"atomic bomb" commit. Reorganized OpenCV directory structure
This commit is contained in:
Vadim Pisarevsky
660
modules/video/src/optflowgf.cpp
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660
modules/video/src/optflowgf.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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//
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// 2D dense optical flow algorithm from the following paper:
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// Gunnar Farneback. "Two-Frame Motion Estimation Based on Polynomial Expansion".
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// Proceedings of the 13th Scandinavian Conference on Image Analysis, Gothenburg, Sweden
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//
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namespace cv
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{
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static void
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FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma )
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{
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int k, x, y;
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assert( src.type() == CV_32FC1 );
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int width = src.cols;
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int height = src.rows;
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AutoBuffer<float> kbuf(n*6 + 3), _row((width + n*2)*3);
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float* g = kbuf + n;
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float* xg = g + n*2 + 1;
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float* xxg = xg + n*2 + 1;
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float *row = (float*)_row + n*3;
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if( sigma < FLT_EPSILON )
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sigma = n*0.3;
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double s = 0.;
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for( x = -n; x <= n; x++ )
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{
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g[x] = (float)std::exp(-x*x/(2*sigma*sigma));
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s += g[x];
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}
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s = 1./s;
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for( x = -n; x <= n; x++ )
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{
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g[x] = (float)(g[x]*s);
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xg[x] = (float)(x*g[x]);
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xxg[x] = (float)(x*x*g[x]);
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}
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Mat_<double> G = Mat_<double>::zeros(6, 6);
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for( y = -n; y <= n; y++ )
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for( x = -n; x <= n; x++ )
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{
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G(0,0) += g[y]*g[x];
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G(1,1) += g[y]*g[x]*x*x;
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G(3,3) += g[y]*g[x]*x*x*x*x;
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G(5,5) += g[y]*g[x]*x*x*y*y;
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}
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//G[0][0] = 1.;
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G(2,2) = G(0,3) = G(0,4) = G(3,0) = G(4,0) = G(1,1);
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G(4,4) = G(3,3);
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G(3,4) = G(4,3) = G(5,5);
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// invG:
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// [ x e e ]
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// [ y ]
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// [ y ]
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// [ e z ]
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// [ e z ]
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// [ u ]
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Mat_<double> invG = G.inv(DECOMP_CHOLESKY);
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double ig11 = invG(1,1), ig03 = invG(0,3), ig33 = invG(3,3), ig55 = invG(5,5);
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dst.create( height, width, CV_32FC(5));
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for( y = 0; y < height; y++ )
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{
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float g0 = g[0], g1, g2;
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float *srow0 = (float*)(src.data + src.step*y), *srow1 = 0;
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float *drow = (float*)(dst.data + dst.step*y);
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// vertical part of convolution
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for( x = 0; x < width; x++ )
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{
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row[x*3] = srow0[x]*g0;
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row[x*3+1] = row[x*3+2] = 0.f;
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}
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for( k = 1; k <= n; k++ )
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{
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g0 = g[k]; g1 = xg[k]; g2 = xxg[k];
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srow0 = (float*)(src.data + src.step*std::max(y-k,0));
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srow1 = (float*)(src.data + src.step*std::min(y+k,height-1));
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for( x = 0; x < width; x++ )
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{
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float p = srow0[x] + srow1[x];
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float t0 = row[x*3] + g0*p;
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float t1 = row[x*3+1] + g1*(srow1[x] - srow0[x]);
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float t2 = row[x*3+2] + g2*p;
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row[x*3] = t0;
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row[x*3+1] = t1;
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row[x*3+2] = t2;
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}
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}
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// horizontal part of convolution
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for( x = 0; x < n*3; x++ )
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{
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row[-1-x] = row[2-x];
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row[width*3+x] = row[width*3+x-3];
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}
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for( x = 0; x < width; x++ )
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{
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g0 = g[0];
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// r1 ~ 1, r2 ~ x, r3 ~ y, r4 ~ x^2, r5 ~ y^2, r6 ~ xy
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double b1 = row[x*3]*g0, b2 = 0, b3 = row[x*3+1]*g0,
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b4 = 0, b5 = row[x*3+2]*g0, b6 = 0;
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for( k = 1; k <= n; k++ )
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{
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double tg = row[(x+k)*3] + row[(x-k)*3];
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g0 = g[k];
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b1 += tg*g0;
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b4 += tg*xxg[k];
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b2 += (row[(x+k)*3] - row[(x-k)*3])*xg[k];
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b3 += (row[(x+k)*3+1] + row[(x-k)*3+1])*g0;
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b6 += (row[(x+k)*3+1] - row[(x-k)*3+1])*xg[k];
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b5 += (row[(x+k)*3+2] + row[(x-k)*3+2])*g0;
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}
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// do not store r1
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drow[x*5+1] = (float)(b2*ig11);
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drow[x*5] = (float)(b3*ig11);
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drow[x*5+3] = (float)(b1*ig03 + b4*ig33);
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drow[x*5+2] = (float)(b1*ig03 + b5*ig33);
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drow[x*5+4] = (float)(b6*ig55);
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}
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}
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row -= n*3;
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}
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/*static void
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FarnebackPolyExpPyr( const Mat& src0, Vector<Mat>& pyr, int maxlevel, int n, double sigma )
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{
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Vector<Mat> imgpyr;
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buildPyramid( src0, imgpyr, maxlevel );
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for( int i = 0; i <= maxlevel; i++ )
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FarnebackPolyExp( imgpyr[i], pyr[i], n, sigma );
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}*/
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static void
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FarnebackUpdateMatrices( const Mat& _R0, const Mat& _R1, const Mat& _flow, Mat& matM, int _y0, int _y1 )
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{
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const int BORDER = 5;
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static const float border[BORDER] = {0.14f, 0.14f, 0.4472f, 0.4472f, 0.4472f};
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int x, y, width = _flow.cols, height = _flow.rows;
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const float* R1 = (float*)_R1.data;
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size_t step1 = _R1.step/sizeof(R1[0]);
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matM.create(height, width, CV_32FC(5));
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for( y = _y0; y < _y1; y++ )
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{
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const float* flow = (float*)(_flow.data + y*_flow.step);
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const float* R0 = (float*)(_R0.data + y*_R0.step);
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float* M = (float*)(matM.data + y*matM.step);
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for( x = 0; x < width; x++ )
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{
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float dx = flow[x*2], dy = flow[x*2+1];
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float fx = x + dx, fy = y + dy;
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#if 1
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int x1 = cvFloor(fx), y1 = cvFloor(fy);
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const float* ptr = R1 + y1*step1 + x1*5;
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float r2, r3, r4, r5, r6;
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fx -= x1; fy -= y1;
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if( (unsigned)x1 < (unsigned)(width-1) &&
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(unsigned)y1 < (unsigned)(height-1) )
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{
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float a00 = (1.f-fx)*(1.f-fy), a01 = fx*(1.f-fy),
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a10 = (1.f-fx)*fy, a11 = fx*fy;
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r2 = a00*ptr[0] + a01*ptr[5] + a10*ptr[step1] + a11*ptr[step1+5];
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r3 = a00*ptr[1] + a01*ptr[6] + a10*ptr[step1+1] + a11*ptr[step1+6];
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r4 = a00*ptr[2] + a01*ptr[7] + a10*ptr[step1+2] + a11*ptr[step1+7];
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r5 = a00*ptr[3] + a01*ptr[8] + a10*ptr[step1+3] + a11*ptr[step1+8];
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r6 = a00*ptr[4] + a01*ptr[9] + a10*ptr[step1+4] + a11*ptr[step1+9];
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r4 = (R0[x*5+2] + r4)*0.5f;
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r5 = (R0[x*5+3] + r5)*0.5f;
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r6 = (R0[x*5+4] + r6)*0.25f;
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}
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#else
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int x1 = cvRound(fx), y1 = cvRound(fy);
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const float* ptr = R1 + y1*step1 + x1*5;
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float r2, r3, r4, r5, r6;
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if( (unsigned)x1 < (unsigned)width &&
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(unsigned)y1 < (unsigned)height )
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{
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r2 = ptr[0];
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r3 = ptr[1];
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r4 = (R0[x*5+2] + ptr[2])*0.5f;
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r5 = (R0[x*5+3] + ptr[3])*0.5f;
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r6 = (R0[x*5+4] + ptr[4])*0.25f;
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}
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#endif
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else
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{
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r2 = r3 = 0.f;
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r4 = R0[x*5+2];
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r5 = R0[x*5+3];
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r6 = R0[x*5+4]*0.5f;
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}
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r2 = (R0[x*5] - r2)*0.5f;
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r3 = (R0[x*5+1] - r3)*0.5f;
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r2 += r4*dy + r6*dx;
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r3 += r6*dy + r5*dx;
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if( (unsigned)(x - BORDER) >= (unsigned)(width - BORDER*2) ||
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(unsigned)(y - BORDER) >= (unsigned)(height - BORDER*2))
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{
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float scale = (x < BORDER ? border[x] : 1.f)*
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(x >= width - BORDER ? border[width - x - 1] : 1.f)*
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(y < BORDER ? border[y] : 1.f)*
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(y >= height - BORDER ? border[height - y - 1] : 1.f);
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r2 *= scale; r3 *= scale; r4 *= scale;
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r5 *= scale; r6 *= scale;
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}
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M[x*5] = r4*r4 + r6*r6; // G(1,1)
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M[x*5+1] = (r4 + r5)*r6; // G(1,2)=G(2,1)
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M[x*5+2] = r5*r5 + r6*r6; // G(2,2)
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M[x*5+3] = r4*r2 + r6*r3; // h(1)
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M[x*5+4] = r6*r2 + r5*r3; // h(2)
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}
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}
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}
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static void
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FarnebackUpdateFlow_Blur( const Mat& _R0, const Mat& _R1,
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Mat& _flow, Mat& matM, int block_size,
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bool update_matrices )
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{
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int x, y, width = _flow.cols, height = _flow.rows;
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int m = block_size/2;
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int y0 = 0, y1;
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int min_update_stripe = std::max((1 << 10)/width, block_size);
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double scale = 1./(block_size*block_size);
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AutoBuffer<double> _vsum((width+m*2+2)*5);
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double* vsum = _vsum + (m+1)*5;
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// init vsum
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const float* srow0 = (const float*)matM.data;
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for( x = 0; x < width*5; x++ )
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vsum[x] = srow0[x]*(m+2);
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for( y = 1; y < m; y++ )
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{
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srow0 = (float*)(matM.data + matM.step*std::min(y,height-1));
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for( x = 0; x < width*5; x++ )
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vsum[x] += srow0[x];
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}
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// compute blur(G)*flow=blur(h)
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for( y = 0; y < height; y++ )
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{
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double g11, g12, g22, h1, h2;
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float* flow = (float*)(_flow.data + _flow.step*y);
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srow0 = (const float*)(matM.data + matM.step*std::max(y-m-1,0));
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const float* srow1 = (const float*)(matM.data + matM.step*std::min(y+m,height-1));
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// vertical blur
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for( x = 0; x < width*5; x++ )
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vsum[x] += srow1[x] - srow0[x];
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// update borders
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for( x = 0; x < (m+1)*5; x++ )
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{
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vsum[-1-x] = vsum[4-x];
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vsum[width*5+x] = vsum[width*5+x-5];
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}
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// init g** and h*
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g11 = vsum[0]*(m+2);
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g12 = vsum[1]*(m+2);
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g22 = vsum[2]*(m+2);
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h1 = vsum[3]*(m+2);
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h2 = vsum[4]*(m+2);
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for( x = 1; x < m; x++ )
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{
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g11 += vsum[x*5];
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g12 += vsum[x*5+1];
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g22 += vsum[x*5+2];
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h1 += vsum[x*5+3];
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h2 += vsum[x*5+4];
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}
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// horizontal blur
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for( x = 0; x < width; x++ )
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{
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g11 += vsum[(x+m)*5] - vsum[(x-m)*5 - 5];
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g12 += vsum[(x+m)*5 + 1] - vsum[(x-m)*5 - 4];
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g22 += vsum[(x+m)*5 + 2] - vsum[(x-m)*5 - 3];
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h1 += vsum[(x+m)*5 + 3] - vsum[(x-m)*5 - 2];
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h2 += vsum[(x+m)*5 + 4] - vsum[(x-m)*5 - 1];
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double g11_ = g11*scale;
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double g12_ = g12*scale;
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double g22_ = g22*scale;
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double h1_ = h1*scale;
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double h2_ = h2*scale;
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double idet = 1./(g11_*g22_ - g12_*g12_+1e-3);
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flow[x*2] = (float)((g11_*h2_-g12_*h1_)*idet);
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flow[x*2+1] = (float)((g22_*h1_-g12_*h2_)*idet);
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}
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||||
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y1 = y == height - 1 ? height : y - block_size;
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if( update_matrices && (y1 == height || y1 >= y0 + min_update_stripe) )
|
||||
{
|
||||
FarnebackUpdateMatrices( _R0, _R1, _flow, matM, y0, y1 );
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||||
y0 = y1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
static void
|
||||
FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1,
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Mat& _flow, Mat& matM, int block_size,
|
||||
bool update_matrices )
|
||||
{
|
||||
int x, y, i, width = _flow.cols, height = _flow.rows;
|
||||
int m = block_size/2;
|
||||
int y0 = 0, y1;
|
||||
int min_update_stripe = std::max((1 << 10)/width, block_size);
|
||||
double sigma = m*0.3, s = 1;
|
||||
|
||||
AutoBuffer<float> _vsum((width+m*2+2)*5 + 16), _hsum(width*5 + 16);
|
||||
AutoBuffer<float, 4096> _kernel((m+1)*5 + 16);
|
||||
AutoBuffer<float*, 1024> _srow(m*2+1);
|
||||
float *vsum = alignPtr((float*)_vsum + (m+1)*5, 16), *hsum = alignPtr((float*)_hsum, 16);
|
||||
float* kernel = (float*)_kernel;
|
||||
const float** srow = (const float**)&_srow[0];
|
||||
kernel[0] = (float)s;
|
||||
|
||||
for( i = 1; i <= m; i++ )
|
||||
{
|
||||
float t = (float)std::exp(-i*i/(2*sigma*sigma) );
|
||||
kernel[i] = t;
|
||||
s += t*2;
|
||||
}
|
||||
|
||||
s = 1./s;
|
||||
for( i = 0; i <= m; i++ )
|
||||
kernel[i] = (float)(kernel[i]*s);
|
||||
|
||||
#if CV_SSE2
|
||||
float* simd_kernel = alignPtr(kernel + m+1, 16);
|
||||
volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE);
|
||||
if( useSIMD )
|
||||
{
|
||||
for( i = 0; i <= m; i++ )
|
||||
_mm_store_ps(simd_kernel + i*4, _mm_set1_ps(kernel[i]));
|
||||
}
|
||||
#endif
|
||||
|
||||
// compute blur(G)*flow=blur(h)
|
||||
for( y = 0; y < height; y++ )
|
||||
{
|
||||
double g11, g12, g22, h1, h2;
|
||||
float* flow = (float*)(_flow.data + _flow.step*y);
|
||||
|
||||
// vertical blur
|
||||
for( i = 0; i <= m; i++ )
|
||||
{
|
||||
srow[m-i] = (const float*)(matM.data + matM.step*std::max(y-i,0));
|
||||
srow[m+i] = (const float*)(matM.data + matM.step*std::min(y+i,height-1));
|
||||
}
|
||||
|
||||
x = 0;
|
||||
#if CV_SSE2
|
||||
if( useSIMD )
|
||||
{
|
||||
for( ; x <= width*5 - 16; x += 16 )
|
||||
{
|
||||
const float *sptr0 = srow[m], *sptr1;
|
||||
__m128 g4 = _mm_load_ps(simd_kernel);
|
||||
__m128 s0, s1, s2, s3;
|
||||
s0 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x), g4);
|
||||
s1 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x + 4), g4);
|
||||
s2 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x + 8), g4);
|
||||
s3 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x + 12), g4);
|
||||
|
||||
for( i = 1; i <= m; i++ )
|
||||
{
|
||||
__m128 x0, x1;
|
||||
sptr0 = srow[m+i], sptr1 = srow[m-i];
|
||||
g4 = _mm_load_ps(simd_kernel + i*4);
|
||||
x0 = _mm_add_ps(_mm_loadu_ps(sptr0 + x), _mm_loadu_ps(sptr1 + x));
|
||||
x1 = _mm_add_ps(_mm_loadu_ps(sptr0 + x + 4), _mm_loadu_ps(sptr1 + x + 4));
|
||||
s0 = _mm_add_ps(s0, _mm_mul_ps(x0, g4));
|
||||
s1 = _mm_add_ps(s1, _mm_mul_ps(x1, g4));
|
||||
x0 = _mm_add_ps(_mm_loadu_ps(sptr0 + x + 8), _mm_loadu_ps(sptr1 + x + 8));
|
||||
x1 = _mm_add_ps(_mm_loadu_ps(sptr0 + x + 12), _mm_loadu_ps(sptr1 + x + 12));
|
||||
s2 = _mm_add_ps(s2, _mm_mul_ps(x0, g4));
|
||||
s3 = _mm_add_ps(s3, _mm_mul_ps(x1, g4));
|
||||
}
|
||||
|
||||
_mm_store_ps(vsum + x, s0);
|
||||
_mm_store_ps(vsum + x + 4, s1);
|
||||
_mm_store_ps(vsum + x + 8, s2);
|
||||
_mm_store_ps(vsum + x + 12, s3);
|
||||
}
|
||||
|
||||
for( ; x <= width*5 - 4; x += 4 )
|
||||
{
|
||||
const float *sptr0 = srow[m], *sptr1;
|
||||
__m128 g4 = _mm_load_ps(simd_kernel);
|
||||
__m128 s0 = _mm_mul_ps(_mm_loadu_ps(sptr0 + x), g4);
|
||||
|
||||
for( i = 1; i <= m; i++ )
|
||||
{
|
||||
sptr0 = srow[m+i], sptr1 = srow[m-i];
|
||||
g4 = _mm_load_ps(simd_kernel + i*4);
|
||||
__m128 x0 = _mm_add_ps(_mm_loadu_ps(sptr0 + x), _mm_loadu_ps(sptr1 + x));
|
||||
s0 = _mm_add_ps(s0, _mm_mul_ps(x0, g4));
|
||||
}
|
||||
_mm_store_ps(vsum + x, s0);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
for( ; x < width*5; x++ )
|
||||
{
|
||||
float s0 = srow[m][x]*kernel[0];
|
||||
for( i = 1; i <= m; i++ )
|
||||
s0 += (srow[m+i][x] + srow[m-i][x])*kernel[i];
|
||||
vsum[x] = s0;
|
||||
}
|
||||
|
||||
// update borders
|
||||
for( x = 0; x < m*5; x++ )
|
||||
{
|
||||
vsum[-1-x] = vsum[4-x];
|
||||
vsum[width*5+x] = vsum[width*5+x-5];
|
||||
}
|
||||
|
||||
// horizontal blur
|
||||
x = 0;
|
||||
#if CV_SSE2
|
||||
if( useSIMD )
|
||||
{
|
||||
for( ; x <= width*5 - 8; x += 8 )
|
||||
{
|
||||
__m128 g4 = _mm_load_ps(simd_kernel);
|
||||
__m128 s0 = _mm_mul_ps(_mm_loadu_ps(vsum + x), g4);
|
||||
__m128 s1 = _mm_mul_ps(_mm_loadu_ps(vsum + x + 4), g4);
|
||||
|
||||
for( i = 1; i <= m; i++ )
|
||||
{
|
||||
g4 = _mm_load_ps(simd_kernel + i*4);
|
||||
__m128 x0 = _mm_add_ps(_mm_loadu_ps(vsum + x - i*5),
|
||||
_mm_loadu_ps(vsum + x + i*5));
|
||||
__m128 x1 = _mm_add_ps(_mm_loadu_ps(vsum + x - i*5 + 4),
|
||||
_mm_loadu_ps(vsum + x + i*5 + 4));
|
||||
s0 = _mm_add_ps(s0, _mm_mul_ps(x0, g4));
|
||||
s1 = _mm_add_ps(s1, _mm_mul_ps(x1, g4));
|
||||
}
|
||||
|
||||
_mm_store_ps(hsum + x, s0);
|
||||
_mm_store_ps(hsum + x + 4, s1);
|
||||
}
|
||||
}
|
||||
#endif
|
||||
for( ; x < width*5; x++ )
|
||||
{
|
||||
float s = vsum[x]*kernel[0];
|
||||
for( i = 1; i <= m; i++ )
|
||||
s += kernel[i]*(vsum[x - i*5] + vsum[x + i*5]);
|
||||
hsum[x] = s;
|
||||
}
|
||||
|
||||
for( x = 0; x < width; x++ )
|
||||
{
|
||||
g11 = hsum[x*5];
|
||||
g12 = hsum[x*5+1];
|
||||
g22 = hsum[x*5+2];
|
||||
h1 = hsum[x*5+3];
|
||||
h2 = hsum[x*5+4];
|
||||
|
||||
double idet = 1./(g11*g22 - g12*g12 + 1e-3);
|
||||
|
||||
flow[x*2] = (float)((g11*h2-g12*h1)*idet);
|
||||
flow[x*2+1] = (float)((g22*h1-g12*h2)*idet);
|
||||
}
|
||||
|
||||
y1 = y == height - 1 ? height : y - block_size;
|
||||
if( update_matrices && (y1 == height || y1 >= y0 + min_update_stripe) )
|
||||
{
|
||||
FarnebackUpdateMatrices( _R0, _R1, _flow, matM, y0, y1 );
|
||||
y0 = y1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void calcOpticalFlowFarneback( const Mat& prev0, const Mat& next0,
|
||||
Mat& flow0, double pyr_scale, int levels, int winsize,
|
||||
int iterations, int poly_n, double poly_sigma, int flags )
|
||||
{
|
||||
const int min_size = 32;
|
||||
const Mat* img[2] = { &prev0, &next0 };
|
||||
Mat fimg;
|
||||
|
||||
int i, k;
|
||||
double scale;
|
||||
Mat prevFlow, flow;
|
||||
|
||||
CV_Assert( prev0.size() == next0.size() && prev0.channels() == next0.channels() &&
|
||||
prev0.channels() == 1 && pyr_scale < 1 );
|
||||
flow0.create( prev0.size(), CV_32FC2 );
|
||||
|
||||
for( k = 0, scale = 1; k < levels; k++ )
|
||||
{
|
||||
scale *= pyr_scale;
|
||||
if( prev0.cols*scale < min_size || prev0.rows*scale < min_size )
|
||||
break;
|
||||
}
|
||||
|
||||
levels = k;
|
||||
|
||||
for( k = levels; k >= 0; k-- )
|
||||
{
|
||||
for( i = 0, scale = 1; i < k; i++ )
|
||||
scale *= pyr_scale;
|
||||
|
||||
double sigma = (1./scale-1)*0.5;
|
||||
int smooth_sz = cvRound(sigma*5)|1;
|
||||
smooth_sz = std::max(smooth_sz, 3);
|
||||
|
||||
int width = cvRound(prev0.cols*scale);
|
||||
int height = cvRound(prev0.rows*scale);
|
||||
|
||||
if( k > 0 )
|
||||
flow.create( height, width, CV_32FC2 );
|
||||
else
|
||||
flow = flow0;
|
||||
|
||||
if( !prevFlow.data )
|
||||
{
|
||||
if( flags & OPTFLOW_USE_INITIAL_FLOW )
|
||||
{
|
||||
resize( flow0, flow, Size(width, height), 0, 0, INTER_AREA );
|
||||
flow *= scale;
|
||||
}
|
||||
else
|
||||
flow = Mat::zeros( height, width, CV_32FC2 );
|
||||
}
|
||||
else
|
||||
{
|
||||
resize( prevFlow, flow, Size(width, height), 0, 0, INTER_LINEAR );
|
||||
flow *= 1./pyr_scale;
|
||||
}
|
||||
|
||||
Mat R[2], I, M;
|
||||
for( i = 0; i < 2; i++ )
|
||||
{
|
||||
img[i]->convertTo(fimg, CV_32F);
|
||||
GaussianBlur(fimg, fimg, Size(smooth_sz, smooth_sz), sigma, sigma);
|
||||
resize( fimg, I, Size(width, height), CV_INTER_LINEAR );
|
||||
FarnebackPolyExp( I, R[i], poly_n, poly_sigma );
|
||||
}
|
||||
|
||||
FarnebackUpdateMatrices( R[0], R[1], flow, M, 0, flow.rows );
|
||||
|
||||
for( i = 0; i < iterations; i++ )
|
||||
{
|
||||
if( flags & OPTFLOW_FARNEBACK_GAUSSIAN )
|
||||
FarnebackUpdateFlow_GaussianBlur( R[0], R[1], flow, M, winsize, i < iterations - 1 );
|
||||
else
|
||||
FarnebackUpdateFlow_Blur( R[0], R[1], flow, M, winsize, i < iterations - 1 );
|
||||
}
|
||||
|
||||
prevFlow = flow;
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
CV_IMPL void cvCalcOpticalFlowFarneback(
|
||||
const CvArr* _prev, const CvArr* _next,
|
||||
CvArr* _flow, double pyr_scale, int levels,
|
||||
int winsize, int iterations, int poly_n,
|
||||
double poly_sigma, int flags )
|
||||
{
|
||||
cv::Mat prev = cv::cvarrToMat(_prev), next = cv::cvarrToMat(_next);
|
||||
cv::Mat flow = cv::cvarrToMat(_flow);
|
||||
CV_Assert( flow.size() == prev.size() && flow.type() == CV_32FC2 );
|
||||
cv::calcOpticalFlowFarneback( prev, next, flow, pyr_scale, levels,
|
||||
winsize, iterations, poly_n, poly_sigma, flags );
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user