`skimage.registration._optical_flow:optical_flow_ilk`

source: /dev/scikit-image/src/skimage/registration/_optical_flow.py :347

Signature

   def     optical_flow_ilk (    reference_image    ,    moving_image    ,  * ,     radius    =  7   ,    num_warp    =  10   ,    gaussian    =  False   ,    prefilter    =  False   ,    dtype    =  <class 'numpy.float32'>     )

Summary

Coarse to fine optical flow estimator.

Extended Summary

The iterative Lucas-Kanade (iLK) solver is applied at each level of the image pyramid. iLK ^[1] is a fast and robust alternative to TVL1 algorithm although less accurate for rendering flat surfaces and object boundaries (see ^[2]).

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.
radius : int, optional: Radius of the window considered around each pixel.
num_warp : int, optional: Number of times moving_image is warped.
gaussian : bool, optional: If True, a Gaussian kernel is used for the local integration. Otherwise, a uniform kernel is used.
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 (reference_image.ndim, M, N[, P[, ...]]): The estimated optical flow components for each axis.

Notes

The implemented algorithm is described in Table2 of ^[1].
Color images are not supported.

Examples

from skimage.color import rgb2gray
from skimage.data import stereo_motorcycle
from skimage.registration import optical_flow_ilk
reference_image, moving_image, disp = stereo_motorcycle()
reference_image = rgb2gray(reference_image)
moving_image = rgb2gray(moving_image)
flow = optical_flow_ilk(moving_image, reference_image)

✓

Aliases

skimage.registration.optical_flow_ilk