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