Static Public Member Functions
static BackgroundSubtractorMOG2	createBackgroundSubtractorMOG2 (int history, double varThreshold, bool detectShadows)
	Creates MOG2 Background Subtractor. More...

static BackgroundSubtractorMOG2	createBackgroundSubtractorMOG2 (int history, double varThreshold)
	Creates MOG2 Background Subtractor. More...

static BackgroundSubtractorMOG2	createBackgroundSubtractorMOG2 (int history)
	Creates MOG2 Background Subtractor. More...

static BackgroundSubtractorMOG2	createBackgroundSubtractorMOG2 ()
	Creates MOG2 Background Subtractor. More...

static BackgroundSubtractorKNN	createBackgroundSubtractorKNN (int history, double dist2Threshold, bool detectShadows)
	Creates KNN Background Subtractor. More...

static BackgroundSubtractorKNN	createBackgroundSubtractorKNN (int history, double dist2Threshold)
	Creates KNN Background Subtractor. More...

static BackgroundSubtractorKNN	createBackgroundSubtractorKNN (int history)
	Creates KNN Background Subtractor. More...

static BackgroundSubtractorKNN	createBackgroundSubtractorKNN ()
	Creates KNN Background Subtractor. More...

static RotatedRect	CamShift (Mat probImage, Rect window, TermCriteria criteria)
	Finds an object center, size, and orientation. More...

static int	meanShift (Mat probImage, Rect window, TermCriteria criteria)
	Finds an object on a back projection image. More...

static int	buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder, bool tryReuseInputImage)
	Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. More...

static int	buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder)
	Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. More...

static int	buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder)
	Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. More...

static int	buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel, bool withDerivatives)
	Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. More...

static int	buildOpticalFlowPyramid (Mat img, List< Mat > pyramid, Size winSize, int maxLevel)
	Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. More...

static void	calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags, double minEigThreshold)
	Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids. More...

static void	calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags)
	Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids. More...

static void	calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria)
	Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids. More...

static void	calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel)
	Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids. More...

static void	calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize)
	Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids. More...

static void	calcOpticalFlowPyrLK (Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err)
	Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids. More...

static void	calcOpticalFlowFarneback (Mat prev, Mat next, Mat flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
	Computes a dense optical flow using the Gunnar Farneback's algorithm. More...

static double	computeECC (Mat templateImage, Mat inputImage, Mat inputMask)
	Computes the Enhanced Correlation Coefficient value between two images [EP08] . More...

static double	computeECC (Mat templateImage, Mat inputImage)
	Computes the Enhanced Correlation Coefficient value between two images [EP08] . More...

static double	findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize)
	Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08] . More...

static double	findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask)

static double	findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria)

static double	findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType)

static double	findTransformECC (Mat templateImage, Mat inputImage, Mat warpMatrix)

static Mat	readOpticalFlow (string path)
	Read a .flo file. More...

static bool	writeOpticalFlow (string path, Mat flow)
	Write a .flo to disk. More...

Public Attributes
const int	OPTFLOW_USE_INITIAL_FLOW = 4

const int	OPTFLOW_LK_GET_MIN_EIGENVALS = 8

const int	OPTFLOW_FARNEBACK_GAUSSIAN = 256

const int	MOTION_TRANSLATION = 0

const int	MOTION_EUCLIDEAN = 1

const int	MOTION_AFFINE = 2

const int	MOTION_HOMOGRAPHY = 3

const int	TrackerSamplerCSC_MODE_INIT_POS = 1

const int	TrackerSamplerCSC_MODE_INIT_NEG = 2

const int	TrackerSamplerCSC_MODE_TRACK_POS = 3

const int	TrackerSamplerCSC_MODE_TRACK_NEG = 4

const int	TrackerSamplerCSC_MODE_DETECT = 5

Member Function Documentation

◆ buildOpticalFlowPyramid() [1/5]

static int OpenCVForUnity.VideoModule.Video.buildOpticalFlowPyramid	(	Mat	img,
		List< Mat >	pyramid,
		Size	winSize,
		int	maxLevel,
		bool	withDerivatives,
		int	pyrBorder,
		int	derivBorder,
		bool	tryReuseInputImage
	)

static

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Parameters

img	8-bit input image.
pyramid	output pyramid.
winSize	window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
maxLevel	0-based maximal pyramid level number.
withDerivatives	set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
pyrBorder	the border mode for pyramid layers.
derivBorder	the border mode for gradients.
tryReuseInputImage	put ROI of input image into the pyramid if possible. You can pass false to force data copying.

Returns: number of levels in constructed pyramid. Can be less than maxLevel.

◆ buildOpticalFlowPyramid() [2/5]

static int OpenCVForUnity.VideoModule.Video.buildOpticalFlowPyramid	(	Mat	img,
		List< Mat >	pyramid,
		Size	winSize,
		int	maxLevel,
		bool	withDerivatives,
		int	pyrBorder,
		int	derivBorder
	)

static

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Parameters

img	8-bit input image.
pyramid	output pyramid.
winSize	window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
maxLevel	0-based maximal pyramid level number.
withDerivatives	set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
pyrBorder	the border mode for pyramid layers.
derivBorder	the border mode for gradients.
tryReuseInputImage	put ROI of input image into the pyramid if possible. You can pass false to force data copying.

Returns: number of levels in constructed pyramid. Can be less than maxLevel.

◆ buildOpticalFlowPyramid() [3/5]

static int OpenCVForUnity.VideoModule.Video.buildOpticalFlowPyramid	(	Mat	img,
		List< Mat >	pyramid,
		Size	winSize,
		int	maxLevel,
		bool	withDerivatives,
		int	pyrBorder
	)

static

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Parameters

img	8-bit input image.
pyramid	output pyramid.
winSize	window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
maxLevel	0-based maximal pyramid level number.
withDerivatives	set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
pyrBorder	the border mode for pyramid layers.
derivBorder	the border mode for gradients.
tryReuseInputImage	put ROI of input image into the pyramid if possible. You can pass false to force data copying.

Returns: number of levels in constructed pyramid. Can be less than maxLevel.

◆ buildOpticalFlowPyramid() [4/5]

static int OpenCVForUnity.VideoModule.Video.buildOpticalFlowPyramid	(	Mat	img,
		List< Mat >	pyramid,
		Size	winSize,
		int	maxLevel,
		bool	withDerivatives
	)

static

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Parameters

img	8-bit input image.
pyramid	output pyramid.
winSize	window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
maxLevel	0-based maximal pyramid level number.
withDerivatives	set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
pyrBorder	the border mode for pyramid layers.
derivBorder	the border mode for gradients.
tryReuseInputImage	put ROI of input image into the pyramid if possible. You can pass false to force data copying.

Returns: number of levels in constructed pyramid. Can be less than maxLevel.

◆ buildOpticalFlowPyramid() [5/5]

static int OpenCVForUnity.VideoModule.Video.buildOpticalFlowPyramid	(	Mat	img,
		List< Mat >	pyramid,
		Size	winSize,
		int	maxLevel
	)

static

Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.

Parameters

img	8-bit input image.
pyramid	output pyramid.
winSize	window size of optical flow algorithm. Must be not less than winSize argument of calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
maxLevel	0-based maximal pyramid level number.
withDerivatives	set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
pyrBorder	the border mode for pyramid layers.
derivBorder	the border mode for gradients.
tryReuseInputImage	put ROI of input image into the pyramid if possible. You can pass false to force data copying.

Returns: number of levels in constructed pyramid. Can be less than maxLevel.

◆ calcOpticalFlowFarneback()

static void OpenCVForUnity.VideoModule.Video.calcOpticalFlowFarneback	(	Mat	prev,
		Mat	next,
		Mat	flow,
		double	pyr_scale,
		int	levels,
		int	winsize,
		int	iterations,
		int	poly_n,
		double	poly_sigma,
		int	flags
	)

static

Computes a dense optical flow using the Gunnar Farneback's algorithm.

Parameters

prev	first 8-bit single-channel input image.
next	second input image of the same size and the same type as prev.
flow	computed flow image that has the same size as prev and type CV_32FC2.
pyr_scale	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	number of pyramid layers including the initial image; levels=1 means that no extra layers are created and only the original images are used.
winsize	averaging window size; larger values increase the algorithm robustness to image noise and give more chances for fast motion detection, but yield more blurred motion field.
iterations	number of iterations the algorithm does at each pyramid level.
poly_n	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	standard deviation of the Gaussian that is used to smooth derivatives used as a basis 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	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.

The function finds an optical flow for each prev pixel using the [Farneback2003] algorithm so that

\[\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\]

Note: Some examples:

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

◆ calcOpticalFlowPyrLK() [1/6]

static void OpenCVForUnity.VideoModule.Video.calcOpticalFlowPyrLK	(	Mat	prevImg,
		Mat	nextImg,
		MatOfPoint2f	prevPts,
		MatOfPoint2f	nextPts,
		MatOfByte	status,
		MatOfFloat	err,
		Size	winSize,
		int	maxLevel,
		TermCriteria	criteria,
		int	flags,
		double	minEigThreshold
	)

static

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Parameters

prevImg	first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
nextImg	second input image or pyramid of the same size and the same type as prevImg.
prevPts	vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.
nextPts	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	output status vector (of unsigned chars); 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.
err	output vector of errors; each element of the vector is set to an error for the corresponding 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	size of the search window at each pyramid level.
maxLevel	0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), 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	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	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	the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [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.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

Note: Some examples:

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

◆ calcOpticalFlowPyrLK() [2/6]

static void OpenCVForUnity.VideoModule.Video.calcOpticalFlowPyrLK	(	Mat	prevImg,
		Mat	nextImg,
		MatOfPoint2f	prevPts,
		MatOfPoint2f	nextPts,
		MatOfByte	status,
		MatOfFloat	err,
		Size	winSize,
		int	maxLevel,
		TermCriteria	criteria,
		int	flags
	)

static

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Parameters

prevImg	first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
nextImg	second input image or pyramid of the same size and the same type as prevImg.
prevPts	vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.
nextPts	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	output status vector (of unsigned chars); 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.
err	output vector of errors; each element of the vector is set to an error for the corresponding 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	size of the search window at each pyramid level.
maxLevel	0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), 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	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	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	the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [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.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

Note: Some examples:

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

◆ calcOpticalFlowPyrLK() [3/6]

static void OpenCVForUnity.VideoModule.Video.calcOpticalFlowPyrLK	(	Mat	prevImg,
		Mat	nextImg,
		MatOfPoint2f	prevPts,
		MatOfPoint2f	nextPts,
		MatOfByte	status,
		MatOfFloat	err,
		Size	winSize,
		int	maxLevel,
		TermCriteria	criteria
	)

static

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Parameters

prevImg	first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
nextImg	second input image or pyramid of the same size and the same type as prevImg.
prevPts	vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.
nextPts	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	output status vector (of unsigned chars); 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.
err	output vector of errors; each element of the vector is set to an error for the corresponding 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	size of the search window at each pyramid level.
maxLevel	0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), 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	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	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	the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [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.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

Note: Some examples:

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

◆ calcOpticalFlowPyrLK() [4/6]

static void OpenCVForUnity.VideoModule.Video.calcOpticalFlowPyrLK	(	Mat	prevImg,
		Mat	nextImg,
		MatOfPoint2f	prevPts,
		MatOfPoint2f	nextPts,
		MatOfByte	status,
		MatOfFloat	err,
		Size	winSize,
		int	maxLevel
	)

static

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Parameters

prevImg	first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
nextImg	second input image or pyramid of the same size and the same type as prevImg.
prevPts	vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.
nextPts	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	output status vector (of unsigned chars); 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.
err	output vector of errors; each element of the vector is set to an error for the corresponding 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	size of the search window at each pyramid level.
maxLevel	0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), 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	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	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	the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [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.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

Note: Some examples:

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

◆ calcOpticalFlowPyrLK() [5/6]

static void OpenCVForUnity.VideoModule.Video.calcOpticalFlowPyrLK	(	Mat	prevImg,
		Mat	nextImg,
		MatOfPoint2f	prevPts,
		MatOfPoint2f	nextPts,
		MatOfByte	status,
		MatOfFloat	err,
		Size	winSize
	)

static

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Parameters

prevImg	first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
nextImg	second input image or pyramid of the same size and the same type as prevImg.
prevPts	vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.
nextPts	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	output status vector (of unsigned chars); 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.
err	output vector of errors; each element of the vector is set to an error for the corresponding 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	size of the search window at each pyramid level.
maxLevel	0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), 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	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	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	the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [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.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

Note: Some examples:

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

◆ calcOpticalFlowPyrLK() [6/6]

static void OpenCVForUnity.VideoModule.Video.calcOpticalFlowPyrLK	(	Mat	prevImg,
		Mat	nextImg,
		MatOfPoint2f	prevPts,
		MatOfPoint2f	nextPts,
		MatOfByte	status,
		MatOfFloat	err
	)

static

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

Parameters

prevImg	first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
nextImg	second input image or pyramid of the same size and the same type as prevImg.
prevPts	vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.
nextPts	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	output status vector (of unsigned chars); 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.
err	output vector of errors; each element of the vector is set to an error for the corresponding 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	size of the search window at each pyramid level.
maxLevel	0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), 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	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	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	the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [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.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00] . The function is parallelized with the TBB library.

Note: Some examples:

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

◆ CamShift()

static RotatedRect OpenCVForUnity.VideoModule.Video.CamShift	(	Mat	probImage,
		Rect	window,
		TermCriteria	criteria
	)

static

Finds an object center, size, and orientation.

Parameters

probImage	Back projection of the object histogram. See calcBackProject.
window	Initial search window.
criteria	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 [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()

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

◆ computeECC() [1/2]

static double OpenCVForUnity.VideoModule.Video.computeECC	(	Mat	templateImage,
		Mat	inputImage,
		Mat	inputMask
	)

static

Computes the Enhanced Correlation Coefficient value between two images [EP08] .

Parameters

templateImage	single-channel template image; CV_8U or CV_32F array.
inputImage	single-channel input image to be warped to provide an image similar to templateImage, same type as templateImage.
inputMask	An optional mask to indicate valid values of inputImage.

See also: findTransformECC

◆ computeECC() [2/2]

static double OpenCVForUnity.VideoModule.Video.computeECC	(	Mat	templateImage,
		Mat	inputImage
	)

static

Computes the Enhanced Correlation Coefficient value between two images [EP08] .

Parameters

templateImage	single-channel template image; CV_8U or CV_32F array.
inputImage	single-channel input image to be warped to provide an image similar to templateImage, same type as templateImage.
inputMask	An optional mask to indicate valid values of inputImage.

See also: findTransformECC

◆ createBackgroundSubtractorKNN() [1/4]

static BackgroundSubtractorKNN OpenCVForUnity.VideoModule.Video.createBackgroundSubtractorKNN	(	int	history,
		double	dist2Threshold,
		bool	detectShadows
	)

static

Creates KNN Background Subtractor.

Parameters

history	Length of the history.
dist2Threshold	Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.
detectShadows	If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

◆ createBackgroundSubtractorKNN() [2/4]

static BackgroundSubtractorKNN OpenCVForUnity.VideoModule.Video.createBackgroundSubtractorKNN	(	int	history,
		double	dist2Threshold
	)

static

Creates KNN Background Subtractor.

Parameters

history	Length of the history.
dist2Threshold	Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.
detectShadows	If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

◆ createBackgroundSubtractorKNN() [3/4]

static BackgroundSubtractorKNN OpenCVForUnity.VideoModule.Video.createBackgroundSubtractorKNN ( int history )

static

Creates KNN Background Subtractor.

Parameters

history	Length of the history.
dist2Threshold	Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.
detectShadows	If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

◆ createBackgroundSubtractorKNN() [4/4]

static BackgroundSubtractorKNN OpenCVForUnity.VideoModule.Video.createBackgroundSubtractorKNN ( )

static

Creates KNN Background Subtractor.

Parameters

history	Length of the history.
dist2Threshold	Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. This parameter does not affect the background update.
detectShadows	If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

◆ createBackgroundSubtractorMOG2() [1/4]

static BackgroundSubtractorMOG2 OpenCVForUnity.VideoModule.Video.createBackgroundSubtractorMOG2	(	int	history,
		double	varThreshold,
		bool	detectShadows
	)

static

Creates MOG2 Background Subtractor.

Parameters

history	Length of the history.
varThreshold	Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update.
detectShadows	If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

◆ createBackgroundSubtractorMOG2() [2/4]

static BackgroundSubtractorMOG2 OpenCVForUnity.VideoModule.Video.createBackgroundSubtractorMOG2	(	int	history,
		double	varThreshold
	)

static

Creates MOG2 Background Subtractor.

Parameters

history	Length of the history.
varThreshold	Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update.
detectShadows	If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

◆ createBackgroundSubtractorMOG2() [3/4]

static BackgroundSubtractorMOG2 OpenCVForUnity.VideoModule.Video.createBackgroundSubtractorMOG2 ( int history )

static

Creates MOG2 Background Subtractor.

Parameters

history	Length of the history.
varThreshold	Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update.
detectShadows	If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

◆ createBackgroundSubtractorMOG2() [4/4]

static BackgroundSubtractorMOG2 OpenCVForUnity.VideoModule.Video.createBackgroundSubtractorMOG2 ( )

static

Creates MOG2 Background Subtractor.

Parameters

history	Length of the history.
varThreshold	Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. This parameter does not affect the background update.
detectShadows	If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false.

◆ findTransformECC() [1/5]

static double OpenCVForUnity.VideoModule.Video.findTransformECC	(	Mat	templateImage,
		Mat	inputImage,
		Mat	warpMatrix,
		int	motionType,
		TermCriteria	criteria,
		Mat	inputMask,
		int	gaussFiltSize
	)

static

Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08] .

Parameters

templateImage	single-channel template image; CV_8U or CV_32F array.
inputImage	single-channel input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage.
warpMatrix	floating-point \(2\times 3\) or \(3\times 3\) mapping matrix (warp).
motionType	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	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	An optional mask to indicate valid values of inputImage.
gaussFiltSize	An optional value indicating size of gaussian blur filter; (DEFAULT: 5)

The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion ([EP08]), that is

\[\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\]

where

\[\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\]

(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.

See also: computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

◆ findTransformECC() [2/5]

static double OpenCVForUnity.VideoModule.Video.findTransformECC	(	Mat	templateImage,
		Mat	inputImage,
		Mat	warpMatrix,
		int	motionType,
		TermCriteria	criteria,
		Mat	inputMask
	)

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ findTransformECC() [3/5]

static double OpenCVForUnity.VideoModule.Video.findTransformECC	(	Mat	templateImage,
		Mat	inputImage,
		Mat	warpMatrix,
		int	motionType,
		TermCriteria	criteria
	)

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ findTransformECC() [4/5]

static double OpenCVForUnity.VideoModule.Video.findTransformECC	(	Mat	templateImage,
		Mat	inputImage,
		Mat	warpMatrix,
		int	motionType
	)

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ findTransformECC() [5/5]

static double OpenCVForUnity.VideoModule.Video.findTransformECC	(	Mat	templateImage,
		Mat	inputImage,
		Mat	warpMatrix
	)

static

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ meanShift()

static int OpenCVForUnity.VideoModule.Video.meanShift	(	Mat	probImage,
		Rect	window,
		TermCriteria	criteria
	)

static

Finds an object on a back projection image.

Parameters

probImage	Back projection of the object histogram. See calcBackProject for details.
window	Initial search window.
criteria	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.

◆ readOpticalFlow()

static Mat OpenCVForUnity.VideoModule.Video.readOpticalFlow ( string path )

static

Read a .flo file.

Parameters

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).

◆ writeOpticalFlow()

static bool OpenCVForUnity.VideoModule.Video.writeOpticalFlow	(	string	path,
		Mat	flow
	)

static

Write a .flo to disk.

Parameters

path	Path to the file to be written
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).

Member Data Documentation

◆ MOTION_AFFINE

const int OpenCVForUnity.VideoModule.Video.MOTION_AFFINE = 2

◆ MOTION_EUCLIDEAN

const int OpenCVForUnity.VideoModule.Video.MOTION_EUCLIDEAN = 1

◆ MOTION_HOMOGRAPHY

const int OpenCVForUnity.VideoModule.Video.MOTION_HOMOGRAPHY = 3

◆ MOTION_TRANSLATION

const int OpenCVForUnity.VideoModule.Video.MOTION_TRANSLATION = 0

◆ OPTFLOW_FARNEBACK_GAUSSIAN

const int OpenCVForUnity.VideoModule.Video.OPTFLOW_FARNEBACK_GAUSSIAN = 256

◆ OPTFLOW_LK_GET_MIN_EIGENVALS

const int OpenCVForUnity.VideoModule.Video.OPTFLOW_LK_GET_MIN_EIGENVALS = 8

◆ OPTFLOW_USE_INITIAL_FLOW

const int OpenCVForUnity.VideoModule.Video.OPTFLOW_USE_INITIAL_FLOW = 4

◆ TrackerSamplerCSC_MODE_DETECT

const int OpenCVForUnity.VideoModule.Video.TrackerSamplerCSC_MODE_DETECT = 5

◆ TrackerSamplerCSC_MODE_INIT_NEG

const int OpenCVForUnity.VideoModule.Video.TrackerSamplerCSC_MODE_INIT_NEG = 2

◆ TrackerSamplerCSC_MODE_INIT_POS

const int OpenCVForUnity.VideoModule.Video.TrackerSamplerCSC_MODE_INIT_POS = 1

◆ TrackerSamplerCSC_MODE_TRACK_NEG

const int OpenCVForUnity.VideoModule.Video.TrackerSamplerCSC_MODE_TRACK_NEG = 4

◆ TrackerSamplerCSC_MODE_TRACK_POS

const int OpenCVForUnity.VideoModule.Video.TrackerSamplerCSC_MODE_TRACK_POS = 3

The documentation for this class was generated from the following file:

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/video/Video.cs

Static Public Member Functions

Public Attributes

Member Function Documentation

◆ buildOpticalFlowPyramid() [1/5]

◆ buildOpticalFlowPyramid() [2/5]

◆ buildOpticalFlowPyramid() [3/5]

◆ buildOpticalFlowPyramid() [4/5]

◆ buildOpticalFlowPyramid() [5/5]

◆ calcOpticalFlowFarneback()

◆ calcOpticalFlowPyrLK() [1/6]

◆ calcOpticalFlowPyrLK() [2/6]

◆ calcOpticalFlowPyrLK() [3/6]

◆ calcOpticalFlowPyrLK() [4/6]

◆ calcOpticalFlowPyrLK() [5/6]

◆ calcOpticalFlowPyrLK() [6/6]

◆ CamShift()

◆ computeECC() [1/2]

◆ computeECC() [2/2]

◆ createBackgroundSubtractorKNN() [1/4]

◆ createBackgroundSubtractorKNN() [2/4]

◆ createBackgroundSubtractorKNN() [3/4]

◆ createBackgroundSubtractorKNN() [4/4]

◆ createBackgroundSubtractorMOG2() [1/4]

◆ createBackgroundSubtractorMOG2() [2/4]

◆ createBackgroundSubtractorMOG2() [3/4]

◆ createBackgroundSubtractorMOG2() [4/4]

◆ findTransformECC() [1/5]

◆ findTransformECC() [2/5]

◆ findTransformECC() [3/5]

◆ findTransformECC() [4/5]

◆ findTransformECC() [5/5]

◆ meanShift()

◆ readOpticalFlow()

◆ writeOpticalFlow()

Member Data Documentation

◆ MOTION_AFFINE

◆ MOTION_EUCLIDEAN

◆ MOTION_HOMOGRAPHY

◆ MOTION_TRANSLATION

◆ OPTFLOW_FARNEBACK_GAUSSIAN

◆ OPTFLOW_LK_GET_MIN_EIGENVALS

◆ OPTFLOW_USE_INITIAL_FLOW

◆ TrackerSamplerCSC_MODE_DETECT

◆ TrackerSamplerCSC_MODE_INIT_NEG

◆ TrackerSamplerCSC_MODE_INIT_POS

◆ TrackerSamplerCSC_MODE_TRACK_NEG

◆ TrackerSamplerCSC_MODE_TRACK_POS