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## org.opencv.video
Class Video

    java.lang.Object
      org.opencv.video.Video

    public class Video

extends java.lang.Object

Field Summary
`static int` | `OPTFLOW_FARNEBACK_GAUSSIAN`
`static int` | `OPTFLOW_LK_GET_MIN_EIGENVALS`
`static int` | `OPTFLOW_USE_INITIAL_FLOW`

Constructor Summary
`Video()`

Method Summary
`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
`clone, equals, finalize, 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.

  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.

  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])

  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.

  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; 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.

  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; 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.

  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; 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/.

  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/.

  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.

  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".

  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

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