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opencv/modules/imgproc/src/histogram.cpp

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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"
#include "opencl_kernels_imgproc.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include "opencv2/core/openvx/ovx_defs.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 if (ranges)
{
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;
}
}
}
}
else
{
CV_Error(Error::StsBadArg, "Either ranges, either uniform ranges should be provided");
}
}
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.empty() )
{
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] = 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 ////////////////////////////////////
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.ptr();
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 )
{
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]++;
}
}
return;
}
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]++;
}
}
return;
}
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 if (_ranges)
{
// 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];
}
}
else
{
CV_Error(Error::StsBadArg, "Either ranges, either uniform ranges should be provided");
}
}
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.ptr();
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 )
{
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 )
{
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;
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];
}
}
}
#ifdef HAVE_IPP
typedef IppStatus(CV_STDCALL * IppiHistogram_C1)(const void* pSrc, int srcStep,
IppiSize roiSize, Ipp32u* pHist, const IppiHistogramSpec* pSpec, Ipp8u* pBuffer);
static IppiHistogram_C1 getIppiHistogramFunction_C1(int type)
{
IppiHistogram_C1 ippFunction =
(type == CV_8UC1) ? (IppiHistogram_C1)ippiHistogram_8u_C1R :
(type == CV_16UC1) ? (IppiHistogram_C1)ippiHistogram_16u_C1R :
(type == CV_32FC1) ? (IppiHistogram_C1)ippiHistogram_32f_C1R :
NULL;
return ippFunction;
}
class ipp_calcHistParallelTLS
{
public:
ipp_calcHistParallelTLS() {}
IppAutoBuffer<IppiHistogramSpec> spec;
IppAutoBuffer<Ipp8u> buffer;
IppAutoBuffer<Ipp32u> thist;
};
class ipp_calcHistParallel: public ParallelLoopBody
{
public:
ipp_calcHistParallel(const Mat &src, Mat &hist, Ipp32s histSize, const float *ranges, bool uniform, bool &ok):
ParallelLoopBody(), m_src(src), m_hist(hist), m_ok(ok)
{
ok = true;
m_uniform = uniform;
m_ranges = ranges;
m_histSize = histSize;
m_type = ippiGetDataType(src.type());
m_levelsNum = histSize+1;
ippiHistogram_C1 = getIppiHistogramFunction_C1(src.type());
m_fullRoi = ippiSize(src.size());
m_bufferSize = 0;
m_specSize = 0;
if(!ippiHistogram_C1)
{
ok = false;
return;
}
if(ippiHistogramGetBufferSize(m_type, m_fullRoi, &m_levelsNum, 1, 1, &m_specSize, &m_bufferSize) < 0)
{
ok = false;
return;
}
hist.setTo(0);
}
virtual void operator() (const Range & range) const CV_OVERRIDE
{
CV_INSTRUMENT_REGION_IPP();
if(!m_ok)
return;
ipp_calcHistParallelTLS *pTls = m_tls.get();
IppiSize roi = {m_src.cols, range.end - range.start };
bool mtLoop = false;
if(m_fullRoi.height != roi.height)
mtLoop = true;
if(!pTls->spec)
{
pTls->spec.allocate(m_specSize);
if(!pTls->spec.get())
{
m_ok = false;
return;
}
pTls->buffer.allocate(m_bufferSize);
if(!pTls->buffer.get() && m_bufferSize)
{
m_ok = false;
return;
}
if(m_uniform)
{
if(ippiHistogramUniformInit(m_type, (Ipp32f*)&m_ranges[0], (Ipp32f*)&m_ranges[1], (Ipp32s*)&m_levelsNum, 1, pTls->spec) < 0)
{
m_ok = false;
return;
}
}
else
{
if(ippiHistogramInit(m_type, (const Ipp32f**)&m_ranges, (Ipp32s*)&m_levelsNum, 1, pTls->spec) < 0)
{
m_ok = false;
return;
}
}
pTls->thist.allocate(m_histSize*sizeof(Ipp32u));
}
if(CV_INSTRUMENT_FUN_IPP(ippiHistogram_C1, m_src.ptr(range.start), (int)m_src.step, roi, pTls->thist, pTls->spec, pTls->buffer) < 0)
{
m_ok = false;
return;
}
if(mtLoop)
{
for(int i = 0; i < m_histSize; i++)
CV_XADD((int*)(m_hist.ptr(i)), *(int*)((Ipp32u*)pTls->thist + i));
}
else
ippiCopy_32s_C1R((Ipp32s*)pTls->thist.get(), sizeof(Ipp32u), (Ipp32s*)m_hist.ptr(), (int)m_hist.step, ippiSize(1, m_histSize));
}
private:
const Mat &m_src;
Mat &m_hist;
Ipp32s m_histSize;
const float *m_ranges;
bool m_uniform;
IppiHistogram_C1 ippiHistogram_C1;
IppiSize m_fullRoi;
IppDataType m_type;
Ipp32s m_levelsNum;
int m_bufferSize;
int m_specSize;
mutable Mutex m_syncMutex;
TLSData<ipp_calcHistParallelTLS> m_tls;
volatile bool &m_ok;
const ipp_calcHistParallel & operator = (const ipp_calcHistParallel & );
};
#endif
}
#ifdef HAVE_OPENVX
namespace cv
{
namespace ovx {
template <> inline bool skipSmallImages<VX_KERNEL_HISTOGRAM>(int w, int h) { return w*h < 2048 * 1536; }
}
static bool openvx_calchist(const Mat& image, OutputArray _hist, const int histSize,
const float* _range)
{
vx_int32 offset = (vx_int32)(_range[0]);
vx_uint32 range = (vx_uint32)(_range[1] - _range[0]);
if (float(offset) != _range[0] || float(range) != (_range[1] - _range[0]))
return false;
size_t total_size = image.total();
int rows = image.dims > 1 ? image.size[0] : 1, cols = rows ? (int)(total_size / rows) : 0;
if (image.dims > 2 && !(image.isContinuous() && cols > 0 && (size_t)rows*cols == total_size))
return false;
try
{
ivx::Context ctx = ovx::getOpenVXContext();
#if VX_VERSION <= VX_VERSION_1_0
if (ctx.vendorID() == VX_ID_KHRONOS && (range % histSize))
return false;
#endif
ivx::Image
img = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(cols, rows, 1, (vx_int32)(image.step[0])), image.data);
ivx::Distribution vxHist = ivx::Distribution::create(ctx, histSize, offset, range);
ivx::IVX_CHECK_STATUS(vxuHistogram(ctx, img, vxHist));
_hist.create(1, &histSize, CV_32F);
Mat hist = _hist.getMat(), ihist = hist;
ihist.flags = (ihist.flags & ~CV_MAT_TYPE_MASK) | CV_32S;
vxHist.copyTo(ihist);
ihist.convertTo(hist, CV_32F);
#ifdef VX_VERSION_1_1
img.swapHandle();
#endif
}
catch (const ivx::RuntimeError & e)
{
VX_DbgThrow(e.what());
}
catch (const ivx::WrapperError & e)
{
VX_DbgThrow(e.what());
}
return true;
}
}
#endif
#ifdef HAVE_IPP
#define IPP_HISTOGRAM_PARALLEL 1
namespace cv
{
static bool ipp_calchist(const Mat &image, Mat &hist, int histSize, const float** ranges, bool uniform, bool accumulate)
{
CV_INSTRUMENT_REGION_IPP();
#if IPP_VERSION_X100 < 201801
// No SSE42 optimization for uniform 32f
if(uniform && image.depth() == CV_32F && cv::ipp::getIppTopFeatures() == ippCPUID_SSE42)
return false;
#endif
// IPP_DISABLE_HISTOGRAM - https://github.com/opencv/opencv/issues/11544
if (uniform && (ranges[0][1] - ranges[0][0]) != histSize)
return false;
Mat ihist = hist;
if(accumulate)
ihist.create(1, &histSize, CV_32S);
bool ok = true;
int threads = ippiSuggestThreadsNum(image, (1+((double)ihist.total()/image.total()))*2);
Range range(0, image.rows);
ipp_calcHistParallel invoker(image, ihist, histSize, ranges[0], uniform, ok);
if(!ok)
return false;
if(IPP_HISTOGRAM_PARALLEL && threads > 1)
parallel_for_(range, invoker, threads*2);
else
invoker(range);
if(ok)
{
if(accumulate)
{
IppiSize histRoi = ippiSize(1, histSize);
IppAutoBuffer<Ipp32f> fhist(histSize*sizeof(Ipp32f));
CV_INSTRUMENT_FUN_IPP(ippiConvert_32s32f_C1R, (Ipp32s*)ihist.ptr(), (int)ihist.step, (Ipp32f*)fhist, sizeof(Ipp32f), histRoi);
CV_INSTRUMENT_FUN_IPP(ippiAdd_32f_C1IR, (Ipp32f*)fhist, sizeof(Ipp32f), (Ipp32f*)hist.ptr(), (int)hist.step, histRoi);
}
else
CV_INSTRUMENT_FUN_IPP(ippiConvert_32s32f_C1R, (Ipp32s*)ihist.ptr(), (int)ihist.step, (Ipp32f*)hist.ptr(), (int)hist.step, ippiSize(1, histSize));
}
return ok;
}
}
#endif
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 )
{
CV_INSTRUMENT_REGION();
CV_OVX_RUN(
images && histSize &&
nimages == 1 && images[0].type() == CV_8UC1 && dims == 1 && _mask.getMat().empty() &&
(!channels || channels[0] == 0) && !accumulate && uniform &&
ranges && ranges[0] &&
!ovx::skipSmallImages<VX_KERNEL_HISTOGRAM>(images[0].cols, images[0].rows),
openvx_calchist(images[0], _hist, histSize[0], ranges[0]))
Mat mask = _mask.getMat();
CV_Assert(dims > 0 && histSize);
const uchar* const histdata = _hist.getMat().ptr();
_hist.create(dims, histSize, CV_32F);
Mat hist = _hist.getMat();
if(histdata != hist.data)
accumulate = false;
CV_IPP_RUN(
nimages == 1 && dims == 1 && channels && channels[0] == 0
&& _mask.empty() && images[0].dims <= 2 && ranges && ranges[0],
ipp_calchist(images[0], hist, histSize[0], ranges, uniform, accumulate));
Mat ihist = hist;
ihist.flags = (ihist.flags & ~CV_MAT_TYPE_MASK)|CV_32S;
if(!accumulate)
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.empty() || 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 if (_ranges)
{
// 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];
}
}
else
{
CV_Error(Error::StsBadArg, "Either ranges, either uniform ranges should be provided");
}
}
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 )
{
CV_Assert(it.ptr != NULL);
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.empty() || 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 )
{
CV_Assert(it.ptr != NULL);
Cv32suf* val = (Cv32suf*)it.ptr;
val->f = (float)val->i;
}
}
}
#ifdef HAVE_OPENCL
enum
{
BINS = 256
};
static bool ocl_calcHist1(InputArray _src, OutputArray _hist, int ddepth = CV_32S)
{
const ocl::Device & dev = ocl::Device::getDefault();
int compunits = dev.maxComputeUnits();
size_t wgs = dev.maxWorkGroupSize();
Size size = _src.size();
bool use16 = size.width % 16 == 0 && _src.offset() % 16 == 0 && _src.step() % 16 == 0;
int kercn = dev.isAMD() && use16 ? 16 : std::min(4, ocl::predictOptimalVectorWidth(_src));
ocl::Kernel k1("calculate_histogram", ocl::imgproc::histogram_oclsrc,
format("-D BINS=%d -D HISTS_COUNT=%d -D WGS=%d -D kercn=%d -D T=%s%s",
BINS, compunits, wgs, kercn,
kercn == 4 ? "int" : ocl::typeToStr(CV_8UC(kercn)),
_src.isContinuous() ? " -D HAVE_SRC_CONT" : ""));
if (k1.empty())
return false;
_hist.create(BINS, 1, ddepth);
UMat src = _src.getUMat(), ghist(1, BINS * compunits, CV_32SC1),
hist = _hist.getUMat();
k1.args(ocl::KernelArg::ReadOnly(src),
ocl::KernelArg::PtrWriteOnly(ghist), (int)src.total());
size_t globalsize = compunits * wgs;
if (!k1.run(1, &globalsize, &wgs, false))
return false;
wgs = std::min<size_t>(ocl::Device::getDefault().maxWorkGroupSize(), BINS);
char cvt[40];
ocl::Kernel k2("merge_histogram", ocl::imgproc::histogram_oclsrc,
format("-D BINS=%d -D HISTS_COUNT=%d -D WGS=%d -D convertToHT=%s -D HT=%s",
BINS, compunits, (int)wgs, ocl::convertTypeStr(CV_32S, ddepth, 1, cvt),
ocl::typeToStr(ddepth)));
if (k2.empty())
return false;
k2.args(ocl::KernelArg::PtrReadOnly(ghist),
ocl::KernelArg::WriteOnlyNoSize(hist));
return k2.run(1, &wgs, &wgs, false);
}
static bool ocl_calcHist(InputArrayOfArrays images, OutputArray hist)
{
std::vector<UMat> v;
images.getUMatVector(v);
return ocl_calcHist1(v[0], hist, CV_32F);
}
#endif
}
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 )
{
CV_INSTRUMENT_REGION();
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 )
{
CV_INSTRUMENT_REGION();
CV_OCL_RUN(images.total() == 1 && channels.size() == 1 && images.channels(0) == 1 &&
channels[0] == 0 && images.isUMatVector() && mask.empty() && !accumulate &&
histSize.size() == 1 && histSize[0] == BINS && ranges.size() == 2 &&
ranges[0] == 0 && ranges[1] == BINS,
ocl_calcHist(images, hist))
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];
const uchar* H = hist.ptr();
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>(((const 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>(((const 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>(((const float*)(H + hstep0*idx0 + hstep1*idx1))[idx2]*scale) : 0;
}
}
}
else
{
for( ; imsize.height--; bproj += bpstep )
{
for( x = 0; x < imsize.width; x++ )
{
const 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>(*(const 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 if (_ranges)
{
// 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++ )
{
const 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>(*(const 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
{
CV_Error(Error::StsBadArg, "Either ranges, either uniform ranges should be provided");
}
}
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];
const uchar* H = hist.ptr();
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>(*(const 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>(*(const float*)(H + idx)*scale) : 0;
}
}
}
else
{
for( ; imsize.height--; bproj += bpstep )
{
for( x = 0; x < imsize.width; x++ )
{
const 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>(*(const 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 )
{
CV_INSTRUMENT_REGION();
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.empty() );
_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 if (_ranges)
{
// 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];
}
}
else
{
CV_Error(Error::StsBadArg, "Either ranges, either uniform ranges should be provided");
}
}
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 )
{
CV_INSTRUMENT_REGION();
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, "");
}
#ifdef HAVE_OPENCL
namespace cv {
static void getUMatIndex(const std::vector<UMat> & um, int cn, int & idx, int & cnidx)
{
int totalChannels = 0;
for (size_t i = 0, size = um.size(); i < size; ++i)
{
int ccn = um[i].channels();
totalChannels += ccn;
if (totalChannels == cn)
{
idx = (int)(i + 1);
cnidx = 0;
return;
}
else if (totalChannels > cn)
{
idx = (int)i;
cnidx = i == 0 ? cn : (cn - totalChannels + ccn);
return;
}
}
idx = cnidx = -1;
}
static bool ocl_calcBackProject( InputArrayOfArrays _images, std::vector<int> channels,
InputArray _hist, OutputArray _dst,
const std::vector<float>& ranges,
float scale, size_t histdims )
{
std::vector<UMat> images;
_images.getUMatVector(images);
size_t nimages = images.size(), totalcn = images[0].channels();
CV_Assert(nimages > 0);
Size size = images[0].size();
int depth = images[0].depth();
//kernels are valid for this type only
if (depth != CV_8U)
return false;
for (size_t i = 1; i < nimages; ++i)
{
const UMat & m = images[i];
totalcn += m.channels();
CV_Assert(size == m.size() && depth == m.depth());
}
std::sort(channels.begin(), channels.end());
for (size_t i = 0; i < histdims; ++i)
CV_Assert(channels[i] < (int)totalcn);
if (histdims == 1)
{
int idx, cnidx;
getUMatIndex(images, channels[0], idx, cnidx);
CV_Assert(idx >= 0);
UMat im = images[idx];
String opts = format("-D histdims=1 -D scn=%d", im.channels());
ocl::Kernel lutk("calcLUT", ocl::imgproc::calc_back_project_oclsrc, opts);
if (lutk.empty())
return false;
size_t lsize = 256;
UMat lut(1, (int)lsize, CV_32SC1), hist = _hist.getUMat(), uranges(ranges, true);
lutk.args(ocl::KernelArg::ReadOnlyNoSize(hist), hist.rows,
ocl::KernelArg::PtrWriteOnly(lut), scale, ocl::KernelArg::PtrReadOnly(uranges));
if (!lutk.run(1, &lsize, NULL, false))
return false;
ocl::Kernel mapk("LUT", ocl::imgproc::calc_back_project_oclsrc, opts);
if (mapk.empty())
return false;
_dst.create(size, depth);
UMat dst = _dst.getUMat();
im.offset += cnidx;
mapk.args(ocl::KernelArg::ReadOnlyNoSize(im), ocl::KernelArg::PtrReadOnly(lut),
ocl::KernelArg::WriteOnly(dst));
size_t globalsize[2] = { (size_t)size.width, (size_t)size.height };
return mapk.run(2, globalsize, NULL, false);
}
else if (histdims == 2)
{
int idx0, idx1, cnidx0, cnidx1;
getUMatIndex(images, channels[0], idx0, cnidx0);
getUMatIndex(images, channels[1], idx1, cnidx1);
CV_Assert(idx0 >= 0 && idx1 >= 0);
UMat im0 = images[idx0], im1 = images[idx1];
// Lut for the first dimension
String opts = format("-D histdims=2 -D scn1=%d -D scn2=%d", im0.channels(), im1.channels());
ocl::Kernel lutk1("calcLUT", ocl::imgproc::calc_back_project_oclsrc, opts);
if (lutk1.empty())
return false;
size_t lsize = 256;
UMat lut(1, (int)lsize<<1, CV_32SC1), uranges(ranges, true), hist = _hist.getUMat();
lutk1.args(hist.rows, ocl::KernelArg::PtrWriteOnly(lut), (int)0, ocl::KernelArg::PtrReadOnly(uranges), (int)0);
if (!lutk1.run(1, &lsize, NULL, false))
return false;
// lut for the second dimension
ocl::Kernel lutk2("calcLUT", ocl::imgproc::calc_back_project_oclsrc, opts);
if (lutk2.empty())
return false;
lut.offset += lsize * sizeof(int);
lutk2.args(hist.cols, ocl::KernelArg::PtrWriteOnly(lut), (int)256, ocl::KernelArg::PtrReadOnly(uranges), (int)2);
if (!lutk2.run(1, &lsize, NULL, false))
return false;
// perform lut
ocl::Kernel mapk("LUT", ocl::imgproc::calc_back_project_oclsrc, opts);
if (mapk.empty())
return false;
_dst.create(size, depth);
UMat dst = _dst.getUMat();
im0.offset += cnidx0;
im1.offset += cnidx1;
mapk.args(ocl::KernelArg::ReadOnlyNoSize(im0), ocl::KernelArg::ReadOnlyNoSize(im1),
ocl::KernelArg::ReadOnlyNoSize(hist), ocl::KernelArg::PtrReadOnly(lut), scale, ocl::KernelArg::WriteOnly(dst));
size_t globalsize[2] = { (size_t)size.width, (size_t)size.height };
return mapk.run(2, globalsize, NULL, false);
}
return false;
}
}
#endif
void cv::calcBackProject( InputArrayOfArrays images, const std::vector<int>& channels,
InputArray hist, OutputArray dst,
const std::vector<float>& ranges,
double scale )
{
CV_INSTRUMENT_REGION();
if (hist.dims() <= 2)
{
#ifdef HAVE_OPENCL
Size histSize = hist.size();
bool _1D = histSize.height == 1 || histSize.width == 1;
size_t histdims = _1D ? 1 : hist.dims();
#endif
CV_OCL_RUN(dst.isUMat() && hist.type() == CV_32FC1 &&
histdims <= 2 && ranges.size() == histdims * 2 && histdims == channels.size(),
ocl_calcBackProject(images, channels, hist, dst, ranges, (float)scale, histdims))
}
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.ptr());
}
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 )
{
CV_INSTRUMENT_REGION();
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;
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 = it.planes[0].ptr<float>();
const float* h2 = it.planes[1].ptr<float>();
const int len = it.planes[0].rows*it.planes[0].cols*H1.channels();
j = 0;
if( (method == CV_COMP_CHISQR) || (method == CV_COMP_CHISQR_ALT))
{
for( ; 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 )
{
#if CV_SIMD_64F
v_float64 v_s1 = vx_setzero_f64();
v_float64 v_s2 = vx_setzero_f64();
v_float64 v_s11 = vx_setzero_f64();
v_float64 v_s12 = vx_setzero_f64();
v_float64 v_s22 = vx_setzero_f64();
for ( ; j <= len - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 v_a = vx_load(h1 + j);
v_float32 v_b = vx_load(h2 + j);
// 0-1
v_float64 v_ad = v_cvt_f64(v_a);
v_float64 v_bd = v_cvt_f64(v_b);
v_s12 = v_muladd(v_ad, v_bd, v_s12);
v_s11 = v_muladd(v_ad, v_ad, v_s11);
v_s22 = v_muladd(v_bd, v_bd, v_s22);
v_s1 += v_ad;
v_s2 += v_bd;
// 2-3
v_ad = v_cvt_f64_high(v_a);
v_bd = v_cvt_f64_high(v_b);
v_s12 = v_muladd(v_ad, v_bd, v_s12);
v_s11 = v_muladd(v_ad, v_ad, v_s11);
v_s22 = v_muladd(v_bd, v_bd, v_s22);
v_s1 += v_ad;
v_s2 += v_bd;
}
s12 += v_reduce_sum(v_s12);
s11 += v_reduce_sum(v_s11);
s22 += v_reduce_sum(v_s22);
s1 += v_reduce_sum(v_s1);
s2 += v_reduce_sum(v_s2);
#elif CV_SIMD && 0 //Disable vectorization for CV_COMP_CORREL if f64 is unsupported due to low precision
v_float32 v_s1 = vx_setzero_f32();
v_float32 v_s2 = vx_setzero_f32();
v_float32 v_s11 = vx_setzero_f32();
v_float32 v_s12 = vx_setzero_f32();
v_float32 v_s22 = vx_setzero_f32();
for (; j <= len - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 v_a = vx_load(h1 + j);
v_float32 v_b = vx_load(h2 + j);
v_s12 = v_muladd(v_a, v_b, v_s12);
v_s11 = v_muladd(v_a, v_a, v_s11);
v_s22 = v_muladd(v_b, v_b, v_s22);
v_s1 += v_a;
v_s2 += v_b;
}
s12 += v_reduce_sum(v_s12);
s11 += v_reduce_sum(v_s11);
s22 += v_reduce_sum(v_s22);
s1 += v_reduce_sum(v_s1);
s2 += v_reduce_sum(v_s2);
#endif
for( ; 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 )
{
#if CV_SIMD_64F
v_float64 v_result = vx_setzero_f64();
for ( ; j <= len - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 v_src = v_min(vx_load(h1 + j), vx_load(h2 + j));
v_result += v_cvt_f64(v_src) + v_cvt_f64_high(v_src);
}
result += v_reduce_sum(v_result);
#elif CV_SIMD
v_float32 v_result = vx_setzero_f32();
for (; j <= len - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 v_src = v_min(vx_load(h1 + j), vx_load(h2 + j));
v_result += v_src;
}
result += v_reduce_sum(v_result);
#endif
for( ; j < len; j++ )
result += std::min(h1[j], h2[j]);
}
else if( method == CV_COMP_BHATTACHARYYA )
{
#if CV_SIMD_64F
v_float64 v_s1 = vx_setzero_f64();
v_float64 v_s2 = vx_setzero_f64();
v_float64 v_result = vx_setzero_f64();
for ( ; j <= len - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 v_a = vx_load(h1 + j);
v_float32 v_b = vx_load(h2 + j);
v_float64 v_ad = v_cvt_f64(v_a);
v_float64 v_bd = v_cvt_f64(v_b);
v_s1 += v_ad;
v_s2 += v_bd;
v_result += v_sqrt(v_ad * v_bd);
v_ad = v_cvt_f64_high(v_a);
v_bd = v_cvt_f64_high(v_b);
v_s1 += v_ad;
v_s2 += v_bd;
v_result += v_sqrt(v_ad * v_bd);
}
s1 += v_reduce_sum(v_s1);
s2 += v_reduce_sum(v_s2);
result += v_reduce_sum(v_result);
#elif CV_SIMD && 0 //Disable vectorization for CV_COMP_BHATTACHARYYA if f64 is unsupported due to low precision
v_float32 v_s1 = vx_setzero_f32();
v_float32 v_s2 = vx_setzero_f32();
v_float32 v_result = vx_setzero_f32();
for (; j <= len - v_float32::nlanes; j += v_float32::nlanes)
{
v_float32 v_a = vx_load(h1 + j);
v_float32 v_b = vx_load(h2 + j);
v_s1 += v_a;
v_s2 += v_b;
v_result += v_sqrt(v_a * v_b);
}
s1 += v_reduce_sum(v_s1);
s2 += v_reduce_sum(v_s2);
result += v_reduce_sum(v_result);
#endif
for( ; j < len; j++ )
{
double a = h1[j];
double b = h2[j];
result += std::sqrt(a*b);
s1 += a;
s2 += b;
}
}
else if( method == CV_COMP_KL_DIV )
{
for( ; j < len; j++ )
{
double p = h1[j];
double q = h2[j];
if( fabs(p) <= DBL_EPSILON ) {
continue;
}
if( fabs(q) <= DBL_EPSILON ) {
q = 1e-10;
}
result += p * std::log( p / q );
}
}
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 )
{
CV_INSTRUMENT_REGION();
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 && method != CV_COMP_KL_DIV )
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 )
{
CV_Assert(it.ptr != NULL);
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 )
{
CV_Assert(it.ptr != NULL);
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 )
{
CV_Assert(it.ptr != NULL);
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 )
{
CV_Assert(it.ptr != NULL);
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 )
{
CV_Assert(it.ptr != NULL);
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 )
{
CV_Assert(it.ptr != NULL);
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 if( method == CV_COMP_KL_DIV )
{
for( i = 0; i < N1; i++, ++it )
{
CV_Assert(it.ptr != NULL);
double v1 = it.value<float>();
const SparseMat::Node* node = it.node();
double v2 = PH2->value<float>(node->idx, (size_t*)&node->hashval);
if( !v2 )
v2 = 1e-10;
result += v1 * std::log( v1 / v2 );
}
}
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 = {0, 0}, maxPt = {0, 0};
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 && method != CV_COMP_KL_DIV )
{
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 if( method == CV_COMP_KL_DIV )
{
cv::SparseMat sH1, sH2;
((const CvSparseMat*)hist1->bins)->copyToSparseMat(sH1);
((const CvSparseMat*)hist2->bins)->copyToSparseMat(sH2);
result = cv::compareHist( sH1, sH2, CV_COMP_KL_DIV );
}
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 )
{
CV_Assert(it.ptr != NULL);
*(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;
cv::Size 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 );
if (dims < 1)
CV_Error( CV_StsOutOfRange, "Invalid number of dimensions");
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 CV_OVERRIDE
{
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 CV_OVERRIDE
{
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));
}
#ifdef HAVE_OPENCL
namespace cv {
static bool ocl_equalizeHist(InputArray _src, OutputArray _dst)
{
const ocl::Device & dev = ocl::Device::getDefault();
int compunits = dev.maxComputeUnits();
size_t wgs = dev.maxWorkGroupSize();
Size size = _src.size();
bool use16 = size.width % 16 == 0 && _src.offset() % 16 == 0 && _src.step() % 16 == 0;
int kercn = dev.isAMD() && use16 ? 16 : std::min(4, ocl::predictOptimalVectorWidth(_src));
ocl::Kernel k1("calculate_histogram", ocl::imgproc::histogram_oclsrc,
format("-D BINS=%d -D HISTS_COUNT=%d -D WGS=%d -D kercn=%d -D T=%s%s",
BINS, compunits, wgs, kercn,
kercn == 4 ? "int" : ocl::typeToStr(CV_8UC(kercn)),
_src.isContinuous() ? " -D HAVE_SRC_CONT" : ""));
if (k1.empty())
return false;
UMat src = _src.getUMat(), ghist(1, BINS * compunits, CV_32SC1);
k1.args(ocl::KernelArg::ReadOnly(src),
ocl::KernelArg::PtrWriteOnly(ghist), (int)src.total());
size_t globalsize = compunits * wgs;
if (!k1.run(1, &globalsize, &wgs, false))
return false;
wgs = std::min<size_t>(ocl::Device::getDefault().maxWorkGroupSize(), BINS);
UMat lut(1, 256, CV_8UC1);
ocl::Kernel k2("calcLUT", ocl::imgproc::histogram_oclsrc,
format("-D BINS=%d -D HISTS_COUNT=%d -D WGS=%d",
BINS, compunits, (int)wgs));
k2.args(ocl::KernelArg::PtrWriteOnly(lut),
ocl::KernelArg::PtrReadOnly(ghist), (int)_src.total());
// calculation of LUT
if (!k2.run(1, &wgs, &wgs, false))
return false;
// execute LUT transparently
LUT(_src, lut, _dst);
return true;
}
}
#endif
#ifdef HAVE_OPENVX
namespace cv
{
static bool openvx_equalize_hist(Mat srcMat, Mat dstMat)
{
using namespace ivx;
try
{
Context context = ovx::getOpenVXContext();
Image srcImage = Image::createFromHandle(context, Image::matTypeToFormat(srcMat.type()),
Image::createAddressing(srcMat), srcMat.data);
Image dstImage = Image::createFromHandle(context, Image::matTypeToFormat(dstMat.type()),
Image::createAddressing(dstMat), dstMat.data);
IVX_CHECK_STATUS(vxuEqualizeHist(context, srcImage, dstImage));
#ifdef VX_VERSION_1_1
//we should take user memory back before release
//(it's not done automatically according to standard)
srcImage.swapHandle(); dstImage.swapHandle();
#endif
}
catch (const RuntimeError & e)
{
VX_DbgThrow(e.what());
}
catch (const WrapperError & e)
{
VX_DbgThrow(e.what());
}
return true;
}
}
#endif
void cv::equalizeHist( InputArray _src, OutputArray _dst )
{
CV_INSTRUMENT_REGION();
CV_Assert( _src.type() == CV_8UC1 );
if (_src.empty())
return;
CV_OCL_RUN(_src.dims() <= 2 && _dst.isUMat(),
ocl_equalizeHist(_src, _dst))
Mat src = _src.getMat();
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
CV_OVX_RUN(!ovx::skipSmallImages<VX_KERNEL_EQUALIZE_HISTOGRAM>(src.cols, src.rows),
openvx_equalize_hist(src, dst))
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. */