Function opencv::video::calc_optical_flow_farneback

pub fn calc_optical_flow_farneback(
    prev: &dyn ToInputArray, 
    next: &dyn ToInputArray, 
    flow: &mut dyn ToInputOutputArray, 
    pyr_scale: f64, 
    levels: i32, 
    winsize: i32, 
    iterations: i32, 
    poly_n: i32, 
    poly_sigma: f64, 
    flags: i32
) -> Result<()>

Computes a dense optical flow using the Gunnar Farneback's algorithm.

Parameters

• prev: first 8-bit single-channel input image.
• next: second input image of the same size and the same type as prev.
• flow: computed flow image that has the same size as prev and type CV_32FC2.
• pyr_scale: parameter, specifying the image scale (<1) to build pyramids for each image; pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous one.
• levels: number of pyramid layers including the initial image; levels=1 means that no extra layers are created and only the original images are used.
• winsize: averaging window size; larger values increase the algorithm robustness to image noise and give more chances for fast motion detection, but yield more blurred motion field.
• iterations: number of iterations the algorithm does at each pyramid level.
• poly_n: size of the pixel neighborhood used to find polynomial expansion in each pixel; larger values mean that the image will be approximated with smoother surfaces, yielding more robust algorithm and more blurred motion field, typically poly_n =5 or 7.
• poly_sigma: standard deviation of the Gaussian that is used to smooth derivatives used as a basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a good value would be poly_sigma=1.5.
• flags: operation flags that can be a combination of the following:
• OPTFLOW_USE_INITIAL_FLOW uses the input flow as an initial flow approximation.
• OPTFLOW_FARNEBACK_GAUSSIAN uses the Gaussian filter instead of a box filter of the same size for optical flow estimation; usually, this option gives z more accurate flow than with a box filter, at the cost of lower speed; normally, winsize for a Gaussian window should be set to a larger value to achieve the same level of robustness.

The function finds an optical flow for each prev pixel using the Farneback2003 algorithm so that

Note:

• An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/cpp/fback.cpp
• (Python) An example using the optical flow algorithm described by Gunnar Farneback can be found at opencv_source_code/samples/python/opt_flow.py