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().