighborhood of each point inpainted that is consideredby the algorithm.
flags (int) – Inpainting method that could be cv::INPAINT_NS or cv::INPAINT_TELEA

Return type:

cv2.typing.MatLike

cv2.insertChannel(src, dst, coi) → dst¶

Inserts a single channel to dst (coi is 0-based index)

See also: mixChannels, merge

Parameters:

src (cv2.typing.MatLike) – input array
dst (cv2.typing.MatLike) – output array
coi (int) – index of channel for insertion

Return type:

cv2.typing.MatLike

cv2.integral(src[, sum[, sdepth]]) → sum¶

@overload

Parameters:

src (cv2.typing.MatLike) –
sum (cv2.typing.MatLike | None) –
sdepth (int) –

Return type:

cv2.typing.MatLike

cv2.integral2(src[, sum[, sqsum[, sdepth[, sqdepth]]]]) → sum, sqsum¶

@overload

Parameters:

src (cv2.typing.MatLike) –
sum (cv2.typing.MatLike | None) –
sqsum (cv2.typing.MatLike | None) –
sdepth (int) –
sqdepth (int) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.integral3(src[, sum[, sqsum[, tilted[, sdepth[, sqdepth]]]]]) → sum, sqsum, tilted¶

Calculates the integral of an image.

The function calculates one or more integral images for the source image as follows:

\[\begin{equation*}\texttt{sum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)\end{equation*}\]

\[\begin{equation*}\texttt{sqsum} (X,Y) = \sum _{x<X,y<Y} \texttt{image} (x,y)^2\end{equation*}\]

\[\begin{equation*}\texttt{tilted} (X,Y) = \sum _{y<Y,abs(x-X+1) \leq Y-y-1} \texttt{image} (x,y)\end{equation*}\]

Using these integral images, you can calculate sum, mean, and standard deviation over a specific up-right or rotated rectangular region of the image in a constant time, for example:

\[\begin{equation*}\sum _{x_1 \leq x < x_2, \, y_1 \leq y < y_2} \texttt{image} (x,y) = \texttt{sum} (x_2,y_2)- \texttt{sum} (x_1,y_2)- \texttt{sum} (x_2,y_1)+ \texttt{sum} (x_1,y_1)\end{equation*}\]

It makes possible to do a fast blurring or fast block correlation with a variable window size, for example. In case of multi-channel images, sums for each channel are accumulated independently.

As a practical example, the next figure shows the calculation of the integral of a straight rectangle Rect(4,4,3,2) and of a tilted rectangle Rect(5,1,2,3) . The selected pixels in the original image are shown, as well as the relative pixels in the integral images sum and tilted .

integral calculation example

Parameters:

src (cv2.typing.MatLike) – input image as \(W \times H\), 8-bit or floating-point (32f or 64f).
sum (cv2.typing.MatLike | None) – integral image as \((W+1)\times (H+1)\) , 32-bit integer or floating-point (32f or 64f).
sqsum (cv2.typing.MatLike | None) – integral image for squared pixel values; it is \((W+1)\times (H+1)\), double-precisionfloating-point (64f) array.
tilted (cv2.typing.MatLike | None) – integral for the image rotated by 45 degrees; it is \((W+1)\times (H+1)\) array withthe same data type as sum.
sdepth (int) – desired depth of the integral and the tilted integral images, CV_32S, CV_32F, orCV_64F.
sqdepth (int) – desired depth of the integral image of squared pixel values, CV_32F or CV_64F.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.intersectConvexConvex(p1, p2[, p12[, handleNested]]) → retval, p12¶

Finds intersection of two convex polygons

Note

intersectConvexConvex doesn’t confirm that both polygons are convex and will return invalid results if they aren’t.

Parameters:

p1 (cv2.typing.MatLike) – First polygon
p2 (cv2.typing.MatLike) – Second polygon
p12 (cv2.typing.MatLike | None) – Output polygon describing the intersecting area
handleNested (bool) – When true, an intersection is found if one of the polygons is fully enclosed in the other.When false, no intersection is found. If the polygons share a side or the vertex of one polygon lies on an edge of the other, they are not considered nested and an intersection will be found regardless of the value of handleNested.

Returns:

Absolute value of area of intersecting polygon

Return type:

tuple[float, cv2.typing.MatLike]

cv2.invert(src[, dst[, flags]]) → retval, dst¶

Finds the inverse or pseudo-inverse of a matrix.

The function cv::invert inverts the matrix src and stores the result in dst . When the matrix src is singular or non-square, the function calculates the pseudo-inverse matrix (the dst matrix) so that norm(src*dst - I) is minimal, where I is an identity matrix.

In case of the #DECOMP_LU method, the function returns non-zero value if the inverse has been successfully calculated and 0 if src is singular.

In case of the #DECOMP_SVD method, the function returns the inverse condition number of src (the ratio of the smallest singular value to the largest singular value) and 0 if src is singular. The SVD method calculates a pseudo-inverse matrix if src is singular.

Similarly to #DECOMP_LU, the method #DECOMP_CHOLESKY works only with non-singular square matrices that should also be symmetrical and positively defined. In this case, the function stores the inverted matrix in dst and returns non-zero. Otherwise, it returns 0.

See also: solve, SVD

Parameters:

src (cv2.typing.MatLike) – input floating-point M x N matrix.
dst (cv2.typing.MatLike | None) – output matrix of N x M size and the same type as src.
flags (int) – inversion method (cv::DecompTypes)

Return type:

tuple[float, cv2.typing.MatLike]

cv2.invertAffineTransform(M[, iM]) → iM¶

Inverts an affine transformation.

The function computes an inverse affine transformation represented by \(2 \times 3\) matrix M:

\[\begin{equation*}\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{21} & a_{22} & b_2 \end{bmatrix}\end{equation*}\]

The result is also a \(2 \times 3\) matrix of the same type as M.

Parameters:

M (cv2.typing.MatLike) – Original affine transformation.
iM (cv2.typing.MatLike | None) – Output reverse affine transformation.

Return type:

cv2.typing.MatLike

cv2.isContourConvex(contour) → retval¶

Tests a contour convexity.

The function tests whether the input contour is convex or not. The contour must be simple, that is, without self-intersections. Otherwise, the function output is undefined.

Parameters:: contour (cv2.typing.MatLike) – Input vector of 2D points, stored in std::vector<> or Mat
Return type:: bool

cv2.kmeans(data, K, bestLabels, criteria, attempts, flags[, centers]) → retval, bestLabels, centers¶

Finds centers of clusters and groups input samples around the clusters.

The function kmeans implements a k-means algorithm that finds the centers of cluster_count clusters and groups the input samples around the clusters. As an output, \(\texttt{bestLabels}_i\) contains a 0-based cluster index for the sample stored in the \(i^{th}\) row of the samples matrix.

@note

(Python) An example on K-means clustering can be found at opencv_source_code/samples/python/kmeans.py

Parameters:: data – Data for clustering. An array of N-Dimensional points with float coordinates is needed.Examples of this array can be:

Mat points(count, 2, CV_32F);
Mat points(count, 1, CV_32FC2);
Mat points(1, count, CV_32FC2);
std::vector<cv::Point2f> points(sampleCount);

Parameters:

K (int) – Number of clusters to split the set by.
bestLabels (cv2.typing.MatLike) – Input/output integer array that stores the cluster indices for every sample.
criteria (cv2.typing.TermCriteria) – The algorithm termination criteria, that is, the maximum number of iterations and/orthe desired accuracy. The accuracy is specified as criteria.epsilon. As soon as each of the cluster centers moves by less than criteria.epsilon on some iteration, the algorithm stops.
attempts (int) – Flag to specify the number of times the algorithm is executed using differentinitial labellings. The algorithm returns the labels that yield the best compactness (see the last function parameter).
flags (int) – Flag that can take values of cv::KmeansFlags
centers (cv2.typing.MatLike | None) – Output matrix of the cluster centers, one row per each cluster center.

Returns:

The function returns the compactness measure that is computed as\begin{equation*}\sum _i | \texttt{samples} _i - \texttt{centers} _{ \texttt{labels} _i} | ^2\end{equation*} after every attempt. The best (minimum) value is chosen and the corresponding labels and the compactness value are returned by the function. Basically, you can use only the core of the function, set the number of attempts to 1, initialize labels each time using a custom algorithm, pass them with the ( flags = #KMEANS_USE_INITIAL_LABELS ) flag, and then choose the best (most-compact) clustering.

Return type:

tuple[float, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.line(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) → img¶

Draws a line segment connecting two points.

The function line draws the line segment between pt1 and pt2 points in the image. The line is clipped by the image boundaries. For non-antialiased lines with integer coordinates, the 8-connected or 4-connected Bresenham algorithm is used. Thick lines are drawn with rounding endings. Antialiased lines are drawn using Gaussian filtering.

Parameters:

img (cv2.typing.MatLike) – Image.
pt1 (cv2.typing.Point) – First point of the line segment.
pt2 (cv2.typing.Point) – Second point of the line segment.
color (cv2.typing.Scalar) – Line color.
thickness (int) – Line thickness.
lineType (int) – Type of the line. See #LineTypes.
shift (int) – Number of fractional bits in the point coordinates.

Return type:

cv2.typing.MatLike

cv2.linearPolar(src, center, maxRadius, flags[, dst]) → dst¶

Remaps an image to polar coordinates space.

@internal Transform the source image using the following transformation (See @ref polar_remaps_reference_image “Polar remaps reference image c)”):

\[\begin{equation*}\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\end{equation*}\]

where

\[\begin{equation*}\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = Kmag \cdot \texttt{magnitude} (I) ,\\ \phi = angle \cdot \texttt{angle} (I) \end{array}\end{equation*}\]

and

\[\begin{equation*}\begin{array}{l} Kx = src.cols / maxRadius \\ Ky = src.rows / 2\Pi \end{array}\end{equation*}\]

@note

The function can not operate in-place.
To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.

Deprecated since version unknown: This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags)

See also: cv::logPolar@endinternal

Parameters:

src (cv2.typing.MatLike) – Source image
dst (cv2.typing.MatLike | None) – Destination image. It will have same size and type as src.
center (cv2.typing.Point2f) – The transformation center;
maxRadius (float) – The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
flags (int) – A combination of interpolation methods, see #InterpolationFlags

Return type:

cv2.typing.MatLike

cv2.log(src[, dst]) → dst¶

Calculates the natural logarithm of every array element.

The function cv::log calculates the natural logarithm of every element of the input array:

\[\begin{equation*}\texttt{dst} (I) = \log (\texttt{src}(I)) \end{equation*}\]

Output on zero, negative and special (NaN, Inf) values is undefined.

See also: exp, cartToPolar, polarToCart, phase, pow, sqrt, magnitude

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src .

Return type:

cv2.typing.MatLike

cv2.logPolar(src, center, M, flags[, dst]) → dst¶

Remaps an image to semilog-polar coordinates space.

@internal Transform the source image using the following transformation (See @ref polar_remaps_reference_image “Polar remaps reference image d)”):

\[\begin{equation*}\begin{array}{l} dst( \rho , \phi ) = src(x,y) \\ dst.size() \leftarrow src.size() \end{array}\end{equation*}\]

where

\[\begin{equation*}\begin{array}{l} I = (dx,dy) = (x - center.x,y - center.y) \\ \rho = M \cdot log_e(\texttt{magnitude} (I)) ,\\ \phi = Kangle \cdot \texttt{angle} (I) \\ \end{array}\end{equation*}\]

and

\[\begin{equation*}\begin{array}{l} M = src.cols / log_e(maxRadius) \\ Kangle = src.rows / 2\Pi \\ \end{array}\end{equation*}\]

The function emulates the human “foveal” vision and can be used for fast scale and rotation-invariant template matching, for object tracking and so forth.

@note

The function can not operate in-place.
To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.

Deprecated since version unknown: This function produces same result as cv::warpPolar(src, dst, src.size(), center, maxRadius, flags+WARP_POLAR_LOG);

See also: cv::linearPolar@endinternal

Parameters:

src (cv2.typing.MatLike) – Source image
dst (cv2.typing.MatLike | None) – Destination image. It will have same size and type as src.
center (cv2.typing.Point2f) – The transformation center; where the output precision is maximal
M (float) – Magnitude scale parameter. It determines the radius of the bounding circle to transform too.
flags (int) – A combination of interpolation methods, see #InterpolationFlags

Return type:

cv2.typing.MatLike

cv2.magnitude(x, y[, magnitude]) → magnitude¶

Calculates the magnitude of 2D vectors.

The function cv::magnitude calculates the magnitude of 2D vectors formed from the corresponding elements of x and y arrays:

\[\begin{equation*}\texttt{dst} (I) = \sqrt{\texttt{x}(I)^2 + \texttt{y}(I)^2}\end{equation*}\]

See also: cartToPolar, polarToCart, phase, sqrt

Parameters:

x (cv2.typing.MatLike) – floating-point array of x-coordinates of the vectors.
y (cv2.typing.MatLike) – floating-point array of y-coordinates of the vectors; it musthave the same size as x.
magnitude (cv2.typing.MatLike | None) – output array of the same size and type as x.

Return type:

cv2.typing.MatLike

cv2.matMulDeriv(A, B[, dABdA[, dABdB]]) → dABdA, dABdB¶

Computes partial derivatives of the matrix product for each multiplied matrix.

The function computes partial derivatives of the elements of the matrix product \(A*B\) with regard to the elements of each of the two input matrices. The function is used to compute the Jacobian matrices in #stereoCalibrate but can also be used in any other similar optimization function.

Parameters:

A (cv2.typing.MatLike) – First multiplied matrix.
B (cv2.typing.MatLike) – Second multiplied matrix.
dABdA (cv2.typing.MatLike | None) – First output derivative matrix d(A*B)/dA of size\(\texttt{A.rows*B.cols} \times {A.rows*A.cols}\) .
dABdB (cv2.typing.MatLike | None) – Second output derivative matrix d(A*B)/dB of size\(\texttt{A.rows*B.cols} \times {B.rows*B.cols}\) .

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.matchShapes(contour1, contour2, method, parameter) → retval¶

Compares two shapes.

The function compares two shapes. All three implemented methods use the Hu invariants (see #HuMoments)

Parameters:

contour1 (cv2.typing.MatLike) – First contour or grayscale image.
contour2 (cv2.typing.MatLike) – Second contour or grayscale image.
method (int) – Comparison method, see #ShapeMatchModes
parameter (float) – Method-specific parameter (not supported now).

Return type:

cv2.matchTemplate(image, templ, method[, result[, mask]]) → result¶

Compares a template against overlapped image regions.

The function slides through image , compares the overlapped patches of size \(w \times h\) against templ using the specified method and stores the comparison results in result . #TemplateMatchModes describes the formulae for the available comparison methods ( \(I\) denotes image, \(T\) template, \(R\) result, \(M\) the optional mask ). The summation is done over template and/or the image patch: \(x' = 0...w-1, y' = 0...h-1\)

After the function finishes the comparison, the best matches can be found as global minimums (when #TM_SQDIFF was used) or maximums (when #TM_CCORR or #TM_CCOEFF was used) using the #minMaxLoc function. In case of a color image, template summation in the numerator and each sum in the denominator is done over all of the channels and separate mean values are used for each channel. That is, the function can take a color template and a color image. The result will still be a single-channel image, which is easier to analyze.

Parameters:

image (cv2.typing.MatLike) – Image where the search is running. It must be 8-bit or 32-bit floating-point.
templ (cv2.typing.MatLike) – Searched template. It must be not greater than the source image and have the samedata type.
result (cv2.typing.MatLike | None) – Map of comparison results. It must be single-channel 32-bit floating-point. If imageis \(W \times H\) and templ is \(w \times h\) , then result is \((W-w+1) \times (H-h+1)\) .
method (int) – Parameter specifying the comparison method, see #TemplateMatchModes
mask (cv2.typing.MatLike | None) – Optional mask. It must have the same size as templ. It must either have the same number of channels as template or only one channel, which is then used for all template and image channels. If the data type is #CV_8U, the mask is interpreted as a binary mask, meaning only elements where mask is nonzero are used and are kept unchanged independent of the actual mask value (weight equals 1). For data tpye #CV_32F, the mask values are used as weights. The exact formulas are documented in #TemplateMatchModes.

Return type:

cv2.typing.MatLike

cv2.max(src1, src2[, dst]) → dst¶

Calculates per-element maximum of two arrays or an array and a scalar.

The function cv::max calculates the per-element maximum of two arrays:

\[\begin{equation*}\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{src2} (I))\end{equation*}\]

or array and a scalar:

\[\begin{equation*}\texttt{dst} (I)= \max ( \texttt{src1} (I), \texttt{value} )\end{equation*}\]

See also: min, compare, inRange, minMaxLoc, @ref MatrixExpressions

Parameters:

src1 (cv2.typing.MatLike) – first input array.
src2 (cv2.typing.MatLike) – second input array of the same size and type as src1 .
dst (cv2.typing.MatLike | None) – output array of the same size and type as src1.

Return type:

cv2.typing.MatLike

cv2.mean(src[, mask]) → retval¶

Calculates an average (mean) of array elements.

The function cv::mean calculates the mean value M of array elements, independently for each channel, and return it:

\[\begin{equation*}\begin{array}{l} N = \sum _{I: \; \texttt{mask} (I) \ne 0} 1 \\ M_c = \left ( \sum _{I: \; \texttt{mask} (I) \ne 0}{ \texttt{mtx} (I)_c} \right )/N \end{array}\end{equation*}\]

When all the mask elements are 0’s, the function returns Scalar::all(0)

See also: countNonZero, meanStdDev, norm, minMaxLoc

Parameters:

src (cv2.typing.MatLike) – input array that should have from 1 to 4 channels so that the result can be stored inScalar_ .
mask (cv2.typing.MatLike | None) – optional operation mask.

Return type:

cv2.typing.Scalar

cv2.meanShift(probImage, window, criteria) → retval, window¶

Finds an object on a back projection image.

Parameters:

probImage (cv2.typing.MatLike) – Back projection of the object histogram. See calcBackProject for details.
window (cv2.typing.Rect) – Initial search window.
criteria (cv2.typing.TermCriteria) – Stop criteria for the iterative search algorithm.returns : Number of iterations CAMSHIFT took to converge. The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in window of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations criteria.maxCount is done or until the window center shifts by less than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search window size or orientation do not change during the search. You can simply pass the output of calcBackProject to this function. But better results can be obtained if you pre-filter the back projection and remove the noise. For example, you can do this by retrieving connected components with findContours , throwing away contours with small area ( contourArea ), and rendering the remaining contours with drawContours.

Return type:

tuple[int, cv2.typing.Rect]

cv2.meanStdDev(src[, mean[, stddev[, mask]]]) → mean, stddev¶

Calculates a mean and standard deviation of array elements.

The function cv::meanStdDev calculates the mean and the standard deviation M of array elements independently for each channel and returns it via the output parameters:

\[\begin{equation*}\begin{array}{l} N = \sum _{I, \texttt{mask} (I) \ne 0} 1 \\ \texttt{mean} _c = \frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \texttt{src} (I)_c}{N} \\ \texttt{stddev} _c = \sqrt{\frac{\sum_{ I: \; \texttt{mask}(I) \ne 0} \left ( \texttt{src} (I)_c - \texttt{mean} _c \right )^2}{N}} \end{array}\end{equation*}\]

When all the mask elements are 0’s, the function returns mean=stddev=Scalar::all(0).

Note

The calculated standard deviation is only the diagonal of thecomplete normalized covariance matrix. If the full matrix is needed, you can reshape the multi-channel array M x N to the single-channel array M*N x mtx.channels() (only possible when the matrix is continuous) and then pass the matrix to calcCovarMatrix .

See also: countNonZero, mean, norm, minMaxLoc, calcCovarMatrix

Parameters:

src (cv2.typing.MatLike) – input array that should have from 1 to 4 channels so that the results can be stored inScalar_ ‘s.
mean (cv2.typing.MatLike | None) – output parameter: calculated mean value.
stddev (cv2.typing.MatLike | None) – output parameter: calculated standard deviation.
mask (cv2.typing.MatLike | None) – optional operation mask.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.medianBlur(src, ksize[, dst]) → dst¶

Blurs an image using the median filter.

The function smoothes an image using the median filter with the \(\texttt{ksize} \times \texttt{ksize}\) aperture. Each channel of a multi-channel image is processed independently. In-place operation is supported.

Note

The median filter uses #BORDER_REPLICATE internally to cope with border pixels, see #BorderTypes

See also: bilateralFilter, blur, boxFilter, GaussianBlur

Parameters:

src (cv2.typing.MatLike) – input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should beCV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U.
dst (cv2.typing.MatLike | None) – destination array of the same size and type as src.
ksize (int) – aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 …

Return type:

cv2.typing.MatLike

cv2.merge(mv[, dst]) → dst¶

@overload

Parameters:

mv (_typing.Sequence[cv2.typing.MatLike]) – input vector of matrices to be merged; all the matrices in mv must have the samesize and the same depth.
dst (cv2.typing.MatLike | None) – output array of the same size and the same depth as mv[0]; The number of channels willbe the total number of channels in the matrix array.

Return type:

cv2.typing.MatLike

cv2.min(src1, src2[, dst]) → dst¶

Calculates per-element minimum of two arrays or an array and a scalar.

The function cv::min calculates the per-element minimum of two arrays:

\[\begin{equation*}\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{src2} (I))\end{equation*}\]

or array and a scalar:

\[\begin{equation*}\texttt{dst} (I)= \min ( \texttt{src1} (I), \texttt{value} )\end{equation*}\]

See also: max, compare, inRange, minMaxLoc

Parameters:

src1 (cv2.typing.MatLike) – first input array.
src2 (cv2.typing.MatLike) – second input array of the same size and type as src1.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src1.

Return type:

cv2.typing.MatLike

cv2.minAreaRect(points) → retval¶

Finds a rotated rectangle of the minimum area enclosing the input 2D point set.

The function calculates and returns the minimum-area bounding rectangle (possibly rotated) for a specified point set. Developer should keep in mind that the returned RotatedRect can contain negative indices when data is close to the containing Mat element boundary.

Parameters:: points (cv2.typing.MatLike) – Input vector of 2D points, stored in std::vector<> or Mat
Return type:: cv2.typing.RotatedRect

cv2.minEnclosingCircle(points) → center, radius¶

Finds a circle of the minimum area enclosing a 2D point set.

The function finds the minimal enclosing circle of a 2D point set using an iterative algorithm.

Parameters:

points (cv2.typing.MatLike) – Input vector of 2D points, stored in std::vector<> or Mat
center – Output center of the circle.
radius – Output radius of the circle.

Return type:

tuple[cv2.typing.Point2f, float]

cv2.minEnclosingTriangle(points[, triangle]) → retval, triangle¶

Finds a triangle of minimum area enclosing a 2D point set and returns its area.

The function finds a triangle of minimum area enclosing the given set of 2D points and returns its area. The output for a given 2D point set is shown in the image below. 2D points are depicted in red and the enclosing triangle in yellow.

Sample output of the minimum enclosing triangle function

The implementation of the algorithm is based on O’Rourke’s @cite ORourke86 and Klee and Laskowski’s @cite KleeLaskowski85 papers. O’Rourke provides a \(\theta(n)\) algorithm for finding the minimal enclosing triangle of a 2D convex polygon with n vertices. Since the #minEnclosingTriangle function takes a 2D point set as input an additional preprocessing step of computing the convex hull of the 2D point set is required. The complexity of the #convexHull function is \(O(n log(n))\) which is higher than \(\theta(n)\). Thus the overall complexity of the function is \(O(n log(n))\).

Parameters:

points (cv2.typing.MatLike) – Input vector of 2D points with depth CV_32S or CV_32F, stored in std::vector<> or Mat
triangle (cv2.typing.MatLike | None) – Output vector of three 2D points defining the vertices of the triangle. The depthof the OutputArray must be CV_32F.

Return type:

tuple[float, cv2.typing.MatLike]

cv2.minMaxLoc(src[, mask]) → minVal, maxVal, minLoc, maxLoc¶

Finds the global minimum and maximum in an array.

The function cv::minMaxLoc finds the minimum and maximum element values and their positions. The extremums are searched across the whole array or, if mask is not an empty array, in the specified array region.

The function do not work with multi-channel arrays. If you need to find minimum or maximum elements across all the channels, use Mat::reshape first to reinterpret the array as single-channel. Or you may extract the particular channel using either extractImageCOI , or mixChannels , or split .

See also: max, min, reduceArgMin, reduceArgMax, compare, inRange, extractImageCOI, mixChannels, split, Mat::reshape

Parameters:

src (cv2.typing.MatLike) – input single-channel array.
minVal – pointer to the returned minimum value; NULL is used if not required.
maxVal – pointer to the returned maximum value; NULL is used if not required.
minLoc – pointer to the returned minimum location (in 2D case); NULL is used if not required.
maxLoc – pointer to the returned maximum location (in 2D case); NULL is used if not required.
mask (cv2.typing.MatLike | None) – optional mask used to select a sub-array.

Return type:

tuple[float, float, cv2.typing.Point, cv2.typing.Point]

cv2.mixChannels(src, dst, fromTo) → dst¶

@overload

Parameters:

src (_typing.Sequence[cv2.typing.MatLike]) – input array or vector of matrices; all of the matrices must have the same size and thesame depth.
dst (_typing.Sequence[cv2.typing.MatLike]) – output array or vector of matrices; all the matrices must be allocated; their size anddepth must be the same as in src[0].
fromTo (_typing.Sequence[int]) – array of index pairs specifying which channels are copied and where; fromTo[k*2] isa 0-based index of the input channel in src, fromTo[k*2+1] is an index of the output channel in dst; the continuous channel numbering is used: the first input image channels are indexed from 0 to src[0].channels()-1, the second input image channels are indexed from src[0].channels() to src[0].channels() + src[1].channels()-1, and so on, the same scheme is used for the output image channels; as a special case, when fromTo[k*2] is negative, the corresponding output channel is filled with zero .

Return type:

_typing.Sequence[cv2.typing.MatLike]

cv2.moments(array[, binaryImage]) → retval¶

Calculates all of the moments up to the third order of a polygon or rasterized shape.

The function computes moments, up to the 3rd order, of a vector shape or a rasterized shape. The results are returned in the structure cv::Moments.

Note

Only applicable to contour moments calculations from Python bindings: Note that the numpytype for the input array should be either np.int32 or np.float32.

See also: contourArea, arcLength

Parameters:

array (cv2.typing.MatLike) – Raster image (single-channel, 8-bit or floating-point 2D array) or an array (\(1 \times N\) or \(N \times 1\) ) of 2D points (Point or Point2f ).
binaryImage (bool) – If it is true, all non-zero image pixels are treated as 1’s. The parameter isused for images only.

Returns:

moments.

Return type:

cv2.typing.Moments

cv2.morphologyEx(src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) → dst¶

Performs advanced morphological transformations.

The function cv::morphologyEx can perform advanced morphological transformations using an erosion and dilation as basic operations.

Any of the operations can be done in-place. In case of multi-channel images, each channel is processed independently.

See also: dilate, erode, getStructuringElement

Note

The number of iterations is the number of times erosion or dilatation operation will be applied.For instance, an opening operation (#MORPH_OPEN) with two iterations is equivalent to apply successively: erode -> erode -> dilate -> dilate (and not erode -> dilate -> erode -> dilate).

Parameters:

src (cv2.typing.MatLike) – Source image. The number of channels can be arbitrary. The depth should be one ofCV_8U, CV_16U, CV_16S, CV_32F or CV_64F.
dst (cv2.typing.MatLike | None) – Destination image of the same size and type as source image.
op (int) – Type of a morphological operation, see #MorphTypes
kernel (cv2.typing.MatLike) – Structuring element. It can be created using #getStructuringElement.
anchor (cv2.typing.Point) – Anchor position with the kernel. Negative values mean that the anchor is at thekernel center.
iterations (int) – Number of times erosion and dilation are applied.
borderType (int) – Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.
borderValue (cv2.typing.Scalar) – Border value in case of a constant border. The default value has a specialmeaning.

Return type:

cv2.typing.MatLike

cv2.moveWindow(winname, x, y) → None¶

Moves the window to the specified position

Parameters:

winname (str) – Name of the window.
x (int) – The new x-coordinate of the window.
y (int) – The new y-coordinate of the window.

Return type:

None

cv2.mulSpectrums(a, b, flags[, c[, conjB]]) → c¶

Performs the per-element multiplication of two Fourier spectrums.

The function cv::mulSpectrums performs the per-element multiplication of the two CCS-packed or complex matrices that are results of a real or complex Fourier transform.

The function, together with dft and idft , may be used to calculate convolution (pass conjB=false ) or correlation (pass conjB=true ) of two arrays rapidly. When the arrays are complex, they are simply multiplied (per element) with an optional conjugation of the second-array elements. When the arrays are real, they are assumed to be CCS-packed (see dft for details).

Parameters:

a (cv2.typing.MatLike) – first input array.
b (cv2.typing.MatLike) – second input array of the same size and type as src1 .
c (cv2.typing.MatLike | None) – output array of the same size and type as src1 .
flags (int) – operation flags; currently, the only supported flag is cv::DFT_ROWS, which indicates thateach row of src1 and src2 is an independent 1D Fourier spectrum. If you do not want to use this flag, then simply add a 0 as value.
conjB (bool) – optional flag that conjugates the second input array before the multiplication (true)or not (false).

Return type:

cv2.typing.MatLike

cv2.mulTransposed(src, aTa[, dst[, delta[, scale[, dtype]]]]) → dst¶

Calculates the product of a matrix and its transposition.

The function cv::mulTransposed calculates the product of src and its transposition:

\[\begin{equation*}\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} )^T ( \texttt{src} - \texttt{delta} )\end{equation*}\]

if aTa=true , and

\[\begin{equation*}\texttt{dst} = \texttt{scale} ( \texttt{src} - \texttt{delta} ) ( \texttt{src} - \texttt{delta} )^T\end{equation*}\]

otherwise. The function is used to calculate the covariance matrix. With zero delta, it can be used as a faster substitute for general matrix product A*B when B=A’

See also: calcCovarMatrix, gemm, repeat, reduce

Parameters:

src (cv2.typing.MatLike) – input single-channel matrix. Note that unlike gemm, thefunction can multiply not only floating-point matrices.
dst (cv2.typing.MatLike | None) – output square matrix.
aTa (bool) – Flag specifying the multiplication ordering. See thedescription below.
delta (cv2.typing.MatLike | None) – Optional delta matrix subtracted from src before themultiplication. When the matrix is empty ( delta=noArray() ), it is assumed to be zero, that is, nothing is subtracted. If it has the same size as src , it is simply subtracted. Otherwise, it is “repeated” (see repeat ) to cover the full src and then subtracted. Type of the delta matrix, when it is not empty, must be the same as the type of created output matrix. See the dtype parameter description below.
scale (float) – Optional scale factor for the matrix product.
dtype (int) – Optional type of the output matrix. When it is negative,the output matrix will have the same type as src . Otherwise, it will be type=CV_MAT_DEPTH(dtype) that should be either CV_32F or CV_64F .

Return type:

cv2.typing.MatLike

cv2.multiply(src1, src2[, dst[, scale[, dtype]]]) → dst¶

Calculates the per-element scaled product of two arrays.

The function multiply calculates the per-element product of two arrays:

\[\begin{equation*}\texttt{dst} (I)= \texttt{saturate} ( \texttt{scale} \cdot \texttt{src1} (I) \cdot \texttt{src2} (I))\end{equation*}\]

There is also a @ref MatrixExpressions -friendly variant of the first function. See Mat::mul .

For a not-per-element matrix product, see gemm .

Note

Saturation is not applied when the output array has the depthCV_32S. You may even get result of an incorrect sign in the case of overflow.

Note

(Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.multiply(src,X) means multiply(src,(X,X,X,X)). multiply(src,(X,)) means multiply(src,(X,0,0,0)).

See also: add, subtract, divide, scaleAdd, addWeighted, accumulate, accumulateProduct, accumulateSquare,Mat::convertTo

Parameters:

src1 (cv2.typing.MatLike) – first input array.
src2 (cv2.typing.MatLike) – second input array of the same size and the same type as src1.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src1.
scale (float) – optional scale factor.
dtype (int) – optional depth of the output array

Return type:

cv2.typing.MatLike

cv2.namedWindow(winname[, flags]) → None¶

Creates a window.

The function namedWindow creates a window that can be used as a placeholder for images and trackbars. Created windows are referred to by their names.

If a window with the same name already exists, the function does nothing.

You can call cv::destroyWindow or cv::destroyAllWindows to close the window and de-allocate any associated memory usage. For a simple program, you do not really have to call these functions because all the resources and windows of the application are closed automatically by the operating system upon exit.

Note

Qt backend supports additional flags: - WINDOW_NORMAL or WINDOW_AUTOSIZE: WINDOW_NORMAL enables you to resize the window, whereas WINDOW_AUTOSIZE adjusts automatically the window size to fit the displayed image (see imshow ), and you cannot change the window size manually.

WINDOW_FREERATIO or WINDOW_KEEPRATIO: WINDOW_FREERATIO adjusts the image with no respect to its ratio, whereas WINDOW_KEEPRATIO keeps the image ratio.
WINDOW_GUI_NORMAL or WINDOW_GUI_EXPANDED: WINDOW_GUI_NORMAL is the old way to draw the window without statusbar and toolbar, whereas WINDOW_GUI_EXPANDED is a new enhanced GUI. By default, flags == WINDOW_AUTOSIZE | WINDOW_KEEPRATIO | WINDOW_GUI_EXPANDED

Parameters:

winname (str) – Name of the window in the window caption that may be used as a window identifier.
flags (int) – Flags of the window. The supported flags are: (cv::WindowFlags)

Return type:

None

cv2.norm(src1[, normType[, mask]]) → retval¶

Calculates an absolute difference norm or a relative difference norm.

This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes.

As example for one array consider the function \(r(x)= \begin{pmatrix} x \\ 1-x \end{pmatrix}, x \in [-1;1]\). The \( L_{1}, L_{2} \) and \( L_{\infty} \) norm for the sample value \(r(-1) = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\) is calculated as follows \f{align*} | r(-1) |{L_1} &= |-1| + |2| = 3 \ | r(-1) |{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \ | r(-1) |{L\infty} &= \max(|-1|,|2|) = 2 \f} and for \(r(0.5) = \begin{pmatrix} 0.5 \\ 0.5 \end{pmatrix}\) the calculation is \f{align*} | r(0.5) |{L_1} &= |0.5| + |0.5| = 1 \ | r(0.5) |{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \ | r(0.5) |{L\infty} &= \max(|0.5|,|0.5|) = 0.5. \f} The following graphic shows all values for the three norm functions \(\| r(x) \|_{L_1}, \| r(x) \|_{L_2}\) and \(\| r(x) \|_{L_\infty}\). It is notable that the \( L_{1} \) norm forms the upper and the \( L_{\infty} \) norm forms the lower border for the example function \( r(x) \). Graphs for the different norm functions from the above example

When the mask parameter is specified and it is not empty, the norm is

If normType is not specified, #NORM_L2 is used. calculated only over the region specified by the mask.

Multi-channel input arrays are treated as single-channel arrays, that is, the results for all channels are combined.

Hamming norms can only be calculated with CV_8U depth arrays.

This version of cv::norm calculates the absolute difference norm or the relative difference norm of arrays src1 and src2. The type of norm to calculate is specified using #NormTypes.

Parameters:

src1 (cv2.typing.MatLike) – first input array.
normType (int) – type of the norm (see #NormTypes).
mask (cv2.typing.MatLike | None) – optional operation mask; it must have the same size as src1 and CV_8UC1 type.
src2 – second input array of the same size and the same type as src1.

Return type:

cv2.normalize(src, dst[, alpha[, beta[, norm_type[, dtype[, mask]]]]]) → dst¶

Normalizes the norm or value range of an array.

The function cv::normalize normalizes scale and shift the input array elements so that

\[\begin{equation*}\| \texttt{dst} \| _{L_p}= \texttt{alpha}\end{equation*}\]

(where p=Inf, 1 or 2) when normType=NORM_INF, NORM_L1, or NORM_L2, respectively; or so that

\[\begin{equation*}\min _I \texttt{dst} (I)= \texttt{alpha} , \, \, \max _I \texttt{dst} (I)= \texttt{beta}\end{equation*}\]

when normType=NORM_MINMAX (for dense arrays only). The optional mask specifies a sub-array to be normalized. This means that the norm or min-n-max are calculated over the sub-array, and then this sub-array is modified to be normalized. If you want to only use the mask to calculate the norm or min-max but modify the whole array, you can use norm and Mat::convertTo.

In case of sparse matrices, only the non-zero values are analyzed and transformed. Because of this, the range transformation for sparse matrices is not allowed since it can shift the zero level.

Possible usage with some positive example data:

    vector<double> positiveData = { 2.0, 8.0, 10.0 };
    vector<double> normalizedData_l1, normalizedData_l2, normalizedData_inf, normalizedData_minmax;

    // Norm to probability (total count)
    // sum(numbers) = 20.0
    // 2.0      0.1     (2.0/20.0)
    // 8.0      0.4     (8.0/20.0)
    // 10.0     0.5     (10.0/20.0)
    normalize(positiveData, normalizedData_l1, 1.0, 0.0, NORM_L1);

    // Norm to unit vector: ||positiveData|| = 1.0
    // 2.0      0.15
    // 8.0      0.62
    // 10.0     0.77
    normalize(positiveData, normalizedData_l2, 1.0, 0.0, NORM_L2);

    // Norm to max element
    // 2.0      0.2     (2.0/10.0)
    // 8.0      0.8     (8.0/10.0)
    // 10.0     1.0     (10.0/10.0)
    normalize(positiveData, normalizedData_inf, 1.0, 0.0, NORM_INF);

    // Norm to range [0.0;1.0]
    // 2.0      0.0     (shift to left border)
    // 8.0      0.75    (6.0/8.0)
    // 10.0     1.0     (shift to right border)
    normalize(positiveData, normalizedData_minmax, 1.0, 0.0, NORM_MINMAX);

See also: norm, Mat::convertTo, SparseMat::convertTo

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike) – output array of the same size as src .
alpha (float) – norm value to normalize to or the lower range boundary in case of the rangenormalization.
beta (float) – upper range boundary in case of the range normalization; it is not used for the normnormalization.
norm_type (int) – normalization type (see cv::NormTypes).
dtype (int) – when negative, the output array has the same type as src; otherwise, it has the samenumber of channels as src and the depth =CV_MAT_DEPTH(dtype).
mask (cv2.typing.MatLike | None) – optional operation mask.

Return type:

cv2.typing.MatLike

cv2.patchNaNs(a[, val]) → a¶

Replaces NaNs by given number

Parameters:

a (cv2.typing.MatLike) – input/output matrix (CV_32F type).
val (float) – value to convert the NaNs

Return type:

cv2.typing.MatLike

cv2.pencilSketch(src[, dst1[, dst2[, sigma_s[, sigma_r[, shade_factor]]]]]) → dst1, dst2¶

Pencil-like non-photorealistic line drawing

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
dst1 (cv2.typing.MatLike | None) – Output 8-bit 1-channel image.
dst2 (cv2.typing.MatLike | None) – Output image with the same size and type as src.
sigma_s (float) –
sigma_r (float) –
shade_factor (float) –

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.perspectiveTransform(src, m[, dst]) → dst¶

Performs the perspective matrix transformation of vectors.

The function cv::perspectiveTransform transforms every element of src by treating it as a 2D or 3D vector, in the following way:

\[\begin{equation*}(x, y, z) \rightarrow (x'/w, y'/w, z'/w)\end{equation*}\]

where

\[\begin{equation*}(x', y', z', w') = \texttt{mat} \cdot \begin{bmatrix} x & y & z & 1 \end{bmatrix}\end{equation*}\]

and

\[\begin{equation*}w = \fork{w'}{if \(w' \ne 0\)}{\infty}{otherwise}\end{equation*}\]

Here a 3D vector transformation is shown. In case of a 2D vector transformation, the z component is omitted.

Note

The function transforms a sparse set of 2D or 3D vectors. If youwant to transform an image using perspective transformation, use warpPerspective . If you have an inverse problem, that is, you want to compute the most probable perspective transformation out of several pairs of corresponding points, you can use getPerspectiveTransform or findHomography .

See also: transform, warpPerspective, getPerspectiveTransform, findHomography

Parameters:

src (cv2.typing.MatLike) – input two-channel or three-channel floating-point array; eachelement is a 2D/3D vector to be transformed.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src.
m (cv2.typing.MatLike) – 3x3 or 4x4 floating-point transformation matrix.

Return type:

cv2.typing.MatLike

cv2.phase(x, y[, angle[, angleInDegrees]]) → angle¶

Calculates the rotation angle of 2D vectors.

The function cv::phase calculates the rotation angle of each 2D vector that is formed from the corresponding elements of x and y :

\[\begin{equation*}\texttt{angle} (I) = \texttt{atan2} ( \texttt{y} (I), \texttt{x} (I))\end{equation*}\]

The angle estimation accuracy is about 0.3 degrees. When x(I)=y(I)=0 , the corresponding angle(I) is set to 0.

Parameters:

x (cv2.typing.MatLike) – input floating-point array of x-coordinates of 2D vectors.
y (cv2.typing.MatLike) – input array of y-coordinates of 2D vectors; it must have thesame size and the same type as x.
angle (cv2.typing.MatLike | None) – output array of vector angles; it has the same size andsame type as x .
angleInDegrees (bool) – when true, the function calculates the angle indegrees, otherwise, they are measured in radians.

Return type:

cv2.typing.MatLike

cv2.phaseCorrelate(src1, src2[, window]) → retval, response¶

The function is used to detect translational shifts that occur between two images.

The operation takes advantage of the Fourier shift theorem for detecting the translational shift in the frequency domain. It can be used for fast image registration as well as motion estimation. For more information please see http://en.wikipedia.org/wiki/Phase_correlation

Calculates the cross-power spectrum of two supplied source arrays. The arrays are padded if needed with getOptimalDFTSize.

The function performs the following equations:

First it applies a Hanning window (see http://en.wikipedia.org/wiki/Hann_function) to each image to remove possible edge effects. This window is cached until the array size changes to speed up processing time.
Next it computes the forward DFTs of each source array:

\[\begin{equation*}\mathbf{G}_a = \mathcal{F}\{src_1\}, \; \mathbf{G}_b = \mathcal{F}\{src_2\}\end{equation*}\]

where \(\mathcal{F}\) is the forward DFT.

It then computes the cross-power spectrum of each frequency domain array:

\[\begin{equation*}R = \frac{ \mathbf{G}_a \mathbf{G}_b^*}{|\mathbf{G}_a \mathbf{G}_b^*|}\end{equation*}\]

Next the cross-correlation is converted back into the time domain via the inverse DFT:

\[\begin{equation*}r = \mathcal{F}^{-1}\{R\}\end{equation*}\]

Finally, it computes the peak location and computes a 5x5 weighted centroid around the peak to achieve sub-pixel accuracy.

\[\begin{equation*}(\Delta x, \Delta y) = \texttt{weightedCentroid} \{\arg \max_{(x, y)}\{r\}\}\end{equation*}\]

If non-zero, the response parameter is computed as the sum of the elements of r within the 5x5 centroid around the peak location. It is normalized to a maximum of 1 (meaning there is a single peak) and will be smaller when there are multiple peaks.

See also: dft, getOptimalDFTSize, idft, mulSpectrums createHanningWindow

Parameters:

src1 (cv2.typing.MatLike) – Source floating point array (CV_32FC1 or CV_64FC1)
src2 (cv2.typing.MatLike) – Source floating point array (CV_32FC1 or CV_64FC1)
window (cv2.typing.MatLike | None) – Floating point array with windowing coefficients to reduce edge effects (optional).
response – Signal power within the 5x5 centroid around the peak, between 0 and 1 (optional).

Returns:

detected phase shift (sub-pixel) between the two arrays.

Return type:

tuple[cv2.typing.Point2d, float]

cv2.pointPolygonTest(contour, pt, measureDist) → retval¶

Performs a point-in-contour test.

The function determines whether the point is inside a contour, outside, or lies on an edge (or coincides with a vertex). It returns positive (inside), negative (outside), or zero (on an edge) value, correspondingly. When measureDist=false , the return value is +1, -1, and 0, respectively. Otherwise, the return value is a signed distance between the point and the nearest contour edge.

See below a sample output of the function where each image pixel is tested against the contour:

sample output

Parameters:

contour (cv2.typing.MatLike) – Input contour.
pt (cv2.typing.Point2f) – Point tested against the contour.
measureDist (bool) – If true, the function estimates the signed distance from the point to thenearest contour edge. Otherwise, the function only checks if the point is inside a contour or not.

Return type:

cv2.polarToCart(magnitude, angle[, x[, y[, angleInDegrees]]]) → x, y¶

Calculates x and y coordinates of 2D vectors from their magnitude and angle.

The function cv::polarToCart calculates the Cartesian coordinates of each 2D vector represented by the corresponding elements of magnitude and angle:

\[\begin{equation*}\begin{array}{l} \texttt{x} (I) = \texttt{magnitude} (I) \cos ( \texttt{angle} (I)) \\ \texttt{y} (I) = \texttt{magnitude} (I) \sin ( \texttt{angle} (I)) \\ \end{array}\end{equation*}\]

The relative accuracy of the estimated coordinates is about 1e-6.

See also: cartToPolar, magnitude, phase, exp, log, pow, sqrt

Parameters:

magnitude (cv2.typing.MatLike) – input floating-point array of magnitudes of 2D vectors;it can be an empty matrix (=Mat()), in this case, the function assumes that all the magnitudes are =1; if it is not empty, it must have the same size and type as angle.
angle (cv2.typing.MatLike) – input floating-point array of angles of 2D vectors.
x (cv2.typing.MatLike | None) – output array of x-coordinates of 2D vectors; it has the samesize and type as angle.
y (cv2.typing.MatLike | None) – output array of y-coordinates of 2D vectors; it has the samesize and type as angle.
angleInDegrees (bool) – when true, the input angles are measured indegrees, otherwise, they are measured in radians.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.pollKey() → retval¶

Polls for a pressed key.

The function pollKey polls for a key event without waiting. It returns the code of the pressed key or -1 if no key was pressed since the last invocation. To wait until a key was pressed, use #waitKey.

Note

The functions #waitKey and #pollKey are the only methods in HighGUI that can fetch and handleGUI events, so one of them needs to be called periodically for normal event processing unless HighGUI is used within an environment that takes care of event processing.

Note

The function only works if there is at least one HighGUI window created and the window isactive. If there are several HighGUI windows, any of them can be active.

Return type:: int

cv2.polylines(img, pts, isClosed, color[, thickness[, lineType[, shift]]]) → img¶

Draws several polygonal curves.

The function cv::polylines draws one or more polygonal curves.

Parameters:

img (cv2.typing.MatLike) – Image.
pts (_typing.Sequence[cv2.typing.MatLike]) – Array of polygonal curves.
isClosed (bool) – Flag indicating whether the drawn polylines are closed or not. If they are closed,the function draws a line from the last vertex of each curve to its first vertex.
color (cv2.typing.Scalar) – Polyline color.
thickness (int) – Thickness of the polyline edges.
lineType (int) – Type of the line segments. See #LineTypes
shift (int) – Number of fractional bits in the vertex coordinates.

Return type:

cv2.typing.MatLike

cv2.pow(src, power[, dst]) → dst¶

Raises every array element to a power.

The function cv::pow raises every element of the input array to power :

\[\begin{equation*}\texttt{dst} (I) = \fork{\texttt{src}(I)^{power}}{if \(\texttt{power}\) is integer}{|\texttt{src}(I)|^{power}}{otherwise}\end{equation*}\]

So, for a non-integer power exponent, the absolute values of input array elements are used. However, it is possible to get true values for negative values using some extra operations. In the example below, computing the 5th root of array src shows:

    Mat mask = src < 0;
    pow(src, 1./5, dst);
    subtract(Scalar::all(0), dst, dst, mask);

For some values of power, such as integer values, 0.5 and -0.5, specialized faster algorithms are used.

Special values (NaN, Inf) are not handled.

See also: sqrt, exp, log, cartToPolar, polarToCart

Parameters:

src (cv2.typing.MatLike) – input array.
power (float) – exponent of power.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src.

Return type:

cv2.typing.MatLike

cv2.preCornerDetect(src, ksize[, dst[, borderType]]) → dst¶

Calculates a feature map for corner detection.

The function calculates the complex spatial derivative-based function of the source image

\[\begin{equation*}\texttt{dst} = (D_x \texttt{src} )^2 \cdot D_{yy} \texttt{src} + (D_y \texttt{src} )^2 \cdot D_{xx} \texttt{src} - 2 D_x \texttt{src} \cdot D_y \texttt{src} \cdot D_{xy} \texttt{src}\end{equation*}\]

where \(D_x\),\(D_y\) are the first image derivatives, \(D_{xx}\),\(D_{yy}\) are the second image derivatives, and \(D_{xy}\) is the mixed derivative.

The corners can be found as local maximums of the functions, as shown below:

    Mat corners, dilated_corners;
    preCornerDetect(image, corners, 3);
    // dilation with 3x3 rectangular structuring element
    dilate(corners, dilated_corners, Mat(), 1);
    Mat corner_mask = corners == dilated_corners;

Parameters:

src (cv2.typing.MatLike) – Source single-channel 8-bit of floating-point image.
dst (cv2.typing.MatLike | None) – Output image that has the type CV_32F and the same size as src .
ksize (int) –
borderType (int) – Pixel extrapolation method. See #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs[, imagePoints[, jacobian[, aspectRatio]]]) → imagePoints, jacobian¶

Projects 3D points to an image plane.

The function computes the 2D projections of 3D points to the image plane, given intrinsic and extrinsic camera parameters. Optionally, the function computes Jacobians -matrices of partial derivatives of image points coordinates (as functions of all the input parameters) with respect to the particular parameters, intrinsic and/or extrinsic. The Jacobians are used during the global optimization in @ref calibrateCamera, @ref solvePnP, and @ref stereoCalibrate. The function itself can also be used to compute a re-projection error, given the current intrinsic and extrinsic parameters.

Note

By setting rvec = tvec = \([0, 0, 0]\), or by setting cameraMatrix to a 3x3 identity matrix,or by passing zero distortion coefficients, one can get various useful partial cases of the function. This means, one can compute the distorted coordinates for a sparse set of points or apply a perspective transformation (and also compute the derivatives) in the ideal zero-distortion setup.

Parameters:

objectPoints (cv2.typing.MatLike) – Array of object points expressed wrt. the world coordinate frame. A 3xN/Nx31-channel or 1xN/Nx1 3-channel (or vector<Point3f> ), where N is the number of points in the view.
rvec (cv2.typing.MatLike) – The rotation vector (@ref Rodrigues) that, together with tvec, performs a change ofbasis from world to camera coordinate system, see @ref calibrateCamera for details.
tvec (cv2.typing.MatLike) – The translation vector, see parameter description above.
cameraMatrix (cv2.typing.MatLike) – Camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\) . If the vector is empty, the zero distortion coefficients are assumed.
imagePoints (cv2.typing.MatLike | None) – Output array of image points, 1xN/Nx1 2-channel, orvector<Point2f> .
jacobian (cv2.typing.MatLike | None) – Optional output 2Nx(10+<numDistCoeffs>) jacobian matrix of derivatives of imagepoints with respect to components of the rotation vector, translation vector, focal lengths, coordinates of the principal point and the distortion coefficients. In the old interface different components of the jacobian are returned via different output parameters.
aspectRatio (float) – Optional “fixed aspect ratio” parameter. If the parameter is not 0, thefunction assumes that the aspect ratio (\(f_x / f_y\)) is fixed and correspondingly adjusts the jacobian matrix.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.putText(img, text, org, fontFace, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) → img¶

Draws a text string.

The function cv::putText renders the specified text string in the image. Symbols that cannot be rendered using the specified font are replaced by question marks. See #getTextSize for a text rendering code example.

Parameters:

img (cv2.typing.MatLike) – Image.
text (str) – Text string to be drawn.
org (cv2.typing.Point) – Bottom-left corner of the text string in the image.
fontFace (int) – Font type, see #HersheyFonts.
fontScale (float) – Font scale factor that is multiplied by the font-specific base size.
color (cv2.typing.Scalar) – Text color.
thickness (int) – Thickness of the lines used to draw a text.
lineType (int) – Line type. See #LineTypes
bottomLeftOrigin (bool) – When true, the image data origin is at the bottom-left corner. Otherwise,it is at the top-left corner.

Return type:

cv2.typing.MatLike

cv2.pyrDown(src[, dst[, dstsize[, borderType]]]) → dst¶

Blurs an image and downsamples it.

By default, size of the output image is computed as Size((src.cols+1)/2, (src.rows+1)/2), but in any case, the following conditions should be satisfied:

\[\begin{equation*}\begin{array}{l} | \texttt{dstsize.width} *2-src.cols| \leq 2 \\ | \texttt{dstsize.height} *2-src.rows| \leq 2 \end{array}\end{equation*}\]

The function performs the downsampling step of the Gaussian pyramid construction. First, it convolves the source image with the kernel:

\[\begin{equation*}\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}\end{equation*}\]

Then, it downsamples the image by rejecting even rows and columns.

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image; it has the specified size and the same type as src.
dstsize (cv2.typing.Size) – size of the output image.
borderType (int) – Pixel extrapolation method, see #BorderTypes (#BORDER_CONSTANT isn’t supported)

Return type:

cv2.typing.MatLike

cv2.pyrMeanShiftFiltering(src, sp, sr[, dst[, maxLevel[, termcrit]]]) → dst¶

Performs initial step of meanshift segmentation of an image.

The function implements the filtering stage of meanshift segmentation, that is, the output of the function is the filtered “posterized” image with color gradients and fine-grain texture flattened. At every pixel (X,Y) of the input image (or down-sized input image, see below) the function executes meanshift iterations, that is, the pixel (X,Y) neighborhood in the joint space-color hyperspace is considered:

\[\begin{equation*}(x,y): X- \texttt{sp} \le x \le X+ \texttt{sp} , Y- \texttt{sp} \le y \le Y+ \texttt{sp} , ||(R,G,B)-(r,g,b)|| \le \texttt{sr}\end{equation*}\]

where (R,G,B) and (r,g,b) are the vectors of color components at (X,Y) and (x,y), respectively (though, the algorithm does not depend on the color space used, so any 3-component color space can be used instead). Over the neighborhood the average spatial value (X’,Y’) and average color vector (R’,G’,B’) are found and they act as the neighborhood center on the next iteration:

\[\begin{equation*}(X,Y)~(X',Y'), (R,G,B)~(R',G',B').\end{equation*}\]

After the iterations over, the color components of the initial pixel (that is, the pixel from where the iterations started) are set to the final value (average color at the last iteration):

\[\begin{equation*}I(X,Y) <- (R*,G*,B*)\end{equation*}\]

When maxLevel > 0, the gaussian pyramid of maxLevel+1 levels is built, and the above procedure is run on the smallest layer first. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. That makes boundaries of color regions sharper. Note that the results will be actually different from the ones obtained by running the meanshift procedure on the whole original image (i.e. when maxLevel==0).

Parameters:

src (cv2.typing.MatLike) – The source 8-bit, 3-channel image.
dst (cv2.typing.MatLike | None) – The destination image of the same format and the same size as the source.
sp (float) – The spatial window radius.
sr (float) – The color window radius.
maxLevel (int) – Maximum level of the pyramid for the segmentation.
termcrit (cv2.typing.TermCriteria) – Termination criteria: when to stop meanshift iterations.

Return type:

cv2.typing.MatLike

cv2.pyrUp(src[, dst[, dstsize[, borderType]]]) → dst¶

Upsamples an image and then blurs it.

By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied:

\[\begin{equation*}\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\end{equation*}\]

The function performs the upsampling step of the Gaussian pyramid construction, though it can actually be used to construct the Laplacian pyramid. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in pyrDown multiplied by 4.

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image. It has the specified size and the same type as src .
dstsize (cv2.typing.Size) – size of the output image.
borderType (int) – Pixel extrapolation method, see #BorderTypes (only #BORDER_DEFAULT is supported)

Return type:

cv2.typing.MatLike

cv2.randShuffle(dst[, iterFactor]) → dst¶

Shuffles the array elements randomly.

The function cv::randShuffle shuffles the specified 1D array by randomly choosing pairs of elements and swapping them. The number of such swap operations will be dst.rows*dst.cols*iterFactor .

See also: RNG, sort

Parameters:

dst (cv2.typing.MatLike) – input/output numerical 1D array.
iterFactor (float) – scale factor that determines the number of random swap operations (see the detailsbelow).
rng – optional random number generator used for shuffling; if it is zero, theRNG () is usedinstead.

Return type:

cv2.typing.MatLike

cv2.randn(dst, mean, stddev) → dst¶

Fills the array with normally distributed random numbers.

The function cv::randn fills the matrix dst with normally distributed random numbers with the specified mean vector and the standard deviation matrix. The generated random numbers are clipped to fit the value range of the output array data type.

See also: RNG, randu

Parameters:

dst (cv2.typing.MatLike) – output array of random numbers; the array must be pre-allocated and have 1 to 4 channels.
mean (cv2.typing.MatLike) – mean value (expectation) of the generated random numbers.
stddev (cv2.typing.MatLike) – standard deviation of the generated random numbers; it can be either a vector (inwhich case a diagonal standard deviation matrix is assumed) or a square matrix.

Return type:

cv2.typing.MatLike

cv2.randu(dst, low, high) → dst¶

Generates a single uniformly-distributed random number or an array of random numbers.

Non-template variant of the function fills the matrix dst with uniformly-distributed random numbers from the specified range:

\[\begin{equation*}\texttt{low} _c \leq \texttt{dst} (I)_c < \texttt{high} _c\end{equation*}\]

See also: RNG, randn, theRNG

Parameters:

dst (cv2.typing.MatLike) – output array of random numbers; the array must be pre-allocated.
low (cv2.typing.MatLike) – inclusive lower boundary of the generated random numbers.
high (cv2.typing.MatLike) – exclusive upper boundary of the generated random numbers.

Return type:

cv2.typing.MatLike

cv2.readOpticalFlow(path) → retval¶

Read a .flo file

@param path Path to the file to be loaded

The function readOpticalFlow loads a flow field from a file and returns it as a single matrix. Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the flow in the horizontal direction (u), second - vertical (v).

Parameters:: path (str) –
Return type:: cv2.typing.MatLike

cv2.recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2[, E[, R[, t[, method[, prob[, threshold[, mask]]]]]]]) → retval, E, R, t, mask¶

Recovers the relative camera rotation and the translation from an estimated essentialmatrix and the corresponding points in two images, using chirality check. Returns the number of inliers that pass the check.

This function decomposes an essential matrix using @ref decomposeEssentialMat and then verifies possible pose hypotheses by doing cheirality check. The cheirality check means that the triangulated 3D points should have positive depth. Some details can be found in @cite Nister03.

This function can be used to process the output E and mask from @ref findEssentialMat. In this scenario, points1 and points2 are the same input for findEssentialMat.:

    // Example. Estimation of fundamental matrix using the RANSAC algorithm
    int point_count = 100;
    vector<Point2f> points1(point_count);
    vector<Point2f> points2(point_count);

    // initialize the points here ...
    for( int i = 0; i < point_count; i++ )
    {
        points1[i] = ...;
        points2[i] = ...;
    }

    // Input: camera calibration of both cameras, for example using intrinsic chessboard calibration.
    Mat cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2;

    // Output: Essential matrix, relative rotation and relative translation.
    Mat E, R, t, mask;

    recoverPose(points1, points2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, E, R, t, mask);

This function decomposes an essential matrix using @ref decomposeEssentialMat and then verifies possible pose hypotheses by doing chirality check. The chirality check means that the triangulated 3D points should have positive depth. Some details can be found in @cite Nister03.

This function can be used to process the output E and mask from @ref findEssentialMat. In this scenario, points1 and points2 are the same input for #findEssentialMat :

    // Example. Estimation of fundamental matrix using the RANSAC algorithm
    int point_count = 100;
    vector<Point2f> points1(point_count);
    vector<Point2f> points2(point_count);

    // initialize the points here ...
    for( int i = 0; i < point_count; i++ )
    {
        points1[i] = ...;
        points2[i] = ...;
    }

    // cametra matrix with both focal lengths = 1, and principal point = (0, 0)
    Mat cameraMatrix = Mat::eye(3, 3, CV_64F);

    Mat E, R, t, mask;

    E = findEssentialMat(points1, points2, cameraMatrix, RANSAC, 0.999, 1.0, mask);
    recoverPose(E, points1, points2, cameraMatrix, R, t, mask);

@overload

This function differs from the one above that it computes camera intrinsic matrix from focal length and principal point:

\[\begin{equation*}A = \begin{bmatrix} f & 0 & x_{pp} \\ 0 & f & y_{pp} \\ 0 & 0 & 1 \end{bmatrix}\end{equation*}\]

@overload

This function differs from the one above that it outputs the triangulated 3D point that are used for the chirality check.

Parameters:

points1 (cv2.typing.MatLike) – Array of N 2D points from the first image. The point coordinates should befloating-point (single or double precision).
points2 (cv2.typing.MatLike) – Array of the second image points of the same size and format as points1.
cameraMatrix1 (cv2.typing.MatLike) – Input/output camera matrix for the first camera, the same as in@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1 (cv2.typing.MatLike) – Input/output vector of distortion coefficients, the same as in@ref calibrateCamera.
cameraMatrix2 (cv2.typing.MatLike) – Input/output camera matrix for the first camera, the same as in@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs2 (cv2.typing.MatLike) – Input/output vector of distortion coefficients, the same as in@ref calibrateCamera.
E (cv2.typing.MatLike | None) – The input essential matrix.
R (cv2.typing.MatLike | None) – Output rotation matrix. Together with the translation vector, this matrix makes up a tuplethat performs a change of basis from the first camera’s coordinate system to the second camera’s coordinate system. Note that, in general, t can not be used for this tuple, see the parameter description below.
t (cv2.typing.MatLike | None) – Output translation vector. This vector is obtained by @ref decomposeEssentialMat andtherefore is only known up to scale, i.e. t is the direction of the translation vector and has unit length.
method – Method for computing an essential matrix.- @ref RANSAC for the RANSAC algorithm.

@ref LMEDS for the LMedS algorithm.

Parameters:

prob (float) – Parameter used for the RANSAC or LMedS methods only. It specifies a desirable level ofconfidence (probability) that the estimated matrix is correct.
threshold (float) – Parameter used for RANSAC. It is the maximum distance from a point to an epipolarline in pixels, beyond which the point is considered an outlier and is not used for computing the final fundamental matrix. It can be set to something like 1-3, depending on the accuracy of the point localization, image resolution, and the image noise.
mask (cv2.typing.MatLike | None) – Input/output mask for inliers in points1 and points2. If it is not empty, then it marksinliers in points1 and points2 for the given essential matrix E. Only these inliers will be used to recover pose. In the output mask only inliers which pass the chirality check.
cameraMatrix – Camera intrinsic matrix \(\cameramatrix{A}\) .Note that this function assumes that points1 and points2 are feature points from cameras with the same camera intrinsic matrix.
focal – Focal length of the camera. Note that this function assumes that points1 and points2are feature points from cameras with same focal length and principal point.
pp – principal point of the camera.
distanceThresh – threshold distance which is used to filter out far away points (i.e. infinitepoints).
triangulatedPoints – 3D points which were reconstructed by triangulation.

Return type:

tuple[int, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) → img¶

Draws a simple, thick, or filled up-right rectangle.

The function cv::rectangle draws a rectangle outline or a filled rectangle whose two opposite corners are pt1 and pt2.

use rec parameter as alternative specification of the drawn rectangle: r.tl() and r.br()-Point(1,1) are opposite corners

Parameters:

img (cv2.typing.MatLike) – Image.
pt1 (cv2.typing.Point) – Vertex of the rectangle.
pt2 (cv2.typing.Point) – Vertex of the rectangle opposite to pt1 .
color (cv2.typing.Scalar) – Rectangle color or brightness (grayscale image).
thickness (int) – Thickness of lines that make up the rectangle. Negative values, like #FILLED,mean that the function has to draw a filled rectangle.
lineType (int) – Type of the line. See #LineTypes
shift (int) – Number of fractional bits in the point coordinates.@overload

Return type:

cv2.typing.MatLike

cv2.rectangleIntersectionArea(a, b) → retval¶

Finds out if there is any intersection between two rectangles *

mainly useful for language bindings
@param a First rectangle
@param b Second rectangle
@return the area of the intersection

Parameters:

a (cv2.typing.Rect2d) –
b (cv2.typing.Rect2d) –

Return type:

cv2.rectify3Collinear(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, cameraMatrix3, distCoeffs3, imgpt1, imgpt3, imageSize, R12, T12, R13, T13, alpha, newImgSize, flags[, R1[, R2[, R3[, P1[, P2[, P3[, Q]]]]]]]) → retval, R1, R2, R3, P1, P2, P3, Q, roi1, roi2¶

Parameters:

cameraMatrix1 (cv2.typing.MatLike) –
distCoeffs1 (cv2.typing.MatLike) –
cameraMatrix2 (cv2.typing.MatLike) –
distCoeffs2 (cv2.typing.MatLike) –
cameraMatrix3 (cv2.typing.MatLike) –
distCoeffs3 (cv2.typing.MatLike) –
imgpt1 (_typing.Sequence[cv2.typing.MatLike]) –
imgpt3 (_typing.Sequence[cv2.typing.MatLike]) –
imageSize (cv2.typing.Size) –
R12 (cv2.typing.MatLike) –
T12 (cv2.typing.MatLike) –
R13 (cv2.typing.MatLike) –
T13 (cv2.typing.MatLike) –
alpha (float) –
newImgSize (cv2.typing.Size) –
flags (int) –
R1 (cv2.typing.MatLike | None) –
R2 (cv2.typing.MatLike | None) –
R3 (cv2.typing.MatLike | None) –
P1 (cv2.typing.MatLike | None) –
P2 (cv2.typing.MatLike | None) –
P3 (cv2.typing.MatLike | None) –
Q (cv2.typing.MatLike | None) –

Return type:

tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.Rect, cv2.typing.Rect]

cv2.redirectError(onError) → None¶

Parameters:: onError (_typing.Callable[[int, str, str, str, int], None] | None) –
Return type:: None

cv2.reduce(src, dim, rtype[, dst[, dtype]]) → dst¶

Reduces a matrix to a vector.

The function #reduce reduces the matrix to a vector by treating the matrix rows/columns as a set of 1D vectors and performing the specified operation on the vectors until a single row/column is obtained. For example, the function can be used to compute horizontal and vertical projections of a raster image. In case of #REDUCE_MAX and #REDUCE_MIN , the output image should have the same type as the source one. In case of #REDUCE_SUM, #REDUCE_SUM2 and #REDUCE_AVG , the output may have a larger element bit-depth to preserve accuracy. And multi-channel arrays are also supported in these two reduction modes.

The following code demonstrates its usage for a single channel matrix. @snippet snippets/core_reduce.cpp example

And the following code demonstrates its usage for a two-channel matrix. @snippet snippets/core_reduce.cpp example2

See also: repeat, reduceArgMin, reduceArgMax

Parameters:

src (cv2.typing.MatLike) – input 2D matrix.
dst (cv2.typing.MatLike | None) – output vector. Its size and type is defined by dim and dtype parameters.
dim (int) – dimension index along which the matrix is reduced. 0 means that the matrix is reduced toa single row. 1 means that the matrix is reduced to a single column.
rtype (int) – reduction operation that could be one of #ReduceTypes
dtype (int) – when negative, the output vector will have the same type as the input matrix,otherwise, its type will be CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()).

Return type:

cv2.typing.MatLike

cv2.reduceArgMax(src, axis[, dst[, lastIndex]]) → dst¶

@brief Finds indices of max elements along provided axis
@note

 - If input or output array is not continuous, this function will create an internal copy.

 - NaN handling is left unspecified, see patchNaNs().

 - The returned index is always in bounds of input matrix.

@param src input single-channel array.
@param dst output array of type CV_32SC1 with the same dimensionality as src,
except for axis being reduced - it should be set to 1.
@param lastIndex whether to get the index of first or last occurrence of max.
@param axis axis to reduce along.
@sa reduceArgMin, minMaxLoc, min, max, compare, reduce

Parameters:

src (cv2.typing.MatLike) –
axis (int) –
dst (cv2.typing.MatLike | None) –
lastIndex (bool) –

Return type:

cv2.typing.MatLike

cv2.reduceArgMin(src, axis[, dst[, lastIndex]]) → dst¶

@brief Finds indices of min elements along provided axis
@note

 - If input or output array is not continuous, this function will create an internal copy.

 - NaN handling is left unspecified, see patchNaNs().

 - The returned index is always in bounds of input matrix.

@param src input single-channel array.
@param dst output array of type CV_32SC1 with the same dimensionality as src,
except for axis being reduced - it should be set to 1.
@param lastIndex whether to get the index of first or last occurrence of min.
@param axis axis to reduce along.
@sa reduceArgMax, minMaxLoc, min, max, compare, reduce

Parameters:

src (cv2.typing.MatLike) –
axis (int) –
dst (cv2.typing.MatLike | None) –
lastIndex (bool) –

Return type:

cv2.typing.MatLike

cv2.remap(src, map1, map2, interpolation[, dst[, borderMode[, borderValue]]]) → dst¶

Applies a generic geometrical transformation to an image.

The function remap transforms the source image using the specified map:

\[\begin{equation*}\texttt{dst} (x,y) = \texttt{src} (map_x(x,y),map_y(x,y))\end{equation*}\]

where values of pixels with non-integer coordinates are computed using one of available interpolation methods. \(map_x\) and \(map_y\) can be encoded as separate floating-point maps in \(map_1\) and \(map_2\) respectively, or interleaved floating-point maps of \((x,y)\) in \(map_1\), or fixed-point maps created by using #convertMaps. The reason you might want to convert from floating to fixed-point representations of a map is that they can yield much faster (~2x) remapping operations. In the converted case, \(map_1\) contains pairs (cvFloor(x), cvFloor(y)) and \(map_2\) contains indices in a table of interpolation coefficients.

This function cannot operate in-place.

Parameters:

src (cv2.typing.MatLike) – Source image.
dst (cv2.typing.MatLike | None) – Destination image. It has the same size as map1 and the same type as src .
map1 (cv2.typing.MatLike) – The first map of either (x,y) points or just x values having the type CV_16SC2 ,CV_32FC1, or CV_32FC2. See #convertMaps for details on converting a floating point representation to fixed-point for speed.
map2 (cv2.typing.MatLike) – The second map of y values having the type CV_16UC1, CV_32FC1, or none (empty mapif map1 is (x,y) points), respectively.
interpolation (int) – Interpolation method (see #InterpolationFlags). The methods #INTER_AREAand #INTER_LINEAR_EXACT are not supported by this function.
borderMode (int) – Pixel extrapolation method (see #BorderTypes). WhenborderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image that corresponds to the “outliers” in the source image are not modified by the function.
borderValue (cv2.typing.Scalar) – Value used in case of a constant border. By default, it is 0.@note Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

Return type:

cv2.typing.MatLike

cv2.repeat(src, ny, nx[, dst]) → dst¶

Fills the output array with repeated copies of the input array.

The function cv::repeat duplicates the input array one or more times along each of the two axes:

\[\begin{equation*}\texttt{dst} _{ij}= \texttt{src} _{i\mod src.rows, \; j\mod src.cols }\end{equation*}\]

The second variant of the function is more convenient to use with @ref MatrixExpressions.

See also: cv::reduce

Parameters:

src (cv2.typing.MatLike) – input array to replicate.
ny (int) – Flag to specify how many times the src is repeated along thevertical axis.
nx (int) – Flag to specify how many times the src is repeated along thehorizontal axis.
dst (cv2.typing.MatLike | None) – output array of the same type as src.

Return type:

cv2.typing.MatLike

cv2.reprojectImageTo3D(disparity, Q[, _3dImage[, handleMissingValues[, ddepth]]]) → _3dImage¶

Reprojects a disparity image to 3D space.

The function transforms a single-channel disparity map to a 3-channel image representing a 3D surface. That is, for each pixel (x,y) and the corresponding disparity d=disparity(x,y) , it computes:

\[\begin{equation*}\begin{bmatrix} X \\ Y \\ Z \\ W \end{bmatrix} = Q \begin{bmatrix} x \\ y \\ \texttt{disparity} (x,y) \\ z \end{bmatrix}.\end{equation*}\]

@sa To reproject a sparse set of points {(x,y,d),…} to 3D space, use perspectiveTransform.

Parameters:

disparity (cv2.typing.MatLike) – Input single-channel 8-bit unsigned, 16-bit signed, 32-bit signed or 32-bitfloating-point disparity image. The values of 8-bit / 16-bit signed formats are assumed to have no fractional bits. If the disparity is 16-bit signed format, as computed by @ref StereoBM or @ref StereoSGBM and maybe other algorithms, it should be divided by 16 (and scaled to float) before being used here.
_3dImage (cv2.typing.MatLike | None) – Output 3-channel floating-point image of the same size as disparity. Each element of_3dImage(x,y) contains 3D coordinates of the point (x,y) computed from the disparity map. If one uses Q obtained by @ref stereoRectify, then the returned points are represented in the first camera’s rectified coordinate system.
Q (cv2.typing.MatLike) – \(4 \times 4\) perspective transformation matrix that can be obtained with@ref stereoRectify.
handleMissingValues (bool) – Indicates, whether the function should handle missing values (i.e.points where the disparity was not computed). If handleMissingValues=true, then pixels with the minimal disparity that corresponds to the outliers (see StereoMatcher::compute ) are transformed to 3D points with a very large Z value (currently set to 10000).
ddepth (int) – The optional output array depth. If it is -1, the output image will have CV_32Fdepth. ddepth can also be set to CV_16S, CV_32S or CV_32F.

Return type:

cv2.typing.MatLike

cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) → dst¶

Resizes an image.

The function resize resizes the image src down to or up to the specified size. Note that the initial dst type or size are not taken into account. Instead, the size and type are derived from the src,dsize,fx, and fy. If you want to resize src so that it fits the pre-created dst, you may call the function as follows:

    // explicitly specify dsize=dst.size(); fx and fy will be computed from that.
    resize(src, dst, dst.size(), 0, 0, interpolation);

If you want to decimate the image by factor of 2 in each direction, you can call the function this way:

    // specify fx and fy and let the function compute the destination image size.
    resize(src, dst, Size(), 0.5, 0.5, interpolation);

To shrink an image, it will generally look best with #INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with #INTER_CUBIC (slow) or #INTER_LINEAR (faster but still looks OK).

See also: warpAffine, warpPerspective, remap

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image; it has the size dsize (when it is non-zero) or the size computed fromsrc.size(), fx, and fy; the type of dst is the same as of src.
dsize (cv2.typing.Size | None) – output image size; if it equals zero (None in Python), it is computed as: \begin{equation*}\texttt{dsize = Size(round(fxsrc.cols), round(fysrc.rows))}\end{equation*} Either dsize or both fx and fy must be non-zero.
fx (float) – scale factor along the horizontal axis; when it equals 0, it is computed as\begin{equation*}\texttt{(double)dsize.width/src.cols}\end{equation*}
fy (float) – scale factor along the vertical axis; when it equals 0, it is computed as\begin{equation*}\texttt{(double)dsize.height/src.rows}\end{equation*}
interpolation (int) – interpolation method, see #InterpolationFlags

Return type:

cv2.typing.MatLike

cv2.resizeWindow(winname, width, height) → None¶

Resizes the window to the specified size

Note

The specified window size is for the image area. Toolbars are not counted.Only windows created without cv::WINDOW_AUTOSIZE flag can be resized.

Parameters:

winname (str) – Window name.
width (int) – The new window width.
height (int) – The new window height.@overload
size – The new window size.

Return type:

None

cv2.rotate(src, rotateCode[, dst]) → dst¶

Rotates a 2D array in multiples of 90 degrees.The function cv::rotate rotates the array in one of three different ways:

Rotate by 90 degrees clockwise (rotateCode = ROTATE_90_CLOCKWISE).
Rotate by 180 degrees clockwise (rotateCode = ROTATE_180).
Rotate by 270 degrees clockwise (rotateCode = ROTATE_90_COUNTERCLOCKWISE).

See also: transpose , repeat , completeSymm, flip, RotateFlags

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike | None) – output array of the same type as src. The size is the same with ROTATE_180,and the rows and cols are switched for ROTATE_90_CLOCKWISE and ROTATE_90_COUNTERCLOCKWISE.
rotateCode (int) – an enum to specify how to rotate the array; see the enum #RotateFlags

Return type:

cv2.typing.MatLike

cv2.rotatedRectangleIntersection(rect1, rect2[, intersectingRegion]) → retval, intersectingRegion¶

Finds out if there is any intersection between two rotated rectangles.

If there is then the vertices of the intersecting region are returned as well.

Below are some examples of intersection configurations. The hatched pattern indicates the intersecting region and the red vertices are returned by the function.

intersection examples

Parameters:

rect1 (cv2.typing.RotatedRect) – First rectangle
rect2 (cv2.typing.RotatedRect) – Second rectangle
intersectingRegion (cv2.typing.MatLike | None) – The output array of the vertices of the intersecting region. It returnsat most 8 vertices. Stored as std::vector<cv::Point2f> or cv::Mat as Mx1 of type CV_32FC2.

Returns:

One of #RectanglesIntersectTypes

Return type:

tuple[int, cv2.typing.MatLike]

cv2.sampsonDistance(pt1, pt2, F) → retval¶

Calculates the Sampson Distance between two points.

The function cv::sampsonDistance calculates and returns the first order approximation of the geometric error as:

\[\begin{equation*} sd( \texttt{pt1} , \texttt{pt2} )= \frac{(\texttt{pt2}^t \cdot \texttt{F} \cdot \texttt{pt1})^2} {((\texttt{F} \cdot \texttt{pt1})(0))^2 + ((\texttt{F} \cdot \texttt{pt1})(1))^2 + ((\texttt{F}^t \cdot \texttt{pt2})(0))^2 + ((\texttt{F}^t \cdot \texttt{pt2})(1))^2} \end{equation*}\]

The fundamental matrix may be calculated using the #findFundamentalMat function. See @cite HartleyZ00 11.4.3 for details.

Parameters:

pt1 (cv2.typing.MatLike) – first homogeneous 2d point
pt2 (cv2.typing.MatLike) – second homogeneous 2d point
F (cv2.typing.MatLike) – fundamental matrix

Returns:

The computed Sampson distance.

Return type:

cv2.scaleAdd(src1, alpha, src2[, dst]) → dst¶

Calculates the sum of a scaled array and another array.

The function scaleAdd is one of the classical primitive linear algebra operations, known as DAXPY or SAXPY in BLAS. It calculates the sum of a scaled array and another array:

\[\begin{equation*}\texttt{dst} (I)= \texttt{scale} \cdot \texttt{src1} (I) + \texttt{src2} (I)\end{equation*}\]

The function can also be emulated with a matrix expression, for example:

    Mat A(3, 3, CV_64F);
    ...
    A.row(0) = A.row(1)*2 + A.row(2);

See also: add, addWeighted, subtract, Mat::dot, Mat::convertTo

Parameters:

src1 (cv2.typing.MatLike) – first input array.
alpha (float) – scale factor for the first array.
src2 (cv2.typing.MatLike) – second input array of the same size and type as src1.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src1.

Return type:

cv2.typing.MatLike

cv2.seamlessClone(src, dst, mask, p, flags[, blend]) → blend¶

Image editing tasks concern either global changes (color/intensity corrections, filters,deformations) or local changes concerned to a selection. Here we are interested in achieving local changes, ones that are restricted to a region manually selected (ROI), in a seamless and effortless manner. The extent of the changes ranges from slight distortions to complete replacement by novel content @cite PM03 .

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
dst (cv2.typing.MatLike) – Input 8-bit 3-channel image.
mask (cv2.typing.MatLike) – Input 8-bit 1 or 3-channel image.
p (cv2.typing.Point) – Point in dst image where object is placed.
blend (cv2.typing.MatLike | None) – Output image with the same size and type as dst.
flags (int) – Cloning method that could be cv::NORMAL_CLONE, cv::MIXED_CLONE or cv::MONOCHROME_TRANSFER

Return type:

cv2.typing.MatLike

cv2.selectROI(windowName, img[, showCrosshair[, fromCenter[, printNotice]]]) → retval¶

Allows users to select a ROI on the given image.

The function creates a window and allows users to select a ROI using the mouse. Controls: use space or enter to finish selection, use key c to cancel selection (function will return the zero cv::Rect).

Note

The function sets it’s own mouse callback for specified window using cv::setMouseCallback(windowName, …).After finish of work an empty callback will be set for the used window. @overload

Parameters:

windowName (str) – name of the window where selection process will be shown.
img (cv2.typing.MatLike) – image to select a ROI.
showCrosshair (bool) – if true crosshair of selection rectangle will be shown.
fromCenter (bool) – if true center of selection will match initial mouse position. In opposite case a corner ofselection rectangle will correspont to the initial mouse position.
printNotice (bool) – if true a notice to select ROI or cancel selection will be printed in console.

Returns:

selected ROI or empty rect if selection canceled.

Return type:

cv2.typing.Rect

cv2.selectROIs(windowName, img[, showCrosshair[, fromCenter[, printNotice]]]) → boundingBoxes¶

Allows users to select multiple ROIs on the given image.

The function creates a window and allows users to select multiple ROIs using the mouse. Controls: use space or enter to finish current selection and start a new one, use esc to terminate multiple ROI selection process.

Note

The function sets it’s own mouse callback for specified window using cv::setMouseCallback(windowName, …).After finish of work an empty callback will be set for the used window.

Parameters:

windowName (str) – name of the window where selection process will be shown.
img (cv2.typing.MatLike) – image to select a ROI.
boundingBoxes – selected ROIs.
showCrosshair (bool) – if true crosshair of selection rectangle will be shown.
fromCenter (bool) – if true center of selection will match initial mouse position. In opposite case a corner ofselection rectangle will correspont to the initial mouse position.
printNotice (bool) – if true a notice to select ROI or cancel selection will be printed in console.

Return type:

_typing.Sequence[cv2.typing.Rect]

cv2.sepFilter2D(src, ddepth, kernelX, kernelY[, dst[, anchor[, delta[, borderType]]]]) → dst¶

Applies a separable linear filter to an image.

The function applies a separable linear filter to the image. That is, first, every row of src is filtered with the 1D kernel kernelX. Then, every column of the result is filtered with the 1D kernel kernelY. The final result shifted by delta is stored in dst .

See also: filter2D, Sobel, GaussianBlur, boxFilter, blur

Parameters:

src (cv2.typing.MatLike) – Source image.
dst (cv2.typing.MatLike | None) – Destination image of the same size and the same number of channels as src .
ddepth (int) – Destination image depth, see @ref filter_depths “combinations”
kernelX (cv2.typing.MatLike) – Coefficients for filtering each row.
kernelY (cv2.typing.MatLike) – Coefficients for filtering each column.
anchor (cv2.typing.Point) – Anchor position within the kernel. The default value \((-1,-1)\) means that the anchoris at the kernel center.
delta (float) – Value added to the filtered results before storing them.
borderType (int) – Pixel extrapolation method, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.setIdentity(mtx[, s]) → mtx¶

Initializes a scaled identity matrix.

The function cv::setIdentity initializes a scaled identity matrix:

\[\begin{equation*}\texttt{mtx} (i,j)= \fork{\texttt{value}}{ if \(i=j\)}{0}{otherwise}\end{equation*}\]

The function can also be emulated using the matrix initializers and the matrix expressions:

    Mat A = Mat::eye(4, 3, CV_32F)*5;
    // A will be set to [[5, 0, 0], [0, 5, 0], [0, 0, 5], [0, 0, 0]]

See also: Mat::zeros, Mat::ones, Mat::setTo, Mat::operator=

Parameters:

mtx (cv2.typing.MatLike) – matrix to initialize (not necessarily square).
s (cv2.typing.Scalar) – value to assign to diagonal elements.

Return type:

cv2.typing.MatLike

cv2.setLogLevel(level) → retval¶

Parameters:: level (int) –
Return type:: int

cv2.setMouseCallback(windowName, onMouse[, param]) → None¶

Parameters:

windowName (str) –
onMouse (_typing.Callable[[int, int, int, int, _typing.Any | None], None]) –
param (_typing.Any | None) –

Return type:

None

cv2.setNumThreads(nthreads) → None¶

OpenCV will try to set the number of threads for subsequent parallel regions.

If threads == 1, OpenCV will disable threading optimizations and run all it’s functions sequentially. Passing threads < 0 will reset threads number to system default. The function is not thread-safe. It must not be called in parallel region or concurrent threads.

OpenCV will try to run its functions with specified threads number, but some behaviour differs from framework:

TBB - User-defined parallel constructions will run with the same threads number, if another is not specified. If later on user creates his own scheduler, OpenCV will use it.
OpenMP - No special defined behaviour.
Concurrency - If threads == 1, OpenCV will disable threading optimizations and run its functions sequentially.
GCD - Supports only values <= 0.
C= - No special defined behaviour.

See also: getNumThreads, getThreadNum

Parameters:: nthreads (int) – Number of threads used by OpenCV.
Return type:: None

cv2.setRNGSeed(seed) → None¶

Sets state of default random number generator.

The function cv::setRNGSeed sets state of default random number generator to custom value.

See also: RNG, randu, randn

Parameters:: seed (int) – new state for default random number generator
Return type:: None

cv2.setTrackbarMax(trackbarname, winname, maxval) → None¶

Sets the trackbar maximum position.

The function sets the maximum position of the specified trackbar in the specified window.

Note

[Qt Backend Only] winname can be empty if the trackbar is attached to the controlpanel.

Parameters:

trackbarname (str) – Name of the trackbar.
winname (str) – Name of the window that is the parent of trackbar.
maxval (int) – New maximum position.

Return type:

None

cv2.setTrackbarMin(trackbarname, winname, minval) → None¶

Sets the trackbar minimum position.

The function sets the minimum position of the specified trackbar in the specified window.

Note

[Qt Backend Only] winname can be empty if the trackbar is attached to the controlpanel.

Parameters:

trackbarname (str) – Name of the trackbar.
winname (str) – Name of the window that is the parent of trackbar.
minval (int) – New minimum position.

Return type:

None

cv2.setTrackbarPos(trackbarname, winname, pos) → None¶

Sets the trackbar position.

The function sets the position of the specified trackbar in the specified window.

Note

[Qt Backend Only] winname can be empty if the trackbar is attached to the controlpanel.

Parameters:

trackbarname (str) – Name of the trackbar.
winname (str) – Name of the window that is the parent of trackbar.
pos (int) – New position.

Return type:

None

cv2.setUseOpenVX(flag) → None¶

Parameters:: flag (bool) –
Return type:: None

cv2.setUseOptimized(onoff) → None¶

Enables or disables the optimized code.

The function can be used to dynamically turn on and off optimized dispatched code (code that uses SSE4.2, AVX/AVX2, and other instructions on the platforms that support it). It sets a global flag that is further checked by OpenCV functions. Since the flag is not checked in the inner OpenCV loops, it is only safe to call the function on the very top level in your application where you can be sure that no other OpenCV function is currently executed.

By default, the optimized code is enabled unless you disable it in CMake. The current status can be retrieved using useOptimized.

Parameters:: onoff (bool) – The boolean flag specifying whether the optimized code should be used (onoff=true)or not (onoff=false).
Return type:: None

cv2.setWindowProperty(winname, prop_id, prop_value) → None¶

Changes parameters of a window dynamically.

The function setWindowProperty enables changing properties of a window.

Parameters:

winname (str) – Name of the window.
prop_id (int) – Window property to edit. The supported operation flags are: (cv::WindowPropertyFlags)
prop_value (float) – New value of the window property. The supported flags are: (cv::WindowFlags)

Return type:

None

cv2.setWindowTitle(winname, title) → None¶

Updates window title

Parameters:

winname (str) – Name of the window.
title (str) – New title.

Return type:

None

cv2.solve(src1, src2[, dst[, flags]]) → retval, dst¶

Solves one or more linear systems or least-squares problems.

The function cv::solve solves a linear system or least-squares problem (the latter is possible with SVD or QR methods, or by specifying the flag #DECOMP_NORMAL ):

\[\begin{equation*}\texttt{dst} = \arg \min _X \| \texttt{src1} \cdot \texttt{X} - \texttt{src2} \|\end{equation*}\]

If #DECOMP_LU or #DECOMP_CHOLESKY method is used, the function returns 1 if src1 (or \(\texttt{src1}^T\texttt{src1}\) ) is non-singular. Otherwise, it returns 0. In the latter case, dst is not valid. Other methods find a pseudo-solution in case of a singular left-hand side part.

Note

If you want to find a unity-norm solution of an under-definedsingular system \(\texttt{src1}\cdot\texttt{dst}=0\) , the function solve will not do the work. Use SVD::solveZ instead.

See also: invert, SVD, eigen

Parameters:

src1 (cv2.typing.MatLike) – input matrix on the left-hand side of the system.
src2 (cv2.typing.MatLike) – input matrix on the right-hand side of the system.
dst (cv2.typing.MatLike | None) – output solution.
flags (int) – solution (matrix inversion) method (#DecompTypes)

Return type:

tuple[bool, cv2.typing.MatLike]

cv2.solveCubic(coeffs[, roots]) → retval, roots¶

Finds the real roots of a cubic equation.

The function solveCubic finds the real roots of a cubic equation:

if coeffs is a 4-element vector:

\[\begin{equation*}\texttt{coeffs} [0] x^3 + \texttt{coeffs} [1] x^2 + \texttt{coeffs} [2] x + \texttt{coeffs} [3] = 0\end{equation*}\]

if coeffs is a 3-element vector:

\[\begin{equation*}x^3 + \texttt{coeffs} [0] x^2 + \texttt{coeffs} [1] x + \texttt{coeffs} [2] = 0\end{equation*}\]

The roots are stored in the roots array.

Parameters:

coeffs (cv2.typing.MatLike) – equation coefficients, an array of 3 or 4 elements.
roots (cv2.typing.MatLike | None) – output array of real roots that has 1 or 3 elements.

Returns:

number of real roots. It can be 0, 1 or 2.

Return type:

tuple[int, cv2.typing.MatLike]

cv2.solveLP(Func, Constr, constr_eps[, z]) → retval, z¶

Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method).

What we mean here by “linear programming problem” (or LP problem, for short) can be formulated as:

\[\begin{equation*}\mbox{Maximize } c\cdot x\\ \mbox{Subject to:}\\ Ax\leq b\\ x\geq 0\end{equation*}\]

Where \(c\) is fixed 1-by-n row-vector, \(A\) is fixed m-by-n matrix, \(b\) is fixed m-by-1 column vector and \(x\) is an arbitrary n-by-1 column vector, which satisfies the constraints.

Simplex algorithm is one of many algorithms that are designed to handle this sort of problems efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve any problem written as above in polynomial time, while simplex method degenerates to exponential time for some special cases), it is well-studied, easy to implement and is shown to work well for real-life purposes.

The particular implementation is taken almost verbatim from Introduction to Algorithms, third edition by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the Bland’s rule http://en.wikipedia.org/wiki/Bland’s_rule is used to prevent cycling.

Parameters:

Func (cv2.typing.MatLike) – This row-vector corresponds to \(c\) in the LP problem formulation (see above). It shouldcontain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, in the latter case it is understood to correspond to \(c^T\).
Constr (cv2.typing.MatLike) – m-by-n+1 matrix, whose rightmost column corresponds to \(b\) in formulation aboveand the remaining to \(A\). It should contain 32- or 64-bit floating point numbers.
z (cv2.typing.MatLike | None) – The solution will be returned here as a column-vector - it corresponds to \(c\) in theformulation above. It will contain 64-bit floating point numbers.
constr_eps (float) – allowed numeric disparity for constraints

Returns:

One of cv::SolveLPResult@overload

Return type:

tuple[int, cv2.typing.MatLike]

cv2.solveP3P(objectPoints, imagePoints, cameraMatrix, distCoeffs, flags[, rvecs[, tvecs]]) → retval, rvecs, tvecs¶

Finds an object pose from 3 3D-2D point correspondences.

The function estimates the object pose given 3 object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients.

@note The solutions are sorted by reprojection errors (lowest to highest).

See also: @ref calib3d_solvePnP

Parameters:

objectPoints (cv2.typing.MatLike) – Array of object points in the object coordinate space, 3x3 1-channel or1x3/3x1 3-channel. vector<Point3f> can be also passed here.
imagePoints (cv2.typing.MatLike) – Array of corresponding image points, 3x2 1-channel or 1x3/3x1 2-channel. vector<Point2f> can be also passed here.
cameraMatrix (cv2.typing.MatLike) – Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Output rotation vectors (see @ref Rodrigues ) that, together with tvecs, brings points fromthe model coordinate system to the camera coordinate system. A P3P problem has up to 4 solutions.
tvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Output translation vectors.
flags – Method for solving a P3P problem:- @ref SOLVEPNP_P3P Method is based on the paper of X.S. Gao, X.-R. Hou, J. Tang, H.-F. Chang “Complete Solution Classification for the Perspective-Three-Point Problem” (@cite gao2003complete).

@ref SOLVEPNP_AP3P Method is based on the paper of T. Ke and S. Roumeliotis. “An Efficient Algebraic Solution to the Perspective-Three-Point Problem” (@cite Ke17).

Return type:: tuple[int, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike]]

cv2.solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]]) → retval, rvec, tvec¶

Finds an object pose from 3D-2D point correspondences.

This function returns the rotation and the translation vectors that transform a 3D point expressed in the object coordinate frame to the camera coordinate frame, using different methods:

P3P methods (@ref SOLVEPNP_P3P, @ref SOLVEPNP_AP3P): need 4 input points to return a unique solution.
@ref SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar.
@ref SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
for all the other flags, number of input points must be >= 4 and object points can be in any configuration.

More information about Perspective-n-Points is described in @ref calib3d_solvePnP

@note

An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
If you are using Python:
- Numpy array slices won’t work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
- The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of #undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
- Thus, given some data D = np.array(…) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
The methods @ref SOLVEPNP_DLS and @ref SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, @ref SOLVEPNP_EPNP method will be used instead.
The minimum number of points is 4 in the general case. In the case of @ref SOLVEPNP_P3P and @ref SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
With @ref SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
With @ref SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
With @ref SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]

-  With @ref SOLVEPNP_SQPNP input points must be >= 3

See also: @ref calib3d_solvePnP

Parameters:

objectPoints (cv2.typing.MatLike) – Array of object points in the object coordinate space, Nx3 1-channel or1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints (cv2.typing.MatLike) – Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix (cv2.typing.MatLike) – Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec (cv2.typing.MatLike | None) – Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points fromthe model coordinate system to the camera coordinate system.
tvec (cv2.typing.MatLike | None) – Output translation vector.
useExtrinsicGuess (bool) – Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function usesthe provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags (int) – Method for solving a PnP problem: see @ref calib3d_solvePnP_flags

Return type:

tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.solvePnPGeneric(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvecs[, tvecs[, useExtrinsicGuess[, flags[, rvec[, tvec[, reprojectionError]]]]]]]) → retval, rvecs, tvecs, reprojectionError¶

Finds an object pose from 3D-2D point correspondences.

This function returns a list of all the possible solutions (a solution is a <rotation vector, translation vector> couple), depending on the number of input points and the chosen method:

P3P methods (@ref SOLVEPNP_P3P, @ref SOLVEPNP_AP3P): 3 or 4 input points. Number of returned solutions can be between 0 and 4 with 3 input points.
@ref SOLVEPNP_IPPE Input points must be >= 4 and object points must be coplanar. Returns 2 solutions.
@ref SOLVEPNP_IPPE_SQUARE Special case suitable for marker pose estimation. Number of input points must be 4 and 2 solutions are returned. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]
for all the other flags, number of input points must be >= 4 and object points can be in any configuration. Only 1 solution is returned.

More information is described in @ref calib3d_solvePnP

@note

An example of how to use solvePnP for planar augmented reality can be found at opencv_source_code/samples/python/plane_ar.py
If you are using Python:
- Numpy array slices won’t work as input because solvePnP requires contiguous arrays (enforced by the assertion using cv::Mat::checkVector() around line 55 of modules/calib3d/src/solvepnp.cpp version 2.4.9)
- The P3P algorithm requires image points to be in an array of shape (N,1,2) due to its calling of #undistortPoints (around line 75 of modules/calib3d/src/solvepnp.cpp version 2.4.9) which requires 2-channel information.
- Thus, given some data D = np.array(…) where D.shape = (N,M), in order to use a subset of it as, e.g., imagePoints, one must effectively copy it into a new array: imagePoints = np.ascontiguousarray(D[:,:2]).reshape((N,1,2))
The methods @ref SOLVEPNP_DLS and @ref SOLVEPNP_UPNP cannot be used as the current implementations are unstable and sometimes give completely wrong results. If you pass one of these two flags, @ref SOLVEPNP_EPNP method will be used instead.
The minimum number of points is 4 in the general case. In the case of @ref SOLVEPNP_P3P and @ref SOLVEPNP_AP3P methods, it is required to use exactly 4 points (the first 3 points are used to estimate all the solutions of the P3P problem, the last one is used to retain the best solution that minimizes the reprojection error).
With @ref SOLVEPNP_ITERATIVE method and useExtrinsicGuess=true, the minimum number of points is 3 (3 points are sufficient to compute a pose but there are up to 4 solutions). The initial solution should be close to the global solution to converge.
With @ref SOLVEPNP_IPPE input points must be >= 4 and object points must be coplanar.
With @ref SOLVEPNP_IPPE_SQUARE this is a special case suitable for marker pose estimation. Number of input points must be 4. Object points must be defined in the following order:
- point 0: [-squareLength / 2, squareLength / 2, 0]
- point 1: [ squareLength / 2, squareLength / 2, 0]
- point 2: [ squareLength / 2, -squareLength / 2, 0]
- point 3: [-squareLength / 2, -squareLength / 2, 0]

See also: @ref calib3d_solvePnP

Parameters:

objectPoints (cv2.typing.MatLike) – Array of object points in the object coordinate space, Nx3 1-channel or1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints (cv2.typing.MatLike) – Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix (cv2.typing.MatLike) – Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Vector of output rotation vectors (see @ref Rodrigues ) that, together with tvecs, brings points fromthe model coordinate system to the camera coordinate system.
tvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Vector of output translation vectors.
useExtrinsicGuess (bool) – Parameter used for #SOLVEPNP_ITERATIVE. If true (1), the function usesthe provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
flags (SolvePnPMethod) – Method for solving a PnP problem: see @ref calib3d_solvePnP_flags
rvec (cv2.typing.MatLike | None) – Rotation vector used to initialize an iterative PnP refinement algorithm, when flag is @ref SOLVEPNP_ITERATIVEand useExtrinsicGuess is set to true.
tvec (cv2.typing.MatLike | None) – Translation vector used to initialize an iterative PnP refinement algorithm, when flag is @ref SOLVEPNP_ITERATIVEand useExtrinsicGuess is set to true.
reprojectionError (cv2.typing.MatLike | None) – Optional vector of reprojection error, that is the RMS error(\( \text{RMSE} = \sqrt{\frac{\sum_{i}^{N} \left ( \hat{y_i} - y_i \right )^2}{N}} \)) between the input image points and the 3D object points projected with the estimated pose.

Return type:

tuple[int, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]

cv2.solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, iterationsCount[, reprojectionError[, confidence[, inliers[, flags]]]]]]]]) → retval, rvec, tvec, inliers¶

Finds an object pose from 3D-2D point correspondences using the RANSAC scheme.

The function estimates an object pose given a set of object points, their corresponding image projections, as well as the camera intrinsic matrix and the distortion coefficients. This function finds such a pose that minimizes reprojection error, that is, the sum of squared distances between the observed projections imagePoints and the projected (using @ref projectPoints ) objectPoints. The use of RANSAC makes the function resistant to outliers.

@note

An example of how to use solvePNPRansac for object detection can be found at opencv_source_code/samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/
The default method used to estimate the camera pose for the Minimal Sample Sets step is #SOLVEPNP_EPNP. Exceptions are:
- if you choose #SOLVEPNP_P3P or #SOLVEPNP_AP3P, these methods will be used.
- if the number of input points is equal to 4, #SOLVEPNP_P3P is used.
The method used to estimate the camera pose using all the inliers is defined by the flags parameters unless it is equal to #SOLVEPNP_P3P or #SOLVEPNP_AP3P. In this case, the method #SOLVEPNP_EPNP will be used instead.

See also: @ref calib3d_solvePnP

Parameters:

objectPoints (cv2.typing.MatLike) – Array of object points in the object coordinate space, Nx3 1-channel or1xN/Nx1 3-channel, where N is the number of points. vector<Point3d> can be also passed here.
imagePoints (cv2.typing.MatLike) – Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,where N is the number of points. vector<Point2d> can be also passed here.
cameraMatrix (cv2.typing.MatLike) – Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec (cv2.typing.MatLike | None) – Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points fromthe model coordinate system to the camera coordinate system.
tvec (cv2.typing.MatLike | None) – Output translation vector.
useExtrinsicGuess (bool) – Parameter used for @ref SOLVEPNP_ITERATIVE. If true (1), the function usesthe provided rvec and tvec values as initial approximations of the rotation and translation vectors, respectively, and further optimizes them.
iterationsCount (int) – Number of iterations.
reprojectionError (float) – Inlier threshold value used by the RANSAC procedure. The parameter valueis the maximum allowed distance between the observed and computed point projections to consider it an inlier.
confidence (float) – The probability that the algorithm produces a useful result.
inliers (cv2.typing.MatLike | None) – Output vector that contains indices of inliers in objectPoints and imagePoints .
flags (int) – Method for solving a PnP problem (see @ref solvePnP ).

Return type:

tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.solvePnPRefineLM(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec[, criteria]) → rvec, tvec¶

Refine a pose (the translation and the rotation that transform a 3D point expressed in the object coordinate frameto the camera coordinate frame) from a 3D-2D point correspondences and starting from an initial solution.

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, according to a Levenberg-Marquardt iterative minimization @cite Madsen04 @cite Eade13 process.

See also: @ref calib3d_solvePnP

Parameters:

objectPoints (cv2.typing.MatLike) – Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel,where N is the number of points. vector<Point3d> can also be passed here.
imagePoints (cv2.typing.MatLike) – Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix (cv2.typing.MatLike) – Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec (cv2.typing.MatLike) – Input/Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points fromthe model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec (cv2.typing.MatLike) – Input/Output translation vector. Input values are used as an initial solution.
criteria (cv2.typing.TermCriteria) – Criteria when to stop the Levenberg-Marquard iterative algorithm.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.solvePnPRefineVVS(objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, tvec[, criteria[, VVSlambda]]) → rvec, tvec¶

The function refines the object pose given at least 3 object points, their corresponding image projections, an initial solution for the rotation and translation vector, as well as the camera intrinsic matrix and the distortion coefficients. The function minimizes the projection error with respect to the rotation and the translation vectors, using a virtual visual servoing (VVS) @cite Chaumette06 @cite Marchand16 scheme.

See also: @ref calib3d_solvePnP

Parameters:

objectPoints (cv2.typing.MatLike) – Array of object points in the object coordinate space, Nx3 1-channel or 1xN/Nx1 3-channel,where N is the number of points. vector<Point3d> can also be passed here.
imagePoints (cv2.typing.MatLike) – Array of corresponding image points, Nx2 1-channel or 1xN/Nx1 2-channel,where N is the number of points. vector<Point2d> can also be passed here.
cameraMatrix (cv2.typing.MatLike) – Input camera intrinsic matrix \(\cameramatrix{A}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\(\distcoeffs\). If the vector is NULL/empty, the zero distortion coefficients are assumed.
rvec (cv2.typing.MatLike) – Input/Output rotation vector (see @ref Rodrigues ) that, together with tvec, brings points fromthe model coordinate system to the camera coordinate system. Input values are used as an initial solution.
tvec (cv2.typing.MatLike) – Input/Output translation vector. Input values are used as an initial solution.
criteria (cv2.typing.TermCriteria) – Criteria when to stop the Levenberg-Marquard iterative algorithm.
VVSlambda (float) – Gain for the virtual visual servoing control law, equivalent to the \(\alpha\)gain in the Damped Gauss-Newton formulation.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.solvePoly(coeffs[, roots[, maxIters]]) → retval, roots¶

Finds the real or complex roots of a polynomial equation.

The function cv::solvePoly finds real and complex roots of a polynomial equation:

\[\begin{equation*}\texttt{coeffs} [n] x^{n} + \texttt{coeffs} [n-1] x^{n-1} + ... + \texttt{coeffs} [1] x + \texttt{coeffs} [0] = 0\end{equation*}\]

Parameters:

coeffs (cv2.typing.MatLike) – array of polynomial coefficients.
roots (cv2.typing.MatLike | None) – output (complex) array of roots.
maxIters (int) – maximum number of iterations the algorithm does.

Return type:

tuple[float, cv2.typing.MatLike]

cv2.sort(src, flags[, dst]) → dst¶

Sorts each row or each column of a matrix.

The function cv::sort sorts each matrix row or each matrix column in ascending or descending order. So you should pass two operation flags to get desired behaviour. If you want to sort matrix rows or columns lexicographically, you can use STL std::sort generic function with the proper comparison predicate.

See also: sortIdx, randShuffle

Parameters:

src (cv2.typing.MatLike) – input single-channel array.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src.
flags (int) – operation flags, a combination of #SortFlags

Return type:

cv2.typing.MatLike

cv2.sortIdx(src, flags[, dst]) → dst¶

Sorts each row or each column of a matrix.

The function cv::sortIdx sorts each matrix row or each matrix column in the ascending or descending order. So you should pass two operation flags to get desired behaviour. Instead of reordering the elements themselves, it stores the indices of sorted elements in the output array. For example:

    Mat A = Mat::eye(3,3,CV_32F), B;
    sortIdx(A, B, SORT_EVERY_ROW + SORT_ASCENDING);
    // B will probably contain
    // (because of equal elements in A some permutations are possible):
    // [[1, 2, 0], [0, 2, 1], [0, 1, 2]]

See also: sort, randShuffle

Parameters:

src (cv2.typing.MatLike) – input single-channel array.
dst (cv2.typing.MatLike | None) – output integer array of the same size as src.
flags (int) – operation flags that could be a combination of cv::SortFlags

Return type:

cv2.typing.MatLike

cv2.spatialGradient(src[, dx[, dy[, ksize[, borderType]]]]) → dx, dy¶

Calculates the first order image derivative in both x and y using a Sobel operator

Equivalent to calling:

Sobel( src, dx, CV_16SC1, 1, 0, 3 );
Sobel( src, dy, CV_16SC1, 0, 1, 3 );

See also: Sobel

Parameters:

src (cv2.typing.MatLike) – input image.
dx (cv2.typing.MatLike | None) – output image with first-order derivative in x.
dy (cv2.typing.MatLike | None) – output image with first-order derivative in y.
ksize (int) – size of Sobel kernel. It must be 3.
borderType (int) – pixel extrapolation method, see #BorderTypes. Only #BORDER_DEFAULT=#BORDER_REFLECT_101 and #BORDER_REPLICATE are supported.

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike]

cv2.split(m[, mv]) → mv¶

@overload

Parameters:

m (cv2.typing.MatLike) – input multi-channel array.
mv (_typing.Sequence[cv2.typing.MatLike] | None) – output vector of arrays; the arrays themselves are reallocated, if needed.

Return type:

_typing.Sequence[cv2.typing.MatLike]

cv2.sqrBoxFilter(src, ddepth, ksize[, dst[, anchor[, normalize[, borderType]]]]) → dst¶

Calculates the normalized sum of squares of the pixel values overlapping the filter.

For every pixel \( (x, y) \) in the source image, the function calculates the sum of squares of those neighboring pixel values which overlap the filter placed over the pixel \( (x, y) \).

The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel.

See also: boxFilter

Parameters:

src (cv2.typing.MatLike) – input image
dst (cv2.typing.MatLike | None) – output image of the same size and type as src
ddepth (int) – the output image depth (-1 to use src.depth())
ksize (cv2.typing.Size) – kernel size
anchor (cv2.typing.Point) – kernel anchor point. The default value of Point(-1, -1) denotes that the anchor is at the kernelcenter.
normalize (bool) – flag, specifying whether the kernel is to be normalized by it’s area or not.
borderType (int) – border mode used to extrapolate pixels outside of the image, see #BorderTypes. #BORDER_WRAP is not supported.

Return type:

cv2.typing.MatLike

cv2.sqrt(src[, dst]) → dst¶

Calculates a square root of array elements.

The function cv::sqrt calculates a square root of each input array element. In case of multi-channel arrays, each channel is processed independently. The accuracy is approximately the same as of the built-in std::sqrt .

Parameters:

src (cv2.typing.MatLike) – input floating-point array.
dst (cv2.typing.MatLike | None) – output array of the same size and type as src.

Return type:

cv2.typing.MatLike

cv2.stackBlur(src, ksize[, dst]) → dst¶

Blurs an image using the stackBlur.

The function applies and stackBlur to an image. stackBlur can generate similar results as Gaussian blur, and the time consumption does not increase with the increase of kernel size. It creates a kind of moving stack of colors whilst scanning through the image. Thereby it just has to add one new block of color to the right side of the stack and remove the leftmost color. The remaining colors on the topmost layer of the stack are either added on or reduced by one, depending on if they are on the right or on the left side of the stack. The only supported borderType is BORDER_REPLICATE. Original paper was proposed by Mario Klingemann, which can be found http://underdestruction.com/2004/02/25/stackblur-2004.

Parameters:

src (cv2.typing.MatLike) – input image. The number of channels can be arbitrary, but the depth should be one ofCV_8U, CV_16U, CV_16S or CV_32F.
dst (cv2.typing.MatLike | None) – output image of the same size and type as src.
ksize (cv2.typing.Size) – stack-blurring kernel size. The ksize.width and ksize.height can differ but they both must bepositive and odd.

Return type:

cv2.typing.MatLike

cv2.startWindowThread() → retval¶

Return type:: int

cv2.stereoCalibrate(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize[, R[, T[, E[, F[, flags[, criteria]]]]]]) → retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F¶

Parameters:

objectPoints (_typing.Sequence[cv2.typing.MatLike]) –
imagePoints1 (_typing.Sequence[cv2.typing.MatLike]) –
imagePoints2 (_typing.Sequence[cv2.typing.MatLike]) –
cameraMatrix1 (cv2.typing.MatLike) –
distCoeffs1 (cv2.typing.MatLike) –
cameraMatrix2 (cv2.typing.MatLike) –
distCoeffs2 (cv2.typing.MatLike) –
imageSize (cv2.typing.Size) –
R (cv2.typing.MatLike | None) –
T (cv2.typing.MatLike | None) –
E (cv2.typing.MatLike | None) –
F (cv2.typing.MatLike | None) –
flags (int) –
criteria (cv2.typing.TermCriteria) –

Return type:

tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.stereoCalibrateExtended(objectPoints, imagePoints1, imagePoints2, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T[, E[, F[, rvecs[, tvecs[, perViewErrors[, flags[, criteria]]]]]]]) → retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F, rvecs, tvecs, perViewErrors¶

Calibrates a stereo camera set up. This function finds the intrinsic parametersfor each of the two cameras and the extrinsic parameters between the two cameras.

The function estimates the transformation between two cameras making a stereo pair. If one computes the poses of an object relative to the first camera and to the second camera, ( \(R_1\),\(T_1\) ) and (\(R_2\),\(T_2\)), respectively, for a stereo camera where the relative position and orientation between the two cameras are fixed, then those poses definitely relate to each other. This means, if the relative position and orientation (\(R\),\(T\)) of the two cameras is known, it is possible to compute (\(R_2\),\(T_2\)) when (\(R_1\),\(T_1\)) is given. This is what the described function does. It computes (\(R\),\(T\)) such that:

\[\begin{equation*}R_2=R R_1\end{equation*}\]

\[\begin{equation*}T_2=R T_1 + T.\end{equation*}\]

Therefore, one can compute the coordinate representation of a 3D point for the second camera’s coordinate system when given the point’s coordinate representation in the first camera’s coordinate system:

\[\begin{equation*}\begin{bmatrix} X_2 \\ Y_2 \\ Z_2 \\ 1 \end{bmatrix} = \begin{bmatrix} R & T \\ 0 & 1 \end{bmatrix} \begin{bmatrix} X_1 \\ Y_1 \\ Z_1 \\ 1 \end{bmatrix}.\end{equation*}\]

Optionally, it computes the essential matrix E:

\[\begin{equation*}E= \vecthreethree{0}{-T_2}{T_1}{T_2}{0}{-T_0}{-T_1}{T_0}{0} R\end{equation*}\]

where \(T_i\) are components of the translation vector \(T\) : \(T=[T_0, T_1, T_2]^T\) . And the function can also compute the fundamental matrix F:

\[\begin{equation*}F = cameraMatrix2^{-T}\cdot E \cdot cameraMatrix1^{-1}\end{equation*}\]

Besides the stereo-related information, the function can also perform a full calibration of each of the two cameras. However, due to the high dimensionality of the parameter space and noise in the input data, the function can diverge from the correct solution. If the intrinsic parameters can be estimated with high accuracy for each of the cameras individually (for example, using #calibrateCamera ), you are recommended to do so and then pass @ref CALIB_FIX_INTRINSIC flag to the function along with the computed intrinsic parameters. Otherwise, if all the parameters are estimated at once, it makes sense to restrict some parameters, for example, pass @ref CALIB_SAME_FOCAL_LENGTH and @ref CALIB_ZERO_TANGENT_DIST flags, which is usually a reasonable assumption.

Similarly to #calibrateCamera, the function minimizes the total re-projection error for all the points in all the available views from both cameras. The function returns the final value of the re-projection error.

Parameters:

objectPoints (_typing.Sequence[cv2.typing.MatLike]) – Vector of vectors of the calibration pattern points. The same structure asin @ref calibrateCamera. For each pattern view, both cameras need to see the same object points. Therefore, objectPoints.size(), imagePoints1.size(), and imagePoints2.size() need to be equal as well as objectPoints[i].size(), imagePoints1[i].size(), and imagePoints2[i].size() need to be equal for each i.
imagePoints1 (_typing.Sequence[cv2.typing.MatLike]) – Vector of vectors of the projections of the calibration pattern points,observed by the first camera. The same structure as in @ref calibrateCamera.
imagePoints2 (_typing.Sequence[cv2.typing.MatLike]) – Vector of vectors of the projections of the calibration pattern points,observed by the second camera. The same structure as in @ref calibrateCamera.
cameraMatrix1 (cv2.typing.MatLike) – Input/output camera intrinsic matrix for the first camera, the same as in@ref calibrateCamera. Furthermore, for the stereo case, additional flags may be used, see below.
distCoeffs1 (cv2.typing.MatLike) – Input/output vector of distortion coefficients, the same as in@ref calibrateCamera.
cameraMatrix2 (cv2.typing.MatLike) – Input/output second camera intrinsic matrix for the second camera. See description forcameraMatrix1.
distCoeffs2 (cv2.typing.MatLike) – Input/output lens distortion coefficients for the second camera. Seedescription for distCoeffs1.
imageSize (cv2.typing.Size) – Size of the image used only to initialize the camera intrinsic matrices.
R (cv2.typing.MatLike) – Output rotation matrix. Together with the translation vector T, this matrix bringspoints given in the first camera’s coordinate system to points in the second camera’s coordinate system. In more technical terms, the tuple of R and T performs a change of basis from the first camera’s coordinate system to the second camera’s coordinate system. Due to its duality, this tuple is equivalent to the position of the first camera with respect to the second camera coordinate system.
T (cv2.typing.MatLike) – Output translation vector, see description above.
E (cv2.typing.MatLike | None) – Output essential matrix.
F (cv2.typing.MatLike | None) – Output fundamental matrix.
rvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Output vector of rotation vectors ( @ref Rodrigues ) estimated for each pattern view in thecoordinate system of the first camera of the stereo pair (e.g. std::vectorcv::Mat). More in detail, each i-th rotation vector together with the corresponding i-th translation vector (see the next output parameter description) brings the calibration pattern from the object coordinate space (in which object points are specified) to the camera coordinate space of the first camera of the stereo pair. In more technical terms, the tuple of the i-th rotation and translation vector performs a change of basis from object coordinate space to camera coordinate space of the first camera of the stereo pair.
tvecs (_typing.Sequence[cv2.typing.MatLike] | None) – Output vector of translation vectors estimated for each pattern view, see parameter descriptionof previous output parameter ( rvecs ).
perViewErrors (cv2.typing.MatLike | None) – Output vector of the RMS re-projection error estimated for each pattern view.
flags – Different flags that may be zero or a combination of the following values:- @ref CALIB_FIX_INTRINSIC Fix cameraMatrix? and distCoeffs? so that only R, T, E, and F matrices are estimated.

@ref CALIB_USE_INTRINSIC_GUESS Optimize some or all of the intrinsic parameters according to the specified flags. Initial values are provided by the user.
@ref CALIB_USE_EXTRINSIC_GUESS R and T contain valid initial values that are optimized further. Otherwise R and T are initialized to the median value of the pattern views (each dimension separately).
@ref CALIB_FIX_PRINCIPAL_POINT Fix the principal points during the optimization.
@ref CALIB_FIX_FOCAL_LENGTH Fix \(f^{(j)}_x\) and \(f^{(j)}_y\) .
@ref CALIB_FIX_ASPECT_RATIO Optimize \(f^{(j)}_y\) . Fix the ratio \(f^{(j)}_x/f^{(j)}_y\) .
@ref CALIB_SAME_FOCAL_LENGTH Enforce \(f^{(0)}_x=f^{(1)}_x\) and \(f^{(0)}_y=f^{(1)}_y\) .
@ref CALIB_ZERO_TANGENT_DIST Set tangential distortion coefficients for each camera to zeros and fix there.
@ref CALIB_FIX_K1,…, @ref CALIB_FIX_K6 Do not change the corresponding radial distortion coefficient during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
@ref CALIB_RATIONAL_MODEL Enable coefficients k4, k5, and k6. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the rational model and return 8 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients.
@ref CALIB_THIN_PRISM_MODEL Coefficients s1, s2, s3 and s4 are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the thin prism model and return 12 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients.
@ref CALIB_FIX_S1_S2_S3_S4 The thin prism distortion coefficients are not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.
@ref CALIB_TILTED_MODEL Coefficients tauX and tauY are enabled. To provide the backward compatibility, this extra flag should be explicitly specified to make the calibration function use the tilted sensor model and return 14 coefficients. If the flag is not set, the function computes and returns only 5 distortion coefficients.
@ref CALIB_FIX_TAUX_TAUY The coefficients of the tilted sensor model are not changed during the optimization. If @ref CALIB_USE_INTRINSIC_GUESS is set, the coefficient from the supplied distCoeffs matrix is used. Otherwise, it is set to 0.

Parameters:: criteria (cv2.typing.TermCriteria) – Termination criteria for the iterative optimization algorithm.
Return type:: tuple[float, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, _typing.Sequence[cv2.typing.MatLike], _typing.Sequence[cv2.typing.MatLike], cv2.typing.MatLike]

cv2.stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T[, R1[, R2[, P1[, P2[, Q[, flags[, alpha[, newImageSize]]]]]]]]) → R1, R2, P1, P2, Q, validPixROI1, validPixROI2¶

Computes rectification transforms for each head of a calibrated stereo camera.

The function computes the rotation matrices for each camera that (virtually) make both camera image planes the same plane. Consequently, this makes all the epipolar lines parallel and thus simplifies the dense stereo correspondence problem. The function takes the matrices computed by #stereoCalibrate as input. As output, it provides two rotation matrices and also two projection matrices in the new coordinates. The function distinguishes the following two cases:

Horizontal stereo: the first and the second camera views are shifted relative to each other mainly along the x-axis (with possible small vertical shift). In the rectified images, the corresponding epipolar lines in the left and right cameras are horizontal and have the same y-coordinate. P1 and P2 look like:

\[\begin{equation*}\texttt{P1} = \begin{bmatrix} f & 0 & cx_1 & 0 \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\end{equation*}\]

\[\begin{equation*}\texttt{P2} = \begin{bmatrix} f & 0 & cx_2 & T_x \cdot f \\ 0 & f & cy & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix} ,\end{equation*}\]

\[\begin{equation*}\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx_1 \\ 0 & 1 & 0 & -cy \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_x} & \frac{cx_1 - cx_2}{T_x} \end{bmatrix} \end{equation*}\]

where \(T_x\) is a horizontal shift between the cameras and \(cx_1=cx_2\) if @ref CALIB_ZERO_DISPARITY is set.
Vertical stereo: the first and the second camera views are shifted relative to each other mainly in the vertical direction (and probably a bit in the horizontal direction too). The epipolar lines in the rectified images are vertical and have the same x-coordinate. P1 and P2 look like:

\[\begin{equation*}\texttt{P1} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_1 & 0 \\ 0 & 0 & 1 & 0 \end{bmatrix}\end{equation*}\]

\[\begin{equation*}\texttt{P2} = \begin{bmatrix} f & 0 & cx & 0 \\ 0 & f & cy_2 & T_y \cdot f \\ 0 & 0 & 1 & 0 \end{bmatrix},\end{equation*}\]

\[\begin{equation*}\texttt{Q} = \begin{bmatrix} 1 & 0 & 0 & -cx \\ 0 & 1 & 0 & -cy_1 \\ 0 & 0 & 0 & f \\ 0 & 0 & -\frac{1}{T_y} & \frac{cy_1 - cy_2}{T_y} \end{bmatrix} \end{equation*}\]

where \(T_y\) is a vertical shift between the cameras and \(cy_1=cy_2\) if @ref CALIB_ZERO_DISPARITY is set.

As you can see, the first three columns of P1 and P2 will effectively be the new “rectified” camera matrices. The matrices, together with R1 and R2 , can then be passed to #initUndistortRectifyMap to initialize the rectification map for each camera.

See below the screenshot from the stereo_calib.cpp sample. Some red horizontal lines pass through the corresponding image regions. This means that the images are well rectified, which is what most stereo correspondence algorithms rely on. The green rectangles are roi1 and roi2 . You see that their interiors are all valid pixels.

Parameters:

cameraMatrix1 (cv2.typing.MatLike) – First camera intrinsic matrix.
distCoeffs1 (cv2.typing.MatLike) – First camera distortion parameters.
cameraMatrix2 (cv2.typing.MatLike) – Second camera intrinsic matrix.
distCoeffs2 (cv2.typing.MatLike) – Second camera distortion parameters.
imageSize (cv2.typing.Size) – Size of the image used for stereo calibration.
R (cv2.typing.MatLike) – Rotation matrix from the coordinate system of the first camera to the second camera,see @ref stereoCalibrate.
T (cv2.typing.MatLike) – Translation vector from the coordinate system of the first camera to the second camera,see @ref stereoCalibrate.
R1 (cv2.typing.MatLike | None) – Output 3x3 rectification transform (rotation matrix) for the first camera. This matrixbrings points given in the unrectified first camera’s coordinate system to points in the rectified first camera’s coordinate system. In more technical terms, it performs a change of basis from the unrectified first camera’s coordinate system to the rectified first camera’s coordinate system.
R2 (cv2.typing.MatLike | None) – Output 3x3 rectification transform (rotation matrix) for the second camera. This matrixbrings points given in the unrectified second camera’s coordinate system to points in the rectified second camera’s coordinate system. In more technical terms, it performs a change of basis from the unrectified second camera’s coordinate system to the rectified second camera’s coordinate system.
P1 (cv2.typing.MatLike | None) – Output 3x4 projection matrix in the new (rectified) coordinate systems for the firstcamera, i.e. it projects points given in the rectified first camera coordinate system into the rectified first camera’s image.
P2 (cv2.typing.MatLike | None) – Output 3x4 projection matrix in the new (rectified) coordinate systems for the secondcamera, i.e. it projects points given in the rectified first camera coordinate system into the rectified second camera’s image.
Q (cv2.typing.MatLike | None) – Output \(4 \times 4\) disparity-to-depth mapping matrix (see @ref reprojectImageTo3D).
flags (int) – Operation flags that may be zero or @ref CALIB_ZERO_DISPARITY . If the flag is set,the function makes the principal points of each camera have the same pixel coordinates in the rectified views. And if the flag is not set, the function may still shift the images in the horizontal or vertical direction (depending on the orientation of epipolar lines) to maximize the useful image area.
alpha (float) – Free scaling parameter. If it is -1 or absent, the function performs the defaultscaling. Otherwise, the parameter should be between 0 and 1. alpha=0 means that the rectified images are zoomed and shifted so that only valid pixels are visible (no black areas after rectification). alpha=1 means that the rectified image is decimated and shifted so that all the pixels from the original images from the cameras are retained in the rectified images (no source image pixels are lost). Any intermediate value yields an intermediate result between those two extreme cases.
newImageSize (cv2.typing.Size) – New image resolution after rectification. The same size should be passed to#initUndistortRectifyMap (see the stereo_calib.cpp sample in OpenCV samples directory). When (0,0) is passed (default), it is set to the original imageSize . Setting it to a larger value can help you preserve details in the original image, especially when there is a big radial distortion.
validPixROI1 – Optional output rectangles inside the rectified images where all the pixelsare valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).
validPixROI2 – Optional output rectangles inside the rectified images where all the pixelsare valid. If alpha=0 , the ROIs cover the whole images. Otherwise, they are likely to be smaller (see the picture below).

Return type:

tuple[cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.MatLike, cv2.typing.Rect, cv2.typing.Rect]

cv2.stereoRectifyUncalibrated(points1, points2, F, imgSize[, H1[, H2[, threshold]]]) → retval, H1, H2¶

Computes a rectification transform for an uncalibrated stereo camera.

The function computes the rectification transformations without knowing intrinsic parameters of the cameras and their relative position in the space, which explains the suffix “uncalibrated”. Another related difference from #stereoRectify is that the function outputs not the rectification transformations in the object (3D) space, but the planar perspective transformations encoded by the homography matrices H1 and H2 . The function implements the algorithm @cite Hartley99 .

@note While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using #calibrateCamera . Then, the images can be corrected using #undistort , or just the point coordinates can be corrected with #undistortPoints .

Parameters:

points1 (cv2.typing.MatLike) – Array of feature points in the first image.
points2 (cv2.typing.MatLike) – The corresponding points in the second image. The same formats as in#findFundamentalMat are supported.
F (cv2.typing.MatLike) – Input fundamental matrix. It can be computed from the same set of point pairs using#findFundamentalMat .
imgSize (cv2.typing.Size) – Size of the image.
H1 (cv2.typing.MatLike | None) – Output rectification homography matrix for the first image.
H2 (cv2.typing.MatLike | None) – Output rectification homography matrix for the second image.
threshold (float) – Optional threshold used to filter out the outliers. If the parameter is greaterthan zero, all the point pairs that do not comply with the epipolar geometry (that is, the points for which \(|\texttt{points2[i]}^T \cdot \texttt{F} \cdot \texttt{points1[i]}|>\texttt{threshold}\) ) are rejected prior to computing the homographies. Otherwise, all the points are considered inliers.

Return type:

tuple[bool, cv2.typing.MatLike, cv2.typing.MatLike]

cv2.stylization(src[, dst[, sigma_s[, sigma_r]]]) → dst¶

Stylization aims to produce digital imagery with a wide variety of effects not focused onphotorealism. Edge-aware filters are ideal for stylization, as they can abstract regions of low contrast while preserving, or enhancing, high-contrast features.

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as src.
sigma_s (float) –
sigma_r (float) –

Return type:

cv2.typing.MatLike

cv2.subtract(src1, src2[, dst[, mask[, dtype]]]) → dst¶

Calculates the per-element difference between two arrays or array and a scalar.

The function subtract calculates:

Difference between two arrays, when both input arrays have the same size and the same number of channels:

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2}(I)) \quad \texttt{if mask}(I) \ne0\end{equation*}\]
Difference between an array and a scalar, when src2 is constructed from Scalar or has the same number of elements as src1.channels():

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1}(I) - \texttt{src2} ) \quad \texttt{if mask}(I) \ne0\end{equation*}\]
Difference between a scalar and an array, when src1 is constructed from Scalar or has the same number of elements as src2.channels():

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} ( \texttt{src1} - \texttt{src2}(I) ) \quad \texttt{if mask}(I) \ne0\end{equation*}\]
The reverse difference between a scalar and an array in the case of SubRS:

\[\begin{equation*}\texttt{dst}(I) = \texttt{saturate} ( \texttt{src2} - \texttt{src1}(I) ) \quad \texttt{if mask}(I) \ne0\end{equation*}\]

where I is a multi-dimensional index of array elements. In case of multi-channel arrays, each channel is processed independently.

The first function in the list above can be replaced with matrix expressions:

    dst = src1 - src2;
    dst -= src1; // equivalent to subtract(dst, src1, dst);

The input arrays and the output array can all have the same or different depths. For example, you can subtract to 8-bit unsigned arrays and store the difference in a 16-bit signed array. Depth of the output array is determined by dtype parameter. In the second and third cases above, as well as in the first case, when src1.depth() == src2.depth(), dtype can be set to the default -1. In this case the output array will have the same depth as the input array, be it src1, src2 or both.

Note

Saturation is not applied when the output array has the depth CV_32S. You may even getresult of an incorrect sign in the case of overflow.

Note

(Python) Be careful to difference behaviour between src1/src2 are single number and they are tuple/array.subtract(src,X) means subtract(src,(X,X,X,X)). subtract(src,(X,)) means subtract(src,(X,0,0,0)).

See also: add, addWeighted, scaleAdd, Mat::convertTo

Parameters:

src1 (cv2.typing.MatLike) – first input array or a scalar.
src2 (cv2.typing.MatLike) – second input array or a scalar.
dst (cv2.typing.MatLike | None) – output array of the same size and the same number of channels as the input array.
mask (cv2.typing.MatLike | None) – optional operation mask; this is an 8-bit single channel array that specifies elementsof the output array to be changed.
dtype (int) – optional depth of the output array

Return type:

cv2.typing.MatLike

cv2.sumElems(src) → retval¶

Calculates the sum of array elements.

The function cv::sum calculates and returns the sum of array elements, independently for each channel.

See also: countNonZero, mean, meanStdDev, norm, minMaxLoc, reduce

Parameters:: src (cv2.typing.MatLike) – input array that must have from 1 to 4 channels.
Return type:: cv2.typing.Scalar

cv2.textureFlattening(src, mask[, dst[, low_threshold[, high_threshold[, kernel_size]]]]) → dst¶

By retaining only the gradients at edge locations, before integrating with the Poisson solver, onewashes out the texture of the selected region, giving its contents a flat aspect. Here Canny Edge %Detector is used.

@note The algorithm assumes that the color of the source image is close to that of the destination. This assumption means that when the colors don’t match, the source image color gets tinted toward the color of the destination image.

Parameters:

src (cv2.typing.MatLike) – Input 8-bit 3-channel image.
mask (cv2.typing.MatLike) – Input 8-bit 1 or 3-channel image.
dst (cv2.typing.MatLike | None) – Output image with the same size and type as src.
low_threshold (float) –
high_threshold (float) – Value > 100.
kernel_size (int) – The size of the Sobel kernel to be used.

Return type:

cv2.typing.MatLike

cv2.threshold(src, thresh, maxval, type[, dst]) → retval, dst¶

Applies a fixed-level threshold to each array element.

The function applies fixed-level thresholding to a multiple-channel array. The function is typically used to get a bi-level (binary) image out of a grayscale image ( #compare could be also used for this purpose) or for removing a noise, that is, filtering out pixels with too small or too large values. There are several types of thresholding supported by the function. They are determined by type parameter.

Also, the special values #THRESH_OTSU or #THRESH_TRIANGLE may be combined with one of the above values. In these cases, the function determines the optimal threshold value using the Otsu’s or Triangle algorithm and uses it instead of the specified thresh.

Note

Currently, the Otsu’s and Triangle methods are implemented only for 8-bit single-channel images.

See also: adaptiveThreshold, findContours, compare, min, max

Parameters:

src (cv2.typing.MatLike) – input array (multiple-channel, 8-bit or 32-bit floating point).
dst (cv2.typing.MatLike | None) – output array of the same size and type and the same number of channels as src.
thresh (float) – threshold value.
maxval (float) – maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholdingtypes.
type (int) – thresholding type (see #ThresholdTypes).

Returns:

the computed threshold value if Otsu’s or Triangle methods used.

Return type:

tuple[float, cv2.typing.MatLike]

cv2.trace(mtx) → retval¶

Returns the trace of a matrix.

The function cv::trace returns the sum of the diagonal elements of the matrix mtx .

\[\begin{equation*}\mathrm{tr} ( \texttt{mtx} ) = \sum _i \texttt{mtx} (i,i)\end{equation*}\]

Parameters:: mtx (cv2.typing.MatLike) – input matrix.
Return type:: cv2.typing.Scalar

cv2.transform(src, m[, dst]) → dst¶

Performs the matrix transformation of every array element.

The function cv::transform performs the matrix transformation of every element of the array src and stores the results in dst :

\[\begin{equation*}\texttt{dst} (I) = \texttt{m} \cdot \texttt{src} (I)\end{equation*}\]

(when m.cols=src.channels() ), or

\[\begin{equation*}\texttt{dst} (I) = \texttt{m} \cdot [ \texttt{src} (I); 1]\end{equation*}\]

(when m.cols=src.channels()+1 )

Every element of the N -channel array src is interpreted as N -element vector that is transformed using the M x N or M x (N+1) matrix m to M-element vector - the corresponding element of the output array dst .

The function may be used for geometrical transformation of N -dimensional points, arbitrary linear color space transformation (such as various kinds of RGB to YUV transforms), shuffling the image channels, and so forth.

See also: perspectiveTransform, getAffineTransform, estimateAffine2D, warpAffine, warpPerspective

Parameters:

src (cv2.typing.MatLike) – input array that must have as many channels (1 to 4) asm.cols or m.cols-1.
dst (cv2.typing.MatLike | None) – output array of the same size and depth as src; it has asmany channels as m.rows.
m (cv2.typing.MatLike) – transformation 2x2 or 2x3 floating-point matrix.

Return type:

cv2.typing.MatLike

cv2.transpose(src[, dst]) → dst¶

Transposes a matrix.

The function cv::transpose transposes the matrix src :

\[\begin{equation*}\texttt{dst} (i,j) = \texttt{src} (j,i)\end{equation*}\]

Note

No complex conjugation is done in case of a complex matrix. Itshould be done separately if needed.

Parameters:

src (cv2.typing.MatLike) – input array.
dst (cv2.typing.MatLike | None) – output array of the same type as src.

Return type:

cv2.typing.MatLike

cv2.transposeND(src, order[, dst]) → dst¶

Transpose for n-dimensional matrices. *

@note Input should be continuous single-channel matrix.
@param src input array.
@param order a permutation of [0,1,..,N-1] where N is the number of axes of src.
The i’th axis of dst will correspond to the axis numbered order[i] of the input.
@param dst output array of the same type as src.

Parameters:

src (cv2.typing.MatLike) –
order (_typing.Sequence[int]) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.triangulatePoints(projMatr1, projMatr2, projPoints1, projPoints2[, points4D]) → points4D¶

This function reconstructs 3-dimensional points (in homogeneous coordinates) by usingtheir observations with a stereo camera.

@note Keep in mind that all input data should be of float type in order for this function to work.

@note If the projection matrices from @ref stereoRectify are used, then the returned points are represented in the first camera’s rectified coordinate system.

@sa reprojectImageTo3D

Parameters:

projMatr1 (cv2.typing.MatLike) – 3x4 projection matrix of the first camera, i.e. this matrix projects 3D pointsgiven in the world’s coordinate system into the first image.
projMatr2 (cv2.typing.MatLike) – 3x4 projection matrix of the second camera, i.e. this matrix projects 3D pointsgiven in the world’s coordinate system into the second image.
projPoints1 (cv2.typing.MatLike) – 2xN array of feature points in the first image. In the case of the c++ version,it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
projPoints2 (cv2.typing.MatLike) – 2xN array of corresponding points in the second image. In the case of the c++version, it can be also a vector of feature points or two-channel matrix of size 1xN or Nx1.
points4D (cv2.typing.MatLike | None) – 4xN array of reconstructed points in homogeneous coordinates. These points arereturned in the world’s coordinate system.

Return type:

cv2.typing.MatLike

cv2.undistort(src, cameraMatrix, distCoeffs[, dst[, newCameraMatrix]]) → dst¶

Transforms an image to compensate for lens distortion.

The function transforms an image to compensate radial and tangential lens distortion.

The function is simply a combination of #initUndistortRectifyMap (with unity R ) and #remap (with bilinear interpolation). See the former function for details of the transformation being performed.

Those pixels in the destination image, for which there is no correspondent pixels in the source image, are filled with zeros (black color).

A particular subset of the source image that will be visible in the corrected image can be regulated by newCameraMatrix. You can use #getOptimalNewCameraMatrix to compute the appropriate newCameraMatrix depending on your requirements.

The camera matrix and the distortion parameters can be determined using #calibrateCamera. If the resolution of images is different from the resolution used at the calibration stage, \(f_x, f_y, c_x\) and \(c_y\) need to be scaled accordingly, while the distortion coefficients remain the same.

Parameters:

src (cv2.typing.MatLike) – Input (distorted) image.
dst (cv2.typing.MatLike | None) – Output (corrected) image that has the same size and type as src .
cameraMatrix (cv2.typing.MatLike) – Input camera matrix \(A = \vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
newCameraMatrix (cv2.typing.MatLike | None) – Camera matrix of the distorted image. By default, it is the same ascameraMatrix but you may additionally scale and shift the result by using a different matrix.

Return type:

cv2.typing.MatLike

cv2.undistortImagePoints(src, cameraMatrix, distCoeffs[, dst[, arg1]]) → dst¶

@brief Compute undistorted image points position
@param src Observed points position, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (or vector<Point2f> ).
@param dst Output undistorted points position (1xN/Nx1 2-channel or vector<Point2f> ).
@param cameraMatrix Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
@param distCoeffs Distortion coefficients

Parameters:

src (cv2.typing.MatLike) –
cameraMatrix (cv2.typing.MatLike) –
distCoeffs (cv2.typing.MatLike) –
dst (cv2.typing.MatLike | None) –
arg1 (cv2.typing.TermCriteria) –

Return type:

cv2.typing.MatLike

cv2.undistortPoints(src, cameraMatrix, distCoeffs[, dst[, R[, P]]]) → dst¶

Computes the ideal point coordinates from the observed point coordinates.

The function is similar to #undistort and #initUndistortRectifyMap but it operates on a sparse set of points instead of a raster image. Also the function performs a reverse transformation to #projectPoints. In case of a 3D object, it does not reconstruct its 3D coordinates, but for a planar object, it does, up to a translation vector, if the proper R is specified.

For each observed point coordinate \((u, v)\) the function computes:

\[\begin{equation*} \begin{array}{l} x^{"} \leftarrow (u - c_x)/f_x \\ y^{"} \leftarrow (v - c_y)/f_y \\ (x',y') = undistort(x^{"},y^{"}, \texttt{distCoeffs}) \\ {[X\,Y\,W]} ^T \leftarrow R*[x' \, y' \, 1]^T \\ x \leftarrow X/W \\ y \leftarrow Y/W \\ \text{only performed if P is specified:} \\ u' \leftarrow x {f'}_x + {c'}_x \\ v' \leftarrow y {f'}_y + {c'}_y \end{array} \end{equation*}\]

where undistort is an approximate iterative algorithm that estimates the normalized original point coordinates out of the normalized distorted point coordinates (“normalized” means that the coordinates do not depend on the camera matrix).

The function can be used for both a stereo camera head or a monocular camera (when R is empty).

Parameters:

src (cv2.typing.MatLike) – Observed point coordinates, 2xN/Nx2 1-channel or 1xN/Nx1 2-channel (CV_32FC2 or CV_64FC2) (orvector<Point2f> ).
dst (cv2.typing.MatLike | None) – Output ideal point coordinates (1xN/Nx1 2-channel or vector<Point2f> ) after undistortion and reverse perspectivetransformation. If matrix P is identity or omitted, dst will contain normalized point coordinates.
cameraMatrix (cv2.typing.MatLike) – Camera matrix \(\vecthreethree{f_x}{0}{c_x}{0}{f_y}{c_y}{0}{0}{1}\) .
distCoeffs (cv2.typing.MatLike) – Input vector of distortion coefficients\((k_1, k_2, p_1, p_2[, k_3[, k_4, k_5, k_6[, s_1, s_2, s_3, s_4[, \tau_x, \tau_y]]]])\) of 4, 5, 8, 12 or 14 elements. If the vector is NULL/empty, the zero distortion coefficients are assumed.
R (cv2.typing.MatLike | None) – Rectification transformation in the object space (3x3 matrix). R1 or R2 computed by#stereoRectify can be passed here. If the matrix is empty, the identity transformation is used.
P (cv2.typing.MatLike | None) – New camera matrix (3x3) or new projection matrix (3x4) \(\begin{bmatrix} {f'}_x & 0 & {c'}_x & t_x \\ 0 & {f'}_y & {c'}_y & t_y \\ 0 & 0 & 1 & t_z \end{bmatrix}\). P1 or P2 computed by#stereoRectify can be passed here. If the matrix is empty, the identity new camera matrix is used.

Return type:

cv2.typing.MatLike

cv2.undistortPointsIter(src, cameraMatrix, distCoeffs, R, P, criteria[, dst]) → dst¶

@overload @note Default version of #undistortPoints does 5 iterations to compute undistorted points.

Parameters:

src (cv2.typing.MatLike) –
cameraMatrix (cv2.typing.MatLike) –
distCoeffs (cv2.typing.MatLike) –
R (cv2.typing.MatLike) –
P (cv2.typing.MatLike) –
criteria (cv2.typing.TermCriteria) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.useOpenVX() → retval¶

Return type:: bool

cv2.useOptimized() → retval¶

Returns the status of optimized code usage.

The function returns true if the optimized code is enabled. Otherwise, it returns false.

Return type:: bool

cv2.validateDisparity(disparity, cost, minDisparity, numberOfDisparities[, disp12MaxDisp]) → disparity¶

Parameters:

disparity (cv2.typing.MatLike) –
cost (cv2.typing.MatLike) –
minDisparity (int) –
numberOfDisparities (int) –
disp12MaxDisp (int) –

Return type:

cv2.typing.MatLike

cv2.vconcat(src[, dst]) → dst¶

@overload @code{.cpp} std::vectorcv::Mat matrices = { cv::Mat(1, 4, CV_8UC1, cv::Scalar(1)), cv::Mat(1, 4, CV_8UC1, cv::Scalar(2)), cv::Mat(1, 4, CV_8UC1, cv::Scalar(3)),};

cv::Mat out;
cv::vconcat( matrices, out );
//out:
//[1,   1,   1,   1;
// 2,   2,   2,   2;
// 3,   3,   3,   3]

@endcode @param src input array or vector of matrices. all of the matrices must have the same number of cols and the same depth @param dst output array. It has the same number of cols and depth as the src, and the sum of rows of the src. same depth.

Parameters:

src (_typing.Sequence[cv2.typing.MatLike]) –
dst (cv2.typing.MatLike | None) –

Return type:

cv2.typing.MatLike

cv2.waitKey([delay]) → retval¶

Waits for a pressed key.

The function waitKey waits for a key event infinitely (when \(\texttt{delay}\leq 0\) ) or for delay milliseconds, when it is positive. Since the OS has a minimum time between switching threads, the function will not wait exactly delay ms, it will wait at least delay ms, depending on what else is running on your computer at that time. It returns the code of the pressed key or -1 if no key was pressed before the specified time had elapsed. To check for a key press but not wait for it, use #pollKey.

Note

The function only works if there is at least one HighGUI window created and the window isactive. If there are several HighGUI windows, any of them can be active.

Parameters:: delay (int) – Delay in milliseconds. 0 is the special value that means “forever”.
Return type:: int

cv2.waitKeyEx([delay]) → retval¶

Similar to #waitKey, but returns full key code.

Note

Key code is implementation specific and depends on used backend: QT/GTK/Win32/etc

Parameters:: delay (int) –
Return type:: int

cv2.warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) → dst¶

Applies an affine transformation to an image.

The function warpAffine transforms the source image using the specified matrix:

\[\begin{equation*}\texttt{dst} (x,y) = \texttt{src} ( \texttt{M} _{11} x + \texttt{M} _{12} y + \texttt{M} _{13}, \texttt{M} _{21} x + \texttt{M} _{22} y + \texttt{M} _{23})\end{equation*}\]

when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with #invertAffineTransform and then put in the formula above instead of M. The function cannot operate in-place.

See also: warpPerspective, resize, remap, getRectSubPix, transform

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image that has the size dsize and the same type as src .
M (cv2.typing.MatLike) – \(2\times 3\) transformation matrix.
dsize (cv2.typing.Size) – size of the output image.
flags (int) – combination of interpolation methods (see #InterpolationFlags) and the optionalflag #WARP_INVERSE_MAP that means that M is the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode (int) – pixel extrapolation method (see #BorderTypes); whenborderMode=#BORDER_TRANSPARENT, it means that the pixels in the destination image corresponding to the “outliers” in the source image are not modified by the function.
borderValue (cv2.typing.Scalar) – value used in case of a constant border; by default, it is 0.

Return type:

cv2.typing.MatLike

cv2.warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) → dst¶

Applies a perspective transformation to an image.

The function warpPerspective transforms the source image using the specified matrix:

\[\begin{equation*}\texttt{dst} (x,y) = \texttt{src} \left ( \frac{M_{11} x + M_{12} y + M_{13}}{M_{31} x + M_{32} y + M_{33}} , \frac{M_{21} x + M_{22} y + M_{23}}{M_{31} x + M_{32} y + M_{33}} \right )\end{equation*}\]

when the flag #WARP_INVERSE_MAP is set. Otherwise, the transformation is first inverted with invert and then put in the formula above instead of M. The function cannot operate in-place.

See also: warpAffine, resize, remap, getRectSubPix, perspectiveTransform

Parameters:

src (cv2.typing.MatLike) – input image.
dst (cv2.typing.MatLike | None) – output image that has the size dsize and the same type as src .
M (cv2.typing.MatLike) – \(3\times 3\) transformation matrix.
dsize (cv2.typing.Size) – size of the output image.
flags (int) – combination of interpolation methods (#INTER_LINEAR or #INTER_NEAREST) and theoptional flag #WARP_INVERSE_MAP, that sets M as the inverse transformation ( \(\texttt{dst}\rightarrow\texttt{src}\) ).
borderMode (int) – pixel extrapolation method (#BORDER_CONSTANT or #BORDER_REPLICATE).
borderValue (cv2.typing.Scalar) – value used in case of a constant border; by default, it equals 0.

Return type:

cv2.typing.MatLike

cv2.warpPolar(src, dsize, center, maxRadius, flags[, dst]) → dst¶

\brief Remaps an image to polar or semilog-polar coordinates space

@anchor polar_remaps_reference_image Polar remaps reference

Transform the source image using the following transformation:

\[\begin{equation*} dst(\rho , \phi ) = src(x,y) \end{equation*}\]

where

\[\begin{equation*} \begin{array}{l} \vec{I} = (x - center.x, \;y - center.y) \\ \phi = Kangle \cdot \texttt{angle} (\vec{I}) \\ \rho = \left\{\begin{matrix} Klin \cdot \texttt{magnitude} (\vec{I}) & default \\ Klog \cdot log_e(\texttt{magnitude} (\vec{I})) & if \; semilog \\ \end{matrix}\right. \end{array} \end{equation*}\]

and

\[\begin{equation*} \begin{array}{l} Kangle = dsize.height / 2\Pi \\ Klin = dsize.width / maxRadius \\ Klog = dsize.width / log_e(maxRadius) \\ \end{array} \end{equation*}\]

\par Linear vs semilog mapping

Polar mapping can be linear or semi-log. Add one of #WarpPolarMode to flags to specify the polar mapping mode.

Linear is the default mode.

The semilog mapping emulates the human “foveal” vision that permit very high acuity on the line of sight (central vision) in contrast to peripheral vision where acuity is minor.

\par Option on dsize:

if both values in dsize <=0 (default), the destination image will have (almost) same area of source bounding circle:

\[\begin{equation*}\begin{array}{l} dsize.area \leftarrow (maxRadius^2 \cdot \Pi) \\ dsize.width = \texttt{cvRound}(maxRadius) \\ dsize.height = \texttt{cvRound}(maxRadius \cdot \Pi) \\ \end{array}\end{equation*}\]

if only dsize.height <= 0, the destination image area will be proportional to the bounding circle area but scaled by Kx * Kx:

\[\begin{equation*}\begin{array}{l} dsize.height = \texttt{cvRound}(dsize.width \cdot \Pi) \\ \end{array} \end{equation*}\]

if both values in dsize > 0 , the destination image will have the given size therefore the area of the bounding circle will be scaled to dsize.

\par Reverse mapping

You can get reverse mapping adding #WARP_INVERSE_MAP to flags \snippet polar_transforms.cpp InverseMap

In addiction, to calculate the original coordinate from a polar mapped coordinate \((rho, phi)->(x, y)\): \snippet polar_transforms.cpp InverseCoordinate

See also: cv::remap

Parameters:

src (cv2.typing.MatLike) – Source image.
dst (cv2.typing.MatLike | None) – Destination image. It will have same type as src.
dsize (cv2.typing.Size) – The destination image size (see description for valid options).
center (cv2.typing.Point2f) – The transformation center.
maxRadius (float) – The radius of the bounding circle to transform. It determines the inverse magnitude scale parameter too.
flags –
A combination of interpolation methods, #InterpolationFlags + #WarpPolarMode. - Add #WARP_POLAR_LINEAR to select linear polar mapping (default)
- Add #WARP_POLAR_LOG to select semilog polar mapping
- Add #WARP_INVERSE_MAP for reverse mapping. @note

The function can not operate in-place.
To calculate magnitude and angle in degrees #cartToPolar is used internally thus angles are measured from 0 to 360 with accuracy about 0.3 degrees.
This function uses #remap. Due to current implementation limitations the size of an input and output images should be less than 32767x32767.

Return type:: cv2.typing.MatLike

cv2.watershed(image, markers) → markers¶

Performs a marker-based image segmentation using the watershed algorithm.

The function implements one of the variants of watershed, non-parametric marker-based segmentation algorithm, described in @cite Meyer92 .

Before passing the image to the function, you have to roughly outline the desired regions in the image markers with positive (>0) indices. So, every region is represented as one or more connected components with the pixel values 1, 2, 3, and so on. Such markers can be retrieved from a binary mask using #findContours and #drawContours (see the watershed.cpp demo). The markers are “seeds” of the future image regions. All the other pixels in markers , whose relation to the outlined regions is not known and should be defined by the algorithm, should be set to 0’s. In the function output, each pixel in markers is set to a value of the “seed” components or to -1 at boundaries between the regions.

Note

Any two neighbor connected components are not necessarily separated by a watershed boundary(-1’s pixels); for example, they can touch each other in the initial marker image passed to the function.

See also: findContours

Parameters:

image (cv2.typing.MatLike) – Input 8-bit 3-channel image.
markers (cv2.typing.MatLike) – Input/output 32-bit single-channel image (map) of markers. It should have the samesize as image .

Return type:

cv2.typing.MatLike

cv2.writeOpticalFlow(path, flow) → retval¶

Write a .flo to disk

@param path Path to the file to be written @param flow Flow field to be stored

The function stores a flow field in a file, returns true on success, false otherwise. The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds to the flow in the horizontal direction (u), second - vertical (v).

Parameters:

path (str) –
flow (cv2.typing.MatLike) –

Return type: