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89 lines
4.1 KiB
ReStructuredText
89 lines
4.1 KiB
ReStructuredText
Motion Analysis
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===============
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.. highlight:: cpp
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CalcOpticalFlowBM
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-----------------
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Calculates the optical flow for two images by using the block matching method.
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.. ocv:cfunction:: void cvCalcOpticalFlowBM( const CvArr* prev, const CvArr* curr, CvSize blockSize, CvSize shiftSize, CvSize maxRange, int usePrevious, CvArr* velx, CvArr* vely )
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.. ocv:pyoldfunction:: cv.CalcOpticalFlowBM(prev, curr, blockSize, shiftSize, maxRange, usePrevious, velx, vely)-> None
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:param prev: First image, 8-bit, single-channel
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:param curr: Second image, 8-bit, single-channel
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:param blockSize: Size of basic blocks that are compared
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:param shiftSize: Block coordinate increments
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:param maxRange: Size of the scanned neighborhood in pixels around the block
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:param usePrevious: Flag that specifies whether to use the input velocity as initial approximations or not.
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:param velx: Horizontal component of the optical flow of
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.. math::
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\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
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size, 32-bit floating-point, single-channel
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:param vely: Vertical component of the optical flow of the same size ``velx`` , 32-bit floating-point, single-channel
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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``
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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``)
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CalcOpticalFlowHS
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-----------------
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Calculates the optical flow for two images using Horn-Schunck algorithm.
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.. ocv:cfunction:: void cvCalcOpticalFlowHS(const CvArr* prev, const CvArr* curr, int usePrevious, CvArr* velx, CvArr* vely, double lambda, CvTermCriteria criteria)
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.. ocv:pyoldfunction:: cv.CalcOpticalFlowHS(prev, curr, usePrevious, velx, vely, lambda, criteria)-> None
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:param prev: First image, 8-bit, single-channel
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:param curr: Second image, 8-bit, single-channel
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:param usePrevious: Flag that specifies whether to use the input velocity as initial approximations or not.
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:param velx: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
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:param vely: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
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:param lambda: Smoothness weight. The larger it is, the smoother optical flow map you get.
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:param criteria: Criteria of termination of velocity computing
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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`.
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CalcOpticalFlowLK
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-----------------
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Calculates the optical flow for two images using Lucas-Kanade algorithm.
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.. ocv:cfunction:: void cvCalcOpticalFlowLK( const CvArr* prev, const CvArr* curr, CvSize winSize, CvArr* velx, CvArr* vely )
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.. ocv:pyoldfunction:: cv.CalcOpticalFlowLK(prev, curr, winSize, velx, vely)-> None
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:param prev: First image, 8-bit, single-channel
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:param curr: Second image, 8-bit, single-channel
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:param winSize: Size of the averaging window used for grouping pixels
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:param velx: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
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:param vely: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
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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`.
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