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2631 lines
82 KiB
2631 lines
82 KiB
/*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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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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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namespace cv |
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{ |
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template<> void Ptr<CvHistogram>::delete_obj() |
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{ cvReleaseHist(&obj); } |
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////////////////// Helper functions ////////////////////// |
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static const size_t OUT_OF_RANGE = (size_t)1 << (sizeof(size_t)*8 - 2); |
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static void |
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calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist, |
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int dims, const float** ranges, const double* uniranges, |
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bool uniform, bool issparse, vector<size_t>& _tab ) |
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{ |
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const int low = 0, high = 256; |
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int i, j; |
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_tab.resize((high-low)*dims); |
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size_t* tab = &_tab[0]; |
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if( uniform ) |
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{ |
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for( i = 0; i < dims; i++ ) |
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{ |
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double a = uniranges[i*2]; |
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double b = uniranges[i*2+1]; |
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int sz = !issparse ? hist.size[i] : shist.size(i); |
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size_t step = !issparse ? hist.step[i] : 1; |
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for( j = low; j < high; j++ ) |
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{ |
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int idx = cvFloor(j*a + b); |
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size_t written_idx; |
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if( (unsigned)idx < (unsigned)sz ) |
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written_idx = idx*step; |
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else |
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written_idx = OUT_OF_RANGE; |
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tab[i*(high - low) + j - low] = written_idx; |
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} |
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} |
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} |
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else |
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{ |
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for( i = 0; i < dims; i++ ) |
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{ |
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int limit = std::min(cvCeil(ranges[i][0]), high); |
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int idx = -1, sz = !issparse ? hist.size[i] : shist.size(i); |
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size_t written_idx = OUT_OF_RANGE; |
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size_t step = !issparse ? hist.step[i] : 1; |
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for(j = low;;) |
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{ |
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for( ; j < limit; j++ ) |
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tab[i*(high - low) + j - low] = written_idx; |
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if( (unsigned)(++idx) < (unsigned)sz ) |
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{ |
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limit = std::min(cvCeil(ranges[i][idx+1]), high); |
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written_idx = idx*step; |
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} |
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else |
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{ |
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for( ; j < high; j++ ) |
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tab[i*(high - low) + j - low] = OUT_OF_RANGE; |
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break; |
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} |
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} |
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} |
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} |
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} |
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static void histPrepareImages( const Mat* images, int nimages, const int* channels, |
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const Mat& mask, int dims, const int* histSize, |
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const float** ranges, bool uniform, |
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vector<uchar*>& ptrs, vector<int>& deltas, |
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Size& imsize, vector<double>& uniranges ) |
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{ |
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int i, j, c; |
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CV_Assert( channels != 0 || nimages == dims ); |
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imsize = images[0].size(); |
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int depth = images[0].depth(), esz1 = (int)images[0].elemSize1(); |
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bool isContinuous = true; |
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ptrs.resize(dims + 1); |
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deltas.resize((dims + 1)*2); |
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for( i = 0; i < dims; i++ ) |
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{ |
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if(!channels) |
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{ |
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j = i; |
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c = 0; |
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CV_Assert( images[j].channels() == 1 ); |
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} |
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else |
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{ |
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c = channels[i]; |
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CV_Assert( c >= 0 ); |
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for( j = 0; j < nimages; c -= images[j].channels(), j++ ) |
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if( c < images[j].channels() ) |
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break; |
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CV_Assert( j < nimages ); |
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} |
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CV_Assert( images[j].size() == imsize && images[j].depth() == depth ); |
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if( !images[j].isContinuous() ) |
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isContinuous = false; |
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ptrs[i] = images[j].data + c*esz1; |
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deltas[i*2] = images[j].channels(); |
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deltas[i*2+1] = (int)(images[j].step/esz1 - imsize.width*deltas[i*2]); |
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} |
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if( mask.data ) |
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{ |
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CV_Assert( mask.size() == imsize && mask.channels() == 1 ); |
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isContinuous = isContinuous && mask.isContinuous(); |
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ptrs[dims] = mask.data; |
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deltas[dims*2] = 1; |
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deltas[dims*2 + 1] = (int)(mask.step/mask.elemSize1()); |
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} |
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if( isContinuous ) |
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{ |
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imsize.width *= imsize.height; |
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imsize.height = 1; |
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} |
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if( !ranges ) |
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{ |
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CV_Assert( depth == CV_8U ); |
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uniranges.resize( dims*2 ); |
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for( i = 0; i < dims; i++ ) |
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{ |
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uniranges[i*2] = histSize[i]/256.; |
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uniranges[i*2+1] = 0; |
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} |
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} |
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else if( uniform ) |
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{ |
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uniranges.resize( dims*2 ); |
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for( i = 0; i < dims; i++ ) |
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{ |
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CV_Assert( ranges[i] && ranges[i][0] < ranges[i][1] ); |
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double low = ranges[i][0], high = ranges[i][1]; |
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double t = histSize[i]/(high - low); |
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uniranges[i*2] = t; |
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uniranges[i*2+1] = -t*low; |
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} |
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} |
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else |
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{ |
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for( i = 0; i < dims; i++ ) |
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{ |
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size_t j, n = histSize[i]; |
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for( j = 0; j < n; j++ ) |
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CV_Assert( ranges[i][j] < ranges[i][j+1] ); |
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} |
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} |
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} |
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////////////////////////////////// C A L C U L A T E H I S T O G R A M //////////////////////////////////// |
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template<typename T> static void |
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calcHist_( vector<uchar*>& _ptrs, const vector<int>& _deltas, |
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Size imsize, Mat& hist, int dims, const float** _ranges, |
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const double* _uniranges, bool uniform ) |
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{ |
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T** ptrs = (T**)&_ptrs[0]; |
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const int* deltas = &_deltas[0]; |
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uchar* H = hist.data; |
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int i, x; |
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const uchar* mask = _ptrs[dims]; |
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int mstep = _deltas[dims*2 + 1]; |
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int size[CV_MAX_DIM]; |
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size_t hstep[CV_MAX_DIM]; |
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for( i = 0; i < dims; i++ ) |
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{ |
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size[i] = hist.size[i]; |
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hstep[i] = hist.step[i]; |
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} |
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if( uniform ) |
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{ |
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const double* uniranges = &_uniranges[0]; |
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if( dims == 1 ) |
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{ |
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double a = uniranges[0], b = uniranges[1]; |
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int sz = size[0], d0 = deltas[0], step0 = deltas[1]; |
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const T* p0 = (const T*)ptrs[0]; |
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for( ; imsize.height--; p0 += step0, mask += mstep ) |
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{ |
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if( !mask ) |
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for( x = 0; x < imsize.width; x++, p0 += d0 ) |
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{ |
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int idx = cvFloor(*p0*a + b); |
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if( (unsigned)idx < (unsigned)sz ) |
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((int*)H)[idx]++; |
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} |
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else |
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for( x = 0; x < imsize.width; x++, p0 += d0 ) |
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if( mask[x] ) |
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{ |
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int idx = cvFloor(*p0*a + b); |
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if( (unsigned)idx < (unsigned)sz ) |
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((int*)H)[idx]++; |
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} |
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} |
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} |
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else if( dims == 2 ) |
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{ |
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double a0 = uniranges[0], b0 = uniranges[1], a1 = uniranges[2], b1 = uniranges[3]; |
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int sz0 = size[0], sz1 = size[1]; |
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int d0 = deltas[0], step0 = deltas[1], |
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d1 = deltas[2], step1 = deltas[3]; |
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size_t hstep0 = hstep[0]; |
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const T* p0 = (const T*)ptrs[0]; |
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const T* p1 = (const T*)ptrs[1]; |
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for( ; imsize.height--; p0 += step0, p1 += step1, mask += mstep ) |
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{ |
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if( !mask ) |
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for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) |
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{ |
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int idx0 = cvFloor(*p0*a0 + b0); |
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int idx1 = cvFloor(*p1*a1 + b1); |
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if( (unsigned)idx0 < (unsigned)sz0 && (unsigned)idx1 < (unsigned)sz1 ) |
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((int*)(H + hstep0*idx0))[idx1]++; |
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} |
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else |
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for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) |
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if( mask[x] ) |
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{ |
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int idx0 = cvFloor(*p0*a0 + b0); |
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int idx1 = cvFloor(*p1*a1 + b1); |
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if( (unsigned)idx0 < (unsigned)sz0 && (unsigned)idx1 < (unsigned)sz1 ) |
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((int*)(H + hstep0*idx0))[idx1]++; |
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} |
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} |
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} |
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else if( dims == 3 ) |
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{ |
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double a0 = uniranges[0], b0 = uniranges[1], |
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a1 = uniranges[2], b1 = uniranges[3], |
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a2 = uniranges[4], b2 = uniranges[5]; |
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int sz0 = size[0], sz1 = size[1], sz2 = size[2]; |
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int d0 = deltas[0], step0 = deltas[1], |
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d1 = deltas[2], step1 = deltas[3], |
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d2 = deltas[4], step2 = deltas[5]; |
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size_t hstep0 = hstep[0], hstep1 = hstep[1]; |
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const T* p0 = (const T*)ptrs[0]; |
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const T* p1 = (const T*)ptrs[1]; |
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const T* p2 = (const T*)ptrs[2]; |
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for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, mask += mstep ) |
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{ |
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if( !mask ) |
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for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) |
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{ |
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int idx0 = cvFloor(*p0*a0 + b0); |
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int idx1 = cvFloor(*p1*a1 + b1); |
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int idx2 = cvFloor(*p2*a2 + b2); |
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if( (unsigned)idx0 < (unsigned)sz0 && |
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(unsigned)idx1 < (unsigned)sz1 && |
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(unsigned)idx2 < (unsigned)sz2 ) |
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((int*)(H + hstep0*idx0 + hstep1*idx1))[idx2]++; |
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} |
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else |
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for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) |
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if( mask[x] ) |
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{ |
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int idx0 = cvFloor(*p0*a0 + b0); |
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int idx1 = cvFloor(*p1*a1 + b1); |
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int idx2 = cvFloor(*p2*a2 + b2); |
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if( (unsigned)idx0 < (unsigned)sz0 && |
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(unsigned)idx1 < (unsigned)sz1 && |
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(unsigned)idx2 < (unsigned)sz2 ) |
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((int*)(H + hstep0*idx0 + hstep1*idx1))[idx2]++; |
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} |
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} |
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} |
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else |
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{ |
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for( ; imsize.height--; mask += mstep ) |
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{ |
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if( !mask ) |
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for( x = 0; x < imsize.width; x++ ) |
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{ |
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uchar* Hptr = H; |
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for( i = 0; i < dims; i++ ) |
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{ |
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int idx = cvFloor(*ptrs[i]*uniranges[i*2] + uniranges[i*2+1]); |
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if( (unsigned)idx >= (unsigned)size[i] ) |
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break; |
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ptrs[i] += deltas[i*2]; |
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Hptr += idx*hstep[i]; |
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} |
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if( i == dims ) |
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++*((int*)Hptr); |
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else |
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for( ; i < dims; i++ ) |
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ptrs[i] += deltas[i*2]; |
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} |
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else |
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for( x = 0; x < imsize.width; x++ ) |
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{ |
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uchar* Hptr = H; |
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i = 0; |
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if( mask[x] ) |
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for( ; i < dims; i++ ) |
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{ |
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int idx = cvFloor(*ptrs[i]*uniranges[i*2] + uniranges[i*2+1]); |
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if( (unsigned)idx >= (unsigned)size[i] ) |
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break; |
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ptrs[i] += deltas[i*2]; |
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Hptr += idx*hstep[i]; |
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} |
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if( i == dims ) |
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++*((int*)Hptr); |
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else |
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for( ; i < dims; i++ ) |
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ptrs[i] += deltas[i*2]; |
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} |
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for( i = 0; i < dims; i++ ) |
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ptrs[i] += deltas[i*2 + 1]; |
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} |
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} |
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} |
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else |
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{ |
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// non-uniform histogram |
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const float* ranges[CV_MAX_DIM]; |
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for( i = 0; i < dims; i++ ) |
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ranges[i] = &_ranges[i][0]; |
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for( ; imsize.height--; mask += mstep ) |
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{ |
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for( x = 0; x < imsize.width; x++ ) |
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{ |
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uchar* Hptr = H; |
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i = 0; |
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if( !mask || mask[x] ) |
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for( ; i < dims; i++ ) |
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{ |
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float v = (float)*ptrs[i]; |
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const float* R = ranges[i]; |
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int idx = -1, sz = size[i]; |
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while( v >= R[idx+1] && ++idx < sz ) |
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; // nop |
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if( (unsigned)idx >= (unsigned)sz ) |
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break; |
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ptrs[i] += deltas[i*2]; |
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Hptr += idx*hstep[i]; |
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} |
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if( i == dims ) |
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++*((int*)Hptr); |
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else |
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for( ; i < dims; i++ ) |
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ptrs[i] += deltas[i*2]; |
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} |
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for( i = 0; i < dims; i++ ) |
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ptrs[i] += deltas[i*2 + 1]; |
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} |
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} |
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} |
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static void |
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calcHist_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas, |
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Size imsize, Mat& hist, int dims, const float** _ranges, |
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const double* _uniranges, bool uniform ) |
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{ |
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uchar** ptrs = &_ptrs[0]; |
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const int* deltas = &_deltas[0]; |
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uchar* H = hist.data; |
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int i, x; |
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const uchar* mask = _ptrs[dims]; |
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int mstep = _deltas[dims*2 + 1]; |
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vector<size_t> _tab; |
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calcHistLookupTables_8u( hist, SparseMat(), dims, _ranges, _uniranges, uniform, false, _tab ); |
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const size_t* tab = &_tab[0]; |
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|
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if( dims == 1 ) |
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{ |
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int d0 = deltas[0], step0 = deltas[1]; |
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int matH[256] = {0}; |
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const uchar* p0 = (const uchar*)ptrs[0]; |
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for( ; imsize.height--; p0 += step0, mask += mstep ) |
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{ |
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if( !mask ) |
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{ |
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if( d0 == 1 ) |
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{ |
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for( x = 0; x <= imsize.width - 4; x += 4 ) |
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{ |
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int t0 = p0[x], t1 = p0[x+1]; |
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matH[t0]++; matH[t1]++; |
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t0 = p0[x+2]; t1 = p0[x+3]; |
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matH[t0]++; matH[t1]++; |
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} |
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p0 += x; |
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} |
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else |
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for( x = 0; x <= imsize.width - 4; x += 4 ) |
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{ |
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int t0 = p0[0], t1 = p0[d0]; |
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matH[t0]++; matH[t1]++; |
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p0 += d0*2; |
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t0 = p0[0]; t1 = p0[d0]; |
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matH[t0]++; matH[t1]++; |
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p0 += d0*2; |
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} |
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for( ; x < imsize.width; x++, p0 += d0 ) |
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matH[*p0]++; |
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} |
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else |
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for( x = 0; x < imsize.width; x++, p0 += d0 ) |
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if( mask[x] ) |
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matH[*p0]++; |
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} |
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for( i = 0; i < 256; i++ ) |
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{ |
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size_t hidx = tab[i]; |
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if( hidx < OUT_OF_RANGE ) |
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*(int*)(H + hidx) += matH[i]; |
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} |
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} |
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else if( dims == 2 ) |
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{ |
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int d0 = deltas[0], step0 = deltas[1], |
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d1 = deltas[2], step1 = deltas[3]; |
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const uchar* p0 = (const uchar*)ptrs[0]; |
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const uchar* p1 = (const uchar*)ptrs[1]; |
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|
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for( ; imsize.height--; p0 += step0, p1 += step1, mask += mstep ) |
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{ |
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if( !mask ) |
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for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) |
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{ |
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size_t idx = tab[*p0] + tab[*p1 + 256]; |
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if( idx < OUT_OF_RANGE ) |
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++*(int*)(H + idx); |
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} |
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else |
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for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) |
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{ |
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size_t idx; |
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if( mask[x] && (idx = tab[*p0] + tab[*p1 + 256]) < OUT_OF_RANGE ) |
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++*(int*)(H + idx); |
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} |
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} |
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} |
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else if( dims == 3 ) |
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{ |
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int d0 = deltas[0], step0 = deltas[1], |
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d1 = deltas[2], step1 = deltas[3], |
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d2 = deltas[4], step2 = deltas[5]; |
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|
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const uchar* p0 = (const uchar*)ptrs[0]; |
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const uchar* p1 = (const uchar*)ptrs[1]; |
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const uchar* p2 = (const uchar*)ptrs[2]; |
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|
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for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, mask += mstep ) |
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{ |
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if( !mask ) |
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for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) |
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{ |
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size_t idx = tab[*p0] + tab[*p1 + 256] + tab[*p2 + 512]; |
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if( idx < OUT_OF_RANGE ) |
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++*(int*)(H + idx); |
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} |
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else |
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for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) |
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{ |
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size_t idx; |
|
if( mask[x] && (idx = tab[*p0] + tab[*p1 + 256] + tab[*p2 + 512]) < OUT_OF_RANGE ) |
|
++*(int*)(H + idx); |
|
} |
|
} |
|
} |
|
else |
|
{ |
|
for( ; imsize.height--; mask += mstep ) |
|
{ |
|
if( !mask ) |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
uchar* Hptr = H; |
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
size_t idx = tab[*ptrs[i] + i*256]; |
|
if( idx >= OUT_OF_RANGE ) |
|
break; |
|
Hptr += idx; |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
|
|
if( i == dims ) |
|
++*((int*)Hptr); |
|
else |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
else |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
uchar* Hptr = H; |
|
int i = 0; |
|
if( mask[x] ) |
|
for( ; i < dims; i++ ) |
|
{ |
|
size_t idx = tab[*ptrs[i] + i*256]; |
|
if( idx >= OUT_OF_RANGE ) |
|
break; |
|
Hptr += idx; |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
|
|
if( i == dims ) |
|
++*((int*)Hptr); |
|
else |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
} |
|
|
|
} |
|
|
|
void cv::calcHist( const Mat* images, int nimages, const int* channels, |
|
InputArray _mask, OutputArray _hist, int dims, const int* histSize, |
|
const float** ranges, bool uniform, bool accumulate ) |
|
{ |
|
Mat mask = _mask.getMat(); |
|
|
|
CV_Assert(dims > 0 && histSize); |
|
|
|
uchar* histdata = _hist.getMat().data; |
|
_hist.create(dims, histSize, CV_32F); |
|
Mat hist = _hist.getMat(), ihist = hist; |
|
ihist.flags = (ihist.flags & ~CV_MAT_TYPE_MASK)|CV_32S; |
|
|
|
if( !accumulate || histdata != hist.data ) |
|
hist = Scalar(0.); |
|
else |
|
hist.convertTo(ihist, CV_32S); |
|
|
|
vector<uchar*> ptrs; |
|
vector<int> deltas; |
|
vector<double> uniranges; |
|
Size imsize; |
|
|
|
CV_Assert( !mask.data || mask.type() == CV_8UC1 ); |
|
histPrepareImages( images, nimages, channels, mask, dims, hist.size, ranges, |
|
uniform, ptrs, deltas, imsize, uniranges ); |
|
const double* _uniranges = uniform ? &uniranges[0] : 0; |
|
|
|
int depth = images[0].depth(); |
|
|
|
if( depth == CV_8U ) |
|
calcHist_8u(ptrs, deltas, imsize, ihist, dims, ranges, _uniranges, uniform ); |
|
else if( depth == CV_16U ) |
|
calcHist_<ushort>(ptrs, deltas, imsize, ihist, dims, ranges, _uniranges, uniform ); |
|
else if( depth == CV_32F ) |
|
calcHist_<float>(ptrs, deltas, imsize, ihist, dims, ranges, _uniranges, uniform ); |
|
else |
|
CV_Error(CV_StsUnsupportedFormat, ""); |
|
|
|
ihist.convertTo(hist, CV_32F); |
|
} |
|
|
|
|
|
namespace cv |
|
{ |
|
|
|
template<typename T> static void |
|
calcSparseHist_( vector<uchar*>& _ptrs, const vector<int>& _deltas, |
|
Size imsize, SparseMat& hist, int dims, const float** _ranges, |
|
const double* _uniranges, bool uniform ) |
|
{ |
|
T** ptrs = (T**)&_ptrs[0]; |
|
const int* deltas = &_deltas[0]; |
|
int i, x; |
|
const uchar* mask = _ptrs[dims]; |
|
int mstep = _deltas[dims*2 + 1]; |
|
const int* size = hist.hdr->size; |
|
int idx[CV_MAX_DIM]; |
|
|
|
if( uniform ) |
|
{ |
|
const double* uniranges = &_uniranges[0]; |
|
|
|
for( ; imsize.height--; mask += mstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
i = 0; |
|
if( !mask || mask[x] ) |
|
for( ; i < dims; i++ ) |
|
{ |
|
idx[i] = cvFloor(*ptrs[i]*uniranges[i*2] + uniranges[i*2+1]); |
|
if( (unsigned)idx[i] >= (unsigned)size[i] ) |
|
break; |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
|
|
if( i == dims ) |
|
++*(int*)hist.ptr(idx, true); |
|
else |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
else |
|
{ |
|
// non-uniform histogram |
|
const float* ranges[CV_MAX_DIM]; |
|
for( i = 0; i < dims; i++ ) |
|
ranges[i] = &_ranges[i][0]; |
|
|
|
for( ; imsize.height--; mask += mstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
i = 0; |
|
|
|
if( !mask || mask[x] ) |
|
for( ; i < dims; i++ ) |
|
{ |
|
float v = (float)*ptrs[i]; |
|
const float* R = ranges[i]; |
|
int j = -1, sz = size[i]; |
|
|
|
while( v >= R[j+1] && ++j < sz ) |
|
; // nop |
|
|
|
if( (unsigned)j >= (unsigned)sz ) |
|
break; |
|
ptrs[i] += deltas[i*2]; |
|
idx[i] = j; |
|
} |
|
|
|
if( i == dims ) |
|
++*(int*)hist.ptr(idx, true); |
|
else |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
|
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
} |
|
|
|
|
|
static void |
|
calcSparseHist_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas, |
|
Size imsize, SparseMat& hist, int dims, const float** _ranges, |
|
const double* _uniranges, bool uniform ) |
|
{ |
|
uchar** ptrs = (uchar**)&_ptrs[0]; |
|
const int* deltas = &_deltas[0]; |
|
int i, x; |
|
const uchar* mask = _ptrs[dims]; |
|
int mstep = _deltas[dims*2 + 1]; |
|
int idx[CV_MAX_DIM]; |
|
vector<size_t> _tab; |
|
|
|
calcHistLookupTables_8u( Mat(), hist, dims, _ranges, _uniranges, uniform, true, _tab ); |
|
const size_t* tab = &_tab[0]; |
|
|
|
for( ; imsize.height--; mask += mstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
int i = 0; |
|
if( !mask || mask[x] ) |
|
for( ; i < dims; i++ ) |
|
{ |
|
size_t hidx = tab[*ptrs[i] + i*256]; |
|
if( hidx >= OUT_OF_RANGE ) |
|
break; |
|
ptrs[i] += deltas[i*2]; |
|
idx[i] = (int)hidx; |
|
} |
|
|
|
if( i == dims ) |
|
++*(int*)hist.ptr(idx,true); |
|
else |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
|
|
|
|
static void calcHist( const Mat* images, int nimages, const int* channels, |
|
const Mat& mask, SparseMat& hist, int dims, const int* histSize, |
|
const float** ranges, bool uniform, bool accumulate, bool keepInt ) |
|
{ |
|
size_t i, N; |
|
|
|
if( !accumulate ) |
|
hist.create(dims, histSize, CV_32F); |
|
else |
|
{ |
|
SparseMatIterator it = hist.begin(); |
|
for( i = 0, N = hist.nzcount(); i < N; i++, ++it ) |
|
{ |
|
Cv32suf* val = (Cv32suf*)it.ptr; |
|
val->i = cvRound(val->f); |
|
} |
|
} |
|
|
|
vector<uchar*> ptrs; |
|
vector<int> deltas; |
|
vector<double> uniranges; |
|
Size imsize; |
|
|
|
CV_Assert( !mask.data || mask.type() == CV_8UC1 ); |
|
histPrepareImages( images, nimages, channels, mask, dims, hist.hdr->size, ranges, |
|
uniform, ptrs, deltas, imsize, uniranges ); |
|
const double* _uniranges = uniform ? &uniranges[0] : 0; |
|
|
|
int depth = images[0].depth(); |
|
if( depth == CV_8U ) |
|
calcSparseHist_8u(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, uniform ); |
|
else if( depth == CV_16U ) |
|
calcSparseHist_<ushort>(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, uniform ); |
|
else if( depth == CV_32F ) |
|
calcSparseHist_<float>(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, uniform ); |
|
else |
|
CV_Error(CV_StsUnsupportedFormat, ""); |
|
|
|
if( !keepInt ) |
|
{ |
|
SparseMatIterator it = hist.begin(); |
|
for( i = 0, N = hist.nzcount(); i < N; i++, ++it ) |
|
{ |
|
Cv32suf* val = (Cv32suf*)it.ptr; |
|
val->f = (float)val->i; |
|
} |
|
} |
|
} |
|
|
|
} |
|
|
|
void cv::calcHist( const Mat* images, int nimages, const int* channels, |
|
InputArray _mask, SparseMat& hist, int dims, const int* histSize, |
|
const float** ranges, bool uniform, bool accumulate ) |
|
{ |
|
Mat mask = _mask.getMat(); |
|
calcHist( images, nimages, channels, mask, hist, dims, histSize, |
|
ranges, uniform, accumulate, false ); |
|
} |
|
|
|
|
|
void cv::calcHist( InputArrayOfArrays images, const vector<int>& channels, |
|
InputArray mask, OutputArray hist, |
|
const vector<int>& histSize, |
|
const vector<float>& ranges, |
|
bool accumulate ) |
|
{ |
|
int i, dims = (int)histSize.size(), rsz = (int)ranges.size(), csz = (int)channels.size(); |
|
int nimages = (int)images.total(); |
|
|
|
CV_Assert(nimages > 0 && dims > 0); |
|
CV_Assert(rsz == dims*2 || (rsz == 0 && images.depth(0) == CV_8U)); |
|
CV_Assert(csz == 0 || csz == dims); |
|
float* _ranges[CV_MAX_DIM]; |
|
if( rsz > 0 ) |
|
{ |
|
for( i = 0; i < rsz/2; i++ ) |
|
_ranges[i] = (float*)&ranges[i*2]; |
|
} |
|
|
|
AutoBuffer<Mat> buf(nimages); |
|
for( i = 0; i < nimages; i++ ) |
|
buf[i] = images.getMat(i); |
|
|
|
calcHist(&buf[0], nimages, csz ? &channels[0] : 0, |
|
mask, hist, dims, &histSize[0], rsz ? (const float**)_ranges : 0, |
|
true, accumulate); |
|
} |
|
|
|
|
|
/////////////////////////////////////// B A C K P R O J E C T //////////////////////////////////// |
|
|
|
namespace cv |
|
{ |
|
|
|
template<typename T, typename BT> static void |
|
calcBackProj_( vector<uchar*>& _ptrs, const vector<int>& _deltas, |
|
Size imsize, const Mat& hist, int dims, const float** _ranges, |
|
const double* _uniranges, float scale, bool uniform ) |
|
{ |
|
T** ptrs = (T**)&_ptrs[0]; |
|
const int* deltas = &_deltas[0]; |
|
uchar* H = hist.data; |
|
int i, x; |
|
BT* bproj = (BT*)_ptrs[dims]; |
|
int bpstep = _deltas[dims*2 + 1]; |
|
int size[CV_MAX_DIM]; |
|
size_t hstep[CV_MAX_DIM]; |
|
|
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
size[i] = hist.size[i]; |
|
hstep[i] = hist.step[i]; |
|
} |
|
|
|
if( uniform ) |
|
{ |
|
const double* uniranges = &_uniranges[0]; |
|
|
|
if( dims == 1 ) |
|
{ |
|
double a = uniranges[0], b = uniranges[1]; |
|
int sz = size[0], d0 = deltas[0], step0 = deltas[1]; |
|
const T* p0 = (const T*)ptrs[0]; |
|
|
|
for( ; imsize.height--; p0 += step0, bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++, p0 += d0 ) |
|
{ |
|
int idx = cvFloor(*p0*a + b); |
|
bproj[x] = (unsigned)idx < (unsigned)sz ? saturate_cast<BT>(((float*)H)[idx]*scale) : 0; |
|
} |
|
} |
|
} |
|
else if( dims == 2 ) |
|
{ |
|
double a0 = uniranges[0], b0 = uniranges[1], |
|
a1 = uniranges[2], b1 = uniranges[3]; |
|
int sz0 = size[0], sz1 = size[1]; |
|
int d0 = deltas[0], step0 = deltas[1], |
|
d1 = deltas[2], step1 = deltas[3]; |
|
size_t hstep0 = hstep[0]; |
|
const T* p0 = (const T*)ptrs[0]; |
|
const T* p1 = (const T*)ptrs[1]; |
|
|
|
for( ; imsize.height--; p0 += step0, p1 += step1, bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) |
|
{ |
|
int idx0 = cvFloor(*p0*a0 + b0); |
|
int idx1 = cvFloor(*p1*a1 + b1); |
|
bproj[x] = (unsigned)idx0 < (unsigned)sz0 && |
|
(unsigned)idx1 < (unsigned)sz1 ? |
|
saturate_cast<BT>(((float*)(H + hstep0*idx0))[idx1]*scale) : 0; |
|
} |
|
} |
|
} |
|
else if( dims == 3 ) |
|
{ |
|
double a0 = uniranges[0], b0 = uniranges[1], |
|
a1 = uniranges[2], b1 = uniranges[3], |
|
a2 = uniranges[4], b2 = uniranges[5]; |
|
int sz0 = size[0], sz1 = size[1], sz2 = size[2]; |
|
int d0 = deltas[0], step0 = deltas[1], |
|
d1 = deltas[2], step1 = deltas[3], |
|
d2 = deltas[4], step2 = deltas[5]; |
|
size_t hstep0 = hstep[0], hstep1 = hstep[1]; |
|
const T* p0 = (const T*)ptrs[0]; |
|
const T* p1 = (const T*)ptrs[1]; |
|
const T* p2 = (const T*)ptrs[2]; |
|
|
|
for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) |
|
{ |
|
int idx0 = cvFloor(*p0*a0 + b0); |
|
int idx1 = cvFloor(*p1*a1 + b1); |
|
int idx2 = cvFloor(*p2*a2 + b2); |
|
bproj[x] = (unsigned)idx0 < (unsigned)sz0 && |
|
(unsigned)idx1 < (unsigned)sz1 && |
|
(unsigned)idx2 < (unsigned)sz2 ? |
|
saturate_cast<BT>(((float*)(H + hstep0*idx0 + hstep1*idx1))[idx2]*scale) : 0; |
|
} |
|
} |
|
} |
|
else |
|
{ |
|
for( ; imsize.height--; bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
uchar* Hptr = H; |
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
int idx = cvFloor(*ptrs[i]*uniranges[i*2] + uniranges[i*2+1]); |
|
if( (unsigned)idx >= (unsigned)size[i] || (_ranges && *ptrs[i] >= _ranges[i][1])) |
|
break; |
|
ptrs[i] += deltas[i*2]; |
|
Hptr += idx*hstep[i]; |
|
} |
|
|
|
if( i == dims ) |
|
bproj[x] = saturate_cast<BT>(*(float*)Hptr*scale); |
|
else |
|
{ |
|
bproj[x] = 0; |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
} |
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
} |
|
else |
|
{ |
|
// non-uniform histogram |
|
const float* ranges[CV_MAX_DIM]; |
|
for( i = 0; i < dims; i++ ) |
|
ranges[i] = &_ranges[i][0]; |
|
|
|
for( ; imsize.height--; bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
uchar* Hptr = H; |
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
float v = (float)*ptrs[i]; |
|
const float* R = ranges[i]; |
|
int idx = -1, sz = size[i]; |
|
|
|
while( v >= R[idx+1] && ++idx < sz ) |
|
; // nop |
|
|
|
if( (unsigned)idx >= (unsigned)sz ) |
|
break; |
|
|
|
ptrs[i] += deltas[i*2]; |
|
Hptr += idx*hstep[i]; |
|
} |
|
|
|
if( i == dims ) |
|
bproj[x] = saturate_cast<BT>(*(float*)Hptr*scale); |
|
else |
|
{ |
|
bproj[x] = 0; |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
} |
|
|
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
} |
|
|
|
|
|
static void |
|
calcBackProj_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas, |
|
Size imsize, const Mat& hist, int dims, const float** _ranges, |
|
const double* _uniranges, float scale, bool uniform ) |
|
{ |
|
uchar** ptrs = &_ptrs[0]; |
|
const int* deltas = &_deltas[0]; |
|
uchar* H = hist.data; |
|
int i, x; |
|
uchar* bproj = _ptrs[dims]; |
|
int bpstep = _deltas[dims*2 + 1]; |
|
vector<size_t> _tab; |
|
|
|
calcHistLookupTables_8u( hist, SparseMat(), dims, _ranges, _uniranges, uniform, false, _tab ); |
|
const size_t* tab = &_tab[0]; |
|
|
|
if( dims == 1 ) |
|
{ |
|
int d0 = deltas[0], step0 = deltas[1]; |
|
uchar matH[256] = {0}; |
|
const uchar* p0 = (const uchar*)ptrs[0]; |
|
|
|
for( i = 0; i < 256; i++ ) |
|
{ |
|
size_t hidx = tab[i]; |
|
if( hidx < OUT_OF_RANGE ) |
|
matH[i] = saturate_cast<uchar>(*(float*)(H + hidx)*scale); |
|
} |
|
|
|
for( ; imsize.height--; p0 += step0, bproj += bpstep ) |
|
{ |
|
if( d0 == 1 ) |
|
{ |
|
for( x = 0; x <= imsize.width - 4; x += 4 ) |
|
{ |
|
uchar t0 = matH[p0[x]], t1 = matH[p0[x+1]]; |
|
bproj[x] = t0; bproj[x+1] = t1; |
|
t0 = matH[p0[x+2]]; t1 = matH[p0[x+3]]; |
|
bproj[x+2] = t0; bproj[x+3] = t1; |
|
} |
|
p0 += x; |
|
} |
|
else |
|
for( x = 0; x <= imsize.width - 4; x += 4 ) |
|
{ |
|
uchar t0 = matH[p0[0]], t1 = matH[p0[d0]]; |
|
bproj[x] = t0; bproj[x+1] = t1; |
|
p0 += d0*2; |
|
t0 = matH[p0[0]]; t1 = matH[p0[d0]]; |
|
bproj[x+2] = t0; bproj[x+3] = t1; |
|
p0 += d0*2; |
|
} |
|
|
|
for( ; x < imsize.width; x++, p0 += d0 ) |
|
bproj[x] = matH[*p0]; |
|
} |
|
} |
|
else if( dims == 2 ) |
|
{ |
|
int d0 = deltas[0], step0 = deltas[1], |
|
d1 = deltas[2], step1 = deltas[3]; |
|
const uchar* p0 = (const uchar*)ptrs[0]; |
|
const uchar* p1 = (const uchar*)ptrs[1]; |
|
|
|
for( ; imsize.height--; p0 += step0, p1 += step1, bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 ) |
|
{ |
|
size_t idx = tab[*p0] + tab[*p1 + 256]; |
|
bproj[x] = idx < OUT_OF_RANGE ? saturate_cast<uchar>(*(float*)(H + idx)*scale) : 0; |
|
} |
|
} |
|
} |
|
else if( dims == 3 ) |
|
{ |
|
int d0 = deltas[0], step0 = deltas[1], |
|
d1 = deltas[2], step1 = deltas[3], |
|
d2 = deltas[4], step2 = deltas[5]; |
|
const uchar* p0 = (const uchar*)ptrs[0]; |
|
const uchar* p1 = (const uchar*)ptrs[1]; |
|
const uchar* p2 = (const uchar*)ptrs[2]; |
|
|
|
for( ; imsize.height--; p0 += step0, p1 += step1, p2 += step2, bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 ) |
|
{ |
|
size_t idx = tab[*p0] + tab[*p1 + 256] + tab[*p2 + 512]; |
|
bproj[x] = idx < OUT_OF_RANGE ? saturate_cast<uchar>(*(float*)(H + idx)*scale) : 0; |
|
} |
|
} |
|
} |
|
else |
|
{ |
|
for( ; imsize.height--; bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
uchar* Hptr = H; |
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
size_t idx = tab[*ptrs[i] + i*256]; |
|
if( idx >= OUT_OF_RANGE ) |
|
break; |
|
ptrs[i] += deltas[i*2]; |
|
Hptr += idx; |
|
} |
|
|
|
if( i == dims ) |
|
bproj[x] = saturate_cast<uchar>(*(float*)Hptr*scale); |
|
else |
|
{ |
|
bproj[x] = 0; |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
} |
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
} |
|
|
|
} |
|
|
|
void cv::calcBackProject( const Mat* images, int nimages, const int* channels, |
|
InputArray _hist, OutputArray _backProject, |
|
const float** ranges, double scale, bool uniform ) |
|
{ |
|
Mat hist = _hist.getMat(); |
|
vector<uchar*> ptrs; |
|
vector<int> deltas; |
|
vector<double> uniranges; |
|
Size imsize; |
|
int dims = hist.dims == 2 && hist.size[1] == 1 ? 1 : hist.dims; |
|
|
|
CV_Assert( dims > 0 && hist.data ); |
|
_backProject.create( images[0].size(), images[0].depth() ); |
|
Mat backProject = _backProject.getMat(); |
|
histPrepareImages( images, nimages, channels, backProject, dims, hist.size, ranges, |
|
uniform, ptrs, deltas, imsize, uniranges ); |
|
const double* _uniranges = uniform ? &uniranges[0] : 0; |
|
|
|
int depth = images[0].depth(); |
|
if( depth == CV_8U ) |
|
calcBackProj_8u(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, (float)scale, uniform); |
|
else if( depth == CV_16U ) |
|
calcBackProj_<ushort, ushort>(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, (float)scale, uniform ); |
|
else if( depth == CV_32F ) |
|
calcBackProj_<float, float>(ptrs, deltas, imsize, hist, dims, ranges, _uniranges, (float)scale, uniform ); |
|
else |
|
CV_Error(CV_StsUnsupportedFormat, ""); |
|
} |
|
|
|
|
|
namespace cv |
|
{ |
|
|
|
template<typename T, typename BT> static void |
|
calcSparseBackProj_( vector<uchar*>& _ptrs, const vector<int>& _deltas, |
|
Size imsize, const SparseMat& hist, int dims, const float** _ranges, |
|
const double* _uniranges, float scale, bool uniform ) |
|
{ |
|
T** ptrs = (T**)&_ptrs[0]; |
|
const int* deltas = &_deltas[0]; |
|
int i, x; |
|
BT* bproj = (BT*)_ptrs[dims]; |
|
int bpstep = _deltas[dims*2 + 1]; |
|
const int* size = hist.hdr->size; |
|
int idx[CV_MAX_DIM]; |
|
const SparseMat_<float>& hist_ = (const SparseMat_<float>&)hist; |
|
|
|
if( uniform ) |
|
{ |
|
const double* uniranges = &_uniranges[0]; |
|
for( ; imsize.height--; bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
idx[i] = cvFloor(*ptrs[i]*uniranges[i*2] + uniranges[i*2+1]); |
|
if( (unsigned)idx[i] >= (unsigned)size[i] ) |
|
break; |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
|
|
if( i == dims ) |
|
bproj[x] = saturate_cast<BT>(hist_(idx)*scale); |
|
else |
|
{ |
|
bproj[x] = 0; |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
} |
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
else |
|
{ |
|
// non-uniform histogram |
|
const float* ranges[CV_MAX_DIM]; |
|
for( i = 0; i < dims; i++ ) |
|
ranges[i] = &_ranges[i][0]; |
|
|
|
for( ; imsize.height--; bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
float v = (float)*ptrs[i]; |
|
const float* R = ranges[i]; |
|
int j = -1, sz = size[i]; |
|
|
|
while( v >= R[j+1] && ++j < sz ) |
|
; // nop |
|
|
|
if( (unsigned)j >= (unsigned)sz ) |
|
break; |
|
idx[i] = j; |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
|
|
if( i == dims ) |
|
bproj[x] = saturate_cast<BT>(hist_(idx)*scale); |
|
else |
|
{ |
|
bproj[x] = 0; |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
} |
|
|
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
} |
|
|
|
|
|
static void |
|
calcSparseBackProj_8u( vector<uchar*>& _ptrs, const vector<int>& _deltas, |
|
Size imsize, const SparseMat& hist, int dims, const float** _ranges, |
|
const double* _uniranges, float scale, bool uniform ) |
|
{ |
|
uchar** ptrs = &_ptrs[0]; |
|
const int* deltas = &_deltas[0]; |
|
int i, x; |
|
uchar* bproj = _ptrs[dims]; |
|
int bpstep = _deltas[dims*2 + 1]; |
|
vector<size_t> _tab; |
|
int idx[CV_MAX_DIM]; |
|
|
|
calcHistLookupTables_8u( Mat(), hist, dims, _ranges, _uniranges, uniform, true, _tab ); |
|
const size_t* tab = &_tab[0]; |
|
|
|
for( ; imsize.height--; bproj += bpstep ) |
|
{ |
|
for( x = 0; x < imsize.width; x++ ) |
|
{ |
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
size_t hidx = tab[*ptrs[i] + i*256]; |
|
if( hidx >= OUT_OF_RANGE ) |
|
break; |
|
idx[i] = (int)hidx; |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
|
|
if( i == dims ) |
|
bproj[x] = saturate_cast<uchar>(hist.value<float>(idx)*scale); |
|
else |
|
{ |
|
bproj[x] = 0; |
|
for( ; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2]; |
|
} |
|
} |
|
for( i = 0; i < dims; i++ ) |
|
ptrs[i] += deltas[i*2 + 1]; |
|
} |
|
} |
|
|
|
} |
|
|
|
void cv::calcBackProject( const Mat* images, int nimages, const int* channels, |
|
const SparseMat& hist, OutputArray _backProject, |
|
const float** ranges, double scale, bool uniform ) |
|
{ |
|
vector<uchar*> ptrs; |
|
vector<int> deltas; |
|
vector<double> uniranges; |
|
Size imsize; |
|
int dims = hist.dims(); |
|
|
|
CV_Assert( dims > 0 ); |
|
_backProject.create( images[0].size(), images[0].depth() ); |
|
Mat backProject = _backProject.getMat(); |
|
histPrepareImages( images, nimages, channels, backProject, |
|
dims, hist.hdr->size, ranges, |
|
uniform, ptrs, deltas, imsize, uniranges ); |
|
const double* _uniranges = uniform ? &uniranges[0] : 0; |
|
|
|
int depth = images[0].depth(); |
|
if( depth == CV_8U ) |
|
calcSparseBackProj_8u(ptrs, deltas, imsize, hist, dims, ranges, |
|
_uniranges, (float)scale, uniform); |
|
else if( depth == CV_16U ) |
|
calcSparseBackProj_<ushort, ushort>(ptrs, deltas, imsize, hist, dims, ranges, |
|
_uniranges, (float)scale, uniform ); |
|
else if( depth == CV_32F ) |
|
calcSparseBackProj_<float, float>(ptrs, deltas, imsize, hist, dims, ranges, |
|
_uniranges, (float)scale, uniform ); |
|
else |
|
CV_Error(CV_StsUnsupportedFormat, ""); |
|
} |
|
|
|
|
|
void cv::calcBackProject( InputArrayOfArrays images, const vector<int>& channels, |
|
InputArray hist, OutputArray dst, |
|
const vector<float>& ranges, |
|
double scale ) |
|
{ |
|
Mat H = hist.getMat(); |
|
bool _1d = H.rows == 1 || H.cols == 1; |
|
int i, dims = H.dims, rsz = (int)ranges.size(), csz = (int)channels.size(); |
|
int nimages = (int)images.total(); |
|
CV_Assert(nimages > 0); |
|
CV_Assert(rsz == dims*2 || (rsz == 2 && _1d) || (rsz == 0 && images.depth(0) == CV_8U)); |
|
CV_Assert(csz == 0 || csz == dims || (csz == 1 && _1d)); |
|
float* _ranges[CV_MAX_DIM]; |
|
if( rsz > 0 ) |
|
{ |
|
for( i = 0; i < rsz/2; i++ ) |
|
_ranges[i] = (float*)&ranges[i*2]; |
|
} |
|
|
|
AutoBuffer<Mat> buf(nimages); |
|
for( i = 0; i < nimages; i++ ) |
|
buf[i] = images.getMat(i); |
|
|
|
calcBackProject(&buf[0], nimages, csz ? &channels[0] : 0, |
|
hist, dst, rsz ? (const float**)_ranges : 0, scale, true); |
|
} |
|
|
|
|
|
////////////////// C O M P A R E H I S T O G R A M S //////////////////////// |
|
|
|
double cv::compareHist( InputArray _H1, InputArray _H2, int method ) |
|
{ |
|
Mat H1 = _H1.getMat(), H2 = _H2.getMat(); |
|
const Mat* arrays[] = {&H1, &H2, 0}; |
|
Mat planes[2]; |
|
NAryMatIterator it(arrays, planes); |
|
double result = 0; |
|
int j, len = (int)it.size; |
|
|
|
CV_Assert( H1.type() == H2.type() && H1.type() == CV_32F ); |
|
|
|
double s1 = 0, s2 = 0, s11 = 0, s12 = 0, s22 = 0; |
|
|
|
CV_Assert( it.planes[0].isContinuous() && it.planes[1].isContinuous() ); |
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it ) |
|
{ |
|
const float* h1 = (const float*)it.planes[0].data; |
|
const float* h2 = (const float*)it.planes[1].data; |
|
len = it.planes[0].rows*it.planes[0].cols; |
|
|
|
if( method == CV_COMP_CHISQR ) |
|
{ |
|
for( j = 0; j < len; j++ ) |
|
{ |
|
double a = h1[j] - h2[j]; |
|
double b = h1[j] + h2[j]; |
|
if( fabs(b) > FLT_EPSILON ) |
|
result += a*a/b; |
|
} |
|
} |
|
else if( method == CV_COMP_CORREL ) |
|
{ |
|
for( j = 0; j < len; j++ ) |
|
{ |
|
double a = h1[j]; |
|
double b = h2[j]; |
|
|
|
s12 += a*b; |
|
s1 += a; |
|
s11 += a*a; |
|
s2 += b; |
|
s22 += b*b; |
|
} |
|
} |
|
else if( method == CV_COMP_INTERSECT ) |
|
{ |
|
for( j = 0; j < len; j++ ) |
|
result += std::min(h1[j], h2[j]); |
|
} |
|
else if( method == CV_COMP_BHATTACHARYYA ) |
|
{ |
|
for( j = 0; j < len; j++ ) |
|
{ |
|
double a = h1[j]; |
|
double b = h2[j]; |
|
result += std::sqrt(a*b); |
|
s1 += a; |
|
s2 += b; |
|
} |
|
} |
|
else |
|
CV_Error( CV_StsBadArg, "Unknown comparison method" ); |
|
} |
|
|
|
if( method == CV_COMP_CORREL ) |
|
{ |
|
size_t total = H1.total(); |
|
double scale = 1./total; |
|
double num = s12 - s1*s2*scale; |
|
double denom2 = (s11 - s1*s1*scale)*(s22 - s2*s2*scale); |
|
result = std::abs(denom2) > DBL_EPSILON ? num/std::sqrt(denom2) : 1.; |
|
} |
|
else if( method == CV_COMP_BHATTACHARYYA ) |
|
{ |
|
s1 *= s2; |
|
s1 = fabs(s1) > FLT_EPSILON ? 1./std::sqrt(s1) : 1.; |
|
result = std::sqrt(std::max(1. - result*s1, 0.)); |
|
} |
|
|
|
return result; |
|
} |
|
|
|
|
|
double cv::compareHist( const SparseMat& H1, const SparseMat& H2, int method ) |
|
{ |
|
double result = 0; |
|
int i, dims = H1.dims(); |
|
|
|
CV_Assert( dims > 0 && dims == H2.dims() && H1.type() == H2.type() && H1.type() == CV_32F ); |
|
for( i = 0; i < dims; i++ ) |
|
CV_Assert( H1.size(i) == H2.size(i) ); |
|
|
|
const SparseMat *PH1 = &H1, *PH2 = &H2; |
|
if( PH1->nzcount() > PH2->nzcount() ) |
|
std::swap(PH1, PH2); |
|
|
|
SparseMatConstIterator it = PH1->begin(); |
|
int N1 = (int)PH1->nzcount(), N2 = (int)PH2->nzcount(); |
|
|
|
if( method == CV_COMP_CHISQR ) |
|
{ |
|
for( i = 0; i < N1; i++, ++it ) |
|
{ |
|
float v1 = it.value<float>(); |
|
const SparseMat::Node* node = it.node(); |
|
float v2 = PH2->value<float>(node->idx, (size_t*)&node->hashval); |
|
if( !v2 ) |
|
result += v1; |
|
else |
|
{ |
|
double a = v1 - v2; |
|
double b = v1 + v2; |
|
if( b > FLT_EPSILON ) |
|
result += a*a/b; |
|
} |
|
} |
|
|
|
it = PH2->begin(); |
|
for( i = 0; i < N2; i++, ++it ) |
|
{ |
|
float v2 = it.value<float>(); |
|
const SparseMat::Node* node = it.node(); |
|
if( !PH1->find<float>(node->idx, (size_t*)&node->hashval) ) |
|
result += v2; |
|
} |
|
} |
|
else if( method == CV_COMP_CORREL ) |
|
{ |
|
double s1 = 0, s2 = 0, s11 = 0, s12 = 0, s22 = 0; |
|
|
|
for( i = 0; i < N1; i++, ++it ) |
|
{ |
|
double v1 = it.value<float>(); |
|
const SparseMat::Node* node = it.node(); |
|
s12 += v1*PH2->value<float>(node->idx, (size_t*)&node->hashval); |
|
s1 += v1; |
|
s11 += v1*v1; |
|
} |
|
|
|
it = PH2->begin(); |
|
for( i = 0; i < N2; i++, ++it ) |
|
{ |
|
double v2 = it.value<float>(); |
|
s2 += v2; |
|
s22 += v2*v2; |
|
} |
|
|
|
size_t total = 1; |
|
for( i = 0; i < H1.dims(); i++ ) |
|
total *= H1.size(i); |
|
double scale = 1./total; |
|
double num = s12 - s1*s2*scale; |
|
double denom2 = (s11 - s1*s1*scale)*(s22 - s2*s2*scale); |
|
result = std::abs(denom2) > DBL_EPSILON ? num/std::sqrt(denom2) : 1.; |
|
} |
|
else if( method == CV_COMP_INTERSECT ) |
|
{ |
|
for( i = 0; i < N1; i++, ++it ) |
|
{ |
|
float v1 = it.value<float>(); |
|
const SparseMat::Node* node = it.node(); |
|
float v2 = PH2->value<float>(node->idx, (size_t*)&node->hashval); |
|
if( v2 ) |
|
result += std::min(v1, v2); |
|
} |
|
} |
|
else if( method == CV_COMP_BHATTACHARYYA ) |
|
{ |
|
double s1 = 0, s2 = 0; |
|
|
|
for( i = 0; i < N1; i++, ++it ) |
|
{ |
|
double v1 = it.value<float>(); |
|
const SparseMat::Node* node = it.node(); |
|
double v2 = PH2->value<float>(node->idx, (size_t*)&node->hashval); |
|
result += std::sqrt(v1*v2); |
|
s1 += v1; |
|
} |
|
|
|
it = PH2->begin(); |
|
for( i = 0; i < N2; i++, ++it ) |
|
s2 += it.value<float>(); |
|
|
|
s1 *= s2; |
|
s1 = fabs(s1) > FLT_EPSILON ? 1./std::sqrt(s1) : 1.; |
|
result = std::sqrt(std::max(1. - result*s1, 0.)); |
|
} |
|
else |
|
CV_Error( CV_StsBadArg, "Unknown comparison method" ); |
|
|
|
return result; |
|
} |
|
|
|
|
|
const int CV_HIST_DEFAULT_TYPE = CV_32F; |
|
|
|
/* Creates new histogram */ |
|
CvHistogram * |
|
cvCreateHist( int dims, int *sizes, CvHistType type, float** ranges, int uniform ) |
|
{ |
|
CvHistogram *hist = 0; |
|
|
|
if( (unsigned)dims > CV_MAX_DIM ) |
|
CV_Error( CV_BadOrder, "Number of dimensions is out of range" ); |
|
|
|
if( !sizes ) |
|
CV_Error( CV_HeaderIsNull, "Null <sizes> pointer" ); |
|
|
|
hist = (CvHistogram *)cvAlloc( sizeof( CvHistogram )); |
|
hist->type = CV_HIST_MAGIC_VAL + ((int)type & 1); |
|
if (uniform) hist->type|= CV_HIST_UNIFORM_FLAG; |
|
hist->thresh2 = 0; |
|
hist->bins = 0; |
|
if( type == CV_HIST_ARRAY ) |
|
{ |
|
hist->bins = cvInitMatNDHeader( &hist->mat, dims, sizes, |
|
CV_HIST_DEFAULT_TYPE ); |
|
cvCreateData( hist->bins ); |
|
} |
|
else if( type == CV_HIST_SPARSE ) |
|
hist->bins = cvCreateSparseMat( dims, sizes, CV_HIST_DEFAULT_TYPE ); |
|
else |
|
CV_Error( CV_StsBadArg, "Invalid histogram type" ); |
|
|
|
if( ranges ) |
|
cvSetHistBinRanges( hist, ranges, uniform ); |
|
|
|
return hist; |
|
} |
|
|
|
|
|
/* Creates histogram wrapping header for given array */ |
|
CV_IMPL CvHistogram* |
|
cvMakeHistHeaderForArray( int dims, int *sizes, CvHistogram *hist, |
|
float *data, float **ranges, int uniform ) |
|
{ |
|
if( !hist ) |
|
CV_Error( CV_StsNullPtr, "Null histogram header pointer" ); |
|
|
|
if( !data ) |
|
CV_Error( CV_StsNullPtr, "Null data pointer" ); |
|
|
|
hist->thresh2 = 0; |
|
hist->type = CV_HIST_MAGIC_VAL; |
|
hist->bins = cvInitMatNDHeader( &hist->mat, dims, sizes, CV_HIST_DEFAULT_TYPE, data ); |
|
|
|
if( ranges ) |
|
{ |
|
if( !uniform ) |
|
CV_Error( CV_StsBadArg, "Only uniform bin ranges can be used here " |
|
"(to avoid memory allocation)" ); |
|
cvSetHistBinRanges( hist, ranges, uniform ); |
|
} |
|
|
|
return hist; |
|
} |
|
|
|
|
|
CV_IMPL void |
|
cvReleaseHist( CvHistogram **hist ) |
|
{ |
|
if( !hist ) |
|
CV_Error( CV_StsNullPtr, "" ); |
|
|
|
if( *hist ) |
|
{ |
|
CvHistogram* temp = *hist; |
|
|
|
if( !CV_IS_HIST(temp)) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header" ); |
|
*hist = 0; |
|
|
|
if( CV_IS_SPARSE_HIST( temp )) |
|
cvReleaseSparseMat( (CvSparseMat**)&temp->bins ); |
|
else |
|
{ |
|
cvReleaseData( temp->bins ); |
|
temp->bins = 0; |
|
} |
|
|
|
if( temp->thresh2 ) |
|
cvFree( &temp->thresh2 ); |
|
cvFree( &temp ); |
|
} |
|
} |
|
|
|
CV_IMPL void |
|
cvClearHist( CvHistogram *hist ) |
|
{ |
|
if( !CV_IS_HIST(hist) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header" ); |
|
cvZero( hist->bins ); |
|
} |
|
|
|
|
|
// Clears histogram bins that are below than threshold |
|
CV_IMPL void |
|
cvThreshHist( CvHistogram* hist, double thresh ) |
|
{ |
|
if( !CV_IS_HIST(hist) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header" ); |
|
|
|
if( !CV_IS_SPARSE_MAT(hist->bins) ) |
|
{ |
|
CvMat mat; |
|
cvGetMat( hist->bins, &mat, 0, 1 ); |
|
cvThreshold( &mat, &mat, thresh, 0, CV_THRESH_TOZERO ); |
|
} |
|
else |
|
{ |
|
CvSparseMat* mat = (CvSparseMat*)hist->bins; |
|
CvSparseMatIterator iterator; |
|
CvSparseNode *node; |
|
|
|
for( node = cvInitSparseMatIterator( mat, &iterator ); |
|
node != 0; node = cvGetNextSparseNode( &iterator )) |
|
{ |
|
float* val = (float*)CV_NODE_VAL( mat, node ); |
|
if( *val <= thresh ) |
|
*val = 0; |
|
} |
|
} |
|
} |
|
|
|
|
|
// Normalizes histogram (make sum of the histogram bins == factor) |
|
CV_IMPL void |
|
cvNormalizeHist( CvHistogram* hist, double factor ) |
|
{ |
|
double sum = 0; |
|
|
|
if( !CV_IS_HIST(hist) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header" ); |
|
|
|
if( !CV_IS_SPARSE_HIST(hist) ) |
|
{ |
|
CvMat mat; |
|
cvGetMat( hist->bins, &mat, 0, 1 ); |
|
sum = cvSum( &mat ).val[0]; |
|
if( fabs(sum) < DBL_EPSILON ) |
|
sum = 1; |
|
cvScale( &mat, &mat, factor/sum, 0 ); |
|
} |
|
else |
|
{ |
|
CvSparseMat* mat = (CvSparseMat*)hist->bins; |
|
CvSparseMatIterator iterator; |
|
CvSparseNode *node; |
|
float scale; |
|
|
|
for( node = cvInitSparseMatIterator( mat, &iterator ); |
|
node != 0; node = cvGetNextSparseNode( &iterator )) |
|
{ |
|
sum += *(float*)CV_NODE_VAL(mat,node); |
|
} |
|
|
|
if( fabs(sum) < DBL_EPSILON ) |
|
sum = 1; |
|
scale = (float)(factor/sum); |
|
|
|
for( node = cvInitSparseMatIterator( mat, &iterator ); |
|
node != 0; node = cvGetNextSparseNode( &iterator )) |
|
{ |
|
*(float*)CV_NODE_VAL(mat,node) *= scale; |
|
} |
|
} |
|
} |
|
|
|
|
|
// Retrieves histogram global min, max and their positions |
|
CV_IMPL void |
|
cvGetMinMaxHistValue( const CvHistogram* hist, |
|
float *value_min, float* value_max, |
|
int* idx_min, int* idx_max ) |
|
{ |
|
double minVal, maxVal; |
|
int i, dims, size[CV_MAX_DIM]; |
|
|
|
if( !CV_IS_HIST(hist) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header" ); |
|
|
|
dims = cvGetDims( hist->bins, size ); |
|
|
|
if( !CV_IS_SPARSE_HIST(hist) ) |
|
{ |
|
CvMat mat; |
|
CvPoint minPt, maxPt; |
|
|
|
cvGetMat( hist->bins, &mat, 0, 1 ); |
|
cvMinMaxLoc( &mat, &minVal, &maxVal, &minPt, &maxPt ); |
|
|
|
if( dims == 1 ) |
|
{ |
|
if( idx_min ) |
|
*idx_min = minPt.y + minPt.x; |
|
if( idx_max ) |
|
*idx_max = maxPt.y + maxPt.x; |
|
} |
|
else if( dims == 2 ) |
|
{ |
|
if( idx_min ) |
|
idx_min[0] = minPt.y, idx_min[1] = minPt.x; |
|
if( idx_max ) |
|
idx_max[0] = maxPt.y, idx_max[1] = maxPt.x; |
|
} |
|
else if( idx_min || idx_max ) |
|
{ |
|
int imin = minPt.y*mat.cols + minPt.x; |
|
int imax = maxPt.y*mat.cols + maxPt.x; |
|
int i; |
|
|
|
for( i = dims - 1; i >= 0; i-- ) |
|
{ |
|
if( idx_min ) |
|
{ |
|
int t = imin / size[i]; |
|
idx_min[i] = imin - t*size[i]; |
|
imin = t; |
|
} |
|
|
|
if( idx_max ) |
|
{ |
|
int t = imax / size[i]; |
|
idx_max[i] = imax - t*size[i]; |
|
imax = t; |
|
} |
|
} |
|
} |
|
} |
|
else |
|
{ |
|
CvSparseMat* mat = (CvSparseMat*)hist->bins; |
|
CvSparseMatIterator iterator; |
|
CvSparseNode *node; |
|
int minv = INT_MAX; |
|
int maxv = INT_MIN; |
|
CvSparseNode* minNode = 0; |
|
CvSparseNode* maxNode = 0; |
|
const int *_idx_min = 0, *_idx_max = 0; |
|
Cv32suf m; |
|
|
|
for( node = cvInitSparseMatIterator( mat, &iterator ); |
|
node != 0; node = cvGetNextSparseNode( &iterator )) |
|
{ |
|
int value = *(int*)CV_NODE_VAL(mat,node); |
|
value = CV_TOGGLE_FLT(value); |
|
if( value < minv ) |
|
{ |
|
minv = value; |
|
minNode = node; |
|
} |
|
|
|
if( value > maxv ) |
|
{ |
|
maxv = value; |
|
maxNode = node; |
|
} |
|
} |
|
|
|
if( minNode ) |
|
{ |
|
_idx_min = CV_NODE_IDX(mat,minNode); |
|
_idx_max = CV_NODE_IDX(mat,maxNode); |
|
m.i = CV_TOGGLE_FLT(minv); minVal = m.f; |
|
m.i = CV_TOGGLE_FLT(maxv); maxVal = m.f; |
|
} |
|
else |
|
{ |
|
minVal = maxVal = 0; |
|
} |
|
|
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
if( idx_min ) |
|
idx_min[i] = _idx_min ? _idx_min[i] : -1; |
|
if( idx_max ) |
|
idx_max[i] = _idx_max ? _idx_max[i] : -1; |
|
} |
|
} |
|
|
|
if( value_min ) |
|
*value_min = (float)minVal; |
|
|
|
if( value_max ) |
|
*value_max = (float)maxVal; |
|
} |
|
|
|
|
|
// Compares two histograms using one of a few methods |
|
CV_IMPL double |
|
cvCompareHist( const CvHistogram* hist1, |
|
const CvHistogram* hist2, |
|
int method ) |
|
{ |
|
int i; |
|
int size1[CV_MAX_DIM], size2[CV_MAX_DIM], total = 1; |
|
|
|
if( !CV_IS_HIST(hist1) || !CV_IS_HIST(hist2) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header[s]" ); |
|
|
|
if( CV_IS_SPARSE_MAT(hist1->bins) != CV_IS_SPARSE_MAT(hist2->bins)) |
|
CV_Error(CV_StsUnmatchedFormats, "One of histograms is sparse and other is not"); |
|
|
|
if( !CV_IS_SPARSE_MAT(hist1->bins) ) |
|
{ |
|
cv::Mat H1((const CvMatND*)hist1->bins), H2((const CvMatND*)hist2->bins); |
|
return cv::compareHist(H1, H2, method); |
|
} |
|
|
|
int dims1 = cvGetDims( hist1->bins, size1 ); |
|
int dims2 = cvGetDims( hist2->bins, size2 ); |
|
|
|
if( dims1 != dims2 ) |
|
CV_Error( CV_StsUnmatchedSizes, |
|
"The histograms have different numbers of dimensions" ); |
|
|
|
for( i = 0; i < dims1; i++ ) |
|
{ |
|
if( size1[i] != size2[i] ) |
|
CV_Error( CV_StsUnmatchedSizes, "The histograms have different sizes" ); |
|
total *= size1[i]; |
|
} |
|
|
|
double result = 0; |
|
CvSparseMat* mat1 = (CvSparseMat*)(hist1->bins); |
|
CvSparseMat* mat2 = (CvSparseMat*)(hist2->bins); |
|
CvSparseMatIterator iterator; |
|
CvSparseNode *node1, *node2; |
|
|
|
if( mat1->heap->active_count > mat2->heap->active_count ) |
|
{ |
|
CvSparseMat* t; |
|
CV_SWAP( mat1, mat2, t ); |
|
} |
|
|
|
if( method == CV_COMP_CHISQR ) |
|
{ |
|
for( node1 = cvInitSparseMatIterator( mat1, &iterator ); |
|
node1 != 0; node1 = cvGetNextSparseNode( &iterator )) |
|
{ |
|
double v1 = *(float*)CV_NODE_VAL(mat1,node1); |
|
uchar* node2_data = cvPtrND( mat2, CV_NODE_IDX(mat1,node1), 0, 0, &node1->hashval ); |
|
if( !node2_data ) |
|
result += v1; |
|
else |
|
{ |
|
double v2 = *(float*)node2_data; |
|
double a = v1 - v2; |
|
double b = v1 + v2; |
|
if( fabs(b) > DBL_EPSILON ) |
|
result += a*a/b; |
|
} |
|
} |
|
|
|
for( node2 = cvInitSparseMatIterator( mat2, &iterator ); |
|
node2 != 0; node2 = cvGetNextSparseNode( &iterator )) |
|
{ |
|
double v2 = *(float*)CV_NODE_VAL(mat2,node2); |
|
if( !cvPtrND( mat1, CV_NODE_IDX(mat2,node2), 0, 0, &node2->hashval )) |
|
result += v2; |
|
} |
|
} |
|
else if( method == CV_COMP_CORREL ) |
|
{ |
|
double s1 = 0, s11 = 0; |
|
double s2 = 0, s22 = 0; |
|
double s12 = 0; |
|
double num, denom2, scale = 1./total; |
|
|
|
for( node1 = cvInitSparseMatIterator( mat1, &iterator ); |
|
node1 != 0; node1 = cvGetNextSparseNode( &iterator )) |
|
{ |
|
double v1 = *(float*)CV_NODE_VAL(mat1,node1); |
|
uchar* node2_data = cvPtrND( mat2, CV_NODE_IDX(mat1,node1), |
|
0, 0, &node1->hashval ); |
|
if( node2_data ) |
|
{ |
|
double v2 = *(float*)node2_data; |
|
s12 += v1*v2; |
|
} |
|
s1 += v1; |
|
s11 += v1*v1; |
|
} |
|
|
|
for( node2 = cvInitSparseMatIterator( mat2, &iterator ); |
|
node2 != 0; node2 = cvGetNextSparseNode( &iterator )) |
|
{ |
|
double v2 = *(float*)CV_NODE_VAL(mat2,node2); |
|
s2 += v2; |
|
s22 += v2*v2; |
|
} |
|
|
|
num = s12 - s1*s2*scale; |
|
denom2 = (s11 - s1*s1*scale)*(s22 - s2*s2*scale); |
|
result = fabs(denom2) > DBL_EPSILON ? num/sqrt(denom2) : 1; |
|
} |
|
else if( method == CV_COMP_INTERSECT ) |
|
{ |
|
for( node1 = cvInitSparseMatIterator( mat1, &iterator ); |
|
node1 != 0; node1 = cvGetNextSparseNode( &iterator )) |
|
{ |
|
float v1 = *(float*)CV_NODE_VAL(mat1,node1); |
|
uchar* node2_data = cvPtrND( mat2, CV_NODE_IDX(mat1,node1), |
|
0, 0, &node1->hashval ); |
|
if( node2_data ) |
|
{ |
|
float v2 = *(float*)node2_data; |
|
if( v1 <= v2 ) |
|
result += v1; |
|
else |
|
result += v2; |
|
} |
|
} |
|
} |
|
else if( method == CV_COMP_BHATTACHARYYA ) |
|
{ |
|
double s1 = 0, s2 = 0; |
|
|
|
for( node1 = cvInitSparseMatIterator( mat1, &iterator ); |
|
node1 != 0; node1 = cvGetNextSparseNode( &iterator )) |
|
{ |
|
double v1 = *(float*)CV_NODE_VAL(mat1,node1); |
|
uchar* node2_data = cvPtrND( mat2, CV_NODE_IDX(mat1,node1), |
|
0, 0, &node1->hashval ); |
|
s1 += v1; |
|
if( node2_data ) |
|
{ |
|
double v2 = *(float*)node2_data; |
|
result += sqrt(v1 * v2); |
|
} |
|
} |
|
|
|
for( node1 = cvInitSparseMatIterator( mat2, &iterator ); |
|
node1 != 0; node1 = cvGetNextSparseNode( &iterator )) |
|
{ |
|
double v2 = *(float*)CV_NODE_VAL(mat2,node1); |
|
s2 += v2; |
|
} |
|
|
|
s1 *= s2; |
|
s1 = fabs(s1) > FLT_EPSILON ? 1./sqrt(s1) : 1.; |
|
result = 1. - result*s1; |
|
result = sqrt(MAX(result,0.)); |
|
} |
|
else |
|
CV_Error( CV_StsBadArg, "Unknown comparison method" ); |
|
|
|
return result; |
|
} |
|
|
|
// copies one histogram to another |
|
CV_IMPL void |
|
cvCopyHist( const CvHistogram* src, CvHistogram** _dst ) |
|
{ |
|
int eq = 0; |
|
int is_sparse; |
|
int i, dims1, dims2; |
|
int size1[CV_MAX_DIM], size2[CV_MAX_DIM], total = 1; |
|
float* ranges[CV_MAX_DIM]; |
|
float** thresh = 0; |
|
CvHistogram* dst; |
|
|
|
if( !_dst ) |
|
CV_Error( CV_StsNullPtr, "Destination double pointer is NULL" ); |
|
|
|
dst = *_dst; |
|
|
|
if( !CV_IS_HIST(src) || (dst && !CV_IS_HIST(dst)) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header[s]" ); |
|
|
|
is_sparse = CV_IS_SPARSE_MAT(src->bins); |
|
dims1 = cvGetDims( src->bins, size1 ); |
|
for( i = 0; i < dims1; i++ ) |
|
total *= size1[i]; |
|
|
|
if( dst && is_sparse == CV_IS_SPARSE_MAT(dst->bins)) |
|
{ |
|
dims2 = cvGetDims( dst->bins, size2 ); |
|
|
|
if( dims1 == dims2 ) |
|
{ |
|
for( i = 0; i < dims1; i++ ) |
|
if( size1[i] != size2[i] ) |
|
break; |
|
} |
|
|
|
eq = i == dims1; |
|
} |
|
|
|
if( !eq ) |
|
{ |
|
cvReleaseHist( _dst ); |
|
dst = cvCreateHist( dims1, size1, !is_sparse ? CV_HIST_ARRAY : CV_HIST_SPARSE, 0, 0 ); |
|
*_dst = dst; |
|
} |
|
|
|
if( CV_HIST_HAS_RANGES( src )) |
|
{ |
|
if( CV_IS_UNIFORM_HIST( src )) |
|
{ |
|
for( i = 0; i < dims1; i++ ) |
|
ranges[i] = (float*)src->thresh[i]; |
|
thresh = ranges; |
|
} |
|
else |
|
thresh = src->thresh2; |
|
cvSetHistBinRanges( dst, thresh, CV_IS_UNIFORM_HIST(src)); |
|
} |
|
|
|
cvCopy( src->bins, dst->bins ); |
|
} |
|
|
|
|
|
// Sets a value range for every histogram bin |
|
CV_IMPL void |
|
cvSetHistBinRanges( CvHistogram* hist, float** ranges, int uniform ) |
|
{ |
|
int dims, size[CV_MAX_DIM], total = 0; |
|
int i, j; |
|
|
|
if( !ranges ) |
|
CV_Error( CV_StsNullPtr, "NULL ranges pointer" ); |
|
|
|
if( !CV_IS_HIST(hist) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header" ); |
|
|
|
dims = cvGetDims( hist->bins, size ); |
|
for( i = 0; i < dims; i++ ) |
|
total += size[i]+1; |
|
|
|
if( uniform ) |
|
{ |
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
if( !ranges[i] ) |
|
CV_Error( CV_StsNullPtr, "One of <ranges> elements is NULL" ); |
|
hist->thresh[i][0] = ranges[i][0]; |
|
hist->thresh[i][1] = ranges[i][1]; |
|
} |
|
|
|
hist->type |= CV_HIST_UNIFORM_FLAG + CV_HIST_RANGES_FLAG; |
|
} |
|
else |
|
{ |
|
float* dim_ranges; |
|
|
|
if( !hist->thresh2 ) |
|
{ |
|
hist->thresh2 = (float**)cvAlloc( |
|
dims*sizeof(hist->thresh2[0])+ |
|
total*sizeof(hist->thresh2[0][0])); |
|
} |
|
dim_ranges = (float*)(hist->thresh2 + dims); |
|
|
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
float val0 = -FLT_MAX; |
|
|
|
if( !ranges[i] ) |
|
CV_Error( CV_StsNullPtr, "One of <ranges> elements is NULL" ); |
|
|
|
for( j = 0; j <= size[i]; j++ ) |
|
{ |
|
float val = ranges[i][j]; |
|
if( val <= val0 ) |
|
CV_Error(CV_StsOutOfRange, "Bin ranges should go in ascenting order"); |
|
val0 = dim_ranges[j] = val; |
|
} |
|
|
|
hist->thresh2[i] = dim_ranges; |
|
dim_ranges += size[i] + 1; |
|
} |
|
|
|
hist->type |= CV_HIST_RANGES_FLAG; |
|
hist->type &= ~CV_HIST_UNIFORM_FLAG; |
|
} |
|
} |
|
|
|
|
|
CV_IMPL void |
|
cvCalcArrHist( CvArr** img, CvHistogram* hist, int accumulate, const CvArr* mask ) |
|
{ |
|
if( !CV_IS_HIST(hist)) |
|
CV_Error( CV_StsBadArg, "Bad histogram pointer" ); |
|
|
|
if( !img ) |
|
CV_Error( CV_StsNullPtr, "Null double array pointer" ); |
|
|
|
int size[CV_MAX_DIM]; |
|
int i, dims = cvGetDims( hist->bins, size); |
|
bool uniform = CV_IS_UNIFORM_HIST(hist); |
|
|
|
cv::vector<cv::Mat> images(dims); |
|
for( i = 0; i < dims; i++ ) |
|
images[i] = cv::cvarrToMat(img[i]); |
|
|
|
cv::Mat _mask; |
|
if( mask ) |
|
_mask = cv::cvarrToMat(mask); |
|
|
|
const float* uranges[CV_MAX_DIM] = {0}; |
|
const float** ranges = 0; |
|
|
|
if( hist->type & CV_HIST_RANGES_FLAG ) |
|
{ |
|
ranges = (const float**)hist->thresh2; |
|
if( uniform ) |
|
{ |
|
for( i = 0; i < dims; i++ ) |
|
uranges[i] = &hist->thresh[i][0]; |
|
ranges = uranges; |
|
} |
|
} |
|
|
|
if( !CV_IS_SPARSE_HIST(hist) ) |
|
{ |
|
cv::Mat H((const CvMatND*)hist->bins); |
|
cv::calcHist( &images[0], (int)images.size(), 0, _mask, |
|
H, cvGetDims(hist->bins), H.size, ranges, uniform, accumulate != 0 ); |
|
} |
|
else |
|
{ |
|
CvSparseMat* sparsemat = (CvSparseMat*)hist->bins; |
|
|
|
if( !accumulate ) |
|
cvZero( hist->bins ); |
|
cv::SparseMat sH(sparsemat); |
|
cv::calcHist( &images[0], (int)images.size(), 0, _mask, sH, sH.dims(), |
|
sH.dims() > 0 ? sH.hdr->size : 0, ranges, uniform, accumulate != 0, true ); |
|
|
|
if( accumulate ) |
|
cvZero( sparsemat ); |
|
|
|
cv::SparseMatConstIterator it = sH.begin(); |
|
int nz = (int)sH.nzcount(); |
|
for( i = 0; i < nz; i++, ++it ) |
|
*(float*)cvPtrND(sparsemat, it.node()->idx, 0, -2) = (float)*(const int*)it.ptr; |
|
} |
|
} |
|
|
|
|
|
CV_IMPL void |
|
cvCalcArrBackProject( CvArr** img, CvArr* dst, const CvHistogram* hist ) |
|
{ |
|
if( !CV_IS_HIST(hist)) |
|
CV_Error( CV_StsBadArg, "Bad histogram pointer" ); |
|
|
|
if( !img ) |
|
CV_Error( CV_StsNullPtr, "Null double array pointer" ); |
|
|
|
int size[CV_MAX_DIM]; |
|
int i, dims = cvGetDims( hist->bins, size ); |
|
|
|
bool uniform = CV_IS_UNIFORM_HIST(hist); |
|
const float* uranges[CV_MAX_DIM] = {0}; |
|
const float** ranges = 0; |
|
|
|
if( hist->type & CV_HIST_RANGES_FLAG ) |
|
{ |
|
ranges = (const float**)hist->thresh2; |
|
if( uniform ) |
|
{ |
|
for( i = 0; i < dims; i++ ) |
|
uranges[i] = &hist->thresh[i][0]; |
|
ranges = uranges; |
|
} |
|
} |
|
|
|
cv::vector<cv::Mat> images(dims); |
|
for( i = 0; i < dims; i++ ) |
|
images[i] = cv::cvarrToMat(img[i]); |
|
|
|
cv::Mat _dst = cv::cvarrToMat(dst); |
|
|
|
CV_Assert( _dst.size() == images[0].size() && _dst.depth() == images[0].depth() ); |
|
|
|
if( !CV_IS_SPARSE_HIST(hist) ) |
|
{ |
|
cv::Mat H((const CvMatND*)hist->bins); |
|
cv::calcBackProject( &images[0], (int)images.size(), |
|
0, H, _dst, ranges, 1, uniform ); |
|
} |
|
else |
|
{ |
|
cv::SparseMat sH((const CvSparseMat*)hist->bins); |
|
cv::calcBackProject( &images[0], (int)images.size(), |
|
0, sH, _dst, ranges, 1, uniform ); |
|
} |
|
} |
|
|
|
|
|
////////////////////// B A C K P R O J E C T P A T C H ///////////////////////// |
|
|
|
CV_IMPL void |
|
cvCalcArrBackProjectPatch( CvArr** arr, CvArr* dst, CvSize patch_size, CvHistogram* hist, |
|
int method, double norm_factor ) |
|
{ |
|
CvHistogram* model = 0; |
|
|
|
IplImage imgstub[CV_MAX_DIM], *img[CV_MAX_DIM]; |
|
IplROI roi; |
|
CvMat dststub, *dstmat; |
|
int i, dims; |
|
int x, y; |
|
CvSize size; |
|
|
|
if( !CV_IS_HIST(hist)) |
|
CV_Error( CV_StsBadArg, "Bad histogram pointer" ); |
|
|
|
if( !arr ) |
|
CV_Error( CV_StsNullPtr, "Null double array pointer" ); |
|
|
|
if( norm_factor <= 0 ) |
|
CV_Error( CV_StsOutOfRange, |
|
"Bad normalization factor (set it to 1.0 if unsure)" ); |
|
|
|
if( patch_size.width <= 0 || patch_size.height <= 0 ) |
|
CV_Error( CV_StsBadSize, "The patch width and height must be positive" ); |
|
|
|
dims = cvGetDims( hist->bins ); |
|
cvNormalizeHist( hist, norm_factor ); |
|
|
|
for( i = 0; i < dims; i++ ) |
|
{ |
|
CvMat stub, *mat; |
|
mat = cvGetMat( arr[i], &stub, 0, 0 ); |
|
img[i] = cvGetImage( mat, &imgstub[i] ); |
|
img[i]->roi = &roi; |
|
} |
|
|
|
dstmat = cvGetMat( dst, &dststub, 0, 0 ); |
|
if( CV_MAT_TYPE( dstmat->type ) != CV_32FC1 ) |
|
CV_Error( CV_StsUnsupportedFormat, "Resultant image must have 32fC1 type" ); |
|
|
|
if( dstmat->cols != img[0]->width - patch_size.width + 1 || |
|
dstmat->rows != img[0]->height - patch_size.height + 1 ) |
|
CV_Error( CV_StsUnmatchedSizes, |
|
"The output map must be (W-w+1 x H-h+1), " |
|
"where the input images are (W x H) each and the patch is (w x h)" ); |
|
|
|
cvCopyHist( hist, &model ); |
|
|
|
size = cvGetMatSize(dstmat); |
|
roi.coi = 0; |
|
roi.width = patch_size.width; |
|
roi.height = patch_size.height; |
|
|
|
for( y = 0; y < size.height; y++ ) |
|
{ |
|
for( x = 0; x < size.width; x++ ) |
|
{ |
|
double result; |
|
roi.xOffset = x; |
|
roi.yOffset = y; |
|
|
|
cvCalcHist( img, model ); |
|
cvNormalizeHist( model, norm_factor ); |
|
result = cvCompareHist( model, hist, method ); |
|
CV_MAT_ELEM( *dstmat, float, y, x ) = (float)result; |
|
} |
|
} |
|
|
|
cvReleaseHist( &model ); |
|
} |
|
|
|
|
|
// Calculates Bayes probabilistic histograms |
|
CV_IMPL void |
|
cvCalcBayesianProb( CvHistogram** src, int count, CvHistogram** dst ) |
|
{ |
|
int i; |
|
|
|
if( !src || !dst ) |
|
CV_Error( CV_StsNullPtr, "NULL histogram array pointer" ); |
|
|
|
if( count < 2 ) |
|
CV_Error( CV_StsOutOfRange, "Too small number of histograms" ); |
|
|
|
for( i = 0; i < count; i++ ) |
|
{ |
|
if( !CV_IS_HIST(src[i]) || !CV_IS_HIST(dst[i]) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram header" ); |
|
|
|
if( !CV_IS_MATND(src[i]->bins) || !CV_IS_MATND(dst[i]->bins) ) |
|
CV_Error( CV_StsBadArg, "The function supports dense histograms only" ); |
|
} |
|
|
|
cvZero( dst[0]->bins ); |
|
// dst[0] = src[0] + ... + src[count-1] |
|
for( i = 0; i < count; i++ ) |
|
cvAdd( src[i]->bins, dst[0]->bins, dst[0]->bins ); |
|
|
|
cvDiv( 0, dst[0]->bins, dst[0]->bins ); |
|
|
|
// dst[i] = src[i]*(1/dst[0]) |
|
for( i = count - 1; i >= 0; i-- ) |
|
cvMul( src[i]->bins, dst[0]->bins, dst[i]->bins ); |
|
} |
|
|
|
|
|
CV_IMPL void |
|
cvCalcProbDensity( const CvHistogram* hist, const CvHistogram* hist_mask, |
|
CvHistogram* hist_dens, double scale ) |
|
{ |
|
if( scale <= 0 ) |
|
CV_Error( CV_StsOutOfRange, "scale must be positive" ); |
|
|
|
if( !CV_IS_HIST(hist) || !CV_IS_HIST(hist_mask) || !CV_IS_HIST(hist_dens) ) |
|
CV_Error( CV_StsBadArg, "Invalid histogram pointer[s]" ); |
|
|
|
{ |
|
CvArr* arrs[] = { hist->bins, hist_mask->bins, hist_dens->bins }; |
|
CvMatND stubs[3]; |
|
CvNArrayIterator iterator; |
|
|
|
cvInitNArrayIterator( 3, arrs, 0, stubs, &iterator ); |
|
|
|
if( CV_MAT_TYPE(iterator.hdr[0]->type) != CV_32FC1 ) |
|
CV_Error( CV_StsUnsupportedFormat, "All histograms must have 32fC1 type" ); |
|
|
|
do |
|
{ |
|
const float* srcdata = (const float*)(iterator.ptr[0]); |
|
const float* maskdata = (const float*)(iterator.ptr[1]); |
|
float* dstdata = (float*)(iterator.ptr[2]); |
|
int i; |
|
|
|
for( i = 0; i < iterator.size.width; i++ ) |
|
{ |
|
float s = srcdata[i]; |
|
float m = maskdata[i]; |
|
if( s > FLT_EPSILON ) |
|
if( m <= s ) |
|
dstdata[i] = (float)(m*scale/s); |
|
else |
|
dstdata[i] = (float)scale; |
|
else |
|
dstdata[i] = (float)0; |
|
} |
|
} |
|
while( cvNextNArraySlice( &iterator )); |
|
} |
|
} |
|
|
|
|
|
CV_IMPL void cvEqualizeHist( const CvArr* srcarr, CvArr* dstarr ) |
|
{ |
|
CvMat sstub, *src = cvGetMat(srcarr, &sstub); |
|
CvMat dstub, *dst = cvGetMat(dstarr, &dstub); |
|
|
|
CV_Assert( CV_ARE_SIZES_EQ(src, dst) && CV_ARE_TYPES_EQ(src, dst) && |
|
CV_MAT_TYPE(src->type) == CV_8UC1 ); |
|
CvSize size = cvGetMatSize(src); |
|
if( CV_IS_MAT_CONT(src->type & dst->type) ) |
|
{ |
|
size.width *= size.height; |
|
size.height = 1; |
|
} |
|
int x, y; |
|
const int hist_sz = 256; |
|
int hist[hist_sz]; |
|
memset(hist, 0, sizeof(hist)); |
|
|
|
for( y = 0; y < size.height; y++ ) |
|
{ |
|
const uchar* sptr = src->data.ptr + src->step*y; |
|
for( x = 0; x < size.width; x++ ) |
|
hist[sptr[x]]++; |
|
} |
|
|
|
float scale = 255.f/(size.width*size.height); |
|
int sum = 0; |
|
uchar lut[hist_sz+1]; |
|
|
|
for( int i = 0; i < hist_sz; i++ ) |
|
{ |
|
sum += hist[i]; |
|
int val = cvRound(sum*scale); |
|
lut[i] = CV_CAST_8U(val); |
|
} |
|
|
|
lut[0] = 0; |
|
for( y = 0; y < size.height; y++ ) |
|
{ |
|
const uchar* sptr = src->data.ptr + src->step*y; |
|
uchar* dptr = dst->data.ptr + dst->step*y; |
|
for( x = 0; x < size.width; x++ ) |
|
dptr[x] = lut[sptr[x]]; |
|
} |
|
} |
|
|
|
|
|
void cv::equalizeHist( InputArray _src, OutputArray _dst ) |
|
{ |
|
Mat src = _src.getMat(); |
|
_dst.create( src.size(), src.type() ); |
|
Mat dst = _dst.getMat(); |
|
CvMat _csrc = src, _cdst = dst; |
|
cvEqualizeHist( &_csrc, &_cdst ); |
|
} |
|
|
|
/* Implementation of RTTI and Generic Functions for CvHistogram */ |
|
#define CV_TYPE_NAME_HIST "opencv-hist" |
|
|
|
static int icvIsHist( const void * ptr ) |
|
{ |
|
return CV_IS_HIST( ((CvHistogram*)ptr) ); |
|
} |
|
|
|
static CvHistogram * icvCloneHist( const CvHistogram * src ) |
|
{ |
|
CvHistogram * dst=NULL; |
|
cvCopyHist(src, &dst); |
|
return dst; |
|
} |
|
|
|
static void *icvReadHist( CvFileStorage * fs, CvFileNode * node ) |
|
{ |
|
CvHistogram * h = 0; |
|
int type = 0; |
|
int is_uniform = 0; |
|
int have_ranges = 0; |
|
|
|
h = (CvHistogram *)cvAlloc( sizeof(CvHistogram) ); |
|
|
|
type = cvReadIntByName( fs, node, "type", 0 ); |
|
is_uniform = cvReadIntByName( fs, node, "is_uniform", 0 ); |
|
have_ranges = cvReadIntByName( fs, node, "have_ranges", 0 ); |
|
h->type = CV_HIST_MAGIC_VAL | type | |
|
(is_uniform ? CV_HIST_UNIFORM_FLAG : 0) | |
|
(have_ranges ? CV_HIST_RANGES_FLAG : 0); |
|
|
|
if(type == CV_HIST_ARRAY) |
|
{ |
|
// read histogram bins |
|
CvMatND* mat = (CvMatND*)cvReadByName( fs, node, "mat" ); |
|
int i, sizes[CV_MAX_DIM]; |
|
|
|
if(!CV_IS_MATND(mat)) |
|
CV_Error( CV_StsError, "Expected CvMatND"); |
|
|
|
for(i=0; i<mat->dims; i++) |
|
sizes[i] = mat->dim[i].size; |
|
|
|
cvInitMatNDHeader( &(h->mat), mat->dims, sizes, mat->type, mat->data.ptr ); |
|
h->bins = &(h->mat); |
|
|
|
// take ownership of refcount pointer as well |
|
h->mat.refcount = mat->refcount; |
|
|
|
// increase refcount so freeing temp header doesn't free data |
|
cvIncRefData( mat ); |
|
|
|
// free temporary header |
|
cvReleaseMatND( &mat ); |
|
} |
|
else |
|
{ |
|
h->bins = cvReadByName( fs, node, "bins" ); |
|
if(!CV_IS_SPARSE_MAT(h->bins)){ |
|
CV_Error( CV_StsError, "Unknown Histogram type"); |
|
} |
|
} |
|
|
|
// read thresholds |
|
if(have_ranges) |
|
{ |
|
int i, dims, size[CV_MAX_DIM], total = 0; |
|
CvSeqReader reader; |
|
CvFileNode * thresh_node; |
|
|
|
dims = cvGetDims( h->bins, size ); |
|
for( i = 0; i < dims; i++ ) |
|
total += size[i]+1; |
|
|
|
thresh_node = cvGetFileNodeByName( fs, node, "thresh" ); |
|
if(!thresh_node) |
|
CV_Error( CV_StsError, "'thresh' node is missing"); |
|
cvStartReadRawData( fs, thresh_node, &reader ); |
|
|
|
if(is_uniform) |
|
{ |
|
for(i=0; i<dims; i++) |
|
cvReadRawDataSlice( fs, &reader, 2, h->thresh[i], "f" ); |
|
h->thresh2 = NULL; |
|
} |
|
else |
|
{ |
|
float* dim_ranges; |
|
h->thresh2 = (float**)cvAlloc( |
|
dims*sizeof(h->thresh2[0])+ |
|
total*sizeof(h->thresh2[0][0])); |
|
dim_ranges = (float*)(h->thresh2 + dims); |
|
for(i=0; i < dims; i++) |
|
{ |
|
h->thresh2[i] = dim_ranges; |
|
cvReadRawDataSlice( fs, &reader, size[i]+1, dim_ranges, "f" ); |
|
dim_ranges += size[i] + 1; |
|
} |
|
} |
|
} |
|
|
|
return h; |
|
} |
|
|
|
static void icvWriteHist( CvFileStorage* fs, const char* name, |
|
const void* struct_ptr, CvAttrList /*attributes*/ ) |
|
{ |
|
const CvHistogram * hist = (const CvHistogram *) struct_ptr; |
|
int sizes[CV_MAX_DIM]; |
|
int dims; |
|
int i; |
|
int is_uniform, have_ranges; |
|
|
|
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_HIST ); |
|
|
|
is_uniform = (CV_IS_UNIFORM_HIST(hist) ? 1 : 0); |
|
have_ranges = (hist->type & CV_HIST_RANGES_FLAG ? 1 : 0); |
|
|
|
cvWriteInt( fs, "type", (hist->type & 1) ); |
|
cvWriteInt( fs, "is_uniform", is_uniform ); |
|
cvWriteInt( fs, "have_ranges", have_ranges ); |
|
if(!CV_IS_SPARSE_HIST(hist)) |
|
cvWrite( fs, "mat", &(hist->mat) ); |
|
else |
|
cvWrite( fs, "bins", hist->bins ); |
|
|
|
// write thresholds |
|
if(have_ranges){ |
|
dims = cvGetDims( hist->bins, sizes ); |
|
cvStartWriteStruct( fs, "thresh", CV_NODE_SEQ + CV_NODE_FLOW ); |
|
if(is_uniform){ |
|
for(i=0; i<dims; i++){ |
|
cvWriteRawData( fs, hist->thresh[i], 2, "f" ); |
|
} |
|
} |
|
else{ |
|
for(i=0; i<dims; i++){ |
|
cvWriteRawData( fs, hist->thresh2[i], sizes[i]+1, "f" ); |
|
} |
|
} |
|
cvEndWriteStruct( fs ); |
|
} |
|
|
|
cvEndWriteStruct( fs ); |
|
} |
|
|
|
|
|
CvType hist_type( CV_TYPE_NAME_HIST, icvIsHist, (CvReleaseFunc)cvReleaseHist, |
|
icvReadHist, icvWriteHist, (CvCloneFunc)icvCloneHist ); |
|
|
|
/* End of file. */ |
|
|
|
|