Package org.opencv.video
Class Video
 java.lang.Object

 org.opencv.video.Video

public class Video extends java.lang.Object


Field Summary
Fields Modifier and Type Field 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

Constructor Summary
Constructors Constructor Description Video()

Method Summary
All Methods Static Methods Concrete Methods Modifier and Type Method Description static int
buildOpticalFlowPyramid(Mat img, java.util.List<Mat> pyramid, Size winSize, int maxLevel)
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.static int
buildOpticalFlowPyramid(Mat img, java.util.List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives)
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.static int
buildOpticalFlowPyramid(Mat img, java.util.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, java.util.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, java.util.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 LucasKanade 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 LucasKanade 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 LucasKanade 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 LucasKanade 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 LucasKanade 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 LucasKanade 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 value between two images CITE: EP08 .static double
computeECC(Mat templateImage, Mat inputImage, Mat inputMask)
Computes the Enhanced Correlation Coefficient value 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 Subtractorstatic BackgroundSubtractorKNN
createBackgroundSubtractorKNN(int history, double dist2Threshold)
Creates KNN Background Subtractorstatic BackgroundSubtractorKNN
createBackgroundSubtractorKNN(int history, double dist2Threshold, boolean detectShadows)
Creates KNN Background Subtractorstatic 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 Subtractorstatic BackgroundSubtractorMOG2
createBackgroundSubtractorMOG2(int history, double varThreshold)
Creates MOG2 Background Subtractorstatic BackgroundSubtractorMOG2
createBackgroundSubtractorMOG2(int history, double varThreshold, boolean detectShadows)
Creates MOG2 Background Subtractorstatic DualTVL1OpticalFlow
createOptFlow_DualTVL1()
Creates instance of cv::DenseOpticalFlowstatic Mat
estimateRigidTransform(Mat src, Mat dst, boolean fullAffine)
Computes an optimal affine transformation between two 2D point sets.static Mat
estimateRigidTransform(Mat src, Mat dst, boolean fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0)
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 int
meanShift(Mat probImage, Rect window, TermCriteria criteria)
Finds an object on a back projection image.



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


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 prefilter 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, java.util.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
 8bit 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
 0based 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, java.util.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
 8bit 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
 0based 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, java.util.List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder)
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. Parameters:
img
 8bit 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
 0based 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, java.util.List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives)
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. Parameters:
img
 8bit 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
 0based 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, java.util.List<Mat> pyramid, Size winSize, int maxLevel)
Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK. Parameters:
img
 8bit 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
 0based 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 LucasKanade method with pyramids. Parameters:
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision floatingpoint 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
 0based 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.
 An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
 (Python) An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py
 (Python) An example using the LucasKanade 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 LucasKanade method with pyramids. Parameters:
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision floatingpoint 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
 0based 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.
 An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
 (Python) An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py
 (Python) An example using the LucasKanade 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 LucasKanade method with pyramids. Parameters:
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision floatingpoint 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
 0based 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.
 An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
 (Python) An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py
 (Python) An example using the LucasKanade 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 LucasKanade method with pyramids. Parameters:
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision floatingpoint 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
 0based 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.
 An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
 (Python) An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py
 (Python) An example using the LucasKanade 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 LucasKanade method with pyramids. Parameters:
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision floatingpoint 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.
 An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
 (Python) An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py
 (Python) An example using the LucasKanade 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 LucasKanade method with pyramids. Parameters:
prevImg
 first 8bit 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 singleprecision floatingpoint numbers.nextPts
 output vector of 2D points (with singleprecision floatingpoint 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.
 An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
 (Python) An example using the LucasKanade optical flow algorithm can be found at opencv_source_code/samples/python/lk_track.py
 (Python) An example using the LucasKanade 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 8bit singlechannel 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.
 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

estimateRigidTransform
public static Mat estimateRigidTransform(Mat src, Mat dst, boolean fullAffine)
Computes an optimal affine transformation between two 2D point sets. Parameters:
src
 First input 2D point set stored in std::vector or Mat, or an image stored in Mat.dst
 Second input 2D point set of the same size and the same type as A, or another image.fullAffine
 If true, the function finds an optimal affine transformation with no additional restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is limited to combinations of translation, rotation, and uniform scaling (4 degrees of freedom). The function finds an optimal affine transform *[Ab]* (a 2 x 3 floatingpoint matrix) that approximates best the affine transformation between: Two point sets Two raster images. In this case, the function first finds some features in the src image and finds the corresponding features in dst image. After that, the problem is reduced to the first case. In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and 2x1 vector *b* so that: \([A^*b^*] = arg \min _{[Ab]} \sum _i \ \texttt{dst}[i]  A { \texttt{src}[i]}^T  b \ ^2\) where src[i] and dst[i] are the ith points in src and dst, respectively \([Ab]\) can be either arbitrary (when fullAffine=true ) or have a form of \(\begin{bmatrix} a_{11} & a_{12} & b_1 \\ a_{12} & a_{11} & b_2 \end{bmatrix}\) when fullAffine=false. SEE: estimateAffine2D, estimateAffinePartial2D, getAffineTransform, getPerspectiveTransform, findHomography Returns:
 automatically generated

estimateRigidTransform
public static Mat estimateRigidTransform(Mat src, Mat dst, boolean fullAffine, int ransacMaxIters, double ransacGoodRatio, int ransacSize0)

computeECC
public static double computeECC(Mat templateImage, Mat inputImage, Mat inputMask)
Computes the Enhanced Correlation Coefficient value between two images CITE: EP08 . Parameters:
templateImage
 singlechannel template image; CV_8U or CV_32F array.inputImage
 singlechannel 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: findTransformECC Returns:
 automatically generated

computeECC
public static double computeECC(Mat templateImage, Mat inputImage)
Computes the Enhanced Correlation Coefficient value between two images CITE: EP08 . Parameters:
templateImage
 singlechannel template image; CV_8U or CV_32F array.inputImage
 singlechannel input image to be warped to provide an image similar to templateImage, same type as templateImage. SEE: findTransformECC Returns:
 automatically generated

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
 singlechannel template image; CV_8U or CV_32F array.inputImage
 singlechannel input image which should be warped with the final warpMatrix in order to provide an image similar to templateImage, same type as templateImage.warpMatrix
 floatingpoint \(2\times 3\) 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)
 Returns:
 automatically generated


createOptFlow_DualTVL1
public static DualTVL1OpticalFlow createOptFlow_DualTVL1()
Creates instance of cv::DenseOpticalFlow Returns:
 automatically generated