--- /dev/null
+/*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 "_cv.h"
+
+//
+// 2D dense optical flow algorithm from the following paper:
+// Gunnar Farneback. "Two-Frame Motion Estimation Based on Polynomial Expansion".
+// Proceedings of the 13th Scandinavian Conference on Image Analysis, Gothenburg, Sweden
+//
+
+namespace cv
+{
+
+static void
+FarnebackPolyExp( const Mat& src, Mat& dst, int n, double sigma )
+{
+ int k, x, y;
+
+ assert( src.type() == CV_32FC1 );
+ int width = src.cols;
+ int height = src.rows;
+ AutoBuffer<float> kbuf(n*6 + 3), _row((width + n*2)*3);
+ float* g = kbuf + n;
+ float* xg = g + n*2 + 1;
+ float* xxg = xg + n*2 + 1;
+ float *row = (float*)_row + n*3;
+
+ if( sigma < FLT_EPSILON )
+ sigma = n*0.3;
+
+ double s = 0.;
+ for( x = -n; x <= n; x++ )
+ {
+ g[x] = (float)std::exp(-x*x/(2*sigma*sigma));
+ s += g[x];
+ }
+
+ s = 1./s;
+ for( x = -n; x <= n; x++ )
+ {
+ g[x] = (float)(g[x]*s);
+ xg[x] = (float)(x*g[x]);
+ xxg[x] = (float)(x*x*g[x]);
+ }
+
+ Mat_<double> G = Mat_<double>::zeros(6, 6);
+
+ for( y = -n; y <= n; y++ )
+ for( x = -n; x <= n; x++ )
+ {
+ G(0,0) += g[y]*g[x];
+ G(1,1) += g[y]*g[x]*x*x;
+ G(3,3) += g[y]*g[x]*x*x*x*x;
+ G(5,5) += g[y]*g[x]*x*x*y*y;
+ }
+
+ //G[0][0] = 1.;
+ G(2,2) = G(0,3) = G(0,4) = G(3,0) = G(4,0) = G(1,1);
+ G(4,4) = G(3,3);
+ G(3,4) = G(4,3) = G(5,5);
+
+ // invG:
+ // [ x e e ]
+ // [ y ]
+ // [ y ]
+ // [ e z ]
+ // [ e z ]
+ // [ u ]
+ Mat_<double> invG = G.inv(DECOMP_CHOLESKY);
+ double ig11 = invG(1,1), ig03 = invG(0,3), ig33 = invG(3,3), ig55 = invG(5,5);
+
+ dst.create( height, width, CV_32FC(5));
+
+ for( y = 0; y < height; y++ )
+ {
+ float g0 = g[0], g1, g2;
+ float *srow0 = (float*)(src.data + src.step*y), *srow1 = 0;
+ float *drow = (float*)(dst.data + dst.step*y);
+
+ // vertical part of convolution
+ for( x = 0; x < width; x++ )
+ {
+ row[x*3] = srow0[x]*g0;
+ row[x*3+1] = row[x*3+2] = 0.f;
+ }
+
+ for( k = 1; k <= n; k++ )
+ {
+ g0 = g[k]; g1 = xg[k]; g2 = xxg[k];
+ srow0 = (float*)(src.data + src.step*std::max(y-k,0));
+ srow1 = (float*)(src.data + src.step*std::min(y+k,height-1));
+
+ for( x = 0; x < width; x++ )
+ {
+ float p = srow0[x] + srow1[x];
+ float t0 = row[x*3] + g0*p;
+ float t1 = row[x*3+1] + g1*(srow1[x] - srow0[x]);
+ float t2 = row[x*3+2] + g2*p;
+
+ row[x*3] = t0;
+ row[x*3+1] = t1;
+ row[x*3+2] = t2;
+ }
+ }
+
+ // horizontal part of convolution
+ for( x = 0; x < n*3; x++ )
+ {
+ row[-1-x] = row[2-x];
+ row[width*3+x] = row[width*3+x-3];
+ }
+
+ for( x = 0; x < width; x++ )
+ {
+ g0 = g[0];
+ // r1 ~ 1, r2 ~ x, r3 ~ y, r4 ~ x^2, r5 ~ y^2, r6 ~ xy
+ double b1 = row[x*3]*g0, b2 = 0, b3 = row[x*3+1]*g0,
+ b4 = 0, b5 = row[x*3+2]*g0, b6 = 0;
+
+ for( k = 1; k <= n; k++ )
+ {
+ double tg = row[(x+k)*3] + row[(x-k)*3];
+ g0 = g[k];
+ b1 += tg*g0;
+ b4 += tg*xxg[k];
+ b2 += (row[(x+k)*3] - row[(x-k)*3])*xg[k];
+ b3 += (row[(x+k)*3+1] + row[(x-k)*3+1])*g0;
+ b6 += (row[(x+k)*3+1] - row[(x-k)*3+1])*xg[k];
+ b5 += (row[(x+k)*3+2] + row[(x-k)*3+2])*g0;
+ }
+
+ // do not store r1
+ drow[x*5+1] = (float)(b2*ig11);
+ drow[x*5] = (float)(b3*ig11);
+ drow[x*5+3] = (float)(b1*ig03 + b4*ig33);
+ drow[x*5+2] = (float)(b1*ig03 + b5*ig33);
+ drow[x*5+4] = (float)(b6*ig55);
+ }
+ }
+
+ row -= n*3;
+}
+
+
+/*static void
+FarnebackPolyExpPyr( const Mat& src0, Vector<Mat>& pyr, int maxlevel, int n, double sigma )
+{
+ Vector<Mat> imgpyr;
+ buildPyramid( src0, imgpyr, maxlevel );
+
+ for( int i = 0; i <= maxlevel; i++ )
+ FarnebackPolyExp( imgpyr[i], pyr[i], n, sigma );
+}*/
+
+
+static void
+FarnebackUpdateMatrices( const Mat& _R0, const Mat& _R1, const Mat& _flow, Mat& _M, int _y0, int _y1 )
+{
+ const int BORDER = 5;
+ static const float border[BORDER] = {0.14f, 0.14f, 0.4472f, 0.4472f, 0.4472f};
+
+ int x, y, width = _flow.cols, height = _flow.rows;
+ const float* R1 = (float*)_R1.data;
+ size_t step1 = _R1.step/sizeof(R1[0]);
+
+ _M.create(height, width, CV_32FC(5));
+
+ for( y = _y0; y < _y1; y++ )
+ {
+ const float* flow = (float*)(_flow.data + y*_flow.step);
+ const float* R0 = (float*)(_R0.data + y*_R0.step);
+ float* M = (float*)(_M.data + y*_M.step);
+
+ for( x = 0; x < width; x++ )
+ {
+ float dx = flow[x*2], dy = flow[x*2+1];
+ float fx = x + dx, fy = y + dy;
+
+#if 1
+ int x1 = cvFloor(fx), y1 = cvFloor(fy);
+ const float* ptr = R1 + y1*step1 + x1*5;
+ float r2, r3, r4, r5, r6;
+
+ fx -= x1; fy -= y1;
+
+ if( (unsigned)x1 < (unsigned)(width-1) &&
+ (unsigned)y1 < (unsigned)(height-1) )
+ {
+ float a00 = (1.f-fx)*(1.f-fy), a01 = fx*(1.f-fy),
+ a10 = (1.f-fx)*fy, a11 = fx*fy;
+
+ r2 = a00*ptr[0] + a01*ptr[5] + a10*ptr[step1] + a11*ptr[step1+5];
+ r3 = a00*ptr[1] + a01*ptr[6] + a10*ptr[step1+1] + a11*ptr[step1+6];
+ r4 = a00*ptr[2] + a01*ptr[7] + a10*ptr[step1+2] + a11*ptr[step1+7];
+ r5 = a00*ptr[3] + a01*ptr[8] + a10*ptr[step1+3] + a11*ptr[step1+8];
+ r6 = a00*ptr[4] + a01*ptr[9] + a10*ptr[step1+4] + a11*ptr[step1+9];
+
+ r4 = (R0[x*5+2] + r4)*0.5f;
+ r5 = (R0[x*5+3] + r5)*0.5f;
+ r6 = (R0[x*5+4] + r6)*0.25f;
+ }
+#else
+ int x1 = cvRound(fx), y1 = cvRound(fy);
+ const float* ptr = R1 + y1*step1 + x1*5;
+ float r2, r3, r4, r5, r6;
+
+ if( (unsigned)x1 < (unsigned)width &&
+ (unsigned)y1 < (unsigned)height )
+ {
+ r2 = ptr[0];
+ r3 = ptr[1];
+ r4 = (R0[x*5+2] + ptr[2])*0.5f;
+ r5 = (R0[x*5+3] + ptr[3])*0.5f;
+ r6 = (R0[x*5+4] + ptr[4])*0.25f;
+ }
+#endif
+ else
+ {
+ r2 = r3 = 0.f;
+ r4 = R0[x*5+2];
+ r5 = R0[x*5+3];
+ r6 = R0[x*5+4]*0.5f;
+ }
+
+ r2 = (R0[x*5] - r2)*0.5f;
+ r3 = (R0[x*5+1] - r3)*0.5f;
+
+ r2 += r4*dy + r6*dx;
+ r3 += r6*dy + r5*dx;
+
+ if( (unsigned)(x - BORDER) >= (unsigned)(width - BORDER*2) ||
+ (unsigned)(y - BORDER) >= (unsigned)(height - BORDER*2))
+ {
+ float scale = (x < BORDER ? border[x] : 1.f)*
+ (x >= width - BORDER ? border[width - x - 1] : 1.f)*
+ (y < BORDER ? border[y] : 1.f)*
+ (y >= height - BORDER ? border[height - y - 1] : 1.f);
+
+ r2 *= scale; r3 *= scale; r4 *= scale;
+ r5 *= scale; r6 *= scale;
+ }
+
+ M[x*5] = r4*r4 + r6*r6; // G(1,1)
+ M[x*5+1] = (r4 + r5)*r6; // G(1,2)=G(2,1)
+ M[x*5+2] = r5*r5 + r6*r6; // G(2,2)
+ M[x*5+3] = r4*r2 + r6*r3; // h(1)
+ M[x*5+4] = r6*r2 + r5*r3; // h(2)
+ }
+ }
+}
+
+
+static void
+FarnebackUpdateFlow_Blur( const Mat& _R0, const Mat& _R1,
+ Mat& _flow, Mat& _M, int block_size,
+ bool update_matrices )
+{
+ int x, y, 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 scale = 1./(block_size*block_size);
+
+ AutoBuffer<double> _vsum((width+m*2+2)*5);
+ double* vsum = _vsum + (m+1)*5;
+
+ // init vsum
+ const float* srow0 = (const float*)_M.data;
+ for( x = 0; x < width*5; x++ )
+ vsum[x] = srow0[x]*(m+2);
+
+ for( y = 1; y < m; y++ )
+ {
+ srow0 = (float*)(_M.data + _M.step*std::min(y,height-1));
+ for( x = 0; x < width*5; x++ )
+ vsum[x] += srow0[x];
+ }
+
+ // 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);
+
+ srow0 = (const float*)(_M.data + _M.step*std::max(y-m-1,0));
+ const float* srow1 = (const float*)(_M.data + _M.step*std::min(y+m,height-1));
+
+ // vertical blur
+ for( x = 0; x < width*5; x++ )
+ vsum[x] += srow1[x] - srow0[x];
+
+ // update borders
+ for( x = 0; x < (m+1)*5; x++ )
+ {
+ vsum[-1-x] = vsum[4-x];
+ vsum[width*5+x] = vsum[width*5+x-5];
+ }
+
+ // init g** and h*
+ g11 = vsum[0]*(m+2);
+ g12 = vsum[1]*(m+2);
+ g22 = vsum[2]*(m+2);
+ h1 = vsum[3]*(m+2);
+ h2 = vsum[4]*(m+2);
+
+ for( x = 1; x < m; x++ )
+ {
+ g11 += vsum[x*5];
+ g12 += vsum[x*5+1];
+ g22 += vsum[x*5+2];
+ h1 += vsum[x*5+3];
+ h2 += vsum[x*5+4];
+ }
+
+ // horizontal blur
+ for( x = 0; x < width; x++ )
+ {
+ g11 += vsum[(x+m)*5] - vsum[(x-m)*5 - 5];
+ g12 += vsum[(x+m)*5 + 1] - vsum[(x-m)*5 - 4];
+ g22 += vsum[(x+m)*5 + 2] - vsum[(x-m)*5 - 3];
+ h1 += vsum[(x+m)*5 + 3] - vsum[(x-m)*5 - 2];
+ h2 += vsum[(x+m)*5 + 4] - vsum[(x-m)*5 - 1];
+
+ double g11_ = g11*scale;
+ double g12_ = g12*scale;
+ double g22_ = g22*scale;
+ double h1_ = h1*scale;
+ double h2_ = h2*scale;
+
+ 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, _M, y0, y1 );
+ y0 = y1;
+ }
+ }
+}
+
+
+static void
+FarnebackUpdateFlow_GaussianBlur( const Mat& _R0, const Mat& _R1,
+ Mat& _flow, Mat& _M, 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);
+ 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*)(_M.data + _M.step*std::max(y-i,0));
+ srow[m+i] = (const float*)(_M.data + _M.step*std::min(y+i,height-1));
+ }
+
+ x = 0;
+#if CV_SSE2
+ 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
+ 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, _M, 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 );
+ 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;
+ }
+}
+
+}
projects / opencv / blobdiff