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pysteps.tracking.lucaskanade.track_features

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pysteps.tracking.lucaskanade.track_features#

pysteps.tracking.lucaskanade.track_features(prvs_image, next_image, points, winsize=(50, 50), nr_levels=3, criteria=(3, 10, 0), flags=0, min_eig_thr=0.0001, verbose=False)#

Interface to the OpenCV Lucas-Kanade feature tracking algorithm (cv.calcOpticalFlowPyrLK).

Parameters:

  • prvs_image (ndarray or MaskedArray) – Array of shape (m, n) containing the first image. Invalid values (Nans or infs) are replaced with the min value.

  • next_image (ndarray or MaskedArray) – Array of shape (m, n) containing the successive image. Invalid values (Nans or infs) are replaced with the min value.

  • points (array_like) – Array of shape (p, 2) indicating the pixel coordinates of the tracking points (corners).

  • winsize (tuple of int, optional) – The winSize parameter in calcOpticalFlowPyrLK. It represents the size of the search window that it is used at each pyramid level. The default is (50, 50).

  • nr_levels (int, optional) – The maxLevel parameter in calcOpticalFlowPyrLK. It represents the 0-based maximal pyramid level number. The default is 3.

  • criteria (tuple of int, optional) – The TermCriteria parameter in calcOpticalFlowPyrLK , which specifies the termination criteria of the iterative search algorithm. The default is (3, 10, 0).

  • flags (int, optional) – Operation flags, see documentation calcOpticalFlowPyrLK. The default is 0.

  • min_eig_thr (float, optional) – The minEigThreshold parameter in calcOpticalFlowPyrLK. The default is 1e-4.

  • verbose (bool, optional) – Print the number of vectors that have been found. The default is False.

Returns:

  • xy (ndarray_) – Array of shape (d, 2) with the x- and y-coordinates of d <= p detected sparse motion vectors.

  • uv (ndarray_) – Array of shape (d, 2) with the u- and v-components of d <= p detected sparse motion vectors.

Notes

The tracking points can be obtained with the pysteps.utils.images.ShiTomasi_detection() routine.

See also

pysteps.motion.lucaskanade.dense_lucaskanade

References

Bouguet, J.-Y.: Pyramidal implementation of the affine Lucas Kanade feature tracker description of the algorithm, Intel Corp., 5, 4, 2001

Lucas, B. D. and Kanade, T.: An iterative image registration technique with an application to stereo vision, in: Proceedings of the 1981 DARPA Imaging Understanding Workshop, pp. 121–130, 1981.

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