opencv_contrib/modules/optflow/src/deepflow.cpp

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#include "precomp.hpp"

namespace cv
{
namespace optflow
{

class OpticalFlowDeepFlow: public DenseOpticalFlow
{
public:
    OpticalFlowDeepFlow();

    void calc( InputArray I0, InputArray I1, InputOutputArray flow );
    void collectGarbage();

protected:
    float sigma; // Gaussian smoothing parameter
    int minSize; // minimal dimension of an image in the pyramid
    float downscaleFactor; // scaling factor in the pyramid
    int fixedPointIterations; // during each level of the pyramid
    int sorIterations; // iterations of SOR
    float alpha; // smoothness assumption weight
    float delta; // color constancy weight
    float gamma; // gradient constancy weight
    float omega; // relaxation factor in SOR

    int maxLayers; // max amount of layers in the pyramid
    int interpolationType;

private:
    std::vector<Mat> buildPyramid( const Mat& src );

};

OpticalFlowDeepFlow::OpticalFlowDeepFlow()
{
    // parameters
    sigma = 0.6f;
    minSize = 25;
    downscaleFactor = 0.95f;
    fixedPointIterations = 5;
    sorIterations = 25;
    alpha = 1.0f;
    delta = 0.5f;
    gamma = 5.0f;
    omega = 1.6f;

    //consts
    interpolationType = INTER_LINEAR;
    maxLayers = 200;
}

std::vector<Mat> OpticalFlowDeepFlow::buildPyramid( const Mat& src )
{
    std::vector<Mat> pyramid;
    pyramid.push_back(src);
    Mat prev = pyramid[0];
    for( int i = 0; i < this->maxLayers; ++i)
    {
        Mat next; //TODO: filtering at each level?
        Size nextSize((int) (prev.cols * downscaleFactor + 0.5f),
                        (int) (prev.rows * downscaleFactor + 0.5f));
        if( nextSize.height <= minSize || nextSize.width <= minSize)
            break;
        resize(prev, next,
                nextSize, 0, 0,
                interpolationType);
        pyramid.push_back(next);
        prev = next;
    }
    return pyramid;
}

void OpticalFlowDeepFlow::calc( InputArray _I0, InputArray _I1, InputOutputArray _flow )
{
    Mat I0temp = _I0.getMat();
    Mat I1temp = _I1.getMat();

    CV_Assert(I0temp.size() == I1temp.size());
    CV_Assert(I0temp.type() == I1temp.type());
    CV_Assert(I0temp.channels() == 1);
    // TODO: currently only grayscale - data term could be computed in color version as well...

    Mat I0, I1;

    I0temp.convertTo(I0, CV_32F);
    I1temp.convertTo(I1, CV_32F);

    _flow.create(I0.size(), CV_32FC2);
    Mat W = _flow.getMat(); // if any data present - will be discarded

    // pre-smooth images
    int kernelLen = ((int)floor(3 * sigma) * 2) + 1;
    Size kernelSize(kernelLen, kernelLen);
    GaussianBlur(I0, I0, kernelSize, sigma);
    GaussianBlur(I1, I1, kernelSize, sigma);
    // build down-sized pyramids
    std::vector<Mat> pyramid_I0 = buildPyramid(I0);
    std::vector<Mat> pyramid_I1 = buildPyramid(I1);
    int levelCount = (int) pyramid_I0.size();

    // initialize the first version of flow estimate to zeros
    Size smallestSize = pyramid_I0[levelCount - 1].size();
    W = Mat::zeros(smallestSize, CV_32FC2);

    for ( int level = levelCount - 1; level >= 0; --level )
    { //iterate through  all levels, beginning with the most coarse
        Ptr<VariationalRefinement> var = createVariationalFlowRefinement();

        var->setAlpha(4 * alpha);
        var->setDelta(delta / 3);
        var->setGamma(gamma / 3);
        var->setFixedPointIterations(fixedPointIterations);
        var->setSorIterations(sorIterations);
        var->setOmega(omega);

        var->calc(pyramid_I0[level], pyramid_I1[level], W);
        if ( level > 0 ) //not the last level
        {
            Mat temp;
            Size newSize = pyramid_I0[level - 1].size();
            resize(W, temp, newSize, 0, 0, interpolationType); //resize calculated flow
            W = temp * (1.0f / downscaleFactor); //scale values
        }
    }
    W.copyTo(_flow);
}

void OpticalFlowDeepFlow::collectGarbage() {}

Ptr<DenseOpticalFlow> createOptFlow_DeepFlow() { return makePtr<OpticalFlowDeepFlow>(); }

}//optflow
}//cv