/*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" #if !defined HAVE_CUDA || defined(CUDA_DISABLER) void cv::cuda::meanShiftSegmentation(InputArray, OutputArray, int, int, int, TermCriteria) { throw_no_cuda(); } #else // Auxiliray stuff namespace { // // Declarations // class DjSets { public: DjSets(int n); int find(int elem); int merge(int set1, int set2); std::vector parent; std::vector rank; std::vector size; private: DjSets(const DjSets&); void operator =(const DjSets&); }; template struct GraphEdge { GraphEdge() {} GraphEdge(int to_, int next_, const T& val_) : to(to_), next(next_), val(val_) {} int to; int next; T val; }; template class Graph { public: typedef GraphEdge Edge; Graph(int numv, int nume_max); void addEdge(int from, int to, const T& val=T()); std::vector start; std::vector edges; int numv; int nume_max; int nume; private: Graph(const Graph&); void operator =(const Graph&); }; struct SegmLinkVal { SegmLinkVal() {} SegmLinkVal(int dr_, int dsp_) : dr(dr_), dsp(dsp_) {} bool operator <(const SegmLinkVal& other) const { return dr + dsp < other.dr + other.dsp; } int dr; int dsp; }; struct SegmLink { SegmLink() {} SegmLink(int from_, int to_, const SegmLinkVal& val_) : from(from_), to(to_), val(val_) {} bool operator <(const SegmLink& other) const { return val < other.val; } int from; int to; SegmLinkVal val; }; // // Implementation // DjSets::DjSets(int n) : parent(n), rank(n, 0), size(n, 1) { for (int i = 0; i < n; ++i) parent[i] = i; } inline int DjSets::find(int elem) { int set = elem; while (set != parent[set]) set = parent[set]; while (elem != parent[elem]) { int next = parent[elem]; parent[elem] = set; elem = next; } return set; } inline int DjSets::merge(int set1, int set2) { if (rank[set1] < rank[set2]) { parent[set1] = set2; size[set2] += size[set1]; return set2; } if (rank[set2] < rank[set1]) { parent[set2] = set1; size[set1] += size[set2]; return set1; } parent[set1] = set2; rank[set2]++; size[set2] += size[set1]; return set2; } template Graph::Graph(int numv_, int nume_max_) : start(numv_, -1), edges(nume_max_) { this->numv = numv_; this->nume_max = nume_max_; nume = 0; } template inline void Graph::addEdge(int from, int to, const T& val) { edges[nume] = Edge(to, start[from], val); start[from] = nume; nume++; } inline int pix(int y, int x, int ncols) { return y * ncols + x; } inline int sqr(int x) { return x * x; } inline int dist2(const cv::Vec4b& lhs, const cv::Vec4b& rhs) { return sqr(lhs[0] - rhs[0]) + sqr(lhs[1] - rhs[1]) + sqr(lhs[2] - rhs[2]); } inline int dist2(const cv::Vec2s& lhs, const cv::Vec2s& rhs) { return sqr(lhs[0] - rhs[0]) + sqr(lhs[1] - rhs[1]); } } // anonymous namespace void cv::cuda::meanShiftSegmentation(InputArray _src, OutputArray _dst, int sp, int sr, int minsize, TermCriteria criteria) { GpuMat src = _src.getGpuMat(); CV_Assert( src.type() == CV_8UC4 ); const int nrows = src.rows; const int ncols = src.cols; const int hr = sr; const int hsp = sp; // Perform mean shift procedure and obtain region and spatial maps GpuMat d_rmap, d_spmap; cuda::meanShiftProc(src, d_rmap, d_spmap, sp, sr, criteria); Mat rmap(d_rmap); Mat spmap(d_spmap); Graph g(nrows * ncols, 4 * (nrows - 1) * (ncols - 1) + (nrows - 1) + (ncols - 1)); // Make region adjacent graph from image Vec4b r1; Vec4b r2[4]; Vec2s sp1; Vec2s sp2[4]; int dr[4]; int dsp[4]; for (int y = 0; y < nrows - 1; ++y) { Vec4b* ry = rmap.ptr(y); Vec4b* ryp = rmap.ptr(y + 1); Vec2s* spy = spmap.ptr(y); Vec2s* spyp = spmap.ptr(y + 1); for (int x = 0; x < ncols - 1; ++x) { r1 = ry[x]; sp1 = spy[x]; r2[0] = ry[x + 1]; r2[1] = ryp[x]; r2[2] = ryp[x + 1]; r2[3] = ryp[x]; sp2[0] = spy[x + 1]; sp2[1] = spyp[x]; sp2[2] = spyp[x + 1]; sp2[3] = spyp[x]; dr[0] = dist2(r1, r2[0]); dr[1] = dist2(r1, r2[1]); dr[2] = dist2(r1, r2[2]); dsp[0] = dist2(sp1, sp2[0]); dsp[1] = dist2(sp1, sp2[1]); dsp[2] = dist2(sp1, sp2[2]); r1 = ry[x + 1]; sp1 = spy[x + 1]; dr[3] = dist2(r1, r2[3]); dsp[3] = dist2(sp1, sp2[3]); g.addEdge(pix(y, x, ncols), pix(y, x + 1, ncols), SegmLinkVal(dr[0], dsp[0])); g.addEdge(pix(y, x, ncols), pix(y + 1, x, ncols), SegmLinkVal(dr[1], dsp[1])); g.addEdge(pix(y, x, ncols), pix(y + 1, x + 1, ncols), SegmLinkVal(dr[2], dsp[2])); g.addEdge(pix(y, x + 1, ncols), pix(y + 1, x, ncols), SegmLinkVal(dr[3], dsp[3])); } } for (int y = 0; y < nrows - 1; ++y) { r1 = rmap.at(y, ncols - 1); r2[0] = rmap.at(y + 1, ncols - 1); sp1 = spmap.at(y, ncols - 1); sp2[0] = spmap.at(y + 1, ncols - 1); dr[0] = dist2(r1, r2[0]); dsp[0] = dist2(sp1, sp2[0]); g.addEdge(pix(y, ncols - 1, ncols), pix(y + 1, ncols - 1, ncols), SegmLinkVal(dr[0], dsp[0])); } for (int x = 0; x < ncols - 1; ++x) { r1 = rmap.at(nrows - 1, x); r2[0] = rmap.at(nrows - 1, x + 1); sp1 = spmap.at(nrows - 1, x); sp2[0] = spmap.at(nrows - 1, x + 1); dr[0] = dist2(r1, r2[0]); dsp[0] = dist2(sp1, sp2[0]); g.addEdge(pix(nrows - 1, x, ncols), pix(nrows - 1, x + 1, ncols), SegmLinkVal(dr[0], dsp[0])); } DjSets comps(g.numv); // Find adjacent components for (int v = 0; v < g.numv; ++v) { for (int e_it = g.start[v]; e_it != -1; e_it = g.edges[e_it].next) { int c1 = comps.find(v); int c2 = comps.find(g.edges[e_it].to); if (c1 != c2 && g.edges[e_it].val.dr < hr && g.edges[e_it].val.dsp < hsp) comps.merge(c1, c2); } } std::vector edges; edges.reserve(g.numv); // Prepare edges connecting differnet components for (int v = 0; v < g.numv; ++v) { int c1 = comps.find(v); for (int e_it = g.start[v]; e_it != -1; e_it = g.edges[e_it].next) { int c2 = comps.find(g.edges[e_it].to); if (c1 != c2) edges.push_back(SegmLink(c1, c2, g.edges[e_it].val)); } } // Sort all graph's edges connecting differnet components (in asceding order) std::sort(edges.begin(), edges.end()); // Exclude small components (starting from the nearest couple) for (size_t i = 0; i < edges.size(); ++i) { int c1 = comps.find(edges[i].from); int c2 = comps.find(edges[i].to); if (c1 != c2 && (comps.size[c1] < minsize || comps.size[c2] < minsize)) comps.merge(c1, c2); } // Compute sum of the pixel's colors which are in the same segment Mat h_src(src); std::vector sumcols(nrows * ncols, Vec4i(0, 0, 0, 0)); for (int y = 0; y < nrows; ++y) { Vec4b* h_srcy = h_src.ptr(y); for (int x = 0; x < ncols; ++x) { int parent = comps.find(pix(y, x, ncols)); Vec4b col = h_srcy[x]; Vec4i& sumcol = sumcols[parent]; sumcol[0] += col[0]; sumcol[1] += col[1]; sumcol[2] += col[2]; } } // Create final image, color of each segment is the average color of its pixels _dst.create(src.size(), src.type()); Mat dst = _dst.getMat(); for (int y = 0; y < nrows; ++y) { Vec4b* dsty = dst.ptr(y); for (int x = 0; x < ncols; ++x) { int parent = comps.find(pix(y, x, ncols)); const Vec4i& sumcol = sumcols[parent]; Vec4b& dstcol = dsty[x]; dstcol[0] = static_cast(sumcol[0] / comps.size[parent]); dstcol[1] = static_cast(sumcol[1] / comps.size[parent]); dstcol[2] = static_cast(sumcol[2] / comps.size[parent]); dstcol[3] = 255; } } } #endif // #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)