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;
    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.

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 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.

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 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.

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

gpu::interpolateFrames

Interpolates 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.