Descriptor types for the Global Patch Collider.

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 .

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

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

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 [190] . 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

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()

DeepFlow optical flow algorithm implementation.

The class implements the DeepFlow optical flow algorithm described in [216] . 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

Creates instance of cv::DenseOpticalFlow.

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

Creates an instance of PCAFlow.

Additional interface to the SimpleFlow algorithm - calcOpticalFlowSF()

Additional interface to the SparseToDenseFlow algorithm - calcOpticalFlowSparseToDense()

Find correspondences between two images.

Parameters

[in] imgFrom First image in a sequence.

[in] imgTo Second image in a sequence.

[out] corr Output vector with pairs of corresponding points.

[in] params Additional matching parameters for fine-tuning.

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.

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 [42] and [24] .