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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
namespace optflow
{
class OpticalFlowDeepFlow: public DenseOpticalFlow
{
public:
OpticalFlowDeepFlow();
void calc( InputArray I0, InputArray I1, InputOutputArray flow ) CV_OVERRIDE;
void collectGarbage() CV_OVERRIDE;
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