458 lines
16 KiB
C++
458 lines
16 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) 2010-2012, Multicoreware, Inc., all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, 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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// @Authors
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// Xiaopeng Fu, fuxiaopeng2222@163.com
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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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#include "opencl_kernels.hpp"
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using namespace cv;
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using namespace cv::ocl;
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static void generateRandomCenter(const 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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// This class is copied from matrix.cpp in core module.
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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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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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int i, j, k, dims = _data.cols, N = _data.rows;
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const float* data = (float*)_data.data;
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size_t step = _data.step/sizeof(data[0]);
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vector<int> _centers(K);
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int* centers = &_centers[0];
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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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void cv::ocl::distanceToCenters(oclMat &dists, oclMat &labels, const oclMat &src, const oclMat ¢ers, int distType, const oclMat &indices)
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{
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CV_Assert(src.cols*src.oclchannels() == centers.cols*centers.oclchannels());
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CV_Assert(src.depth() == CV_32F && centers.depth() == CV_32F);
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bool is_label_row_major = false;
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ensureSizeIsEnough(1, src.rows, CV_32FC1, dists);
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if(labels.empty() || (!labels.empty() && labels.rows == src.rows && labels.cols == 1))
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{
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ensureSizeIsEnough(src.rows, 1, CV_32SC1, labels);
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is_label_row_major = true;
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}
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CV_Assert(distType == NORM_L1 || distType == NORM_L2SQR);
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std::stringstream build_opt_ss;
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build_opt_ss
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<< (distType == NORM_L1 ? "-D L1_DIST" : "-D L2SQR_DIST")
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<< (indices.empty() ? "" : " -D USE_INDEX");
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String build_opt = build_opt_ss.str();
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const int src_step = (int)(src.oclchannels() * src.step / src.elemSize());
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const int centers_step = (int)(centers.oclchannels() * centers.step / centers.elemSize());
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const int colsNumb = centers.cols*centers.oclchannels();
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const int label_step = is_label_row_major ? (int)(labels.step / labels.elemSize()) : 1;
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String kernelname = "distanceToCenters";
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const int number_of_input = indices.empty() ? src.rows : indices.size().area();
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const int src_offset = (int)src.offset/src.elemSize();
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const int centers_offset = (int)centers.offset/centers.elemSize();
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size_t globalThreads[3] = {number_of_input, 1, 1};
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vector<pair<size_t, const void *> > args;
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args.push_back(make_pair(sizeof(cl_mem), (void *)&src.data));
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args.push_back(make_pair(sizeof(cl_mem), (void *)¢ers.data));
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if(!indices.empty())
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{
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args.push_back(make_pair(sizeof(cl_mem), (void *)&indices.data));
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}
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args.push_back(make_pair(sizeof(cl_mem), (void *)&labels.data));
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args.push_back(make_pair(sizeof(cl_mem), (void *)&dists.data));
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args.push_back(make_pair(sizeof(cl_int), (void *)&colsNumb));
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args.push_back(make_pair(sizeof(cl_int), (void *)&src_step));
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args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_step));
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args.push_back(make_pair(sizeof(cl_int), (void *)&label_step));
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args.push_back(make_pair(sizeof(cl_int), (void *)&number_of_input));
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args.push_back(make_pair(sizeof(cl_int), (void *)¢ers.rows));
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args.push_back(make_pair(sizeof(cl_int), (void *)&src_offset));
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args.push_back(make_pair(sizeof(cl_int), (void *)¢ers_offset));
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openCLExecuteKernel(Context::getContext(), &kmeans_kernel,
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kernelname, globalThreads, NULL, args, -1, -1, build_opt.c_str());
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}
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///////////////////////////////////k - means /////////////////////////////////////////////////////////
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double cv::ocl::kmeans(const oclMat &_src, int K, oclMat &_bestLabels,
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TermCriteria criteria, int attempts, int flags, oclMat &_centers)
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{
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const int SPP_TRIALS = 3;
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bool isrow = _src.rows == 1 && _src.oclchannels() > 1;
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int N = !isrow ? _src.rows : _src.cols;
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int dims = (!isrow ? _src.cols : 1) * _src.oclchannels();
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int type = _src.depth();
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attempts = std::max(attempts, 1);
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CV_Assert(type == CV_32F && K > 0 );
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CV_Assert( N >= K );
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Mat _labels;
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if( flags & CV_KMEANS_USE_INITIAL_LABELS )
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{
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CV_Assert( (_bestLabels.cols == 1 || _bestLabels.rows == 1) &&
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_bestLabels.cols * _bestLabels.rows == N &&
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_bestLabels.type() == CV_32S );
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_bestLabels.download(_labels);
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}
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else
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{
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if( !((_bestLabels.cols == 1 || _bestLabels.rows == 1) &&
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_bestLabels.cols * _bestLabels.rows == N &&
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_bestLabels.type() == CV_32S &&
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_bestLabels.isContinuous()))
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_bestLabels.create(N, 1, CV_32S);
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_labels.create(_bestLabels.size(), _bestLabels.type());
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}
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int* labels = _labels.ptr<int>();
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Mat data;
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_src.download(data);
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Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
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vector<int> counters(K);
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vector<Vec2f> _box(dims);
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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>();
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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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if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
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break;
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// assign labels
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oclMat _dists(1, N, CV_64F);
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_bestLabels.upload(_labels);
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_centers.upload(centers);
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distanceToCenters(_dists, _bestLabels, _src, _centers);
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Mat dists;
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_dists.download(dists);
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_bestLabels.download(_labels);
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float* dist = dists.ptr<float>(0);
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compactness = 0;
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for( i = 0; i < N; i++ )
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{
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compactness += (double)dist[i];
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}
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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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}
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}
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return best_compactness;
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}
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