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bundles / skimage 0.26.1rc0.dev0+git20260530.b607368ff / skimage / registration / _optical_flow / optical_flow_tvl1

function

skimage.registration._optical_flow:optical_flow_tvl1

source: /dev/scikit-image/src/skimage/registration/_optical_flow.py :166

Signature

def   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'> )

Summary

Coarse to fine optical flow estimator.

Extended Summary

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_image : ndarray, shape (M, N[, P[, ...]])

The first grayscale image of the sequence.

moving_image : ndarray, shape (M, N[, P[, ...]])

The second grayscale image of the sequence.

attachment : float, optional

Attachment parameter ( in [1]). The smaller this parameter is, the smoother the returned result will be.

tightness : float, optional

Tightness parameter ( in [1]). It should have a small value in order to maintain attachment and regularization parts in correspondence.

num_warp : int, optional

Number of times moving_image is warped.

num_iter : int, optional

Number of fixed point iteration.

tol : float, optional

Tolerance used as stopping criterion based on the L² distance between two consecutive values of (u, v).

prefilter : bool, 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.

dtype : dtype, 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

flow : ndarray, shape (image0.ndim, M, N[, P[, ...]])

The estimated optical flow components for each axis.

Notes

Color images are not supported.

Examples

from skimage.color import rgb2gray
from skimage.data import stereo_motorcycle
from skimage.registration import optical_flow_tvl1
image0, image1, disp = stereo_motorcycle()
image0 = rgb2gray(image0)
image1 = rgb2gray(image1)
flow = optical_flow_tvl1(image1, image0)
✓

Aliases

  • skimage.registration.optical_flow_tvl1

Referenced by

This package