skimage2.registration.optical_flow_tvl1#

skimage2.registration.optical_flow_tvl1(reference_image, moving_image, *, attachment=15, tightness=0.3, num_warp=5, num_iter=10, tol=0.0001, prefilter=False, dtype=<class 'numpy.float32'>)[source]#

Coarse to fine optical flow estimator.

The TV-L1 solver is applied at each level of the image pyramid. TV-L1 is a popular algorithm for optical flow estimation introduced by Zack et al. [1], improved in [2] and detailed in [3].

Parameters:

reference_imagendarray, shape (M, N[, P[, …]]): The first grayscale image of the sequence.
moving_imagendarray, shape (M, N[, P[, …]]): The second grayscale image of the sequence.
attachmentfloat, optional: Attachment parameter (\(\lambda\) in [1]). The smaller this parameter is, the smoother the returned result will be.
tightnessfloat, optional: Tightness parameter (\(\theta\) in [1]). It should have a small value in order to maintain attachment and regularization parts in correspondence.
num_warpint, optional: Number of times moving_image is warped.
num_iterint, optional: Number of fixed point iteration.
tolfloat, optional: Tolerance used as stopping criterion based on the L² distance between two consecutive values of (u, v).
prefilterbool, optional: Whether to prefilter the estimated optical flow before each image warp. When True, a median filter with window size 3 along each axis is applied. This helps to remove potential outliers.
dtypedtype, optional: Output data type: must be floating point. Single precision provides good results and saves memory usage and computation time compared to double precision.

Returns:

flowndarray, shape (image0.ndim, M, N[, P[, …]]): The estimated optical flow components for each axis.

Notes

Color images are not supported.

References

[1] (1,2,3)

Zach, C., Pock, T., & Bischof, H. (2007, September). A duality based approach for realtime TV-L 1 optical flow. In Joint pattern recognition symposium (pp. 214-223). Springer, Berlin, Heidelberg. DOI:10.1007/978-3-540-74936-3_22

[2]

Wedel, A., Pock, T., Zach, C., Bischof, H., & Cremers, D. (2009). An improved algorithm for TV-L 1 optical flow. In Statistical and geometrical approaches to visual motion analysis (pp. 23-45). Springer, Berlin, Heidelberg. DOI:10.1007/978-3-642-03061-1_2

[3]

Pérez, J. S., Meinhardt-Llopis, E., & Facciolo, G. (2013). TV-L1 optical flow estimation. Image Processing On Line, 2013, 137-150. DOI:10.5201/ipol.2013.26

Examples

>>> from _skimage2.color import rgb2gray
>>> from _skimage2.data import stereo_motorcycle
>>> from _skimage2.registration import optical_flow_tvl1
>>> image0, image1, disp = stereo_motorcycle()
>>> # --- Convert the images to gray level: color is not supported.
>>> image0 = rgb2gray(image0)
>>> image1 = rgb2gray(image1)
>>> flow = optical_flow_tvl1(image1, image0)