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# Motion Analysis

## CalcOpticalFlowBM

Calculates the optical flow for two images by using the block matching method.

C:`` void `cvCalcOpticalFlowBM`(const CvArr* prev, const CvArr* curr, CvSize block_size, CvSize shift_size, CvSize max_range, int use_previous, CvArr* velx, CvArr* vely)

Python:`` `cv.``CalcOpticalFlowBM`(prev, curr, blockSize, shiftSize, max_range, usePrevious, velx, vely) → None

  Parameters: | prev – First image, 8-bit, single-channel curr – Second image, 8-bit, single-channel block_size – Size of basic blocks that are compared shift_size – Block coordinate increments max_range – Size of the scanned neighborhood in pixels around the block use_previous – Flag that specifies whether to use the input velocity as initial approximations or not. velx – Horizontal component of the optical flow of \left \lfloor \frac{\texttt{prev->width} - \texttt{block\_size.width}}{\texttt{shift\_size.width}} \right \rfloor \times \left \lfloor \frac{\texttt{prev->height} - \texttt{block\_size.height}}{\texttt{shift\_size.height}} \right \rfloor size, 32-bit floating-point, single-channel 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 `block_size.width x block_size.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 `use_previous=1`)

## CalcOpticalFlowHS

Calculates the optical flow for two images using Horn-Schunck algorithm.

C:`` void `cvCalcOpticalFlowHS`(const CvArr* prev, const CvArr* curr, int use_previous, CvArr* velx, CvArr* vely, double lambda, CvTermCriteria criteria)

Python:`` `cv.``CalcOpticalFlowHS`(prev, curr, usePrevious, velx, vely, lambda, criteria) → None

  Parameters: | prev – First image, 8-bit, single-channel curr – Second image, 8-bit, single-channel use_previous – Flag that specifies whether to use the input velocity as initial approximations or not. velx – Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel vely – Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel lambda – Smoothness weight. The larger it is, the smoother optical flow map you get. 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 `calcOpticalFlowPyrLK()`. To track all the pixels, use `calcOpticalFlowFarneback()`.

## CalcOpticalFlowLK

Calculates the optical flow for two images using Lucas-Kanade algorithm.

C:`` void `cvCalcOpticalFlowLK`(const CvArr* prev, const CvArr* curr, CvSize win_size, CvArr* velx, CvArr* vely)

Python:`` `cv.``CalcOpticalFlowLK`(prev, curr, winSize, velx, vely) → None

  Parameters: | prev – First image, 8-bit, single-channel curr – Second image, 8-bit, single-channel win_size – Size of the averaging window used for grouping pixels velx – Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel 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 `calcOpticalFlowPyrLK()`. To track all the pixels, use `calcOpticalFlowFarneback()`.

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