OpenCV  3.1.0 Open Source Computer Vision
Optical Flow Algorithms

## Functions

double cv::motempl::calcGlobalOrientation (InputArray orientation, InputArray mask, InputArray mhi, double timestamp, double duration)
Calculates a global motion orientation in a selected region. More...

void cv::motempl::calcMotionGradient (InputArray mhi, OutputArray mask, OutputArray orientation, double delta1, double delta2, int apertureSize=3)
Calculates a gradient orientation of a motion history image. More...

void cv::optflow::calcOpticalFlowSF (InputArray from, InputArray to, OutputArray flow, int layers, int averaging_block_size, int max_flow)

void cv::optflow::calcOpticalFlowSF (InputArray from, InputArray to, OutputArray flow, int layers, int averaging_block_size, int max_flow, double sigma_dist, double sigma_color, int postprocess_window, double sigma_dist_fix, double sigma_color_fix, double occ_thr, int upscale_averaging_radius, double upscale_sigma_dist, double upscale_sigma_color, double speed_up_thr)
Calculate an optical flow using "SimpleFlow" algorithm. More...

void cv::optflow::calcOpticalFlowSparseToDense (InputArray from, InputArray to, OutputArray flow, int grid_step=8, int k=128, float sigma=0.05f, bool use_post_proc=true, float fgs_lambda=500.0f, float fgs_sigma=1.5f)
Fast dense optical flow based on PyrLK sparse matches interpolation. More...

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_DeepFlow ()
DeepFlow optical flow algorithm implementation. More...

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_Farneback ()
Additional interface to the Farneback's algorithm - calcOpticalFlowFarneback() More...

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_SimpleFlow ()
Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF() More...

Ptr< DenseOpticalFlow > cv::optflow::createOptFlow_SparseToDense ()
Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense() More...

void cv::motempl::segmentMotion (InputArray mhi, OutputArray segmask, std::vector< Rect > &boundingRects, double timestamp, double segThresh)
Splits a motion history image into a few parts corresponding to separate independent motions (for example, left hand, right hand). More...

void cv::motempl::updateMotionHistory (InputArray silhouette, InputOutputArray mhi, double timestamp, double duration)
Updates the motion history image by a moving silhouette. More...

bool cv::optflow::writeOpticalFlow (const String &path, InputArray flow)
Write a .flo to disk. More...

## Detailed Description

Dense optical flow algorithms compute motion for each point:

Motion templates is alternative technique for detecting motion and computing its direction. See samples/motempl.py.

## Function Documentation

 double cv::motempl::calcGlobalOrientation ( InputArray orientation, InputArray mask, InputArray mhi, double timestamp, double duration )

Calculates a global motion orientation in a selected region.

Parameters
 orientation Motion gradient orientation image calculated by the function calcMotionGradient mask Mask image. It may be a conjunction of a valid gradient mask, also calculated by calcMotionGradient , and the mask of a region whose direction needs to be calculated. mhi Motion history image calculated by updateMotionHistory . timestamp Timestamp passed to updateMotionHistory . duration Maximum duration of a motion track in milliseconds, passed to updateMotionHistory

The function calculates an average motion direction in the selected region and returns the angle between 0 degrees and 360 degrees. The average direction is computed from the weighted orientation histogram, where a recent motion has a larger weight and the motion occurred in the past has a smaller weight, as recorded in mhi .

 void cv::motempl::calcMotionGradient ( InputArray mhi, OutputArray mask, OutputArray orientation, double delta1, double delta2, int apertureSize = 3 )

Calculates a gradient orientation of a motion history image.

Parameters
 mhi Motion history single-channel floating-point image. mask Output mask image that has the type CV_8UC1 and the same size as mhi . Its non-zero elements mark pixels where the motion gradient data is correct. orientation Output motion gradient orientation image that has the same type and the same size as mhi . Each pixel of the image is a motion orientation, from 0 to 360 degrees. delta1 Minimal (or maximal) allowed difference between mhi values within a pixel neighborhood. delta2 Maximal (or minimal) allowed difference between mhi values within a pixel neighborhood. That is, the function finds the minimum ( $$m(x,y)$$ ) and maximum ( $$M(x,y)$$ ) mhi values over $$3 \times 3$$ neighborhood of each pixel and marks the motion orientation at $$(x, y)$$ as valid only if $\min ( \texttt{delta1} , \texttt{delta2} ) \le M(x,y)-m(x,y) \le \max ( \texttt{delta1} , \texttt{delta2} ).$ apertureSize Aperture size of the Sobel operator.

The function calculates a gradient orientation at each pixel $$(x, y)$$ as:

$\texttt{orientation} (x,y)= \arctan{\frac{d\texttt{mhi}/dy}{d\texttt{mhi}/dx}}$

In fact, fastAtan2 and phase are used so that the computed angle is measured in degrees and covers the full range 0..360. Also, the mask is filled to indicate pixels where the computed angle is valid.

Note
• (Python) An example on how to perform a motion template technique can be found at opencv_source_code/samples/python2/motempl.py
 void cv::optflow::calcOpticalFlowSF ( InputArray from, InputArray to, OutputArray flow, int layers, int averaging_block_size, int max_flow )

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

 void cv::optflow::calcOpticalFlowSF ( InputArray from, InputArray to, OutputArray flow, int layers, int averaging_block_size, int max_flow, double sigma_dist, double sigma_color, int postprocess_window, double sigma_dist_fix, double sigma_color_fix, double occ_thr, int upscale_averaging_radius, double upscale_sigma_dist, double upscale_sigma_color, double speed_up_thr )

Calculate an optical flow using "SimpleFlow" algorithm.

Parameters
 from First 8-bit 3-channel image. to Second 8-bit 3-channel image of the same size as prev flow computed flow image that has the same size as prev and type CV_32FC2 layers Number of layers averaging_block_size Size of block through which we sum up when calculate cost function for pixel max_flow maximal flow that we search at each level sigma_dist vector smooth spatial sigma parameter sigma_color vector smooth color sigma parameter postprocess_window window size for postprocess cross bilateral filter sigma_dist_fix spatial sigma for postprocess cross bilateralf filter sigma_color_fix color sigma for postprocess cross bilateral filter occ_thr threshold for detecting occlusions upscale_averaging_radius window size for bilateral upscale operation upscale_sigma_dist spatial sigma for bilateral upscale operation upscale_sigma_color color sigma for bilateral upscale operation speed_up_thr threshold to detect point with irregular flow - where flow should be recalculated after upscale

See [134] . And site of project - http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/.

Note
• An example using the simpleFlow algorithm can be found at samples/simpleflow_demo.cpp
 void cv::optflow::calcOpticalFlowSparseToDense ( InputArray from, InputArray to, OutputArray flow, int grid_step = 8, int k = 128, float sigma = 0.05f, bool use_post_proc = true, float fgs_lambda = 500.0f, float fgs_sigma = 1.5f )

Fast dense optical flow based on PyrLK sparse matches interpolation.

Parameters
 from first 8-bit 3-channel or 1-channel image. to second 8-bit 3-channel or 1-channel image of the same size as from flow computed flow image that has the same size as from and CV_32FC2 type grid_step stride used in sparse match computation. Lower values usually result in higher quality but slow down the algorithm. k number of nearest-neighbor matches considered, when fitting a locally affine model. Lower values can make the algorithm noticeably faster at the cost of some quality degradation. sigma parameter defining how fast the weights decrease in the locally-weighted affine fitting. Higher values can help preserve fine details, lower values can help to get rid of the noise in the output flow. use_post_proc defines whether the ximgproc::fastGlobalSmootherFilter() is used for post-processing after interpolation fgs_lambda see the respective parameter of the ximgproc::fastGlobalSmootherFilter() fgs_sigma see the respective parameter of the ximgproc::fastGlobalSmootherFilter()
 Ptr cv::optflow::createOptFlow_DeepFlow ( )

DeepFlow optical flow algorithm implementation.

The class implements the DeepFlow optical flow algorithm described in [150] . See also http://lear.inrialpes.fr/src/deepmatching/ . Parameters - class fields - that may be modified after creating a class instance:

• member float alpha Smoothness assumption weight
• member float delta Color constancy assumption weight
• member float gamma Gradient constancy weight
• member float sigma Gaussian smoothing parameter
• member int minSize Minimal dimension of an image in the pyramid (next, smaller images in the pyramid are generated until one of the dimensions reaches this size)
• member float downscaleFactor Scaling factor in the image pyramid (must be < 1)
• member int fixedPointIterations How many iterations on each level of the pyramid
• member int sorIterations Iterations of Succesive Over-Relaxation (solver)
• member float omega Relaxation factor in SOR
 Ptr cv::optflow::createOptFlow_Farneback ( )

Additional interface to the Farneback's algorithm - calcOpticalFlowFarneback()

 Ptr cv::optflow::createOptFlow_SimpleFlow ( )

Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF()

 Ptr cv::optflow::createOptFlow_SparseToDense ( )

Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense()

 Mat cv::optflow::readOpticalFlow ( const String & path )

Parameters
 path Path to the file to be loaded

The function readOpticalFlow loads a flow field from a file and returns it as a single matrix. Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the flow in the horizontal direction (u), second - vertical (v).

 void cv::motempl::segmentMotion ( InputArray mhi, OutputArray segmask, std::vector< Rect > & boundingRects, double timestamp, double segThresh )

Splits a motion history image into a few parts corresponding to separate independent motions (for example, left hand, right hand).

Parameters
 mhi Motion history image. segmask Image where the found mask should be stored, single-channel, 32-bit floating-point. boundingRects Vector containing ROIs of motion connected components. timestamp Current time in milliseconds or other units. segThresh Segmentation threshold that is recommended to be equal to the interval between motion history "steps" or greater.

The function finds all of the motion segments and marks them in segmask with individual values (1,2,...). It also computes a vector with ROIs of motion connected components. After that the motion direction for every component can be calculated with calcGlobalOrientation using the extracted mask of the particular component.

 void cv::motempl::updateMotionHistory ( InputArray silhouette, InputOutputArray mhi, double timestamp, double duration )

Updates the motion history image by a moving silhouette.

Parameters
 silhouette Silhouette mask that has non-zero pixels where the motion occurs. mhi Motion history image that is updated by the function (single-channel, 32-bit floating-point). timestamp Current time in milliseconds or other units. duration Maximal duration of the motion track in the same units as timestamp .

The function updates the motion history image as follows:

$\texttt{mhi} (x,y)= \forkthree{\texttt{timestamp}}{if $$\texttt{silhouette}(x,y) \ne 0$$}{0}{if $$\texttt{silhouette}(x,y) = 0$$ and $$\texttt{mhi} < (\texttt{timestamp} - \texttt{duration})$$}{\texttt{mhi}(x,y)}{otherwise}$

That is, MHI pixels where the motion occurs are set to the current timestamp , while the pixels where the motion happened last time a long time ago are cleared.

The function, together with calcMotionGradient and calcGlobalOrientation , implements a motion templates technique described in [30] and [16] .

 bool cv::optflow::writeOpticalFlow ( const String & path, InputArray flow )

Write a .flo to disk.

Parameters
 path Path to the file to be written flow Flow field to be stored

The function stores a flow field in a file, returns true on success, false otherwise. The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds to the flow in the horizontal direction (u), second - vertical (v).