calcOpticalFlowFarneback

最新推荐文章于 2023-08-16 12:29:50 发布

转载最新推荐文章于 2023-08-16 12:29:50 发布 · 339 阅读

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原文链接：http://www.cnblogs.com/casperwin/p/6557662.html

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#python

本文详细介绍了使用Gunnar Farneback算法计算密集光流的方法，包括C++、C及Python等不同编程语言的具体实现方式。文章还讨论了算法参数的作用，如金字塔缩放比例、窗口大小等，并提供了使用该算法的示例。

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calcOpticalFlowFarneback

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

C++: void calcOpticalFlowFarneback (InputArray prev, InputArray next, InputOutputArray flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags )

C: void cvCalcOpticalFlowFarneback (const CvArr* prev, const CvArr* next, CvArr* flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags )

Python: cv2. calcOpticalFlowFarneback (prev, next, flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags ) → flow

Parameters:

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 $\texttt{winsize}\times\texttt{winsize}$ 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.

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 $\texttt{winsize}\times\texttt{winsize}$ 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

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

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/python2/opt_flow.py

转载于:https://www.cnblogs.com/casperwin/p/6557662.html