calcOpticalFlowFarneback

Motion and trackingfunctionOpenCV 5.0.0

import { calcOpticalFlowFarneback } from '@banou/opencv-wasm'

Use after await initOpenCV(). See the initialization and named imports guide.

Explore visually: Farneback dense optical flow →

Estimate a displacement vector at every pixel using local polynomial approximations.

VISUAL WALKTHROUGHMotion

REFERENCE INPUTTwo consecutive frames

The object moves right and upward; most of the background remains still.

→

RESULTDense displacement field

Mint vectors follow the moving object. Grey vectors represent the nearly static background; each vector belongs to an image location.

Illustrative example. The stages explain the method; they are not a live OpenCV execution.

example: samples/python/snippets/opt_flow.py An example to show optical flow in python Computes a dense optical flow using the Gunnar Farneback's algorithm.

Note: Some examples:

• 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

calcOpticalFlowFarneback(prev: Mat, next: Mat, flow: Mat, pyr_scale: number, levels: number, winsize: number, iterations: number, poly_n: number, poly_sigma: number, flags: number): void;

prev

first 8-bit single-channel input image.

next

second input image of the same size and the same type as prev.

flow

Input/output value, modified by the native operation. 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 \texttt{winsize}\times\texttt{winsize} 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

\texttt{prev} (y,x)  \sim \texttt{next} ( y + \texttt{flow} (y,x)[1],  x + \texttt{flow} (y,x)[0])

Upstream declaration ↗

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