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opencv/modules/imgproc/src/histogram.cpp
kocheganovvm 086db9d6db Introduce CV_COMP_CHISQR_ALT, an alternative method to calculate ChiSquare Histogram comparison 
There's some disagreement about the correct formula.
has its supporters, however, for texture analysis, the newly introduced formula became
standard. The commit enables both uses without breaking backward compatibility.

First contributor of this commit was sperrholz.
2013-08-12 13:51:52 +04:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv
{
////////////////// Helper functions //////////////////////
static const size_t OUT_OF_RANGE = (size_t)1 << (sizeof(size_t)*8 - 2);
static void
calcHistLookupTables_8u( const Mat& hist, const SparseMat& shist,
int dims, const float** ranges, const double* uniranges,
bool uniform, bool issparse, std::vector<size_t>& _tab )
{
const int low = 0, high = 256;
int i, j;
_tab.resize((high-low)*dims);
size_t* tab = &_tab[0];
if( uniform )
{
for( i = 0; i < dims; i++ )
{
double a = uniranges[i*2];
double b = uniranges[i*2+1];
int sz = !issparse ? hist.size[i] : shist.size(i);
size_t step = !issparse ? hist.step[i] : 1;
for( j = low; j < high; j++ )
{
int idx = cvFloor(j*a + b);
size_t written_idx;
if( (unsigned)idx < (unsigned)sz )
written_idx = idx*step;
else
written_idx = OUT_OF_RANGE;
tab[i*(high - low) + j - low] = written_idx;
}
}
}
else
{
for( i = 0; i < dims; i++ )
{
int limit = std::min(cvCeil(ranges[i][0]), high);
int idx = -1, sz = !issparse ? hist.size[i] : shist.size(i);
size_t written_idx = OUT_OF_RANGE;
size_t step = !issparse ? hist.step[i] : 1;
for(j = low;;)
{
for( ; j < limit; j++ )
tab[i*(high - low) + j - low] = written_idx;
if( (unsigned)(++idx) < (unsigned)sz )
{
limit = std::min(cvCeil(ranges[i][idx+1]), high);
written_idx = idx*step;
}
else
{
for( ; j < high; j++ )
tab[i*(high - low) + j - low] = OUT_OF_RANGE;
break;
}
}
}
}
}
static void histPrepareImages( const Mat* images, int nimages, const int* channels,
const Mat& mask, int dims, const int* histSize,
const float** ranges, bool uniform,
std::vector<uchar*>& ptrs, std::vector<int>& deltas,
Size& imsize, std::vector<double>& uniranges )
{
int i, j, c;
CV_Assert( channels != 0 || nimages == dims );
imsize = images[0].size();
int depth = images[0].depth(), esz1 = (int)images[0].elemSize1();
bool isContinuous = true;
ptrs.resize(dims + 1);
deltas.resize((dims + 1)*2);
for( i = 0; i < dims; i++ )
{
if(!channels)
{
j = i;
c = 0;
CV_Assert( images[j].channels() == 1 );
}
else
{
c = channels[i];
CV_Assert( c >= 0 );
for( j = 0; j < nimages; c -= images[j].channels(), j++ )
if( c < images[j].channels() )
break;
CV_Assert( j < nimages );
}
CV_Assert( images[j].size() == imsize && images[j].depth() == depth );
if( !images[j].isContinuous() )
isContinuous = false;
ptrs[i] = images[j].data + c*esz1;
deltas[i*2] = images[j].channels();
deltas[i*2+1] = (int)(images[j].step/esz1 - imsize.width*deltas[i*2]);
}
if( mask.data )
{
CV_Assert( mask.size() == imsize && mask.channels() == 1 );
isContinuous = isContinuous && mask.isContinuous();
ptrs[dims] = mask.data;
deltas[dims*2] = 1;
deltas[dims*2 + 1] = (int)(mask.step/mask.elemSize1());
}
#ifndef HAVE_TBB
if( isContinuous )
{
imsize.width *= imsize.height;
imsize.height = 1;
}
#endif
if( !ranges )
{
CV_Assert( depth == CV_8U );
uniranges.resize( dims*2 );
for( i = 0; i < dims; i++ )
{
uniranges[i*2] = histSize[i]/256.;
uniranges[i*2+1] = 0;
}
}
else if( uniform )
{
uniranges.resize( dims*2 );
for( i = 0; i < dims; i++ )
{
CV_Assert( ranges[i] && ranges[i][0] < ranges[i][1] );
double low = ranges[i][0], high = ranges[i][1];
double t = histSize[i]/(high - low);
uniranges[i*2] = t;
uniranges[i*2+1] = -t*low;
}
}
else
{
for( i = 0; i < dims; i++ )
{
size_t n = histSize[i];
for(size_t k = 0; k < n; k++ )
CV_Assert( ranges[i][k] < ranges[i][k+1] );
}
}
}
////////////////////////////////// C A L C U L A T E H I S T O G R A M ////////////////////////////////////
#ifdef HAVE_TBB
enum {one = 1, two, three}; // array elements number
template<typename T>
class calcHist1D_Invoker
{
public:
calcHist1D_Invoker( const std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Mat& hist, const double* _uniranges, int sz, int dims,
Size& imageSize )
: mask_(_ptrs[dims]),
mstep_(_deltas[dims*2 + 1]),
imageWidth_(imageSize.width),
histogramSize_(hist.size()), histogramType_(hist.type()),
globalHistogram_((tbb::atomic<int>*)hist.data)
{
p_[0] = ((T**)&_ptrs[0])[0];
step_[0] = (&_deltas[0])[1];
d_[0] = (&_deltas[0])[0];
a_[0] = (&_uniranges[0])[0];
b_[0] = (&_uniranges[0])[1];
size_[0] = sz;
}
void operator()( const BlockedRange& range ) const
{
T* p0 = p_[0] + range.begin() * (step_[0] + imageWidth_*d_[0]);
uchar* mask = mask_ + range.begin()*mstep_;
for( int row = range.begin(); row < range.end(); row++, p0 += step_[0] )
{
if( !mask_ )
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0] )
{
int idx = cvFloor(*p0*a_[0] + b_[0]);
if( (unsigned)idx < (unsigned)size_[0] )
{
globalHistogram_[idx].fetch_and_add(1);
}
}
}
else
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0] )
{
if( mask[x] )
{
int idx = cvFloor(*p0*a_[0] + b_[0]);
if( (unsigned)idx < (unsigned)size_[0] )
{
globalHistogram_[idx].fetch_and_add(1);
}
}
}
mask += mstep_;
}
}
}
private:
calcHist1D_Invoker operator=(const calcHist1D_Invoker&);
T* p_[one];
uchar* mask_;
int step_[one];
int d_[one];
int mstep_;
double a_[one];
double b_[one];
int size_[one];
int imageWidth_;
Size histogramSize_;
int histogramType_;
tbb::atomic<int>* globalHistogram_;
};
template<typename T>
class calcHist2D_Invoker
{
public:
calcHist2D_Invoker( const std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Mat& hist, const double* _uniranges, const int* size,
int dims, Size& imageSize, size_t* hstep )
: mask_(_ptrs[dims]),
mstep_(_deltas[dims*2 + 1]),
imageWidth_(imageSize.width),
histogramSize_(hist.size()), histogramType_(hist.type()),
globalHistogram_(hist.data)
{
p_[0] = ((T**)&_ptrs[0])[0]; p_[1] = ((T**)&_ptrs[0])[1];
step_[0] = (&_deltas[0])[1]; step_[1] = (&_deltas[0])[3];
d_[0] = (&_deltas[0])[0]; d_[1] = (&_deltas[0])[2];
a_[0] = (&_uniranges[0])[0]; a_[1] = (&_uniranges[0])[2];
b_[0] = (&_uniranges[0])[1]; b_[1] = (&_uniranges[0])[3];
size_[0] = size[0]; size_[1] = size[1];
hstep_[0] = hstep[0];
}
void operator()(const BlockedRange& range) const
{
T* p0 = p_[0] + range.begin()*(step_[0] + imageWidth_*d_[0]);
T* p1 = p_[1] + range.begin()*(step_[1] + imageWidth_*d_[1]);
uchar* mask = mask_ + range.begin()*mstep_;
for( int row = range.begin(); row < range.end(); row++, p0 += step_[0], p1 += step_[1] )
{
if( !mask_ )
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0], p1 += d_[1] )
{
int idx0 = cvFloor(*p0*a_[0] + b_[0]);
int idx1 = cvFloor(*p1*a_[1] + b_[1]);
if( (unsigned)idx0 < (unsigned)size_[0] && (unsigned)idx1 < (unsigned)size_[1] )
( (tbb::atomic<int>*)(globalHistogram_ + hstep_[0]*idx0) )[idx1].fetch_and_add(1);
}
}
else
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0], p1 += d_[1] )
{
if( mask[x] )
{
int idx0 = cvFloor(*p0*a_[0] + b_[0]);
int idx1 = cvFloor(*p1*a_[1] + b_[1]);
if( (unsigned)idx0 < (unsigned)size_[0] && (unsigned)idx1 < (unsigned)size_[1] )
((tbb::atomic<int>*)(globalHistogram_ + hstep_[0]*idx0))[idx1].fetch_and_add(1);
}
}
mask += mstep_;
}
}
}
private:
calcHist2D_Invoker operator=(const calcHist2D_Invoker&);
T* p_[two];
uchar* mask_;
int step_[two];
int d_[two];
int mstep_;
double a_[two];
double b_[two];
int size_[two];
const int imageWidth_;
size_t hstep_[one];
Size histogramSize_;
int histogramType_;
uchar* globalHistogram_;
};
template<typename T>
class calcHist3D_Invoker
{
public:
calcHist3D_Invoker( const std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Size imsize, Mat& hist, const double* uniranges, int _dims,
size_t* hstep, int* size )
: mask_(_ptrs[_dims]),
mstep_(_deltas[_dims*2 + 1]),
imageWidth_(imsize.width),
globalHistogram_(hist.data)
{
p_[0] = ((T**)&_ptrs[0])[0]; p_[1] = ((T**)&_ptrs[0])[1]; p_[2] = ((T**)&_ptrs[0])[2];
step_[0] = (&_deltas[0])[1]; step_[1] = (&_deltas[0])[3]; step_[2] = (&_deltas[0])[5];
d_[0] = (&_deltas[0])[0]; d_[1] = (&_deltas[0])[2]; d_[2] = (&_deltas[0])[4];
a_[0] = uniranges[0]; a_[1] = uniranges[2]; a_[2] = uniranges[4];
b_[0] = uniranges[1]; b_[1] = uniranges[3]; b_[2] = uniranges[5];
size_[0] = size[0]; size_[1] = size[1]; size_[2] = size[2];
hstep_[0] = hstep[0]; hstep_[1] = hstep[1];
}
void operator()( const BlockedRange& range ) const
{
T* p0 = p_[0] + range.begin()*(imageWidth_*d_[0] + step_[0]);
T* p1 = p_[1] + range.begin()*(imageWidth_*d_[1] + step_[1]);
T* p2 = p_[2] + range.begin()*(imageWidth_*d_[2] + step_[2]);
uchar* mask = mask_ + range.begin()*mstep_;
for( int i = range.begin(); i < range.end(); i++, p0 += step_[0], p1 += step_[1], p2 += step_[2] )
{
if( !mask_ )
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0], p1 += d_[1], p2 += d_[2] )
{
int idx0 = cvFloor(*p0*a_[0] + b_[0]);
int idx1 = cvFloor(*p1*a_[1] + b_[1]);
int idx2 = cvFloor(*p2*a_[2] + b_[2]);
if( (unsigned)idx0 < (unsigned)size_[0] &&
(unsigned)idx1 < (unsigned)size_[1] &&
(unsigned)idx2 < (unsigned)size_[2] )
{
( (tbb::atomic<int>*)(globalHistogram_ + hstep_[0]*idx0 + hstep_[1]*idx1) )[idx2].fetch_and_add(1);
}
}
}
else
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0], p1 += d_[1], p2 += d_[2] )
{
if( mask[x] )
{
int idx0 = cvFloor(*p0*a_[0] + b_[0]);
int idx1 = cvFloor(*p1*a_[1] + b_[1]);
int idx2 = cvFloor(*p2*a_[2] + b_[2]);
if( (unsigned)idx0 < (unsigned)size_[0] &&
(unsigned)idx1 < (unsigned)size_[1] &&
(unsigned)idx2 < (unsigned)size_[2] )
{
( (tbb::atomic<int>*)(globalHistogram_ + hstep_[0]*idx0 + hstep_[1]*idx1) )[idx2].fetch_and_add(1);
}
}
}
mask += mstep_;
}
}
}
static bool isFit( const Mat& histogram, const Size imageSize )
{
return ( imageSize.width * imageSize.height >= 320*240
&& histogram.total() >= 8*8*8 );
}
private:
calcHist3D_Invoker operator=(const calcHist3D_Invoker&);
T* p_[three];
uchar* mask_;
int step_[three];
int d_[three];
const int mstep_;
double a_[three];
double b_[three];
int size_[three];
int imageWidth_;
size_t hstep_[two];
uchar* globalHistogram_;
};
class CalcHist1D_8uInvoker
{
public:
CalcHist1D_8uInvoker( const std::vector<uchar*>& ptrs, const std::vector<int>& deltas,
Size imsize, Mat& hist, int dims, const std::vector<size_t>& tab,
tbb::mutex* lock )
: mask_(ptrs[dims]),
mstep_(deltas[dims*2 + 1]),
imageWidth_(imsize.width),
imageSize_(imsize),
histSize_(hist.size()), histType_(hist.type()),
tab_((size_t*)&tab[0]),
histogramWriteLock_(lock),
globalHistogram_(hist.data)
{
p_[0] = (&ptrs[0])[0];
step_[0] = (&deltas[0])[1];
d_[0] = (&deltas[0])[0];
}
void operator()( const BlockedRange& range ) const
{
int localHistogram[256] = { 0, };
uchar* mask = mask_;
uchar* p0 = p_[0];
int x;
tbb::mutex::scoped_lock lock;
if( !mask_ )
{
int n = (imageWidth_ - 4) / 4 + 1;
int tail = imageWidth_ - n*4;
int xN = 4*n;
p0 += (xN*d_[0] + tail*d_[0] + step_[0]) * range.begin();
}
else
{
p0 += (imageWidth_*d_[0] + step_[0]) * range.begin();
mask += mstep_*range.begin();
}
for( int i = range.begin(); i < range.end(); i++, p0 += step_[0] )
{
if( !mask_ )
{
if( d_[0] == 1 )
{
for( x = 0; x <= imageWidth_ - 4; x += 4 )
{
int t0 = p0[x], t1 = p0[x+1];
localHistogram[t0]++; localHistogram[t1]++;
t0 = p0[x+2]; t1 = p0[x+3];
localHistogram[t0]++; localHistogram[t1]++;
}
p0 += x;
}
else
{
for( x = 0; x <= imageWidth_ - 4; x += 4 )
{
int t0 = p0[0], t1 = p0[d_[0]];
localHistogram[t0]++; localHistogram[t1]++;
p0 += d_[0]*2;
t0 = p0[0]; t1 = p0[d_[0]];
localHistogram[t0]++; localHistogram[t1]++;
p0 += d_[0]*2;
}
}
for( ; x < imageWidth_; x++, p0 += d_[0] )
{
localHistogram[*p0]++;
}
}
else
{
for( x = 0; x < imageWidth_; x++, p0 += d_[0] )
{
if( mask[x] )
{
localHistogram[*p0]++;
}
}
mask += mstep_;
}
}
lock.acquire(*histogramWriteLock_);
for(int i = 0; i < 256; i++ )
{
size_t hidx = tab_[i];
if( hidx < OUT_OF_RANGE )
{
*(int*)((globalHistogram_ + hidx)) += localHistogram[i];
}
}
lock.release();
}
static bool isFit( const Mat& histogram, const Size imageSize )
{
return ( histogram.total() >= 8
&& imageSize.width * imageSize.height >= 160*120 );
}
private:
uchar* p_[one];
uchar* mask_;
int mstep_;
int step_[one];
int d_[one];
int imageWidth_;
Size imageSize_;
Size histSize_;
int histType_;
size_t* tab_;
tbb::mutex* histogramWriteLock_;
uchar* globalHistogram_;
};
class CalcHist2D_8uInvoker
{
public:
CalcHist2D_8uInvoker( const std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Size imsize, Mat& hist, int dims, const std::vector<size_t>& _tab,
tbb::mutex* lock )
: mask_(_ptrs[dims]),
mstep_(_deltas[dims*2 + 1]),
imageWidth_(imsize.width),
histSize_(hist.size()), histType_(hist.type()),
tab_((size_t*)&_tab[0]),
histogramWriteLock_(lock),
globalHistogram_(hist.data)
{
p_[0] = (uchar*)(&_ptrs[0])[0]; p_[1] = (uchar*)(&_ptrs[0])[1];
step_[0] = (&_deltas[0])[1]; step_[1] = (&_deltas[0])[3];
d_[0] = (&_deltas[0])[0]; d_[1] = (&_deltas[0])[2];
}
void operator()( const BlockedRange& range ) const
{
uchar* p0 = p_[0] + range.begin()*(step_[0] + imageWidth_*d_[0]);
uchar* p1 = p_[1] + range.begin()*(step_[1] + imageWidth_*d_[1]);
uchar* mask = mask_ + range.begin()*mstep_;
Mat localHist = Mat::zeros(histSize_, histType_);
uchar* localHistData = localHist.data;
tbb::mutex::scoped_lock lock;
for(int i = range.begin(); i < range.end(); i++, p0 += step_[0], p1 += step_[1])
{
if( !mask_ )
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0], p1 += d_[1] )
{
size_t idx = tab_[*p0] + tab_[*p1 + 256];
if( idx < OUT_OF_RANGE )
{
++*(int*)(localHistData + idx);
}
}
}
else
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0], p1 += d_[1] )
{
size_t idx;
if( mask[x] && (idx = tab_[*p0] + tab_[*p1 + 256]) < OUT_OF_RANGE )
{
++*(int*)(localHistData + idx);
}
}
mask += mstep_;
}
}
lock.acquire(*histogramWriteLock_);
for(int i = 0; i < histSize_.width*histSize_.height; i++)
{
((int*)globalHistogram_)[i] += ((int*)localHistData)[i];
}
lock.release();
}
static bool isFit( const Mat& histogram, const Size imageSize )
{
return ( (histogram.total() > 4*4 && histogram.total() <= 116*116
&& imageSize.width * imageSize.height >= 320*240)
|| (histogram.total() > 116*116 && imageSize.width * imageSize.height >= 1280*720) );
}
private:
uchar* p_[two];
uchar* mask_;
int step_[two];
int d_[two];
int mstep_;
int imageWidth_;
Size histSize_;
int histType_;
size_t* tab_;
tbb::mutex* histogramWriteLock_;
uchar* globalHistogram_;
};
class CalcHist3D_8uInvoker
{
public:
CalcHist3D_8uInvoker( const std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Size imsize, Mat& hist, int dims, const std::vector<size_t>& tab )
: mask_(_ptrs[dims]),
mstep_(_deltas[dims*2 + 1]),
histogramSize_(hist.size.p), histogramType_(hist.type()),
imageWidth_(imsize.width),
tab_((size_t*)&tab[0]),
globalHistogram_(hist.data)
{
p_[0] = (uchar*)(&_ptrs[0])[0]; p_[1] = (uchar*)(&_ptrs[0])[1]; p_[2] = (uchar*)(&_ptrs[0])[2];
step_[0] = (&_deltas[0])[1]; step_[1] = (&_deltas[0])[3]; step_[2] = (&_deltas[0])[5];
d_[0] = (&_deltas[0])[0]; d_[1] = (&_deltas[0])[2]; d_[2] = (&_deltas[0])[4];
}
void operator()( const BlockedRange& range ) const
{
uchar* p0 = p_[0] + range.begin()*(step_[0] + imageWidth_*d_[0]);
uchar* p1 = p_[1] + range.begin()*(step_[1] + imageWidth_*d_[1]);
uchar* p2 = p_[2] + range.begin()*(step_[2] + imageWidth_*d_[2]);
uchar* mask = mask_ + range.begin()*mstep_;
for(int i = range.begin(); i < range.end(); i++, p0 += step_[0], p1 += step_[1], p2 += step_[2] )
{
if( !mask_ )
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0], p1 += d_[1], p2 += d_[2] )
{
size_t idx = tab_[*p0] + tab_[*p1 + 256] + tab_[*p2 + 512];
if( idx < OUT_OF_RANGE )
{
( *(tbb::atomic<int>*)(globalHistogram_ + idx) ).fetch_and_add(1);
}
}
}
else
{
for( int x = 0; x < imageWidth_; x++, p0 += d_[0], p1 += d_[1], p2 += d_[2] )
{
size_t idx;
if( mask[x] && (idx = tab_[*p0] + tab_[*p1 + 256] + tab_[*p2 + 512]) < OUT_OF_RANGE )
{
(*(tbb::atomic<int>*)(globalHistogram_ + idx)).fetch_and_add(1);
}
}
mask += mstep_;
}
}
}
static bool isFit( const Mat& histogram, const Size imageSize )
{
return ( histogram.total() >= 128*128*128
&& imageSize.width * imageSize.width >= 320*240 );
}
private:
uchar* p_[three];
uchar* mask_;
int mstep_;
int step_[three];
int d_[three];
int* histogramSize_;
int histogramType_;
int imageWidth_;
size_t* tab_;
uchar* globalHistogram_;
};
static void
callCalcHist2D_8u( std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Size imsize, Mat& hist, int dims, std::vector<size_t>& _tab )
{
int grainSize = imsize.height / tbb::task_scheduler_init::default_num_threads();
tbb::mutex histogramWriteLock;
CalcHist2D_8uInvoker body(_ptrs, _deltas, imsize, hist, dims, _tab, &histogramWriteLock);
parallel_for(BlockedRange(0, imsize.height, grainSize), body);
}
static void
callCalcHist3D_8u( std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Size imsize, Mat& hist, int dims, std::vector<size_t>& _tab )
{
CalcHist3D_8uInvoker body(_ptrs, _deltas, imsize, hist, dims, _tab);
parallel_for(BlockedRange(0, imsize.height), body);
}
#endif
template<typename T> static void
calcHist_( std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Size imsize, Mat& hist, int dims, const float** _ranges,
const double* _uniranges, bool uniform )
{
T** ptrs = (T**)&_ptrs[0];
const int* deltas = &_deltas[0];
uchar* H = hist.data;
int i, x;
const uchar* mask = _ptrs[dims];
int mstep = _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 )
{
#ifdef HAVE_TBB
calcHist1D_Invoker<T> body(_ptrs, _deltas, hist, _uniranges, size[0], dims, imsize);
parallel_for(BlockedRange(0, imsize.height), body);
#else
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, mask += mstep )
{
if( !mask )
for( x = 0; x < imsize.width; x++, p0 += d0 )
{
int idx = cvFloor(*p0*a + b);
if( (unsigned)idx < (unsigned)sz )
((int*)H)[idx]++;
}
else
for( x = 0; x < imsize.width; x++, p0 += d0 )
if( mask[x] )
{
int idx = cvFloor(*p0*a + b);
if( (unsigned)idx < (unsigned)sz )
((int*)H)[idx]++;
}
}
#endif //HAVE_TBB
return;
}
else if( dims == 2 )
{
#ifdef HAVE_TBB
calcHist2D_Invoker<T> body(_ptrs, _deltas, hist, _uniranges, size, dims, imsize, hstep);
parallel_for(BlockedRange(0, imsize.height), body);
#else
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, mask += mstep )
{
if( !mask )
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 )
{
int idx0 = cvFloor(*p0*a0 + b0);
int idx1 = cvFloor(*p1*a1 + b1);
if( (unsigned)idx0 < (unsigned)sz0 && (unsigned)idx1 < (unsigned)sz1 )
((int*)(H + hstep0*idx0))[idx1]++;
}
else
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 )
if( mask[x] )
{
int idx0 = cvFloor(*p0*a0 + b0);
int idx1 = cvFloor(*p1*a1 + b1);
if( (unsigned)idx0 < (unsigned)sz0 && (unsigned)idx1 < (unsigned)sz1 )
((int*)(H + hstep0*idx0))[idx1]++;
}
}
#endif //HAVE_TBB
return;
}
else if( dims == 3 )
{
#ifdef HAVE_TBB
if( calcHist3D_Invoker<T>::isFit(hist, imsize) )
{
calcHist3D_Invoker<T> body(_ptrs, _deltas, imsize, hist, uniranges, dims, hstep, size);
parallel_for(BlockedRange(0, imsize.height), body);
return;
}
#endif
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, mask += mstep )
{
if( !mask )
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);
if( (unsigned)idx0 < (unsigned)sz0 &&
(unsigned)idx1 < (unsigned)sz1 &&
(unsigned)idx2 < (unsigned)sz2 )
((int*)(H + hstep0*idx0 + hstep1*idx1))[idx2]++;
}
else
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 )
if( mask[x] )
{
int idx0 = cvFloor(*p0*a0 + b0);
int idx1 = cvFloor(*p1*a1 + b1);
int idx2 = cvFloor(*p2*a2 + b2);
if( (unsigned)idx0 < (unsigned)sz0 &&
(unsigned)idx1 < (unsigned)sz1 &&
(unsigned)idx2 < (unsigned)sz2 )
((int*)(H + hstep0*idx0 + hstep1*idx1))[idx2]++;
}
}
}
else
{
for( ; imsize.height--; mask += mstep )
{
if( !mask )
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] )
break;
ptrs[i] += deltas[i*2];
Hptr += idx*hstep[i];
}
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;
i = 0;
if( mask[x] )
for( ; i < dims; i++ )
{
int idx = cvFloor(*ptrs[i]*uniranges[i*2] + uniranges[i*2+1]);
if( (unsigned)idx >= (unsigned)size[i] )
break;
ptrs[i] += deltas[i*2];
Hptr += idx*hstep[i];
}
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];
}
}
}
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++ )
{
uchar* Hptr = H;
i = 0;
if( !mask || mask[x] )
for( ; 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 )
++*((int*)Hptr);
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_8u( std::vector<uchar*>& _ptrs, const std::vector<int>& _deltas,
Size imsize, Mat& hist, int dims, const float** _ranges,
const double* _uniranges, bool uniform )
{
uchar** ptrs = &_ptrs[0];
const int* deltas = &_deltas[0];
uchar* H = hist.data;
int x;
const uchar* mask = _ptrs[dims];
int mstep = _deltas[dims*2 + 1];
std::vector<size_t> _tab;
calcHistLookupTables_8u( hist, SparseMat(), dims, _ranges, _uniranges, uniform, false, _tab );
const size_t* tab = &_tab[0];
if( dims == 1 )
{
#ifdef HAVE_TBB
if( CalcHist1D_8uInvoker::isFit(hist, imsize) )
{
int treadsNumber = tbb::task_scheduler_init::default_num_threads();
int grainSize = imsize.height/treadsNumber;
tbb::mutex histogramWriteLock;
CalcHist1D_8uInvoker body(_ptrs, _deltas, imsize, hist, dims, _tab, &histogramWriteLock);
parallel_for(BlockedRange(0, imsize.height, grainSize), body);
return;
}
#endif
int d0 = deltas[0], step0 = deltas[1];
int matH[256] = { 0, };
const uchar* p0 = (const uchar*)ptrs[0];
for( ; imsize.height--; p0 += step0, mask += mstep )
{
if( !mask )
{
if( d0 == 1 )
{
for( x = 0; x <= imsize.width - 4; x += 4 )
{
int t0 = p0[x], t1 = p0[x+1];
matH[t0]++; matH[t1]++;
t0 = p0[x+2]; t1 = p0[x+3];
matH[t0]++; matH[t1]++;
}
p0 += x;
}
else
for( x = 0; x <= imsize.width - 4; x += 4 )
{
int t0 = p0[0], t1 = p0[d0];
matH[t0]++; matH[t1]++;
p0 += d0*2;
t0 = p0[0]; t1 = p0[d0];
matH[t0]++; matH[t1]++;
p0 += d0*2;
}
for( ; x < imsize.width; x++, p0 += d0 )
matH[*p0]++;
}
else
for( x = 0; x < imsize.width; x++, p0 += d0 )
if( mask[x] )
matH[*p0]++;
}
for(int i = 0; i < 256; i++ )
{
size_t hidx = tab[i];
if( hidx < OUT_OF_RANGE )
*(int*)(H + hidx) += matH[i];
}
}
else if( dims == 2 )
{
#ifdef HAVE_TBB
if( CalcHist2D_8uInvoker::isFit(hist, imsize) )
{
callCalcHist2D_8u(_ptrs, _deltas, imsize, hist, dims, _tab);
return;
}
#endif
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, mask += mstep )
{
if( !mask )
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 )
{
size_t idx = tab[*p0] + tab[*p1 + 256];
if( idx < OUT_OF_RANGE )
++*(int*)(H + idx);
}
else
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1 )
{
size_t idx;
if( mask[x] && (idx = tab[*p0] + tab[*p1 + 256]) < OUT_OF_RANGE )
++*(int*)(H + idx);
}
}
}
else if( dims == 3 )
{
#ifdef HAVE_TBB
if( CalcHist3D_8uInvoker::isFit(hist, imsize) )
{
callCalcHist3D_8u(_ptrs, _deltas, imsize, hist, dims, _tab);
return;
}
#endif
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, mask += mstep )
{
if( !mask )
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 )
{
size_t idx = tab[*p0] + tab[*p1 + 256] + tab[*p2 + 512];
if( idx < OUT_OF_RANGE )
++*(int*)(H + idx);
}
else
for( x = 0; x < imsize.width; x++, p0 += d0, p1 += d1, p2 += d2 )
{
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;
int i = 0;
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];
}
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(int 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);
std::vector<uchar*> ptrs;
std::vector<int> deltas;
std::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_( std::vector<uchar*>& _ptrs, const std::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( std::vector<uchar*>& _ptrs, const std::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 x;
const uchar* mask = _ptrs[dims];
int mstep = _deltas[dims*2 + 1];
int idx[CV_MAX_DIM];
std::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(int 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);
}
}
std::vector<uchar*> ptrs;
std::vector<int> deltas;
std::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 std::vector<int>& channels,
InputArray mask, OutputArray hist,
const std::vector<int>& histSize,
const std::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_( std::vector<uchar*>& _ptrs, const std::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( std::vector<uchar*>& _ptrs, const std::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];
std::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();
std::vector<uchar*> ptrs;
std::vector<int> deltas;
std::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_( std::vector<uchar*>& _ptrs, const std::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( std::vector<uchar*>& _ptrs, const std::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];
std::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 )
{
std::vector<uchar*> ptrs;
std::vector<int> deltas;
std::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 std::vector<int>& channels,
InputArray hist, OutputArray dst,
const std::vector<float>& ranges,
double scale )
{
Mat H0 = hist.getMat(), H;
int hcn = H0.channels();
if( hcn > 1 )
{
CV_Assert( H0.isContinuous() );
int hsz[CV_CN_MAX+1];
memcpy(hsz, &H0.size[0], H0.dims*sizeof(hsz[0]));
hsz[H0.dims] = hcn;
H = Mat(H0.dims+1, hsz, H0.depth(), H0.data);
}
else
H = H0;
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.depth() == 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*H1.channels();
if( (method == CV_COMP_CHISQR) || (method == CV_COMP_CHISQR_ALT))
{
for( j = 0; j < len; j++ )
{
double a = h1[j] - h2[j];
double b = (method == CV_COMP_CHISQR) ? h1[j] : h1[j] + h2[j];
if( fabs(b) > DBL_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_CHISQR_ALT )
result *= 2;
else 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() && method != CV_COMP_CHISQR && method != CV_COMP_CHISQR_ALT)
std::swap(PH1, PH2);
SparseMatConstIterator it = PH1->begin();
int N1 = (int)PH1->nzcount(), N2 = (int)PH2->nzcount();
if( (method == CV_COMP_CHISQR) || (method == CV_COMP_CHISQR_ALT) )
{
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);
double a = v1 - v2;
double b = (method == CV_COMP_CHISQR) ? v1 : v1 + v2;
if( fabs(b) > DBL_EPSILON )
result += a*a/b;
}
}
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" );
if( method == CV_COMP_CHISQR_ALT )
result *= 2;
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 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;
for(int 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(int 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 = cv::cvarrToMat(hist1->bins);
cv::Mat H2 = cv::cvarrToMat(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 && method != CV_COMP_CHISQR && method != CV_COMP_CHISQR_ALT)
{
CvSparseMat* t;
CV_SWAP( mat1, mat2, t );
}
if( (method == CV_COMP_CHISQR) || (method == CV_COMP_CHISQR_ALT) )
{
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 );
double v2 = node2_data ? *(float*)node2_data : 0.f;
double a = v1 - v2;
double b = (method == CV_COMP_CHISQR) ? v1 : v1 + v2;
if( fabs(b) > DBL_EPSILON )
result += a*a/b;
}
}
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" );
if( method == CV_COMP_CHISQR_ALT )
result *= 2;
return result;
}
// copies one histogram to another
CV_IMPL void
cvCopyHist( const CvHistogram* src, CvHistogram** _dst )
{
if( !_dst )
CV_Error( CV_StsNullPtr, "Destination double pointer is NULL" );
CvHistogram* dst = *_dst;
if( !CV_IS_HIST(src) || (dst && !CV_IS_HIST(dst)) )
CV_Error( CV_StsBadArg, "Invalid histogram header[s]" );
bool eq = false;
int size1[CV_MAX_DIM];
bool is_sparse = CV_IS_SPARSE_MAT(src->bins);
int dims1 = cvGetDims( src->bins, size1 );
if( dst && (is_sparse == CV_IS_SPARSE_MAT(dst->bins)))
{
int size2[CV_MAX_DIM];
int dims2 = cvGetDims( dst->bins, size2 );
if( dims1 == dims2 )
{
int i;
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 ))
{
float* ranges[CV_MAX_DIM];
float** thresh = 0;
if( CV_IS_UNIFORM_HIST( src ))
{
for( int 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);
std::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 = cv::cvarrToMat(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->copyToSparseMat(sH);
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;
}
}
std::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 = cv::cvarrToMat(hist->bins);
cv::calcBackProject( &images[0], (int)images.size(),
0, H, _dst, ranges, 1, uniform );
}
else
{
cv::SparseMat sH;
((const CvSparseMat*)hist->bins)->copyToSparseMat(sH);
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 ));
}
}
class EqualizeHistCalcHist_Invoker : public cv::ParallelLoopBody
{
public:
enum {HIST_SZ = 256};
EqualizeHistCalcHist_Invoker(cv::Mat& src, int* histogram, cv::Mutex* histogramLock)
: src_(src), globalHistogram_(histogram), histogramLock_(histogramLock)
{ }
void operator()( const cv::Range& rowRange ) const
{
int localHistogram[HIST_SZ] = {0, };
const size_t sstep = src_.step;
int width = src_.cols;
int height = rowRange.end - rowRange.start;
if (src_.isContinuous())
{
width *= height;
height = 1;
}
for (const uchar* ptr = src_.ptr<uchar>(rowRange.start); height--; ptr += sstep)
{
int x = 0;
for (; x <= width - 4; x += 4)
{
int t0 = ptr[x], t1 = ptr[x+1];
localHistogram[t0]++; localHistogram[t1]++;
t0 = ptr[x+2]; t1 = ptr[x+3];
localHistogram[t0]++; localHistogram[t1]++;
}
for (; x < width; ++x)
localHistogram[ptr[x]]++;
}
cv::AutoLock lock(*histogramLock_);
for( int i = 0; i < HIST_SZ; i++ )
globalHistogram_[i] += localHistogram[i];
}
static bool isWorthParallel( const cv::Mat& src )
{
return ( src.total() >= 640*480 );
}
private:
EqualizeHistCalcHist_Invoker& operator=(const EqualizeHistCalcHist_Invoker&);
cv::Mat& src_;
int* globalHistogram_;
cv::Mutex* histogramLock_;
};
class EqualizeHistLut_Invoker : public cv::ParallelLoopBody
{
public:
EqualizeHistLut_Invoker( cv::Mat& src, cv::Mat& dst, int* lut )
: src_(src),
dst_(dst),
lut_(lut)
{ }
void operator()( const cv::Range& rowRange ) const
{
const size_t sstep = src_.step;
const size_t dstep = dst_.step;
int width = src_.cols;
int height = rowRange.end - rowRange.start;
int* lut = lut_;
if (src_.isContinuous() && dst_.isContinuous())
{
width *= height;
height = 1;
}
const uchar* sptr = src_.ptr<uchar>(rowRange.start);
uchar* dptr = dst_.ptr<uchar>(rowRange.start);
for (; height--; sptr += sstep, dptr += dstep)
{
int x = 0;
for (; x <= width - 4; x += 4)
{
int v0 = sptr[x];
int v1 = sptr[x+1];
int x0 = lut[v0];
int x1 = lut[v1];
dptr[x] = (uchar)x0;
dptr[x+1] = (uchar)x1;
v0 = sptr[x+2];
v1 = sptr[x+3];
x0 = lut[v0];
x1 = lut[v1];
dptr[x+2] = (uchar)x0;
dptr[x+3] = (uchar)x1;
}
for (; x < width; ++x)
dptr[x] = (uchar)lut[sptr[x]];
}
}
static bool isWorthParallel( const cv::Mat& src )
{
return ( src.total() >= 640*480 );
}
private:
EqualizeHistLut_Invoker& operator=(const EqualizeHistLut_Invoker&);
cv::Mat& src_;
cv::Mat& dst_;
int* lut_;
};
CV_IMPL void cvEqualizeHist( const CvArr* srcarr, CvArr* dstarr )
{
cv::equalizeHist(cv::cvarrToMat(srcarr), cv::cvarrToMat(dstarr));
}
void cv::equalizeHist( InputArray _src, OutputArray _dst )
{
Mat src = _src.getMat();
CV_Assert( src.type() == CV_8UC1 );
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
if(src.empty())
return;
Mutex histogramLockInstance;
const int hist_sz = EqualizeHistCalcHist_Invoker::HIST_SZ;
int hist[hist_sz] = {0,};
int lut[hist_sz];
EqualizeHistCalcHist_Invoker calcBody(src, hist, &histogramLockInstance);
EqualizeHistLut_Invoker lutBody(src, dst, lut);
cv::Range heightRange(0, src.rows);
if(EqualizeHistCalcHist_Invoker::isWorthParallel(src))
parallel_for_(heightRange, calcBody);
else
calcBody(heightRange);
int i = 0;
while (!hist[i]) ++i;
int total = (int)src.total();
if (hist[i] == total)
{
dst.setTo(i);
return;
}
float scale = (hist_sz - 1.f)/(total - hist[i]);
int sum = 0;
for (lut[i++] = 0; i < hist_sz; ++i)
{
sum += hist[i];
lut[i] = saturate_cast<uchar>(sum * scale);
}
if(EqualizeHistLut_Invoker::isWorthParallel(src))
parallel_for_(heightRange, lutBody);
else
lutBody(heightRange);
}
// ----------------------------------------------------------------------
/* 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. */
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