Android Open Source - Operation-Valkyrie Video






From Project

Back to project page Operation-Valkyrie.

License

The source code is released under:

Terms and conditions Preamble: This Agreement, signed on Jun 10, 2012 [hereinafter: Effective Date] governs the relationship between the Enduser, a private person, (hereinafter: Licensee) and Paul N...

If you think the Android project Operation-Valkyrie listed in this page is inappropriate, such as containing malicious code/tools or violating the copyright, please email info at java2s dot com, thanks.

Java Source Code

//
// This file is auto-generated. Please don't modify it!
////from   w w  w .ja  v  a 2s . c  o  m
package org.opencv.video;

import java.util.List;
import org.opencv.core.Mat;
import org.opencv.core.MatOfByte;
import org.opencv.core.MatOfFloat;
import org.opencv.core.MatOfPoint2f;
import org.opencv.core.MatOfRect;
import org.opencv.core.Rect;
import org.opencv.core.RotatedRect;
import org.opencv.core.Size;
import org.opencv.core.TermCriteria;
import org.opencv.utils.Converters;

public class Video {

    private static final int
            CV_LKFLOW_INITIAL_GUESSES = 4,
            CV_LKFLOW_GET_MIN_EIGENVALS = 8;


    public static final int
            OPTFLOW_USE_INITIAL_FLOW = CV_LKFLOW_INITIAL_GUESSES,
            OPTFLOW_LK_GET_MIN_EIGENVALS = CV_LKFLOW_GET_MIN_EIGENVALS,
            OPTFLOW_FARNEBACK_GAUSSIAN = 256;


    //
    // C++:  RotatedRect CamShift(Mat probImage, Rect& window, TermCriteria criteria)
    //

/**
 * <p>Finds an object center, size, and orientation.</p>
 *
 * <p>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 <code>RotatedRect.boundingRect()</code>.</p>
 *
 * <p>See the OpenCV sample <code>camshiftdemo.c</code> that tracks colored
 * objects.</p>
 *
 * @param probImage Back projection of the object histogram. See
 * "calcBackProject".
 * @param window Initial search window.
 * @param criteria Stop criteria for the underlying "meanShift".
 *
 * <p>:returns: (in old interfaces) Number of iterations CAMSHIFT took to converge</p>
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#camshift">org.opencv.video.Video.CamShift</a>
 */
    public static RotatedRect CamShift(Mat probImage, Rect window, TermCriteria criteria)
    {
        double[] window_out = new double[4];
        RotatedRect retVal = new RotatedRect(CamShift_0(probImage.nativeObj, window.x, window.y, window.width, window.height, window_out, criteria.type, criteria.maxCount, criteria.epsilon));
        if(window!=null){ window.x = (int)window_out[0]; window.y = (int)window_out[1]; window.width = (int)window_out[2]; window.height = (int)window_out[3]; }
        return retVal;
    }


    //
    // C++:  int buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true)
    //

/**
 * <p>Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK".</p>
 *
 * @param img 8-bit input image.
 * @param pyramid output pyramid.
 * @param winSize window size of optical flow algorithm. Must be not less than
 * <code>winSize</code> argument of "calcOpticalFlowPyrLK". It is needed to
 * calculate required padding for pyramid levels.
 * @param maxLevel 0-based maximal pyramid level number.
 * @param withDerivatives set to precompute gradients for the every pyramid
 * level. If pyramid is constructed without the gradients then "calcOpticalFlowPyrLK"
 * will calculate them internally.
 * @param pyrBorder the border mode for pyramid layers.
 * @param derivBorder the border mode for gradients.
 * @param tryReuseInputImage put ROI of input image into the pyramid if
 * possible. You can pass <code>false</code> to force data copying.
 *
 * <p>:return: number of levels in constructed pyramid. Can be less than
 * <code>maxLevel</code>.</p>
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#buildopticalflowpyramid">org.opencv.video.Video.buildOpticalFlowPyramid</a>
 */
    public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage)
    {
        Mat pyramid_mat = new Mat();
        int retVal = buildOpticalFlowPyramid_0(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives, pyrBorder, derivBorder, tryReuseInputImage);
        Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
        return retVal;
    }

/**
 * <p>Constructs the image pyramid which can be passed to "calcOpticalFlowPyrLK".</p>
 *
 * @param img 8-bit input image.
 * @param pyramid output pyramid.
 * @param winSize window size of optical flow algorithm. Must be not less than
 * <code>winSize</code> argument of "calcOpticalFlowPyrLK". It is needed to
 * calculate required padding for pyramid levels.
 * @param maxLevel 0-based maximal pyramid level number.
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#buildopticalflowpyramid">org.opencv.video.Video.buildOpticalFlowPyramid</a>
 */
    public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel)
    {
        Mat pyramid_mat = new Mat();
        int retVal = buildOpticalFlowPyramid_1(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel);
        Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
        return retVal;
    }


    //
    // C++:  double calcGlobalOrientation(Mat orientation, Mat mask, Mat mhi, double timestamp, double duration)
    //

/**
 * <p>Calculates a global motion orientation in a selected region.</p>
 *
 * <p>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 <code>mhi</code>.</p>
 *
 * @param orientation Motion gradient orientation image calculated by the
 * function "calcMotionGradient".
 * @param 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.
 * @param mhi Motion history image calculated by "updateMotionHistory".
 * @param timestamp Timestamp passed to "updateMotionHistory".
 * @param duration Maximum duration of a motion track in milliseconds, passed to
 * "updateMotionHistory".
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#calcglobalorientation">org.opencv.video.Video.calcGlobalOrientation</a>
 */
    public static double calcGlobalOrientation(Mat orientation, Mat mask, Mat mhi, double timestamp, double duration)
    {

        double retVal = calcGlobalOrientation_0(orientation.nativeObj, mask.nativeObj, mhi.nativeObj, timestamp, duration);

        return retVal;
    }


    //
    // C++:  void calcMotionGradient(Mat mhi, Mat& mask, Mat& orientation, double delta1, double delta2, int apertureSize = 3)
    //

/**
 * <p>Calculates a gradient orientation of a motion history image.</p>
 *
 * <p>The function calculates a gradient orientation at each pixel <em>(x, y)</em>
 * as:</p>
 *
 * <p><em>orientation(x,y)= arctan((dmhi/dy)/(dmhi/dx))</em></p>
 *
 * <p>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
 * <code>mask</code> is filled to indicate pixels where the computed angle is
 * valid.</p>
 *
 * @param mhi Motion history single-channel floating-point image.
 * @param mask Output mask image that has the type <code>CV_8UC1</code> and the
 * same size as <code>mhi</code>. Its non-zero elements mark pixels where the
 * motion gradient data is correct.
 * @param orientation Output motion gradient orientation image that has the same
 * type and the same size as <code>mhi</code>. Each pixel of the image is a
 * motion orientation, from 0 to 360 degrees.
 * @param delta1 Minimal (or maximal) allowed difference between
 * <code>mhi</code> values within a pixel neighborhood.
 * @param delta2 Maximal (or minimal) allowed difference between
 * <code>mhi</code> values within a pixel neighborhood. That is, the function
 * finds the minimum (<em>m(x,y)</em>) and maximum (<em>M(x,y)</em>)
 * <code>mhi</code> values over <em>3 x 3</em> neighborhood of each pixel and
 * marks the motion orientation at <em>(x, y)</em> as valid only if
 *
 * <p><em>min(delta1, delta2) <= M(x,y)-m(x,y) <= max(delta1, delta2).</em></p>
 * @param apertureSize Aperture size of the "Sobel" operator.
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#calcmotiongradient">org.opencv.video.Video.calcMotionGradient</a>
 */
    public static void calcMotionGradient(Mat mhi, Mat mask, Mat orientation, double delta1, double delta2, int apertureSize)
    {

        calcMotionGradient_0(mhi.nativeObj, mask.nativeObj, orientation.nativeObj, delta1, delta2, apertureSize);

        return;
    }

/**
 * <p>Calculates a gradient orientation of a motion history image.</p>
 *
 * <p>The function calculates a gradient orientation at each pixel <em>(x, y)</em>
 * as:</p>
 *
 * <p><em>orientation(x,y)= arctan((dmhi/dy)/(dmhi/dx))</em></p>
 *
 * <p>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
 * <code>mask</code> is filled to indicate pixels where the computed angle is
 * valid.</p>
 *
 * @param mhi Motion history single-channel floating-point image.
 * @param mask Output mask image that has the type <code>CV_8UC1</code> and the
 * same size as <code>mhi</code>. Its non-zero elements mark pixels where the
 * motion gradient data is correct.
 * @param orientation Output motion gradient orientation image that has the same
 * type and the same size as <code>mhi</code>. Each pixel of the image is a
 * motion orientation, from 0 to 360 degrees.
 * @param delta1 Minimal (or maximal) allowed difference between
 * <code>mhi</code> values within a pixel neighborhood.
 * @param delta2 Maximal (or minimal) allowed difference between
 * <code>mhi</code> values within a pixel neighborhood. That is, the function
 * finds the minimum (<em>m(x,y)</em>) and maximum (<em>M(x,y)</em>)
 * <code>mhi</code> values over <em>3 x 3</em> neighborhood of each pixel and
 * marks the motion orientation at <em>(x, y)</em> as valid only if
 *
 * <p><em>min(delta1, delta2) <= M(x,y)-m(x,y) <= max(delta1, delta2).</em></p>
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#calcmotiongradient">org.opencv.video.Video.calcMotionGradient</a>
 */
    public static void calcMotionGradient(Mat mhi, Mat mask, Mat orientation, double delta1, double delta2)
    {

        calcMotionGradient_1(mhi.nativeObj, mask.nativeObj, orientation.nativeObj, delta1, delta2);

        return;
    }


    //
    // C++:  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)
    //

/**
 * <p>Computes a dense optical flow using the Gunnar Farneback's algorithm.</p>
 *
 * <p>The function finds an optical flow for each <code>prev</code> pixel using the
 * [Farneback2003] algorithm so that</p>
 *
 * <p><em>prev(y,x) ~ next(y + flow(y,x)[1], x + flow(y,x)[0])</em></p>
 *
 * @param prev First 8-bit single-channel input image.
 * @param next Second input image of the same size and the same type as
 * <code>prev</code>.
 * @param flow Computed flow image that has the same size as <code>prev</code>
 * and type <code>CV_32FC2</code>.
 * @param pyr_scale Parameter specifying the image scale (<1) to build pyramids
 * for each image. <code>pyr_scale=0.5</code> means a classical pyramid, where
 * each next layer is twice smaller than the previous one.
 * @param levels Number of pyramid layers including the initial image.
 * <code>levels=1</code> means that no extra layers are created and only the
 * original images are used.
 * @param 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.
 * @param iterations Number of iterations the algorithm does at each pyramid
 * level.
 * @param 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, <code>poly_n</code> =5 or 7.
 * @param poly_sigma Standard deviation of the Gaussian that is used to smooth
 * derivatives used as a basis for the polynomial expansion. For
 * <code>poly_n=5</code>, you can set <code>poly_sigma=1.1</code>. For
 * <code>poly_n=7</code>, a good value would be <code>poly_sigma=1.5</code>.
 * @param flags Operation flags that can be a combination of the following:
 * <ul>
 *   <li> OPTFLOW_USE_INITIAL_FLOW Use the input <code>flow</code> as an initial
 * flow approximation.
 *   <li> OPTFLOW_FARNEBACK_GAUSSIAN Use the Gaussian <em>winsizexwinsize</em>
 * 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, <code>winsize</code> for a Gaussian window
 * should be set to a larger value to achieve the same level of robustness.
 * </ul>
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#calcopticalflowfarneback">org.opencv.video.Video.calcOpticalFlowFarneback</a>
 */
    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)
    {

        calcOpticalFlowFarneback_0(prev.nativeObj, next.nativeObj, flow.nativeObj, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags);

        return;
    }


    //
    // C++:  void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, vector_Point2f prevPts, vector_Point2f& nextPts, vector_uchar& status, vector_float& err, Size winSize = Size(21,21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
    //

/**
 * <p>Calculates an optical flow for a sparse feature set using the iterative
 * Lucas-Kanade method with pyramids.</p>
 *
 * <p>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.</p>
 *
 * @param prevImg First 8-bit input image or pyramid constructed by
 * "buildOpticalFlowPyramid".
 * @param nextImg Second input image or pyramid of the same size and the same
 * type as <code>prevImg</code>.
 * @param prevPts Vector of 2D points for which the flow needs to be found. The
 * point coordinates must be single-precision floating-point numbers.
 * @param 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 <code>OPTFLOW_USE_INITIAL_FLOW</code> flag
 * is passed, the vector must have the same size as in the input.
 * @param 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.
 * @param err Output vector of errors. Each element of the vector is set to a
 * error for the corresponding feature. A type of the error measure can be set
 * in <code>flags</code> parameter. If the flow wasn't found then the error is
 * not defined (use the <code>status</code> parameter to find such cases).
 * @param winSize Size of the search window at each pyramid level.
 * @param 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 <code>maxLevel</code>.
 * @param criteria Parameter specifying the termination criteria of the
 * iterative search algorithm (after the specified maximum number of iterations
 * <code>criteria.maxCount</code> or when the search window moves by less than
 * <code>criteria.epsilon</code>.
 * @param flags Operation flags:
 * <ul>
 *   <li> OPTFLOW_USE_INITIAL_FLOW Use initial estimations stored in
 * <code>nextPts</code>. If the flag is not set, then <code>prevPts</code> is
 * copied to <code>nextPts</code> and is considered as the initial estimate.
 *   <li> OPTFLOW_LK_GET_MIN_EIGENVALS Use minimum eigen values as a error
 * measure (see <code>minEigThreshold</code> 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.
 * </ul>
 * @param minEigThreshold The algorithm computes a 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 then <code>minEigThreshold</code> then a corresponding
 * feature is filtered out and its flow is not computed. So it allows to remove
 * bad points earlier and speed up the computation.
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#calcopticalflowpyrlk">org.opencv.video.Video.calcOpticalFlowPyrLK</a>
 */
    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)
    {
        Mat prevPts_mat = prevPts;
        Mat nextPts_mat = nextPts;
        Mat status_mat = status;
        Mat err_mat = err;
        calcOpticalFlowPyrLK_0(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon, flags, minEigThreshold);

        return;
    }

/**
 * <p>Calculates an optical flow for a sparse feature set using the iterative
 * Lucas-Kanade method with pyramids.</p>
 *
 * <p>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.</p>
 *
 * @param prevImg First 8-bit input image or pyramid constructed by
 * "buildOpticalFlowPyramid".
 * @param nextImg Second input image or pyramid of the same size and the same
 * type as <code>prevImg</code>.
 * @param prevPts Vector of 2D points for which the flow needs to be found. The
 * point coordinates must be single-precision floating-point numbers.
 * @param 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 <code>OPTFLOW_USE_INITIAL_FLOW</code> flag
 * is passed, the vector must have the same size as in the input.
 * @param 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.
 * @param err Output vector of errors. Each element of the vector is set to a
 * error for the corresponding feature. A type of the error measure can be set
 * in <code>flags</code> parameter. If the flow wasn't found then the error is
 * not defined (use the <code>status</code> parameter to find such cases).
 * @param winSize Size of the search window at each pyramid level.
 * @param 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 <code>maxLevel</code>.
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#calcopticalflowpyrlk">org.opencv.video.Video.calcOpticalFlowPyrLK</a>
 */
    public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel)
    {
        Mat prevPts_mat = prevPts;
        Mat nextPts_mat = nextPts;
        Mat status_mat = status;
        Mat err_mat = err;
        calcOpticalFlowPyrLK_1(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel);

        return;
    }

/**
 * <p>Calculates an optical flow for a sparse feature set using the iterative
 * Lucas-Kanade method with pyramids.</p>
 *
 * <p>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.</p>
 *
 * @param prevImg First 8-bit input image or pyramid constructed by
 * "buildOpticalFlowPyramid".
 * @param nextImg Second input image or pyramid of the same size and the same
 * type as <code>prevImg</code>.
 * @param prevPts Vector of 2D points for which the flow needs to be found. The
 * point coordinates must be single-precision floating-point numbers.
 * @param 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 <code>OPTFLOW_USE_INITIAL_FLOW</code> flag
 * is passed, the vector must have the same size as in the input.
 * @param 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.
 * @param err Output vector of errors. Each element of the vector is set to a
 * error for the corresponding feature. A type of the error measure can be set
 * in <code>flags</code> parameter. If the flow wasn't found then the error is
 * not defined (use the <code>status</code> parameter to find such cases).
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#calcopticalflowpyrlk">org.opencv.video.Video.calcOpticalFlowPyrLK</a>
 */
    public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err)
    {
        Mat prevPts_mat = prevPts;
        Mat nextPts_mat = nextPts;
        Mat status_mat = status;
        Mat err_mat = err;
        calcOpticalFlowPyrLK_2(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj);

        return;
    }


    //
    // C++:  Mat estimateRigidTransform(Mat src, Mat dst, bool fullAffine)
    //

/**
 * <p>Computes an optimal affine transformation between two 2D point sets.</p>
 *
 * <p>The function finds an optimal affine transform *[A|b]* (a <code>2 x 3</code>
 * floating-point matrix) that approximates best the affine transformation
 * between:</p>
 * <ul>
 *   <li> Two point sets
 *   <li> Two raster images. In this case, the function first finds some
 * features in the <code>src</code> image and finds the corresponding features
 * in <code>dst</code> image. After that, the problem is reduced to the first
 * case.
 * </ul>
 *
 * <p>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:</p>
 *
 * <p><em>[A^*|b^*] = arg min _([A|b]) sum _i|dst[i] - A (src[i])^T - b| ^2</em></p>
 *
 * <p>where <code>src[i]</code> and <code>dst[i]</code> are the i-th points in
 * <code>src</code> and <code>dst</code>, respectively</p>
 *
 * <p><em>[A|b]</em> can be either arbitrary (when <code>fullAffine=true</code>) or
 * have a form of</p>
 *
 * <p><em>a_11 a_12 b_1
 * -a_12 a_11 b_2 </em></p>
 *
 * <p>when <code>fullAffine=false</code>.</p>
 *
 * @param src First input 2D point set stored in <code>std.vector</code> or
 * <code>Mat</code>, or an image stored in <code>Mat</code>.
 * @param dst Second input 2D point set of the same size and the same type as
 * <code>A</code>, or another image.
 * @param 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 <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#estimaterigidtransform">org.opencv.video.Video.estimateRigidTransform</a>
 * @see org.opencv.calib3d.Calib3d#findHomography
 * @see org.opencv.imgproc.Imgproc#getAffineTransform
 * @see org.opencv.imgproc.Imgproc#getPerspectiveTransform
 */
    public static Mat estimateRigidTransform(Mat src, Mat dst, boolean fullAffine)
    {

        Mat retVal = new Mat(estimateRigidTransform_0(src.nativeObj, dst.nativeObj, fullAffine));

        return retVal;
    }


    //
    // C++:  int meanShift(Mat probImage, Rect& window, TermCriteria criteria)
    //

/**
 * <p>Finds an object on a back projection image.</p>
 *
 * <p>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 <code>window</code> 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 <code>criteria.maxCount</code> is
 * done or until the window center shifts by less than <code>criteria.epsilon</code>.
 * 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".</p>
 *
 * @param probImage Back projection of the object histogram. See
 * "calcBackProject" for details.
 * @param window Initial search window.
 * @param criteria Stop criteria for the iterative search algorithm.
 *
 * <p>:returns: Number of iterations CAMSHIFT took to converge.</p>
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#meanshift">org.opencv.video.Video.meanShift</a>
 */
    public static int meanShift(Mat probImage, Rect window, TermCriteria criteria)
    {
        double[] window_out = new double[4];
        int retVal = meanShift_0(probImage.nativeObj, window.x, window.y, window.width, window.height, window_out, criteria.type, criteria.maxCount, criteria.epsilon);
        if(window!=null){ window.x = (int)window_out[0]; window.y = (int)window_out[1]; window.width = (int)window_out[2]; window.height = (int)window_out[3]; }
        return retVal;
    }


    //
    // C++:  void segmentMotion(Mat mhi, Mat& segmask, vector_Rect& boundingRects, double timestamp, double segThresh)
    //

/**
 * <p>Splits a motion history image into a few parts corresponding to separate
 * independent motions (for example, left hand, right hand).</p>
 *
 * <p>The function finds all of the motion segments and marks them in
 * <code>segmask</code> 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.</p>
 *
 * @param mhi Motion history image.
 * @param segmask Image where the found mask should be stored, single-channel,
 * 32-bit floating-point.
 * @param boundingRects Vector containing ROIs of motion connected components.
 * @param timestamp Current time in milliseconds or other units.
 * @param segThresh Segmentation threshold that is recommended to be equal to
 * the interval between motion history "steps" or greater.
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#segmentmotion">org.opencv.video.Video.segmentMotion</a>
 */
    public static void segmentMotion(Mat mhi, Mat segmask, MatOfRect boundingRects, double timestamp, double segThresh)
    {
        Mat boundingRects_mat = boundingRects;
        segmentMotion_0(mhi.nativeObj, segmask.nativeObj, boundingRects_mat.nativeObj, timestamp, segThresh);

        return;
    }


    //
    // C++:  void updateMotionHistory(Mat silhouette, Mat& mhi, double timestamp, double duration)
    //

/**
 * <p>Updates the motion history image by a moving silhouette.</p>
 *
 * <p>The function updates the motion history image as follows:</p>
 *
 * <p><em>mhi(x,y)= timestamp if silhouette(x,y) != 0; 0 if silhouette(x,y) = 0 and
 * mhi < (timestamp - duration); mhi(x,y) otherwise</em></p>
 *
 * <p>That is, MHI pixels where the motion occurs are set to the current
 * <code>timestamp</code>, while the pixels where the motion happened last time
 * a long time ago are cleared.</p>
 *
 * <p>The function, together with "calcMotionGradient" and "calcGlobalOrientation",
 * implements a motion templates technique described in [Davis97] and
 * [Bradski00].
 * See also the OpenCV sample <code>motempl.c</code> that demonstrates the use
 * of all the motion template functions.</p>
 *
 * @param silhouette Silhouette mask that has non-zero pixels where the motion
 * occurs.
 * @param mhi Motion history image that is updated by the function
 * (single-channel, 32-bit floating-point).
 * @param timestamp Current time in milliseconds or other units.
 * @param duration Maximal duration of the motion track in the same units as
 * <code>timestamp</code>.
 *
 * @see <a href="http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html#updatemotionhistory">org.opencv.video.Video.updateMotionHistory</a>
 */
    public static void updateMotionHistory(Mat silhouette, Mat mhi, double timestamp, double duration)
    {

        updateMotionHistory_0(silhouette.nativeObj, mhi.nativeObj, timestamp, duration);

        return;
    }




    //
    // native stuff
    //
    static { System.loadLibrary("opencv_java"); }

    // C++:  RotatedRect CamShift(Mat probImage, Rect& window, TermCriteria criteria)
    private static native double[] CamShift_0(long probImage_nativeObj, int window_x, int window_y, int window_width, int window_height, double[] window_out, int criteria_type, int criteria_maxCount, double criteria_epsilon);

    // C++:  int buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true)
    private static native int buildOpticalFlowPyramid_0(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, boolean withDerivatives, int pyrBorder, int derivBorder, boolean tryReuseInputImage);
    private static native int buildOpticalFlowPyramid_1(long img_nativeObj, long pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel);

    // C++:  double calcGlobalOrientation(Mat orientation, Mat mask, Mat mhi, double timestamp, double duration)
    private static native double calcGlobalOrientation_0(long orientation_nativeObj, long mask_nativeObj, long mhi_nativeObj, double timestamp, double duration);

    // C++:  void calcMotionGradient(Mat mhi, Mat& mask, Mat& orientation, double delta1, double delta2, int apertureSize = 3)
    private static native void calcMotionGradient_0(long mhi_nativeObj, long mask_nativeObj, long orientation_nativeObj, double delta1, double delta2, int apertureSize);
    private static native void calcMotionGradient_1(long mhi_nativeObj, long mask_nativeObj, long orientation_nativeObj, double delta1, double delta2);

    // C++:  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)
    private static native void calcOpticalFlowFarneback_0(long prev_nativeObj, long next_nativeObj, long flow_nativeObj, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags);

    // C++:  void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, vector_Point2f prevPts, vector_Point2f& nextPts, vector_uchar& status, vector_float& err, Size winSize = Size(21,21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
    private static native void calcOpticalFlowPyrLK_0(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, int criteria_type, int criteria_maxCount, double criteria_epsilon, int flags, double minEigThreshold);
    private static native void calcOpticalFlowPyrLK_1(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel);
    private static native void calcOpticalFlowPyrLK_2(long prevImg_nativeObj, long nextImg_nativeObj, long prevPts_mat_nativeObj, long nextPts_mat_nativeObj, long status_mat_nativeObj, long err_mat_nativeObj);

    // C++:  Mat estimateRigidTransform(Mat src, Mat dst, bool fullAffine)
    private static native long estimateRigidTransform_0(long src_nativeObj, long dst_nativeObj, boolean fullAffine);

    // C++:  int meanShift(Mat probImage, Rect& window, TermCriteria criteria)
    private static native int meanShift_0(long probImage_nativeObj, int window_x, int window_y, int window_width, int window_height, double[] window_out, int criteria_type, int criteria_maxCount, double criteria_epsilon);

    // C++:  void segmentMotion(Mat mhi, Mat& segmask, vector_Rect& boundingRects, double timestamp, double segThresh)
    private static native void segmentMotion_0(long mhi_nativeObj, long segmask_nativeObj, long boundingRects_mat_nativeObj, double timestamp, double segThresh);

    // C++:  void updateMotionHistory(Mat silhouette, Mat& mhi, double timestamp, double duration)
    private static native void updateMotionHistory_0(long silhouette_nativeObj, long mhi_nativeObj, double timestamp, double duration);

}




Java Source Code List

org.opencv.android.Utils.java
org.opencv.calib3d.Calib3d.java
org.opencv.calib3d.StereoBM.java
org.opencv.calib3d.StereoSGBM.java
org.opencv.core.Algorithm.java
org.opencv.core.Core.java
org.opencv.core.CvException.java
org.opencv.core.CvType.java
org.opencv.core.MatOfByte.java
org.opencv.core.MatOfDMatch.java
org.opencv.core.MatOfDouble.java
org.opencv.core.MatOfFloat4.java
org.opencv.core.MatOfFloat6.java
org.opencv.core.MatOfFloat.java
org.opencv.core.MatOfInt4.java
org.opencv.core.MatOfInt.java
org.opencv.core.MatOfKeyPoint.java
org.opencv.core.MatOfPoint2f.java
org.opencv.core.MatOfPoint3.java
org.opencv.core.MatOfPoint3f.java
org.opencv.core.MatOfPoint.java
org.opencv.core.MatOfRect.java
org.opencv.core.Mat.java
org.opencv.core.Point3.java
org.opencv.core.Point.java
org.opencv.core.Range.java
org.opencv.core.Rect.java
org.opencv.core.RotatedRect.java
org.opencv.core.Scalar.java
org.opencv.core.Size.java
org.opencv.core.TermCriteria.java
org.opencv.features2d.DMatch.java
org.opencv.features2d.DescriptorExtractor.java
org.opencv.features2d.DescriptorMatcher.java
org.opencv.features2d.FeatureDetector.java
org.opencv.features2d.Features2d.java
org.opencv.features2d.GenericDescriptorMatcher.java
org.opencv.features2d.KeyPoint.java
org.opencv.highgui.Highgui.java
org.opencv.highgui.VideoCapture.java
org.opencv.imgproc.Imgproc.java
org.opencv.imgproc.Moments.java
org.opencv.imgproc.Subdiv2D.java
org.opencv.ml.CvANN_MLP_TrainParams.java
org.opencv.ml.CvANN_MLP.java
org.opencv.ml.CvBoostParams.java
org.opencv.ml.CvBoost.java
org.opencv.ml.CvDTreeParams.java
org.opencv.ml.CvDTree.java
org.opencv.ml.CvERTrees.java
org.opencv.ml.CvGBTreesParams.java
org.opencv.ml.CvGBTrees.java
org.opencv.ml.CvKNearest.java
org.opencv.ml.CvNormalBayesClassifier.java
org.opencv.ml.CvParamGrid.java
org.opencv.ml.CvRTParams.java
org.opencv.ml.CvRTrees.java
org.opencv.ml.CvSVMParams.java
org.opencv.ml.CvSVM.java
org.opencv.ml.CvStatModel.java
org.opencv.ml.EM.java
org.opencv.ml.Ml.java
org.opencv.objdetect.CascadeClassifier.java
org.opencv.objdetect.HOGDescriptor.java
org.opencv.objdetect.Objdetect.java
org.opencv.photo.Photo.java
org.opencv.utils.Converters.java
org.opencv.video.BackgroundSubtractorMOG.java
org.opencv.video.BackgroundSubtractor.java
org.opencv.video.KalmanFilter.java
org.opencv.video.Video.java
valkyrie.colorpicker.ColorPickerDialog.java
valkyrie.colorpicker.ColorPicker.java
valkyrie.file.DecodeBitmaps.java
valkyrie.file.FileManager.java
valkyrie.filter.FilterAssets.java
valkyrie.filter.FilterInternalStorage.java
valkyrie.filter.FilterManager.java
valkyrie.filter.FilterUIPosition.java
valkyrie.filter.IFilter.java
valkyrie.filter.ascii.Ascii.java
valkyrie.filter.ascii.Converter.java
valkyrie.filter.ascii.Font.java
valkyrie.filter.ascii.Options.java
valkyrie.filter.canny.Canny.java
valkyrie.filter.grayscale.Grayscale.java
valkyrie.filter.nofilter.NoFilter.java
valkyrie.ui.IUpdateableUI.java
valkyrie.ui.LayoutManager.java
valkyrie.ui.MainActivity.java
valkyrie.ui.UpdateableRelativeLayout.java
valkyrie.ui.gallery.AboutActivity.java
valkyrie.ui.gallery.GalleryActivity.java
valkyrie.ui.gallery.ImageAdapter.java
valkyrie.ui.gallery.ShowPicActivity.java
valkyrie.ui.preview.CameraPreviewViewCV.java
valkyrie.widget.MultiDirectionSlidingDrawer.java
valkyrie.widget.TouchImageView.java