2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
388 lines
10 KiB
C++
388 lines
10 KiB
C++
/*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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#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
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void cv::gpu::meanShiftSegmentation(const GpuMat&, Mat&, int, int, int, TermCriteria) { throw_nogpu(); }
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#else
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// Auxiliray stuff
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namespace
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{
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//
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// Declarations
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//
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class DjSets
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{
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public:
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DjSets(int n);
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int find(int elem);
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int merge(int set1, int set2);
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std::vector<int> parent;
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std::vector<int> rank;
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std::vector<int> size;
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private:
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DjSets(const DjSets&);
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void operator =(const DjSets&);
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};
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template <typename T>
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struct GraphEdge
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{
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GraphEdge() {}
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GraphEdge(int to_, int next_, const T& val_) : to(to_), next(next_), val(val_) {}
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int to;
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int next;
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T val;
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};
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template <typename T>
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class Graph
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{
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public:
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typedef GraphEdge<T> Edge;
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Graph(int numv, int nume_max);
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void addEdge(int from, int to, const T& val=T());
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std::vector<int> start;
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std::vector<Edge> edges;
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int numv;
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int nume_max;
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int nume;
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private:
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Graph(const Graph&);
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void operator =(const Graph&);
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};
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struct SegmLinkVal
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{
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SegmLinkVal() {}
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SegmLinkVal(int dr_, int dsp_) : dr(dr_), dsp(dsp_) {}
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bool operator <(const SegmLinkVal& other) const
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{
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return dr + dsp < other.dr + other.dsp;
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}
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int dr;
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int dsp;
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};
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struct SegmLink
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{
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SegmLink() {}
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SegmLink(int from_, int to_, const SegmLinkVal& val_)
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: from(from_), to(to_), val(val_) {}
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bool operator <(const SegmLink& other) const
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{
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return val < other.val;
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}
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int from;
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int to;
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SegmLinkVal val;
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};
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//
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// Implementation
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//
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DjSets::DjSets(int n) : parent(n), rank(n, 0), size(n, 1)
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{
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for (int i = 0; i < n; ++i)
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parent[i] = i;
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}
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inline int DjSets::find(int elem)
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{
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int set = elem;
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while (set != parent[set])
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set = parent[set];
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while (elem != parent[elem])
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{
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int next = parent[elem];
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parent[elem] = set;
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elem = next;
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}
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return set;
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}
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inline int DjSets::merge(int set1, int set2)
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{
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if (rank[set1] < rank[set2])
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{
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parent[set1] = set2;
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size[set2] += size[set1];
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return set2;
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}
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if (rank[set2] < rank[set1])
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{
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parent[set2] = set1;
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size[set1] += size[set2];
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return set1;
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}
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parent[set1] = set2;
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rank[set2]++;
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size[set2] += size[set1];
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return set2;
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}
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template <typename T>
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Graph<T>::Graph(int numv_, int nume_max_) : start(numv_, -1), edges(nume_max_)
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{
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this->numv = numv_;
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this->nume_max = nume_max_;
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nume = 0;
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}
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template <typename T>
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inline void Graph<T>::addEdge(int from, int to, const T& val)
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{
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edges[nume] = Edge(to, start[from], val);
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start[from] = nume;
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nume++;
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}
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inline int pix(int y, int x, int ncols)
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{
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return y * ncols + x;
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}
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inline int sqr(int x)
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{
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return x * x;
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}
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inline int dist2(const cv::Vec4b& lhs, const cv::Vec4b& rhs)
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{
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return sqr(lhs[0] - rhs[0]) + sqr(lhs[1] - rhs[1]) + sqr(lhs[2] - rhs[2]);
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}
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inline int dist2(const cv::Vec2s& lhs, const cv::Vec2s& rhs)
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{
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return sqr(lhs[0] - rhs[0]) + sqr(lhs[1] - rhs[1]);
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}
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} // anonymous namespace
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void cv::gpu::meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize, TermCriteria criteria)
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{
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CV_Assert(src.type() == CV_8UC4);
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const int nrows = src.rows;
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const int ncols = src.cols;
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const int hr = sr;
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const int hsp = sp;
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// Perform mean shift procedure and obtain region and spatial maps
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GpuMat d_rmap, d_spmap;
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meanShiftProc(src, d_rmap, d_spmap, sp, sr, criteria);
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Mat rmap(d_rmap);
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Mat spmap(d_spmap);
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Graph<SegmLinkVal> g(nrows * ncols, 4 * (nrows - 1) * (ncols - 1)
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+ (nrows - 1) + (ncols - 1));
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// Make region adjacent graph from image
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Vec4b r1;
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Vec4b r2[4];
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Vec2s sp1;
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Vec2s sp2[4];
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int dr[4];
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int dsp[4];
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for (int y = 0; y < nrows - 1; ++y)
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{
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Vec4b* ry = rmap.ptr<Vec4b>(y);
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Vec4b* ryp = rmap.ptr<Vec4b>(y + 1);
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Vec2s* spy = spmap.ptr<Vec2s>(y);
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Vec2s* spyp = spmap.ptr<Vec2s>(y + 1);
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for (int x = 0; x < ncols - 1; ++x)
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{
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r1 = ry[x];
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sp1 = spy[x];
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r2[0] = ry[x + 1];
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r2[1] = ryp[x];
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r2[2] = ryp[x + 1];
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r2[3] = ryp[x];
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sp2[0] = spy[x + 1];
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sp2[1] = spyp[x];
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sp2[2] = spyp[x + 1];
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sp2[3] = spyp[x];
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dr[0] = dist2(r1, r2[0]);
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dr[1] = dist2(r1, r2[1]);
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dr[2] = dist2(r1, r2[2]);
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dsp[0] = dist2(sp1, sp2[0]);
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dsp[1] = dist2(sp1, sp2[1]);
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dsp[2] = dist2(sp1, sp2[2]);
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r1 = ry[x + 1];
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sp1 = spy[x + 1];
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dr[3] = dist2(r1, r2[3]);
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dsp[3] = dist2(sp1, sp2[3]);
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g.addEdge(pix(y, x, ncols), pix(y, x + 1, ncols), SegmLinkVal(dr[0], dsp[0]));
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g.addEdge(pix(y, x, ncols), pix(y + 1, x, ncols), SegmLinkVal(dr[1], dsp[1]));
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g.addEdge(pix(y, x, ncols), pix(y + 1, x + 1, ncols), SegmLinkVal(dr[2], dsp[2]));
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g.addEdge(pix(y, x + 1, ncols), pix(y + 1, x, ncols), SegmLinkVal(dr[3], dsp[3]));
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}
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}
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for (int y = 0; y < nrows - 1; ++y)
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{
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r1 = rmap.at<Vec4b>(y, ncols - 1);
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r2[0] = rmap.at<Vec4b>(y + 1, ncols - 1);
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sp1 = spmap.at<Vec2s>(y, ncols - 1);
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sp2[0] = spmap.at<Vec2s>(y + 1, ncols - 1);
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dr[0] = dist2(r1, r2[0]);
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dsp[0] = dist2(sp1, sp2[0]);
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g.addEdge(pix(y, ncols - 1, ncols), pix(y + 1, ncols - 1, ncols), SegmLinkVal(dr[0], dsp[0]));
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}
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for (int x = 0; x < ncols - 1; ++x)
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{
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r1 = rmap.at<Vec4b>(nrows - 1, x);
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r2[0] = rmap.at<Vec4b>(nrows - 1, x + 1);
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sp1 = spmap.at<Vec2s>(nrows - 1, x);
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sp2[0] = spmap.at<Vec2s>(nrows - 1, x + 1);
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dr[0] = dist2(r1, r2[0]);
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dsp[0] = dist2(sp1, sp2[0]);
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g.addEdge(pix(nrows - 1, x, ncols), pix(nrows - 1, x + 1, ncols), SegmLinkVal(dr[0], dsp[0]));
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}
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DjSets comps(g.numv);
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// Find adjacent components
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for (int v = 0; v < g.numv; ++v)
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{
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for (int e_it = g.start[v]; e_it != -1; e_it = g.edges[e_it].next)
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{
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int c1 = comps.find(v);
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int c2 = comps.find(g.edges[e_it].to);
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if (c1 != c2 && g.edges[e_it].val.dr < hr && g.edges[e_it].val.dsp < hsp)
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comps.merge(c1, c2);
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}
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}
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std::vector<SegmLink> edges;
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edges.reserve(g.numv);
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// Prepare edges connecting differnet components
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for (int v = 0; v < g.numv; ++v)
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{
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int c1 = comps.find(v);
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for (int e_it = g.start[v]; e_it != -1; e_it = g.edges[e_it].next)
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{
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int c2 = comps.find(g.edges[e_it].to);
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if (c1 != c2)
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edges.push_back(SegmLink(c1, c2, g.edges[e_it].val));
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}
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}
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// Sort all graph's edges connecting differnet components (in asceding order)
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sort(edges.begin(), edges.end());
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// Exclude small components (starting from the nearest couple)
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for (size_t i = 0; i < edges.size(); ++i)
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{
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int c1 = comps.find(edges[i].from);
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int c2 = comps.find(edges[i].to);
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if (c1 != c2 && (comps.size[c1] < minsize || comps.size[c2] < minsize))
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comps.merge(c1, c2);
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}
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// Compute sum of the pixel's colors which are in the same segment
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Mat h_src(src);
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std::vector<Vec4i> sumcols(nrows * ncols, Vec4i(0, 0, 0, 0));
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for (int y = 0; y < nrows; ++y)
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{
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Vec4b* h_srcy = h_src.ptr<Vec4b>(y);
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for (int x = 0; x < ncols; ++x)
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{
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int parent = comps.find(pix(y, x, ncols));
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Vec4b col = h_srcy[x];
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Vec4i& sumcol = sumcols[parent];
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sumcol[0] += col[0];
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sumcol[1] += col[1];
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sumcol[2] += col[2];
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}
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}
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// Create final image, color of each segment is the average color of its pixels
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dst.create(src.size(), src.type());
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for (int y = 0; y < nrows; ++y)
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{
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Vec4b* dsty = dst.ptr<Vec4b>(y);
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for (int x = 0; x < ncols; ++x)
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{
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int parent = comps.find(pix(y, x, ncols));
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const Vec4i& sumcol = sumcols[parent];
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Vec4b& dstcol = dsty[x];
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dstcol[0] = static_cast<uchar>(sumcol[0] / comps.size[parent]);
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dstcol[1] = static_cast<uchar>(sumcol[1] / comps.size[parent]);
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dstcol[2] = static_cast<uchar>(sumcol[2] / comps.size[parent]);
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}
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}
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}
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#endif // #if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
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