pysteps.motion.proesmans.proesmans¶

pysteps.motion.proesmans.proesmans(input_images, lam=50.0, num_iter=100, num_levels=6, filter_std=0.0, verbose=True, full_output=False)¶

Implementation of the anisotropic diffusion method of Proesmans et al. (1994).

Parameters

input_images: array_like: Array of shape (2, m, n) containing the first and second input image.
lam: float: Multiplier of the smoothness term. Smaller values give a smoother motion field.
num_iter: float: The number of iterations to use.
num_levels: int: The number of image pyramid levels to use.
filter_std: float: Standard deviation of an optional Gaussian filter that is applied before computing the optical flow.
verbose: bool, optional: Verbosity enabled if True (default).
full_output: bool, optional: If True, the output is a two-element tuple containing the forward-backward advection and consistency fields. The first element is shape (2, 2, m, n), where the index along the first dimension refers to the forward and backward advection fields. The second element is an array of shape (2, m, n), where the index along the first dimension refers to the forward and backward consistency fields. Default: False.

Returns

out: ndarray: If full_output=False, the advection field having shape (2, m, n), where out[0, :, :] contains the x-components of the motion vectors and out[1, :, :] contains the y-components. The velocities are in units of pixels / timestep, where timestep is the time difference between the two input images.

References

[PvGPO94]