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opencv/modules/imgproc/src/histogram.cpp

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"atomic bomb" commit. Reorganized OpenCV directory structure
2010-05-12 01:44:00 +08:00
/*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 MatND& hist, const SparseMat& shist,
const float** ranges,
const double* uniranges,
bool uniform, bool issparse, vector<size_t>& _tab )
{
const int low = 0, high = 256;
int i, j, dims = !issparse ? hist.dims : shist.dims();
_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,
vector<uchar*>& ptrs, vector<int>& deltas,
Size& imsize, vector<double>& uniranges )
{
int i, j, c;
if(!channels)
CV_Assert( 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());
}
if( isContinuous )
{
imsize.width *= imsize.height;
imsize.height = 1;
}
if( !ranges )
{
CV_Assert( depth == CV_8U );
uniranges.resize( dims*2 );
for( i = 0; i < dims; i++ )
{
uniranges[i*2] = 1;
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 j, n = histSize[i];
for( j = 0; j < n; j++ )
CV_Assert( ranges[i][j] < ranges[i][j+1] );
}
}
}
////////////////////////////////// C A L C U L A T E H I S T O G R A M ////////////////////////////////////
template<typename T> static void
calcHist_( vector<uchar*>& _ptrs, const vector<int>& _deltas,
Size imsize, MatND& hist, 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, dims = hist.dims;
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 )
{
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]++;
}
}
}
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, 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]++;
}
}
}
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, 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( vector<uchar*>& _ptrs, const vector<int>& _deltas,
Size imsize, MatND& hist, const float** _ranges,
const double* _uniranges, bool uniform )
{
uchar** ptrs = &_ptrs[0];
const int* deltas = &_deltas[0];
uchar* H = hist.data;
int i, x, dims = hist.dims;
const uchar* mask = _ptrs[dims];
int mstep = _deltas[dims*2 + 1];
vector<size_t> _tab;
calcHistLookupTables_8u( hist, SparseMat(), _ranges, _uniranges, uniform, false, _tab );
const size_t* tab = &_tab[0];
if( dims == 1 )
{
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( i = 0; i < 256; i++ )
{
size_t hidx = tab[i];
if( hidx < OUT_OF_RANGE )
*(int*)(H + hidx) += matH[i];
}
}
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, 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 )
{
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;
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 calcHist( const Mat* images, int nimages, const int* channels,
const Mat& mask, MatND& hist, int dims, const int* histSize,
const float** ranges, bool uniform, bool accumulate )
{
CV_Assert(dims > 0 && histSize);
hist.create(dims, histSize, CV_32F);
MatND ihist = hist;
ihist.flags = (ihist.flags & ~CV_MAT_TYPE_MASK)|CV_32S;
if( !accumulate )
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, hist.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, ranges, _uniranges, uniform );
else if( depth == CV_32F )
calcHist_<float>(ptrs, deltas, imsize, ihist, ranges, _uniranges, uniform );
ihist.convertTo(hist, CV_32F);
}
template<typename T> static void
calcSparseHist_( vector<uchar*>& _ptrs, const vector<int>& _deltas,
Size imsize, SparseMat& hist, const float** _ranges,
const double* _uniranges, bool uniform )
{
T** ptrs = (T**)&_ptrs[0];
const int* deltas = &_deltas[0];
int i, x, dims = hist.dims();
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, const float** _ranges,
const double* _uniranges, bool uniform )
{
uchar** ptrs = (uchar**)&_ptrs[0];
const int* deltas = &_deltas[0];
int i, x, dims = hist.dims();
const uchar* mask = _ptrs[dims];
int mstep = _deltas[dims*2 + 1];
int idx[CV_MAX_DIM];
vector<size_t> _tab;
calcHistLookupTables_8u( MatND(), hist, _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 )
{
SparseMatIterator it;
size_t i, N;
if( !accumulate )
hist.create(dims, histSize, CV_32F);
else
for( i = 0, N = hist.nzcount(); i < N; i++, ++it )
{
int* value = (int*)it.ptr;
*value = cvRound(*(const float*)value);
}
vector<uchar*> ptrs;
vector<int> deltas;
vector<double> uniranges;
Size imsize;
CV_Assert( !mask.data || mask.type() == CV_8UC1 );
histPrepareImages( images, nimages, channels, mask, hist.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, ranges, _uniranges, uniform );
else if( depth == CV_32F )
calcSparseHist_<float>(ptrs, deltas, imsize, hist, ranges, _uniranges, uniform );
if( !keepInt )
for( i = 0, N = hist.nzcount(); i < N; i++, ++it )
{
int* value = (int*)it.ptr;
*(float*)value = (float)*value;
}
}
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 )
{
calcHist( images, nimages, channels, mask, hist, dims, histSize,
ranges, uniform, accumulate, false );
}
/////////////////////////////////////// B A C K P R O J E C T ////////////////////////////////////
template<typename T, typename BT> static void
calcBackProj_( vector<uchar*>& _ptrs, const vector<int>& _deltas,
Size imsize, const MatND& hist, 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, dims = hist.dims;
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] )
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 MatND& hist, 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, dims = hist.dims;
uchar* bproj = _ptrs[dims];
int bpstep = _deltas[dims*2 + 1];
vector<size_t> _tab;
calcHistLookupTables_8u( hist, SparseMat(), _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 calcBackProject( const Mat* images, int nimages, const int* channels,
const MatND& hist, Mat& backProject,
const float** ranges, double scale, bool uniform )
{
vector<uchar*> ptrs;
vector<int> deltas;
vector<double> uniranges;
Size imsize;
CV_Assert( hist.dims > 0 && hist.data );
backProject.create( images[0].size(), images[0].depth() );
histPrepareImages( images, nimages, channels, backProject, hist.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, ranges, _uniranges, (float)scale, uniform);
else if( depth == CV_32F )
calcBackProj_<float, float>(ptrs, deltas, imsize, hist, ranges, _uniranges, (float)scale, uniform );
}
template<typename T, typename BT> static void
calcSparseBackProj_( vector<uchar*>& _ptrs, const vector<int>& _deltas,
Size imsize, const SparseMat& hist, const float** _ranges,
const double* _uniranges, float scale, bool uniform )
{
T** ptrs = (T**)&_ptrs[0];
const int* deltas = &_deltas[0];
int i, x, dims = hist.dims();
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, const float** _ranges,
const double* _uniranges, float scale, bool uniform )
{
uchar** ptrs = &_ptrs[0];
const int* deltas = &_deltas[0];
int i, x, dims = hist.dims();
uchar* bproj = _ptrs[dims];
int bpstep = _deltas[dims*2 + 1];
vector<size_t> _tab;
int idx[CV_MAX_DIM];
calcHistLookupTables_8u( MatND(), hist, _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 calcBackProject( const Mat* images, int nimages, const int* channels,
const SparseMat& hist, Mat& backProject,
const float** ranges, double scale, bool uniform )
{
vector<uchar*> ptrs;
vector<int> deltas;
vector<double> uniranges;
Size imsize;
CV_Assert( hist.dims() > 0 );
backProject.create( images[0].size(), images[0].depth() );
histPrepareImages( images, nimages, channels, backProject,
hist.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, ranges, _uniranges, (float)scale, uniform);
else if( depth == CV_32F )
calcSparseBackProj_<float, float>(ptrs, deltas, imsize, hist, ranges, _uniranges, (float)scale, uniform );
}
////////////////// C O M P A R E H I S T O G R A M S ////////////////////////
double compareHist( const MatND& H1, const MatND& H2, int method )
{
NAryMatNDIterator it(H1, H2);
double result = 0;
int i, len;
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( 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( i = 0; i < len; i++ )
{
double a = h1[i] - h2[i];
double b = h1[i] + h2[i];
if( fabs(b) > FLT_EPSILON )
result += a*a/b;
}
}
else if( method == CV_COMP_CORREL )
{
for( i = 0; i < len; i++ )
{
double a = h1[i];
double b = h2[i];
s12 += a*b;
s1 += a;
s11 += a*a;
s2 += b;
s22 += b*b;
}
}
else if( method == CV_COMP_INTERSECT )
{
for( i = 0; i < len; i++ )
result += std::min(h1[i], h2[i]);
}
else if( method == CV_COMP_BHATTACHARYYA )
{
for( i = 0; i < len; i++ )
{
double a = h1[i];
double b = h2[i];
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 = 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_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 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;
}
template<> void Ptr<CvHistogram>::delete_obj()
{ cvReleaseHist(&obj); }
}
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::MatND 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::MatND H((const CvMatND*)hist->bins);
cv::calcHist( &images[0], (int)images.size(), 0, _mask,
H, H.dims, 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::MatND 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( const Mat& src, Mat& dst )
{
dst.create( src.size(), src.type() );
CvMat _src = src, _dst = dst;
cvEqualizeHist( &_src, &_dst );
}
/* 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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