Video Analysis

gpu::BroxOpticalFlow

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

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.

See also

goodFeaturesToTrack()

gpu::GoodFeaturesToTrackDetector_GPU::GoodFeaturesToTrackDetector_GPU

Constructor.

C++: 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)

Parameters:

• maxCorners – Maximum number of corners to return. If there are more corners than are found, the strongest of them is returned.
• 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 gpu::cornerMinEigenVal() ) or the Harris function response (see 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.
• minDistance – Minimum possible Euclidean distance between the returned corners.
• blockSize – Size of an average block for computing a derivative covariation matrix over each pixel neighborhood. See cornerEigenValsAndVecs() .
• useHarrisDetector – Parameter indicating whether to use a Harris detector (see gpu::cornerHarris()) or gpu::cornerMinEigenVal().
• harrisK – Free parameter of the Harris detector.

gpu::GoodFeaturesToTrackDetector_GPU::operator ()

Finds the most prominent corners in the image.

C++: void gpu::GoodFeaturesToTrackDetector_GPU::operator()(const GpuMat& image, GpuMat& corners, const GpuMat& mask=GpuMat())

Parameters:

• image – Input 8-bit, single-channel image.
• corners – Output vector of detected corners (it will be one row matrix with CV_32FC2 type).
• 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.

See also

goodFeaturesToTrack()

gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory

Releases inner buffers memory.

C++: void gpu::GoodFeaturesToTrackDetector_GPU::releaseMemory()

gpu::FarnebackOpticalFlow

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.

C++: void gpu::FarnebackOpticalFlow::operator()(const GpuMat& frame0, const GpuMat& frame1, GpuMat& flowx, GpuMat& flowy, Stream& s=Stream::Null())

Parameters:

• frame0 – First 8-bit gray-scale input image
• frame1 – Second 8-bit gray-scale input image
• flowx – Flow horizontal component
• flowy – Flow vertical component
• s – Stream

See also

calcOpticalFlowFarneback()

gpu::FarnebackOpticalFlow::releaseMemory

Releases unused auxiliary memory buffers.

C++: void gpu::FarnebackOpticalFlow::releaseMemory()

gpu::PyrLKOpticalFlow

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;

    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.

See also

calcOpticalFlowPyrLK()

gpu::PyrLKOpticalFlow::sparse

Calculate an optical flow for a sparse feature set.

C++: void gpu::PyrLKOpticalFlow::sparse(const GpuMat& prevImg, const GpuMat& nextImg, const GpuMat& prevPts, GpuMat& nextPts, GpuMat& status, GpuMat* err=0)

Parameters:

• prevImg – First 8-bit input image (supports both grayscale and color images).
• nextImg – Second input image of the same size and the same type as prevImg .
• prevPts – Vector of 2D points for which the flow needs to be found. It must be one row matrix with CV_32FC2 type.
• 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.
• 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.
• err – Output vector (CV_32FC1 type) that contains min eigen value. It can be NULL, if not needed.

See also

calcOpticalFlowPyrLK()

gpu::PyrLKOpticalFlow::dense

Calculate dense optical flow.

C++: void gpu::PyrLKOpticalFlow::dense(const GpuMat& prevImg, const GpuMat& nextImg, GpuMat& u, GpuMat& v, GpuMat* err=0)

Parameters:

• prevImg – First 8-bit grayscale input image.
• nextImg – Second input image of the same size and the same type as prevImg .
• u – Horizontal component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
• v – Vertical component of the optical flow of the same size as input images, 32-bit floating-point, single-channel
• err – Output vector (CV_32FC1 type) that contains min eigen value. It can be NULL, if not needed.

gpu::PyrLKOpticalFlow::releaseMemory

Releases inner buffers memory.

C++: void gpu::PyrLKOpticalFlow::releaseMemory()

gpu::interpolateFrames

Interpolate frames (images) using provided optical flow (displacement field).

C++: 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())

Parameters:

• frame0 – First frame (32-bit floating point images, single channel).
• frame1 – Second frame. Must have the same type and size as frame0 .
• fu – Forward horizontal displacement.
• fv – Forward vertical displacement.
• bu – Backward horizontal displacement.
• bv – Backward vertical displacement.
• pos – New frame position.
• newFrame – Output image.
• 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.
• stream – Stream for the asynchronous version.

[Brox2004]

Brox, A. Bruhn, N. Papenberg, J. Weickert. High accuracy optical flow estimation based on a theory for warping. ECCV 2004.