Motion Analysis =============== .. highlight:: cpp CalcOpticalFlowBM ----------------- Calculates the optical flow for two images by using the block matching method. .. ocv:cfunction:: void cvCalcOpticalFlowBM( const CvArr* prev, const CvArr* curr, CvSize blockSize, CvSize shiftSize, CvSize maxRange, int usePrevious, CvArr* velx, CvArr* vely ) .. ocv:pyoldfunction:: cv.CalcOpticalFlowBM(prev, curr, blockSize, shiftSize, maxRange, usePrevious, velx, vely)-> None :param prev: First image, 8-bit, single-channel :param curr: Second image, 8-bit, single-channel :param blockSize: Size of basic blocks that are compared :param shiftSize: Block coordinate increments :param maxRange: Size of the scanned neighborhood in pixels around the block :param usePrevious: Flag that specifies whether to use the input velocity as initial approximations or not. :param velx: Horizontal component of the optical flow of .. math:: \left \lfloor \frac{\texttt{prev->width} - \texttt{blockSize.width}}{\texttt{shiftSize.width}} \right \rfloor \times \left \lfloor \frac{\texttt{prev->height} - \texttt{blockSize.height}}{\texttt{shiftSize.height}} \right \rfloor size, 32-bit floating-point, single-channel :param vely: Vertical component of the optical flow of the same size velx , 32-bit floating-point, single-channel The function calculates the optical flow for overlapped blocks blockSize.width x blockSize.height pixels each, thus the velocity fields are smaller than the original images. For every block in prev the functions tries to find a similar block in curr in some neighborhood of the original block or shifted by (velx(x0,y0), vely(x0,y0)) block as has been calculated by previous function call (if usePrevious=1) CalcOpticalFlowHS ----------------- Calculates the optical flow for two images using Horn-Schunck algorithm. .. ocv:cfunction:: void cvCalcOpticalFlowHS(const CvArr* prev, const CvArr* curr, int usePrevious, CvArr* velx, CvArr* vely, double lambda, CvTermCriteria criteria) .. ocv:pyoldfunction:: cv.CalcOpticalFlowHS(prev, curr, usePrevious, velx, vely, lambda, criteria)-> None :param prev: First image, 8-bit, single-channel :param curr: Second image, 8-bit, single-channel :param usePrevious: Flag that specifies whether to use the input velocity as initial approximations or not. :param velx: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel :param vely: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel :param lambda: Smoothness weight. The larger it is, the smoother optical flow map you get. :param criteria: Criteria of termination of velocity computing The function computes the flow for every pixel of the first input image using the Horn and Schunck algorithm [Horn81]_. The function is obsolete. To track sparse features, use :ocv:func:calcOpticalFlowPyrLK. To track all the pixels, use :ocv:func:calcOpticalFlowFarneback. CalcOpticalFlowLK ----------------- Calculates the optical flow for two images using Lucas-Kanade algorithm. .. ocv:cfunction:: void cvCalcOpticalFlowLK( const CvArr* prev, const CvArr* curr, CvSize winSize, CvArr* velx, CvArr* vely ) .. ocv:pyoldfunction:: cv.CalcOpticalFlowLK(prev, curr, winSize, velx, vely)-> None :param prev: First image, 8-bit, single-channel :param curr: Second image, 8-bit, single-channel :param winSize: Size of the averaging window used for grouping pixels :param velx: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel :param vely: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel The function computes the flow for every pixel of the first input image using the Lucas and Kanade algorithm [Lucas81]_. The function is obsolete. To track sparse features, use :ocv:func:calcOpticalFlowPyrLK. To track all the pixels, use :ocv:func:calcOpticalFlowFarneback.