Video Analysis ============== .. highlight:: cpp gpu::BroxOpticalFlow -------------------- .. ocv:class:: gpu::BroxOpticalFlow Class computing the optical flow for two images using Brox et al Optical Flow algorithm ([Brox2004]_). :: class BroxOpticalFlow { public: BroxOpticalFlow(float alpha_, float gamma_, float scale_factor_, int inner_iterations_, int outer_iterations_, int solver_iterations_); //! Compute optical flow //! frame0 - source frame (supports only CV_32FC1 type) //! frame1 - frame to track (with the same size and type as frame0) //! u - flow horizontal component (along x axis) //! v - flow vertical component (along y axis) void operator ()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& u, GpuMat& v, Stream& stream = Stream::Null()); //! flow smoothness float alpha; //! gradient constancy importance float gamma; //! pyramid scale factor float scale_factor; //! number of lagged non-linearity iterations (inner loop) int inner_iterations; //! number of warping iterations (number of pyramid levels) int outer_iterations; //! number of linear system solver iterations int solver_iterations; GpuMat buf; }; gpu::GoodFeaturesToTrackDetector_GPU ------------------------------------ .. ocv:class:: gpu::GoodFeaturesToTrackDetector_GPU Class used for strong corners detection on an image. :: class GoodFeaturesToTrackDetector_GPU { public: explicit GoodFeaturesToTrackDetector_GPU(int maxCorners_ = 1000, double qualityLevel_ = 0.01, double minDistance_ = 0.0, int blockSize_ = 3, bool useHarrisDetector_ = false, double harrisK_ = 0.04); void operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat()); int maxCorners; double qualityLevel; double minDistance; int blockSize; bool useHarrisDetector; double harrisK; void releaseMemory(); }; The class finds the most prominent corners in the image. .. seealso:: :ocv:func:`goodFeaturesToTrack` gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU --------------------------------------------------------------------- Constructor. .. ocv:function:: gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU(int maxCorners = 1000, double qualityLevel = 0.01, double minDistance = 0.0, int blockSize = 3, bool useHarrisDetector = false, double harrisK = 0.04) :param maxCorners: Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned. :param qualityLevel: Parameter characterizing the minimal accepted quality of image corners. The parameter value is multiplied by the best corner quality measure, which is the minimal eigenvalue (see :ocv:func:`gpu::cornerMinEigenVal` ) or the Harris function response (see :ocv:func:`gpu::cornerHarris` ). The corners with the quality measure less than the product are rejected. For example, if the best corner has the quality measure = 1500, and the ``qualityLevel=0.01`` , then all the corners with the quality measure less than 15 are rejected. :param minDistance: Minimum possible Euclidean distance between the returned corners. :param blockSize: Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See :ocv:func:`cornerEigenValsAndVecs` . :param useHarrisDetector: Parameter indicating whether to use a Harris detector (see :ocv:func:`gpu::cornerHarris`) or :ocv:func:`gpu::cornerMinEigenVal`. :param harrisK: Free parameter of the Harris detector. gpu::GoodFeaturesToTrackDetector_GPU::operator () ------------------------------------------------- Finds the most prominent corners in the image. .. ocv:function:: void gpu::GoodFeaturesToTrackDetector_GPU::operator ()(const GpuMat& image, GpuMat& corners, const GpuMat& mask = GpuMat()) :param image: Input 8-bit, single-channel image. :param corners: Output vector of detected corners (it will be one row matrix with CV_32FC2 type). :param mask: Optional region of interest. If the image is not empty (it needs to have the type ``CV_8UC1`` and the same size as ``image`` ), it specifies the region in which the corners are detected. .. seealso:: :ocv:func:`goodFeaturesToTrack` gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory --------------------------------------------------- Releases inner buffers memory. .. ocv:function:: void gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory() gpu::FarnebackOpticalFlow ------------------------- .. ocv:class:: gpu::FarnebackOpticalFlow Class computing a dense optical flow using the Gunnar Farneback’s algorithm. :: class CV_EXPORTS FarnebackOpticalFlow { public: FarnebackOpticalFlow() { numLevels = 5; pyrScale = 0.5; fastPyramids = false; winSize = 13; numIters = 10; polyN = 5; polySigma = 1.1; flags = 0; } int numLevels; double pyrScale; bool fastPyramids; int winSize; int numIters; int polyN; double polySigma; int flags; void operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null()); void releaseMemory(); private: /* hidden */ }; gpu::FarnebackOpticalFlow::operator () -------------------------------------- Computes a dense optical flow using the Gunnar Farneback’s algorithm. .. ocv:function:: void gpu::FarnebackOpticalFlow::operator ()(const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s = Stream::Null()) :param frame0: First 8-bit gray-scale input image :param frame1: Second 8-bit gray-scale input image :param flowx: Flow horizontal component :param flowy: Flow vertical component :param s: Stream .. seealso:: :ocv:func:`calcOpticalFlowFarneback` gpu::FarnebackOpticalFlow::releaseMemory ---------------------------------------- Releases unused auxiliary memory buffers. .. ocv:function:: void gpu::FarnebackOpticalFlow::releaseMemory() gpu::PyrLKOpticalFlow --------------------- .. ocv:class:: gpu::PyrLKOpticalFlow Class used for calculating an optical flow. :: class PyrLKOpticalFlow { public: PyrLKOpticalFlow(); void sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err = 0); void dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0); Size winSize; int maxLevel; int iters; double derivLambda; bool useInitialFlow; float minEigThreshold; bool getMinEigenVals; void releaseMemory(); }; The class can calculate an optical flow for a sparse feature set or dense optical flow using the iterative Lucas-Kanade method with pyramids. .. seealso:: :ocv:func:`calcOpticalFlowPyrLK` gpu::PyrLKOpticalFlow::sparse ----------------------------- Calculate an optical flow for a sparse feature set. .. ocv:function:: void gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err = 0) :param prevImg: First 8-bit input image (supports both grayscale and color images). :param nextImg: Second input image of the same size and the same type as ``prevImg`` . :param prevPts: Vector of 2D points for which the flow needs to be found. It must be one row matrix with CV_32FC2 type. :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 ``useInitialFlow`` is true, the vector must have the same size as in the input. :param status: Output status vector (CV_8UC1 type). 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 (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if ``getMinEigenVals`` is checked. It can be NULL, if not needed. .. seealso:: :ocv:func:`calcOpticalFlowPyrLK` gpu::PyrLKOpticalFlow::dense ----------------------------- Calculate dense optical flow. .. ocv:function:: void gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err = 0) :param prevImg: First 8-bit grayscale input image. :param nextImg: Second input image of the same size and the same type as ``prevImg`` . :param u: Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel :param v: Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel :param err: Output vector (CV_32FC1 type) that contains the difference between patches around the original and moved points or min eigen value if ``getMinEigenVals`` is checked. It can be NULL, if not needed. gpu::PyrLKOpticalFlow::releaseMemory ------------------------------------ Releases inner buffers memory. .. ocv:function:: void gpu::PyrLKOpticalFlow::releaseMemory() gpu::interpolateFrames ---------------------- Interpolates frames (images) using provided optical flow (displacement field). .. ocv:function:: void gpu::interpolateFrames(const GpuMat& frame0, const GpuMat& frame1, const GpuMat& fu, const GpuMat& fv, const GpuMat& bu, const GpuMat& bv, float pos, GpuMat& newFrame, GpuMat& buf, Stream& stream = Stream::Null()) :param frame0: First frame (32-bit floating point images, single channel). :param frame1: Second frame. Must have the same type and size as ``frame0`` . :param fu: Forward horizontal displacement. :param fv: Forward vertical displacement. :param bu: Backward horizontal displacement. :param bv: Backward vertical displacement. :param pos: New frame position. :param newFrame: Output image. :param buf: Temporary buffer, will have width x 6*height size, CV_32FC1 type and contain 6 GpuMat: occlusion masks for first frame, occlusion masks for second, interpolated forward horizontal flow, interpolated forward vertical flow, interpolated backward horizontal flow, interpolated backward vertical flow. :param stream: Stream for the asynchronous version. .. [Brox2004] T. Brox, A. Bruhn, N. Papenberg, J. Weickert. *High accuracy optical flow estimation based on a theory for warping*. ECCV 2004.