org.opencv.video

Class Video

java.lang.Object

org.opencv.video.Video

public class Video
extends java.lang.Object

Field Summary

Fields

Modifier and Type	Field and Description
static int	OPTFLOW_FARNEBACK_GAUSSIAN
static int	OPTFLOW_LK_GET_MIN_EIGENVALS
static int	OPTFLOW_USE_INITIAL_FLOW

Constructor Summary

Constructors

Constructor and Description
Video()

Method Summary

Methods

Modifier and Type	Method and Description
static int	buildOpticalFlowPyramid(Mat img, java.util.List<Mat> pyramid, Size winSize, int maxLevel) Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK".
static int	buildOpticalFlowPyramid(Mat img, java.util.List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage) Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK".
static double	calcGlobalOrientation(Mat orientation, Mat mask, Mat mhi, double timestamp, double duration) Calculates a global motion orientation in a selected region.
static void	calcMotionGradient(Mat mhi, Mat mask, Mat orientation, double delta1, double delta2) Calculates a gradient orientation of a motion history image.
static void	calcMotionGradient(Mat mhi, Mat mask, Mat orientation, double delta1, double delta2, int apertureSize) Calculates a gradient orientation of a motion history image.
static void	calcOpticalFlowFarneback(Mat prev, Mat next, Mat flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags) Computes a dense optical flow using the Gunnar Farneback's algorithm.
static void	calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err) Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
static void	calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel) Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
static void	calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags, double minEigThreshold) Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.
static void	calcOpticalFlowSF(Mat from, Mat to, Mat flow, int layers, int averaging_block_size, int max_flow) Calculate an optical flow using "SimpleFlow" algorithm.
static void	calcOpticalFlowSF(Mat from, Mat to, Mat 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.
static RotatedRect	CamShift(Mat probImage, Rect window, TermCriteria criteria) Finds an object center, size, and orientation.
static Mat	estimateRigidTransform(Mat src, Mat dst, boolean fullAffine) Computes an optimal affine transformation between two 2D point sets.
static int	meanShift(Mat probImage, Rect window, TermCriteria criteria) Finds an object on a back projection image.
static void	segmentMotion(Mat mhi, Mat segmask, MatOfRect 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).
static void	updateMotionHistory(Mat silhouette, Mat mhi, double timestamp, double duration) Updates the motion history image by a moving silhouette.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

OPTFLOW_FARNEBACK_GAUSSIAN

public static final int OPTFLOW_FARNEBACK_GAUSSIAN

See Also:

Constant Field Values

OPTFLOW_LK_GET_MIN_EIGENVALS

public static final int OPTFLOW_LK_GET_MIN_EIGENVALS

See Also:

Constant Field Values

OPTFLOW_USE_INITIAL_FLOW

public static final int OPTFLOW_USE_INITIAL_FLOW

See Also:

Constant Field Values

Constructor Detail

Video

public Video()

Method Detail

buildOpticalFlowPyramid

public static int buildOpticalFlowPyramid(Mat img,
                          java.util.List<Mat> pyramid,
                          Size winSize,
                          int maxLevel)

Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK".

Parameters:

img - 8-bit input image.

pyramid - output pyramid.

winSize - window size of optical flow algorithm. Must be not less than winSize argument of "calcOpticalFlowPyrLK". It is needed to calculate required padding for pyramid levels.

maxLevel - 0-based maximal pyramid level number.

See Also:

org.opencv.video.Video.buildOpticalFlowPyramid

buildOpticalFlowPyramid

public static int buildOpticalFlowPyramid(Mat img,
                          java.util.List<Mat> pyramid,
                          Size winSize,
                          int maxLevel,
                          boolean withDerivatives,
                          int pyrBorder,
                          int derivBorder,
                          boolean tryReuseInputImage)

Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK".

Parameters:

img - 8-bit input image.

pyramid - output pyramid.

winSize - window size of optical flow algorithm. Must be not less than winSize argument of "calcOpticalFlowPyrLK". It is needed to calculate required padding for pyramid levels.

maxLevel - 0-based maximal pyramid level number.

withDerivatives - set to precompute gradients for the every pyramid level. If pyramid is constructed without the gradients then "calcOpticalFlowPyrLK" will calculate them internally.

pyrBorder - the border mode for pyramid layers.

derivBorder - the border mode for gradients.

tryReuseInputImage - put ROI of input image into the pyramid if possible. You can pass false to force data copying.

:return: number of levels in constructed pyramid. Can be less than maxLevel.

See Also:

org.opencv.video.Video.buildOpticalFlowPyramid

calcGlobalOrientation

public static double calcGlobalOrientation(Mat orientation,
                           Mat mask,
                           Mat mhi,
                           double timestamp,
                           double duration)

Calculates a global motion orientation in a selected region.

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.

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".

See Also:

org.opencv.video.Video.calcGlobalOrientation

calcMotionGradient

public static void calcMotionGradient(Mat mhi,
                      Mat mask,
                      Mat orientation,
                      double delta1,
                      double delta2)

Calculates a gradient orientation of a motion history image.

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

orientation(x,y)= arctan((dmhi/dy)/(dmhi/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

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 x 3 neighborhood of each pixel and marks the motion orientation at (x, y) as valid only if

min(delta1, delta2) <= M(x,y)-m(x,y) <= max(delta1, delta2).

See Also:

org.opencv.video.Video.calcMotionGradient

calcMotionGradient

public static void calcMotionGradient(Mat mhi,
                      Mat mask,
                      Mat orientation,
                      double delta1,
                      double delta2,
                      int apertureSize)

Calculates a gradient orientation of a motion history image.

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

orientation(x,y)= arctan((dmhi/dy)/(dmhi/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

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 x 3 neighborhood of each pixel and marks the motion orientation at (x, y) as valid only if

min(delta1, delta2) <= M(x,y)-m(x,y) <= max(delta1, delta2).

apertureSize - Aperture size of the "Sobel" operator.

See Also:

org.opencv.video.Video.calcMotionGradient

calcOpticalFlowFarneback

public static void calcOpticalFlowFarneback(Mat prev,
                            Mat next,
                            Mat flow,
                            double pyr_scale,
                            int levels,
                            int winsize,
                            int iterations,
                            int poly_n,
                            double poly_sigma,
                            int flags)

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

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

prev(y,x) ~ next(y + flow(y,x)[1], x + 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

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 winsizexwinsize 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.

See Also:

org.opencv.video.Video.calcOpticalFlowFarneback

calcOpticalFlowPyrLK

public static void calcOpticalFlowPyrLK(Mat prevImg,
                        Mat nextImg,
                        MatOfPoint2f prevPts,
                        MatOfPoint2f nextPts,
                        MatOfByte status,
                        MatOfFloat err)

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.

Note:

• An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
• (Python) An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/python2/lk_track.py
• (Python) An example using the Lucas-Kanade tracker for homography matching can be found at opencv_source_code/samples/python2/lk_homography.py

Parameters:

prevImg - first 8-bit input image or pyramid constructed by "buildOpticalFlowPyramid".

nextImg - second input image or pyramid of the same size and the same type as prevImg.

prevPts - vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.

nextPts - output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.

status - output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0.

err - output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases).

See Also:

org.opencv.video.Video.calcOpticalFlowPyrLK

calcOpticalFlowPyrLK

public static void calcOpticalFlowPyrLK(Mat prevImg,
                        Mat nextImg,
                        MatOfPoint2f prevPts,
                        MatOfPoint2f nextPts,
                        MatOfByte status,
                        MatOfFloat err,
                        Size winSize,
                        int maxLevel)

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.

Note:

• An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
• (Python) An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/python2/lk_track.py
• (Python) An example using the Lucas-Kanade tracker for homography matching can be found at opencv_source_code/samples/python2/lk_homography.py

Parameters:

prevImg - first 8-bit input image or pyramid constructed by "buildOpticalFlowPyramid".

nextImg - second input image or pyramid of the same size and the same type as prevImg.

prevPts - vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.

nextPts - output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.

status - output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0.

err - output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases).

winSize - size of the search window at each pyramid level.

maxLevel - 0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel.

See Also:

org.opencv.video.Video.calcOpticalFlowPyrLK

calcOpticalFlowPyrLK

public static void calcOpticalFlowPyrLK(Mat prevImg,
                        Mat nextImg,
                        MatOfPoint2f prevPts,
                        MatOfPoint2f nextPts,
                        MatOfByte status,
                        MatOfFloat err,
                        Size winSize,
                        int maxLevel,
                        TermCriteria criteria,
                        int flags,
                        double minEigThreshold)

Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with pyramids.

The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See [Bouguet00]. The function is parallelized with the TBB library.

Note:

• An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/cpp/lkdemo.cpp
• (Python) An example using the Lucas-Kanade optical flow algorithm can be found at opencv_source_code/samples/python2/lk_track.py
• (Python) An example using the Lucas-Kanade tracker for homography matching can be found at opencv_source_code/samples/python2/lk_homography.py

Parameters:

prevImg - first 8-bit input image or pyramid constructed by "buildOpticalFlowPyramid".

nextImg - second input image or pyramid of the same size and the same type as prevImg.

prevPts - vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.

nextPts - output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.

status - output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0.

err - output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't found then the error is not defined (use the status parameter to find such cases).

winSize - size of the search window at each pyramid level.

maxLevel - 0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel.

criteria - parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon.

flags - operation flags:

• OPTFLOW_USE_INITIAL_FLOW uses initial estimations, stored in nextPts; if the flag is not set, then prevPts is copied to nextPts and is considered the initial estimate.
• OPTFLOW_LK_GET_MIN_EIGENVALS use minimum eigen values as an error measure (see minEigThreshold description); if the flag is not set, then L1 distance between patches around the original and a moved point, divided by number of pixels in a window, is used as a error measure.

minEigThreshold - the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost.

See Also:

org.opencv.video.Video.calcOpticalFlowPyrLK

calcOpticalFlowSF

public static void calcOpticalFlowSF(Mat from,
                     Mat to,
                     Mat flow,
                     int layers,
                     int averaging_block_size,
                     int max_flow)

Calculate an optical flow using "SimpleFlow" algorithm.

See [Tao2012]. And site of project - http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/.

Note:

• An example using the simpleFlow algorithm can be found at opencv_source_code/samples/cpp/simpleflow_demo.cpp

Parameters:

from - a from

to - a to

flow - a flow

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

See Also:

org.opencv.video.Video.calcOpticalFlowSF

calcOpticalFlowSF

public static void calcOpticalFlowSF(Mat from,
                     Mat to,
                     Mat 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.

See [Tao2012]. And site of project - http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/.

Note:

• An example using the simpleFlow algorithm can be found at opencv_source_code/samples/cpp/simpleflow_demo.cpp

Parameters:

from - a from

to - a to

flow - a flow

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 - a upscale_averaging_radius

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 Also:

org.opencv.video.Video.calcOpticalFlowSF

CamShift

public static RotatedRect CamShift(Mat probImage,
                   Rect window,
                   TermCriteria criteria)

Finds an object center, size, and orientation.

The function implements the CAMSHIFT object tracking algorithm [Bradski98]. First, it finds an object center using "meanShift" and then adjusts the window size and finds the optimal rotation. The function returns the rotated rectangle structure that includes the object position, size, and orientation. The next position of the search window can be obtained with RotatedRect.boundingRect().

See the OpenCV sample camshiftdemo.c that tracks colored objects.

Note:

• (Python) A sample explaining the camshift tracking algorithm can be found at opencv_source_code/samples/python2/camshift.py

Parameters:

probImage - Back projection of the object histogram. See "calcBackProject".

window - Initial search window.

criteria - Stop criteria for the underlying "meanShift".

:returns: (in old interfaces) Number of iterations CAMSHIFT took to converge

See Also:

org.opencv.video.Video.CamShift

estimateRigidTransform

public static Mat estimateRigidTransform(Mat src,
                         Mat dst,
                         boolean fullAffine)

Computes an optimal affine transformation between two 2D point sets.

The function finds an optimal affine transform *[A|b]* (a 2 x 3 floating-point matrix) that approximates best the affine transformation between:

• Two point sets
• Two raster images. In this case, the function first finds some features in the src image and finds the corresponding features in dst image. After that, the problem is reduced to the first case.

In case of point sets, the problem is formulated as follows: you need to find a 2x2 matrix *A* and 2x1 vector *b* so that:

[A^*|b^*] = arg min _([A|b]) sum _i|dst[i] - A (src[i])^T - b| ^2

where src[i] and dst[i] are the i-th points in src and dst, respectively

[A|b] can be either arbitrary (when fullAffine=true) or have a form of

a_11 a_12 b_1 -a_12 a_11 b_2

when fullAffine=false.

Parameters:

src - First input 2D point set stored in std.vector or Mat, or an image stored in Mat.

dst - Second input 2D point set of the same size and the same type as A, or another image.

fullAffine - If true, the function finds an optimal affine transformation with no additional restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is limited to combinations of translation, rotation, and uniform scaling (5 degrees of freedom).

See Also:

org.opencv.video.Video.estimateRigidTransform, Calib3d.findHomography(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f, int, double, org.opencv.core.Mat), Imgproc.getAffineTransform(org.opencv.core.MatOfPoint2f, org.opencv.core.MatOfPoint2f), Imgproc.getPerspectiveTransform(org.opencv.core.Mat, org.opencv.core.Mat)

meanShift

public static int meanShift(Mat probImage,
            Rect window,
            TermCriteria criteria)

Finds an object on a back projection image.

The function implements the iterative object search algorithm. It takes the input back projection of an object and the initial position. The mass center in window of the back projection image is computed and the search window center shifts to the mass center. The procedure is repeated until the specified number of iterations criteria.maxCount is done or until the window center shifts by less than criteria.epsilon. The algorithm is used inside "CamShift" and, unlike "CamShift", the search window size or orientation do not change during the search. You can simply pass the output of "calcBackProject" to this function. But better results can be obtained if you pre-filter the back projection and remove the noise. For example, you can do this by retrieving connected components with "findContours", throwing away contours with small area ("contourArea"), and rendering the remaining contours with "drawContours".

Note:

• A mean-shift tracking sample can be found at opencv_source_code/samples/cpp/camshiftdemo.cpp

Parameters:

probImage - Back projection of the object histogram. See "calcBackProject" for details.

window - Initial search window.

criteria - Stop criteria for the iterative search algorithm.

:returns: Number of iterations CAMSHIFT took to converge.

See Also:

org.opencv.video.Video.meanShift

segmentMotion

public static void segmentMotion(Mat mhi,
                 Mat segmask,
                 MatOfRect 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).

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.

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.

See Also:

org.opencv.video.Video.segmentMotion

updateMotionHistory

public static void updateMotionHistory(Mat silhouette,
                       Mat mhi,
                       double timestamp,
                       double duration)

Updates the motion history image by a moving silhouette.

The function updates the motion history image as follows:

mhi(x,y)= timestamp if silhouette(x,y) != 0; 0 if silhouette(x,y) = 0 and mhi <(timestamp - duration); 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 [Davis97] and [Bradski00]. See also the OpenCV sample motempl.c that demonstrates the use of all the motion template functions.

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.

See Also:

org.opencv.video.Video.updateMotionHistory