implemented mean shift segmentation with elimination of small segments, added tests
This commit is contained in:
Alexey Spizhevoy
parent
5386a8e7cb
commit
970dd7f593
@ -473,6 +473,10 @@ namespace cv
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CV_EXPORTS void meanShiftProc(const GpuMat& src, GpuMat& dstr, GpuMat& dstsp, int sp, int sr,
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TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
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//! Does mean shift segmentation with elimiation of small regions.
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CV_EXPORTS void meanShiftSegmentation(const GpuMat& src, Mat& dst, int sp, int sr, int minsize,
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TermCriteria criteria = TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 5, 1));
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//! Does coloring of disparity image: [0..ndisp) -> [0..240, 1, 1] in HSV.
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//! Supported types of input disparity: CV_8U, CV_16S.
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//! Output disparity has CV_8UC4 type in BGRA format (alpha = 255).
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475
modules/gpu/src/mssegmentation.cpp
Normal file
475
modules/gpu/src/mssegmentation.cpp
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@ -0,0 +1,475 @@
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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 <time.h>
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#include <vector>
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#include "precomp.hpp"
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#if !defined(HAVE_CUDA)
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namespace cv
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{
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namespace gpu
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{
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void meanShiftSegmentation(const GpuMat&, Mat&, int, int, int, TermCriteria) { throw_nogpu(); }
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} // namespace gpu
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} // namespace cv
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#else
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//#define _MSSEGMENTATION_DBG
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#ifdef _MSSEGMENTATION_DBG
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#include <iostream>
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#define LOG(s) std::cout << (s) << std::endl
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#define LOG2(s1, s2) std::cout << (s1) << (s2) << std::endl
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#define DBG(code) code
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#else
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#define LOG(s1)
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#define LOG2(s1, s2)
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#define DBG(code)
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#endif
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#define PIX(y, x) ((y) * ncols + (x))
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using namespace std;
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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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~DjSets();
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int find(int elem) const;
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int merge(int set1, int set2);
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int* parent;
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int* rank;
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int* size;
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private:
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DjSets(const DjSets&) {}
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DjSets 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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~Graph();
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void addEdge(int from, int to, const T& val=T());
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int* start;
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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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Graph 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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int from;
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int to;
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SegmLinkVal val;
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};
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struct SegmLinkCmp
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{
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bool operator ()(const SegmLink& lhs, const SegmLink& rhs) const
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{
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return lhs.val < rhs.val;
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}
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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)
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{
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parent = new int[n];
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rank = new int[n];
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size = new int[n];
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for (int i = 0; i < n; ++i)
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{
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parent[i] = i;
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rank[i] = 0;
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size[i] = 1;
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}
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}
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DjSets::~DjSets()
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{
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delete[] parent;
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delete[] rank;
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delete[] size;
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}
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inline int DjSets::find(int elem) const
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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)
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{
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this->numv = numv;
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this->nume_max = nume_max;
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start = new int[numv];
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for (int i = 0; i < numv; ++i)
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start[i] = -1;
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edges = new Edge[nume_max];
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nume = 0;
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}
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template <typename T>
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Graph<T>::~Graph()
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{
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delete[] start;
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delete[] edges;
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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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Edge* edge = edges + nume;
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new (edge) SegmLink(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 sqr(int x)
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{
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return x * x;
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}
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} // anonymous namespace
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namespace cv
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{
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namespace gpu
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{
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void 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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DBG(clock_t start = clock());
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// Perform mean shift procedure and obtain region and spatial maps
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GpuMat h_rmap, h_spmap;
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meanShiftProc(src, h_rmap, h_spmap, sp, sr, criteria);
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Mat rmap = h_rmap;
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Mat spmap = h_spmap;
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LOG2("meanshift:", clock() - start);
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DBG(start = clock());
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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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LOG2("ragalloc:", clock() - start);
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DBG(start = clock());
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// Make region adjacent graph from image
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// TODO: SSE?
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Vec4b r1;
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Vec4b r2[4];
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Point_<short> sp1;
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Point_<short> 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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Point_<short>* spy = spmap.ptr<Point_<short> >(y);
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Point_<short>* spyp = spmap.ptr<Point_<short> >(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] = sqr(r1[0] - r2[0][0]) + sqr(r1[1] - r2[0][1]) + sqr(r1[2] - r2[0][2]);
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dr[1] = sqr(r1[0] - r2[1][0]) + sqr(r1[1] - r2[1][1]) + sqr(r1[2] - r2[1][2]);
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dr[2] = sqr(r1[0] - r2[2][0]) + sqr(r1[1] - r2[2][1]) + sqr(r1[2] - r2[2][2]);
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dsp[0] = sqr(sp1.x - sp2[0].x) + sqr(sp1.y - sp2[0].y);
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dsp[1] = sqr(sp1.x - sp2[1].x) + sqr(sp1.y - sp2[1].y);
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dsp[2] = sqr(sp1.x - sp2[2].x) + sqr(sp1.y - sp2[2].y);
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r1 = ry[x + 1];
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sp1 = spy[x + 1];
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dr[3] = sqr(r1[0] - r2[3][0]) + sqr(r1[1] - r2[3][1]) + sqr(r1[2] - r2[3][2]);
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dsp[3] = sqr(sp1.x - sp2[3].x) + sqr(sp1.y - sp2[3].y);
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g.addEdge(PIX(y, x), PIX(y, x + 1), SegmLinkVal(dr[0], dsp[0]));
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g.addEdge(PIX(y, x), PIX(y + 1, x), SegmLinkVal(dr[1], dsp[1]));
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g.addEdge(PIX(y, x), PIX(y + 1, x + 1), SegmLinkVal(dr[2], dsp[2]));
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g.addEdge(PIX(y, x + 1), PIX(y, x + 1), 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<Point_<short> >(y, ncols - 1);
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sp2[0] = spmap.at<Point_<short> >(y + 1, ncols - 1);
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dr[0] = sqr(r1[0] - r2[0][0]) + sqr(r1[1] - r2[0][1]) + sqr(r1[2] - r2[0][2]);
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dsp[0] = sqr(sp1.x - sp2[0].x) + sqr(sp1.y - sp2[0].y);
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g.addEdge(PIX(y, ncols - 1), PIX(y + 1, ncols - 1), 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<Point_<short> >(nrows - 1, x);
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sp2[0] = spmap.at<Point_<short> >(nrows - 1, x + 1);
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dr[0] = sqr(r1[0] - r2[0][0]) + sqr(r1[1] - r2[0][1]) + sqr(r1[2] - r2[0][2]);
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dsp[0] = sqr(sp1.x - sp2[0].x) + sqr(sp1.y - sp2[0].y);
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g.addEdge(PIX(nrows - 1, x), PIX(nrows - 1, x + 1), SegmLinkVal(dr[0], dsp[0]));
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}
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LOG2("raginit:", clock() - start);
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DBG(start = clock());
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DjSets comps(g.numv);
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LOG2("djsetinit:", clock() - start);
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DBG(start = clock());
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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 comp1 = comps.find(v);
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int comp2 = comps.find(g.edges[e_it].to);
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if (comp1 != comp2 && g.edges[e_it].val.dr < hr
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&& g.edges[e_it].val.dsp < hsp)
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comps.merge(comp1, comp2);
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}
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}
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LOG2("findadjacent:", clock() - start);
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DBG(start = clock());
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vector<SegmLink> edges;
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edges.reserve(g.numv);
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LOG2("initedges:", clock() - start);
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DBG(start = clock());
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for (int v = 0; v < g.numv; ++v)
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{
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int comp1 = 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 comp2 = comps.find(g.edges[e_it].to);
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if (comp1 != comp2)
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edges.push_back(SegmLink(comp1, comp2, g.edges[e_it].val));
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}
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}
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LOG2("prepareforsort:", clock() - start);
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DBG(start = clock());
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// Sort all graph's edges connecting differnet components (in asceding order)
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sort(edges.begin(), edges.end(), SegmLinkCmp());
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LOG2("sortedges:", clock() - start);
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DBG(start = clock());
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// Exclude small components (starting from the nearest couple)
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vector<SegmLink>::iterator e_it = edges.begin();
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for (; e_it != edges.end(); ++e_it)
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{
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int comp1 = comps.find(e_it->from);
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int comp2 = comps.find(e_it->to);
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if (comp1 != comp2 && (comps.size[comp1] < minsize || comps.size[comp2] < minsize))
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comps.merge(comp1, comp2);
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}
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LOG2("excludesmall:", clock() - start);
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DBG(start = clock());
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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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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));
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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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LOG2("computesum:", clock() - start);
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DBG(start = clock());
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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)
|
||||
{
|
||||
Vec4b* dsty = dst.ptr<Vec4b>(y);
|
||||
for (int x = 0; x < ncols; ++x)
|
||||
{
|
||||
int parent = comps.find(PIX(y, x));
|
||||
const Vec4i& sumcol = sumcols[parent];
|
||||
Vec4b& dstcol = dsty[x];
|
||||
dstcol[0] = static_cast<uchar>(sumcol[0] / comps.size[parent]);
|
||||
dstcol[1] = static_cast<uchar>(sumcol[1] / comps.size[parent]);
|
||||
dstcol[2] = static_cast<uchar>(sumcol[2] / comps.size[parent]);
|
||||
}
|
||||
}
|
||||
|
||||
LOG2("createfinal:", clock() - start);
|
||||
}
|
||||
|
||||
} // namespace gpu
|
||||
} // namespace cv
|
||||
|
||||
#endif // #if !defined (HAVE_CUDA)
|
||||
103
tests/gpu/src/mssegmentation.cpp
Normal file
103
tests/gpu/src/mssegmentation.cpp
Normal file
@ -0,0 +1,103 @@
|
||||
/*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.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 <opencv2/opencv.hpp>
|
||||
#include <opencv2/gpu/gpu.hpp>
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <iosfwd>
|
||||
#include "gputest.hpp"
|
||||
using namespace cv;
|
||||
using namespace cv::gpu;
|
||||
using namespace std;
|
||||
|
||||
struct CV_GpuMeanShiftSegmentationTest : public CvTest {
|
||||
CV_GpuMeanShiftSegmentationTest() : CvTest( "GPU-MeanShiftSegmentation", "MeanShiftSegmentation" ) {}
|
||||
|
||||
void run(int)
|
||||
{
|
||||
try
|
||||
{
|
||||
Mat img_rgb = imread(string(ts->get_data_path()) + "meanshift/cones.png");
|
||||
if (img_rgb.empty())
|
||||
{
|
||||
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
|
||||
return;
|
||||
}
|
||||
|
||||
Mat img;
|
||||
cvtColor(img_rgb, img, CV_BGR2BGRA);
|
||||
|
||||
for (int minsize = 0; minsize < 2000; minsize = (minsize + 1) * 2)
|
||||
{
|
||||
stringstream path;
|
||||
path << ts->get_data_path() << "meanshift/cones_segmented_sp10_sr10_minsize" << minsize << ".png";
|
||||
|
||||
Mat dst;
|
||||
meanShiftSegmentation((GpuMat)img, dst, 10, 10, minsize);
|
||||
Mat dst_rgb;
|
||||
cvtColor(dst, dst_rgb, CV_BGRA2BGR);
|
||||
|
||||
//imwrite(path.str(), dst_rgb);
|
||||
Mat dst_ref = imread(path.str());
|
||||
if (dst_ref.empty())
|
||||
{
|
||||
ts->set_failed_test_info(CvTS::FAIL_MISSING_TEST_DATA);
|
||||
return;
|
||||
}
|
||||
if (abs(cv::norm(dst_rgb - dst_ref, NORM_INF)) > 1e-3)
|
||||
{
|
||||
ts->printf(CvTS::LOG, "\ndiffers from image *minsize%d.png\n", minsize);
|
||||
ts->set_failed_test_info(CvTS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
catch (const cv::Exception& e)
|
||||
{
|
||||
if (!check_and_treat_gpu_exception(e, ts))
|
||||
throw;
|
||||
return;
|
||||
}
|
||||
|
||||
ts->set_failed_test_info(CvTS::OK);
|
||||
}
|
||||
} ms_segm_test;
|
||||
Loading…
x
Reference in New Issue
Block a user