org.opencv.video

Class Video

java.lang.Object

org.opencv.video.Video

public class Video
extends Object

Field Summary

Fields

Modifier and Type	Field and Description
static int	MOTION_AFFINE
static int	MOTION_EUCLIDEAN
static int	MOTION_HOMOGRAPHY
static int	MOTION_TRANSLATION
static int	OPTFLOW_FARNEBACK_GAUSSIAN
static int	OPTFLOW_LK_GET_MIN_EIGENVALS
static int	OPTFLOW_USE_INITIAL_FLOW
static int	TrackerSamplerCSC_MODE_DETECT
static int	TrackerSamplerCSC_MODE_INIT_NEG
static int	TrackerSamplerCSC_MODE_INIT_POS
static int	TrackerSamplerCSC_MODE_TRACK_NEG
static int	TrackerSamplerCSC_MODE_TRACK_POS

Constructor Summary

Constructors

Constructor and Description
Video()

Method Summary

Modifier and Type	Method and Description
static int	buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel) Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
static int	buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives) Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
static int	buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder) Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
static int	buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder) Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
static int	buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage) Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
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.
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.
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.
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.
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.
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.
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.
static RotatedRect	CamShift(Mat probImage, Rect window, TermCriteria criteria) Finds an object center, size, and orientation.
static double	computeECC(Mat templateImage, Mat inputImage) Computes the Enhanced Correlation Coefficient (ECC) value between two images The Enhanced Correlation Coefficient (ECC) is a normalized measure of similarity between two images CITE: EP08.
static double	computeECC(Mat templateImage, Mat inputImage, Mat inputMask) Computes the Enhanced Correlation Coefficient (ECC) value between two images The Enhanced Correlation Coefficient (ECC) is a normalized measure of similarity between two images CITE: EP08.
static BackgroundSubtractorKNN	createBackgroundSubtractorKNN() Creates KNN Background Subtractor whether a pixel is close to that sample.
static BackgroundSubtractorKNN	createBackgroundSubtractorKNN(int history) Creates KNN Background Subtractor
static BackgroundSubtractorKNN	createBackgroundSubtractorKNN(int history, double dist2Threshold) Creates KNN Background Subtractor
static BackgroundSubtractorKNN	createBackgroundSubtractorKNN(int history, double dist2Threshold, boolean detectShadows) Creates KNN Background Subtractor
static BackgroundSubtractorMOG2	createBackgroundSubtractorMOG2() Creates MOG2 Background Subtractor to decide whether a pixel is well described by the background model.
static BackgroundSubtractorMOG2	createBackgroundSubtractorMOG2(int history) Creates MOG2 Background Subtractor
static BackgroundSubtractorMOG2	createBackgroundSubtractorMOG2(int history, double varThreshold) Creates MOG2 Background Subtractor
static BackgroundSubtractorMOG2	createBackgroundSubtractorMOG2(int history, double varThreshold, boolean detectShadows) Creates MOG2 Background Subtractor
static double	findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix)
static double	findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType)
static double	findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria)
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, Mat inputMask, int gaussFiltSize) Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 .
static double	findTransformECCWithMask(Mat templateImage, Mat inputImage, Mat templateMask, Mat inputMask, Mat warpMatrix) Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 using validity masks for both the template and the input images.
static double	findTransformECCWithMask(Mat templateImage, Mat inputImage, Mat templateMask, Mat inputMask, Mat warpMatrix, int motionType) Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 using validity masks for both the template and the input images.
static double	findTransformECCWithMask(Mat templateImage, Mat inputImage, Mat templateMask, Mat inputMask, Mat warpMatrix, int motionType, TermCriteria criteria) Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 using validity masks for both the template and the input images.
static double	findTransformECCWithMask(Mat templateImage, Mat inputImage, Mat templateMask, Mat inputMask, Mat warpMatrix, int motionType, TermCriteria criteria, int gaussFiltSize) Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 using validity masks for both the template and the input images.
static int	meanShift(Mat probImage, Rect window, TermCriteria criteria) Finds an object on a back projection image.
static Mat	readOpticalFlow(String path) Read a .flo file
static boolean	writeOpticalFlow(String path, Mat flow) Write a .flo to disk

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

OPTFLOW_USE_INITIAL_FLOW

public static final int OPTFLOW_USE_INITIAL_FLOW

See Also:

Constant Field Values

OPTFLOW_LK_GET_MIN_EIGENVALS

public static final int OPTFLOW_LK_GET_MIN_EIGENVALS

See Also:

Constant Field Values

OPTFLOW_FARNEBACK_GAUSSIAN

public static final int OPTFLOW_FARNEBACK_GAUSSIAN

See Also:

Constant Field Values

MOTION_TRANSLATION

public static final int MOTION_TRANSLATION

See Also:

Constant Field Values

MOTION_EUCLIDEAN

public static final int MOTION_EUCLIDEAN

See Also:

Constant Field Values

MOTION_AFFINE

public static final int MOTION_AFFINE

See Also:

Constant Field Values

MOTION_HOMOGRAPHY

public static final int MOTION_HOMOGRAPHY

See Also:

Constant Field Values

TrackerSamplerCSC_MODE_INIT_POS

public static final int TrackerSamplerCSC_MODE_INIT_POS

See Also:

Constant Field Values

TrackerSamplerCSC_MODE_INIT_NEG

public static final int TrackerSamplerCSC_MODE_INIT_NEG

See Also:

Constant Field Values

TrackerSamplerCSC_MODE_TRACK_POS

public static final int TrackerSamplerCSC_MODE_TRACK_POS

See Also:

Constant Field Values

TrackerSamplerCSC_MODE_TRACK_NEG

public static final int TrackerSamplerCSC_MODE_TRACK_NEG

See Also:

Constant Field Values

TrackerSamplerCSC_MODE_DETECT

public static final int TrackerSamplerCSC_MODE_DETECT

See Also:

Constant Field Values

Constructor Detail

Video

public Video()

Method Detail

createBackgroundSubtractorMOG2

public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history,
                                                                      double varThreshold,
                                                                      boolean detectShadows)

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.

Returns:

automatically generated

createBackgroundSubtractorMOG2

public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history,
                                                                      double varThreshold)

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. speed a bit, so if you do not need this feature, set the parameter to false.

Returns:

automatically generated

createBackgroundSubtractorMOG2

public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history)

Creates MOG2 Background Subtractor

Parameters:

history - Length of the history. to decide whether a pixel is well described by the background model. This parameter does not affect the background update. speed a bit, so if you do not need this feature, set the parameter to false.

Returns:

automatically generated

createBackgroundSubtractorMOG2

public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2()

Creates MOG2 Background Subtractor to decide whether a pixel is well described by the background model. This parameter does not affect the background update. speed a bit, so if you do not need this feature, set the parameter to false.

Returns:

automatically generated

createBackgroundSubtractorKNN

public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history,
                                                                    double dist2Threshold,
                                                                    boolean detectShadows)

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.

Returns:

automatically generated

createBackgroundSubtractorKNN

public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history,
                                                                    double dist2Threshold)

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. speed a bit, so if you do not need this feature, set the parameter to false.

Returns:

automatically generated

createBackgroundSubtractorKNN

public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history)

Creates KNN Background Subtractor

Parameters:

history - Length of the history. whether a pixel is close to that sample. This parameter does not affect the background update. speed a bit, so if you do not need this feature, set the parameter to false.

Returns:

automatically generated

createBackgroundSubtractorKNN

public static BackgroundSubtractorKNN createBackgroundSubtractorKNN()

Creates KNN Background Subtractor whether a pixel is close to that sample. This parameter does not affect the background update. speed a bit, so if you do not need this feature, set the parameter to false.

Returns:

automatically generated

CamShift

public static RotatedRect CamShift(Mat probImage,
                                   Rect window,
                                   TermCriteria criteria)

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 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() 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

Returns:

automatically generated

meanShift

public static int meanShift(Mat probImage,
                            Rect window,
                            TermCriteria criteria)

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.

Returns:

automatically generated

buildOpticalFlowPyramid

public static int buildOpticalFlowPyramid(Mat img,
                                          List<Mat> pyramid,
                                          Size winSize,
                                          int maxLevel,
                                          boolean withDerivatives,
                                          int pyrBorder,
                                          int derivBorder,
                                          boolean tryReuseInputImage)

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

public static int buildOpticalFlowPyramid(Mat img,
                                          List<Mat> pyramid,
                                          Size winSize,
                                          int maxLevel,
                                          boolean withDerivatives,
                                          int pyrBorder,
                                          int derivBorder)

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. to force data copying.

Returns:

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

buildOpticalFlowPyramid

public static int buildOpticalFlowPyramid(Mat img,
                                          List<Mat> pyramid,
                                          Size winSize,
                                          int maxLevel,
                                          boolean withDerivatives,
                                          int pyrBorder)

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. to force data copying.

Returns:

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

buildOpticalFlowPyramid

public static int buildOpticalFlowPyramid(Mat img,
                                          List<Mat> pyramid,
                                          Size winSize,
                                          int maxLevel,
                                          boolean withDerivatives)

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. to force data copying.

Returns:

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

buildOpticalFlowPyramid

public static int buildOpticalFlowPyramid(Mat img,
                                          List<Mat> pyramid,
                                          Size winSize,
                                          int maxLevel)

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. constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally. to force data copying.

Returns:

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

calcOpticalFlowPyrLK

public 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.

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 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. 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. 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

public 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.

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.

optical 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. 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. 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

public 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.

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.

• 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.

optical 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. 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. 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

public 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.

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. (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon.

• 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.

optical 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. 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. 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

public 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.

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. 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. (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon.

• 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.

optical 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. 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. 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

public 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.

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). 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. (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon.

• 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.

optical 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. 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. 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

calcOpticalFlowFarneback

public 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.

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 CITE: 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

computeECC

public static double computeECC(Mat templateImage,
                                Mat inputImage,
                                Mat inputMask)

Computes the Enhanced Correlation Coefficient (ECC) value between two images The Enhanced Correlation Coefficient (ECC) is a normalized measure of similarity between two images CITE: EP08. The result lies in the range [-1, 1], where 1 corresponds to perfect similarity (modulo affine shift and scale), 0 indicates no correlation, and -1 indicates perfect negative correlation. For single-channel images, the ECC is defined as: \( \mathrm{ECC}(I, T) = \frac{\sum_{x} (I(x) - \mu_I)(T(x) - \mu_T)} {\sqrt{\sum_{x} (I(x) - \mu_I)^2} \cdot \sqrt{\sum_{x} (T(x) - \mu_T)^2}} \) For multi-channel images (e.g., 3-channel RGB), the formula generalizes to: \( \mathrm{ECC}(I, T) = \frac{\sum_{x} \sum_{c=1}^{C} (I_c(x) - \mu_{I_c})(T_c(x) - \mu_{T_c})} {\sqrt{\sum_{x} \sum_{c=1}^{C} (I_c(x) - \mu_{I_c})^2} \cdot \sqrt{\sum_{x} \sum_{c=1}^{C} (T_c(x) - \mu_{T_c})^2}} \) Where:

• \(I_c(x), T_c(x)\) are the values of channel \(c\) at spatial location \(x\),
• \(\mu_{I_c}, \mu_{T_c}\) are the mean values of channel \(c\) over the masked region (if provided),
• \(C\) is the number of channels (only 1 and 3 are currently supported),
• The sums run over all pixels \(x\) in the image domain (optionally restricted by mask).

Parameters:

templateImage - Input template image; must have either 1 or 3 channels and be of type CV_8U, CV_16U, CV_32F, or CV_64F.

inputImage - Input image to be compared with the template; must have the same type and number of channels as templateImage.

inputMask - Optional single-channel mask to specify the valid region of interest in inputImage and templateImage.

Returns:

The ECC similarity coefficient in the range [-1, 1]. SEE: findTransformECC

computeECC

public static double computeECC(Mat templateImage,
                                Mat inputImage)

Computes the Enhanced Correlation Coefficient (ECC) value between two images The Enhanced Correlation Coefficient (ECC) is a normalized measure of similarity between two images CITE: EP08. The result lies in the range [-1, 1], where 1 corresponds to perfect similarity (modulo affine shift and scale), 0 indicates no correlation, and -1 indicates perfect negative correlation. For single-channel images, the ECC is defined as: \( \mathrm{ECC}(I, T) = \frac{\sum_{x} (I(x) - \mu_I)(T(x) - \mu_T)} {\sqrt{\sum_{x} (I(x) - \mu_I)^2} \cdot \sqrt{\sum_{x} (T(x) - \mu_T)^2}} \) For multi-channel images (e.g., 3-channel RGB), the formula generalizes to: \( \mathrm{ECC}(I, T) = \frac{\sum_{x} \sum_{c=1}^{C} (I_c(x) - \mu_{I_c})(T_c(x) - \mu_{T_c})} {\sqrt{\sum_{x} \sum_{c=1}^{C} (I_c(x) - \mu_{I_c})^2} \cdot \sqrt{\sum_{x} \sum_{c=1}^{C} (T_c(x) - \mu_{T_c})^2}} \) Where:

• \(I_c(x), T_c(x)\) are the values of channel \(c\) at spatial location \(x\),
• \(\mu_{I_c}, \mu_{T_c}\) are the mean values of channel \(c\) over the masked region (if provided),
• \(C\) is the number of channels (only 1 and 3 are currently supported),
• The sums run over all pixels \(x\) in the image domain (optionally restricted by mask).

Parameters:

templateImage - Input template image; must have either 1 or 3 channels and be of type CV_8U, CV_16U, CV_32F, or CV_64F.

inputImage - Input image to be compared with the template; must have the same type and number of channels as templateImage.

Returns:

The ECC similarity coefficient in the range [-1, 1]. SEE: findTransformECC

findTransformECC

public 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 CITE: EP08 .

Parameters:

templateImage - 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.

inputImage - 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 single channel 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 (CITE: 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: computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

Returns:

automatically generated

findTransformECC

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

findTransformECC

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

findTransformECC

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

findTransformECC

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

findTransformECCWithMask

public static double findTransformECCWithMask(Mat templateImage,
                                              Mat inputImage,
                                              Mat templateMask,
                                              Mat inputMask,
                                              Mat warpMatrix,
                                              int motionType,
                                              TermCriteria criteria,
                                              int gaussFiltSize)

Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 using validity masks for both the template and the input images. This function extends findTransformECC() by adding a mask for the template image. The Enhanced Correlation Coefficient is evaluated only over pixels that are valid in both images: on each iteration inputMask is warped into the template frame and combined with templateMask, and only the intersection of these masks contributes to the objective function.

Parameters:

templateImage - 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.

inputImage - input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage.

templateMask - single-channel 8-bit mask for templateImage indicating valid pixels to be used in the alignment. Must have the same size as templateImage.

inputMask - single-channel 8-bit mask for inputImage indicating valid pixels before warping. Must have the same size as inputImage.

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.

gaussFiltSize - size of the Gaussian blur filter used for smoothing images and masks before computing the alignment (DEFAULT: 5). SEE: findTransformECC, computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

Returns:

automatically generated

findTransformECCWithMask

public static double findTransformECCWithMask(Mat templateImage,
                                              Mat inputImage,
                                              Mat templateMask,
                                              Mat inputMask,
                                              Mat warpMatrix,
                                              int motionType,
                                              TermCriteria criteria)

Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 using validity masks for both the template and the input images. This function extends findTransformECC() by adding a mask for the template image. The Enhanced Correlation Coefficient is evaluated only over pixels that are valid in both images: on each iteration inputMask is warped into the template frame and combined with templateMask, and only the intersection of these masks contributes to the objective function.

Parameters:

templateImage - 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.

inputImage - input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage.

templateMask - single-channel 8-bit mask for templateImage indicating valid pixels to be used in the alignment. Must have the same size as templateImage.

inputMask - single-channel 8-bit mask for inputImage indicating valid pixels before warping. Must have the same size as inputImage.

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. before computing the alignment (DEFAULT: 5). SEE: findTransformECC, computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

Returns:

automatically generated

findTransformECCWithMask

public static double findTransformECCWithMask(Mat templateImage,
                                              Mat inputImage,
                                              Mat templateMask,
                                              Mat inputMask,
                                              Mat warpMatrix,
                                              int motionType)

Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 using validity masks for both the template and the input images. This function extends findTransformECC() by adding a mask for the template image. The Enhanced Correlation Coefficient is evaluated only over pixels that are valid in both images: on each iteration inputMask is warped into the template frame and combined with templateMask, and only the intersection of these masks contributes to the objective function.

Parameters:

templateImage - 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.

inputImage - input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage.

templateMask - single-channel 8-bit mask for templateImage indicating valid pixels to be used in the alignment. Must have the same size as templateImage.

inputMask - single-channel 8-bit mask for inputImage indicating valid pixels before warping. Must have the same size as inputImage.

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.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. before computing the alignment (DEFAULT: 5). SEE: findTransformECC, computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

Returns:

automatically generated

findTransformECCWithMask

public static double findTransformECCWithMask(Mat templateImage,
                                              Mat inputImage,
                                              Mat templateMask,
                                              Mat inputMask,
                                              Mat warpMatrix)

Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 using validity masks for both the template and the input images. This function extends findTransformECC() by adding a mask for the template image. The Enhanced Correlation Coefficient is evaluated only over pixels that are valid in both images: on each iteration inputMask is warped into the template frame and combined with templateMask, and only the intersection of these masks contributes to the objective function.

Parameters:

templateImage - 1 or 3 channel template image; CV_8U, CV_16U, CV_32F, CV_64F type.

inputImage - input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage.

templateMask - single-channel 8-bit mask for templateImage indicating valid pixels to be used in the alignment. Must have the same size as templateImage.

inputMask - single-channel 8-bit mask for inputImage indicating valid pixels before warping. Must have the same size as inputImage.

warpMatrix - floating-point \(2\times 3\) or \(3\times 3\) mapping matrix (warp).

• 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.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. before computing the alignment (DEFAULT: 5). SEE: findTransformECC, computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography

Returns:

automatically generated

readOpticalFlow

public static Mat readOpticalFlow(String path)

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).

Returns:

automatically generated

writeOpticalFlow

public static boolean writeOpticalFlow(String path,
                                       Mat flow)

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).

Returns:

automatically generated