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468 lines
15 KiB
C++
468 lines
15 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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// Copyright (C) 2013, OpenCV Foundation, 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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////////////////////////////////////////// kmeans ////////////////////////////////////////////
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namespace cv
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{
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static void generateRandomCenter(const std::vector<Vec2f>& box, float* center, RNG& rng)
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{
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size_t j, dims = box.size();
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float margin = 1.f/dims;
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for( j = 0; j < dims; j++ )
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center[j] = ((float)rng*(1.f+margin*2.f)-margin)*(box[j][1] - box[j][0]) + box[j][0];
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}
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class KMeansPPDistanceComputer : public ParallelLoopBody
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{
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public:
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KMeansPPDistanceComputer( float *_tdist2,
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const float *_data,
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const float *_dist,
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int _dims,
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size_t _step,
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size_t _stepci )
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: tdist2(_tdist2),
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data(_data),
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dist(_dist),
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dims(_dims),
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step(_step),
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stepci(_stepci) { }
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void operator()( const cv::Range& range ) const
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{
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CV_TRACE_FUNCTION();
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const int begin = range.start;
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const int end = range.end;
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for ( int i = begin; i<end; i++ )
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{
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tdist2[i] = std::min(normL2Sqr(data + step*i, data + stepci, dims), dist[i]);
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}
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}
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private:
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KMeansPPDistanceComputer& operator=(const KMeansPPDistanceComputer&); // to quiet MSVC
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float *tdist2;
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const float *data;
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const float *dist;
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const int dims;
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const size_t step;
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const size_t stepci;
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};
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/*
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k-means center initialization using the following algorithm:
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Arthur & Vassilvitskii (2007) k-means++: The Advantages of Careful Seeding
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*/
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static void generateCentersPP(const Mat& _data, Mat& _out_centers,
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int K, RNG& rng, int trials)
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{
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CV_TRACE_FUNCTION();
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int i, j, k, dims = _data.cols, N = _data.rows;
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const float* data = _data.ptr<float>(0);
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size_t step = _data.step/sizeof(data[0]);
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std::vector<int> _centers(K);
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int* centers = &_centers[0];
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std::vector<float> _dist(N*3);
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float* dist = &_dist[0], *tdist = dist + N, *tdist2 = tdist + N;
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double sum0 = 0;
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centers[0] = (unsigned)rng % N;
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for( i = 0; i < N; i++ )
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{
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dist[i] = normL2Sqr(data + step*i, data + step*centers[0], dims);
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sum0 += dist[i];
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}
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for( k = 1; k < K; k++ )
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{
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double bestSum = DBL_MAX;
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int bestCenter = -1;
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for( j = 0; j < trials; j++ )
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{
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double p = (double)rng*sum0, s = 0;
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for( i = 0; i < N-1; i++ )
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if( (p -= dist[i]) <= 0 )
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break;
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int ci = i;
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parallel_for_(Range(0, N),
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KMeansPPDistanceComputer(tdist2, data, dist, dims, step, step*ci));
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for( i = 0; i < N; i++ )
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{
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s += tdist2[i];
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}
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if( s < bestSum )
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{
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bestSum = s;
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bestCenter = ci;
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std::swap(tdist, tdist2);
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}
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}
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centers[k] = bestCenter;
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sum0 = bestSum;
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std::swap(dist, tdist);
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}
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for( k = 0; k < K; k++ )
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{
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const float* src = data + step*centers[k];
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float* dst = _out_centers.ptr<float>(k);
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for( j = 0; j < dims; j++ )
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dst[j] = src[j];
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}
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}
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class KMeansDistanceComputer : public ParallelLoopBody
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{
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public:
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KMeansDistanceComputer( double *_distances,
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int *_labels,
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const Mat& _data,
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const Mat& _centers,
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bool _onlyDistance = false )
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: distances(_distances),
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labels(_labels),
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data(_data),
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centers(_centers),
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onlyDistance(_onlyDistance)
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{
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}
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void operator()( const Range& range ) const
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{
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const int begin = range.start;
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const int end = range.end;
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const int K = centers.rows;
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const int dims = centers.cols;
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for( int i = begin; i<end; ++i)
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{
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const float *sample = data.ptr<float>(i);
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if (onlyDistance)
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{
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const float* center = centers.ptr<float>(labels[i]);
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distances[i] = normL2Sqr(sample, center, dims);
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continue;
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}
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int k_best = 0;
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double min_dist = DBL_MAX;
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for( int k = 0; k < K; k++ )
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{
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const float* center = centers.ptr<float>(k);
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const double dist = normL2Sqr(sample, center, dims);
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if( min_dist > dist )
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{
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min_dist = dist;
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k_best = k;
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}
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}
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distances[i] = min_dist;
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labels[i] = k_best;
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}
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}
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private:
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KMeansDistanceComputer& operator=(const KMeansDistanceComputer&); // to quiet MSVC
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double *distances;
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int *labels;
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const Mat& data;
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const Mat& centers;
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bool onlyDistance;
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};
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}
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double cv::kmeans( InputArray _data, int K,
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InputOutputArray _bestLabels,
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TermCriteria criteria, int attempts,
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int flags, OutputArray _centers )
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{
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CV_INSTRUMENT_REGION()
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const int SPP_TRIALS = 3;
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Mat data0 = _data.getMat();
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bool isrow = data0.rows == 1;
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int N = isrow ? data0.cols : data0.rows;
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int dims = (isrow ? 1 : data0.cols)*data0.channels();
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int type = data0.depth();
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attempts = std::max(attempts, 1);
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CV_Assert( data0.dims <= 2 && type == CV_32F && K > 0 );
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CV_Assert( N >= K );
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Mat data(N, dims, CV_32F, data0.ptr(), isrow ? dims * sizeof(float) : static_cast<size_t>(data0.step));
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_bestLabels.create(N, 1, CV_32S, -1, true);
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Mat _labels, best_labels = _bestLabels.getMat();
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if( flags & CV_KMEANS_USE_INITIAL_LABELS )
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{
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CV_Assert( (best_labels.cols == 1 || best_labels.rows == 1) &&
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best_labels.cols*best_labels.rows == N &&
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best_labels.type() == CV_32S &&
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best_labels.isContinuous());
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best_labels.copyTo(_labels);
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}
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else
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{
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if( !((best_labels.cols == 1 || best_labels.rows == 1) &&
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best_labels.cols*best_labels.rows == N &&
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best_labels.type() == CV_32S &&
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best_labels.isContinuous()))
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best_labels.create(N, 1, CV_32S);
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_labels.create(best_labels.size(), best_labels.type());
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}
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int* labels = _labels.ptr<int>();
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Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
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std::vector<int> counters(K);
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std::vector<Vec2f> _box(dims);
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Mat dists(1, N, CV_64F);
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Vec2f* box = &_box[0];
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double best_compactness = DBL_MAX, compactness = 0;
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RNG& rng = theRNG();
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int a, iter, i, j, k;
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if( criteria.type & TermCriteria::EPS )
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criteria.epsilon = std::max(criteria.epsilon, 0.);
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else
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criteria.epsilon = FLT_EPSILON;
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criteria.epsilon *= criteria.epsilon;
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if( criteria.type & TermCriteria::COUNT )
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criteria.maxCount = std::min(std::max(criteria.maxCount, 2), 100);
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else
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criteria.maxCount = 100;
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if( K == 1 )
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{
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attempts = 1;
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criteria.maxCount = 2;
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}
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const float* sample = data.ptr<float>(0);
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for( j = 0; j < dims; j++ )
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box[j] = Vec2f(sample[j], sample[j]);
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for( i = 1; i < N; i++ )
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{
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sample = data.ptr<float>(i);
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for( j = 0; j < dims; j++ )
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{
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float v = sample[j];
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box[j][0] = std::min(box[j][0], v);
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box[j][1] = std::max(box[j][1], v);
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}
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}
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for( a = 0; a < attempts; a++ )
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{
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double max_center_shift = DBL_MAX;
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for( iter = 0;; )
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{
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swap(centers, old_centers);
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if( iter == 0 && (a > 0 || !(flags & KMEANS_USE_INITIAL_LABELS)) )
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{
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if( flags & KMEANS_PP_CENTERS )
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generateCentersPP(data, centers, K, rng, SPP_TRIALS);
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else
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{
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for( k = 0; k < K; k++ )
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generateRandomCenter(_box, centers.ptr<float>(k), rng);
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}
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}
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else
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{
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if( iter == 0 && a == 0 && (flags & KMEANS_USE_INITIAL_LABELS) )
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{
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for( i = 0; i < N; i++ )
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CV_Assert( (unsigned)labels[i] < (unsigned)K );
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}
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// compute centers
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centers = Scalar(0);
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for( k = 0; k < K; k++ )
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counters[k] = 0;
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for( i = 0; i < N; i++ )
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{
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sample = data.ptr<float>(i);
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k = labels[i];
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float* center = centers.ptr<float>(k);
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j=0;
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#if CV_ENABLE_UNROLLED
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for(; j <= dims - 4; j += 4 )
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{
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float t0 = center[j] + sample[j];
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float t1 = center[j+1] + sample[j+1];
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center[j] = t0;
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center[j+1] = t1;
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t0 = center[j+2] + sample[j+2];
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t1 = center[j+3] + sample[j+3];
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center[j+2] = t0;
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center[j+3] = t1;
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}
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#endif
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for( ; j < dims; j++ )
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center[j] += sample[j];
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counters[k]++;
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}
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if( iter > 0 )
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max_center_shift = 0;
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for( k = 0; k < K; k++ )
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{
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if( counters[k] != 0 )
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continue;
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// if some cluster appeared to be empty then:
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// 1. find the biggest cluster
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// 2. find the farthest from the center point in the biggest cluster
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// 3. exclude the farthest point from the biggest cluster and form a new 1-point cluster.
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int max_k = 0;
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for( int k1 = 1; k1 < K; k1++ )
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{
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if( counters[max_k] < counters[k1] )
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max_k = k1;
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}
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double max_dist = 0;
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int farthest_i = -1;
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float* new_center = centers.ptr<float>(k);
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float* old_center = centers.ptr<float>(max_k);
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float* _old_center = temp.ptr<float>(); // normalized
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float scale = 1.f/counters[max_k];
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for( j = 0; j < dims; j++ )
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_old_center[j] = old_center[j]*scale;
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for( i = 0; i < N; i++ )
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{
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if( labels[i] != max_k )
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continue;
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sample = data.ptr<float>(i);
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double dist = normL2Sqr(sample, _old_center, dims);
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if( max_dist <= dist )
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{
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max_dist = dist;
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farthest_i = i;
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}
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}
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counters[max_k]--;
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counters[k]++;
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labels[farthest_i] = k;
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sample = data.ptr<float>(farthest_i);
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for( j = 0; j < dims; j++ )
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{
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old_center[j] -= sample[j];
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new_center[j] += sample[j];
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}
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}
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for( k = 0; k < K; k++ )
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{
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float* center = centers.ptr<float>(k);
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CV_Assert( counters[k] != 0 );
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float scale = 1.f/counters[k];
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for( j = 0; j < dims; j++ )
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center[j] *= scale;
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if( iter > 0 )
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{
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double dist = 0;
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const float* old_center = old_centers.ptr<float>(k);
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for( j = 0; j < dims; j++ )
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{
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double t = center[j] - old_center[j];
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dist += t*t;
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}
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max_center_shift = std::max(max_center_shift, dist);
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}
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}
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}
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bool isLastIter = (++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon);
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// assign labels
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dists = 0;
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double* dist = dists.ptr<double>(0);
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parallel_for_(Range(0, N), KMeansDistanceComputer(dist, labels, data, centers, isLastIter));
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compactness = sum(dists)[0];
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if (isLastIter)
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break;
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}
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if( compactness < best_compactness )
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{
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best_compactness = compactness;
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if( _centers.needed() )
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centers.copyTo(_centers);
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_labels.copyTo(best_labels);
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}
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}
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return best_compactness;
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}
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