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opencv/modules/imgproc/src/thresh.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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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
{
template <typename T>
static inline T threshBinary(const T& src, const T& thresh, const T& maxval)
{
return src > thresh ? maxval : 0;
}
template <typename T>
static inline T threshBinaryInv(const T& src, const T& thresh, const T& maxval)
{
return src <= thresh ? maxval : 0;
}
template <typename T>
static inline T threshTrunc(const T& src, const T& thresh)
{
return std::min(src, thresh);
}
template <typename T>
static inline T threshToZero(const T& src, const T& thresh)
{
return src > thresh ? src : 0;
}
template <typename T>
static inline T threshToZeroInv(const T& src, const T& thresh)
{
return src <= thresh ? src : 0;
}
template <typename T>
static void threshGeneric(Size roi, const T* src, size_t src_step, T* dst,
size_t dst_step, T thresh, T maxval, int type)
{
int i = 0, j;
switch (type)
{
case THRESH_BINARY:
for (; i < roi.height; i++, src += src_step, dst += dst_step)
for (j = 0; j < roi.width; j++)
dst[j] = threshBinary<T>(src[j], thresh, maxval);
return;
case THRESH_BINARY_INV:
for (; i < roi.height; i++, src += src_step, dst += dst_step)
for (j = 0; j < roi.width; j++)
dst[j] = threshBinaryInv<T>(src[j], thresh, maxval);
return;
case THRESH_TRUNC:
for (; i < roi.height; i++, src += src_step, dst += dst_step)
for (j = 0; j < roi.width; j++)
dst[j] = threshTrunc<T>(src[j], thresh);
return;
case THRESH_TOZERO:
for (; i < roi.height; i++, src += src_step, dst += dst_step)
for (j = 0; j < roi.width; j++)
dst[j] = threshToZero<T>(src[j], thresh);
return;
case THRESH_TOZERO_INV:
for (; i < roi.height; i++, src += src_step, dst += dst_step)
for (j = 0; j < roi.width; j++)
dst[j] = threshToZeroInv<T>(src[j], thresh);
return;
default:
CV_Error( CV_StsBadArg, "" ); return;
}
}
static void
thresh_8u( const Mat& _src, Mat& _dst, uchar thresh, uchar maxval, int type )
{
Size roi = _src.size();
roi.width *= _src.channels();
size_t src_step = _src.step;
size_t dst_step = _dst.step;
if( _src.isContinuous() && _dst.isContinuous() )
{
roi.width *= roi.height;
roi.height = 1;
src_step = dst_step = roi.width;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::useTegra() && tegra::thresh_8u(_src, _dst, roi.width, roi.height, thresh, maxval, type))
return;
#endif
#if defined(HAVE_IPP)
CV_IPP_CHECK()
{
IppiSize sz = { roi.width, roi.height };
CV_SUPPRESS_DEPRECATED_START
switch( type )
{
case THRESH_TRUNC:
if (_src.data == _dst.data && CV_INSTRUMENT_FUN_IPP(ippiThreshold_GT_8u_C1IR, _dst.ptr(), (int)dst_step, sz, thresh) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
if (CV_INSTRUMENT_FUN_IPP(ippiThreshold_GT_8u_C1R, _src.ptr(), (int)src_step, _dst.ptr(), (int)dst_step, sz, thresh) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
case THRESH_TOZERO:
if (_src.data == _dst.data && CV_INSTRUMENT_FUN_IPP(ippiThreshold_LTVal_8u_C1IR, _dst.ptr(), (int)dst_step, sz, thresh+1, 0) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
if (CV_INSTRUMENT_FUN_IPP(ippiThreshold_LTVal_8u_C1R, _src.ptr(), (int)src_step, _dst.ptr(), (int)dst_step, sz, thresh + 1, 0) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
case THRESH_TOZERO_INV:
if (_src.data == _dst.data && CV_INSTRUMENT_FUN_IPP(ippiThreshold_GTVal_8u_C1IR, _dst.ptr(), (int)dst_step, sz, thresh, 0) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
if (CV_INSTRUMENT_FUN_IPP(ippiThreshold_GTVal_8u_C1R, _src.ptr(), (int)src_step, _dst.ptr(), (int)dst_step, sz, thresh, 0) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
}
CV_SUPPRESS_DEPRECATED_END
}
#endif
int j = 0;
const uchar* src = _src.ptr();
uchar* dst = _dst.ptr();
#if CV_SIMD128
bool useSIMD = checkHardwareSupport( CV_CPU_SSE2 ) || checkHardwareSupport( CV_CPU_NEON );
if( useSIMD )
{
v_uint8x16 thresh_u = v_setall_u8( thresh );
v_uint8x16 maxval16 = v_setall_u8( maxval );
switch( type )
{
case THRESH_BINARY:
for( int i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
for( j = 0; j <= roi.width - 16; j += 16 )
{
v_uint8x16 v0;
v0 = v_load( src + j );
v0 = thresh_u < v0;
v0 = v0 & maxval16;
v_store( dst + j, v0 );
}
}
break;
case THRESH_BINARY_INV:
for( int i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
for( j = 0; j <= roi.width - 16; j += 16 )
{
v_uint8x16 v0;
v0 = v_load( src + j );
v0 = v0 <= thresh_u;
v0 = v0 & maxval16;
v_store( dst + j, v0 );
}
}
break;
case THRESH_TRUNC:
for( int i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
for( j = 0; j <= roi.width - 16; j += 16 )
{
v_uint8x16 v0;
v0 = v_load( src + j );
v0 = v0 - ( v0 - thresh_u );
v_store( dst + j, v0 );
}
}
break;
case THRESH_TOZERO:
for( int i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
for( j = 0; j <= roi.width - 16; j += 16 )
{
v_uint8x16 v0;
v0 = v_load( src + j );
v0 = ( thresh_u < v0 ) & v0;
v_store( dst + j, v0 );
}
}
break;
case THRESH_TOZERO_INV:
for( int i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
for( j = 0; j <= roi.width - 16; j += 16 )
{
v_uint8x16 v0;
v0 = v_load( src + j );
v0 = ( v0 <= thresh_u ) & v0;
v_store( dst + j, v0 );
}
}
break;
}
}
#endif
int j_scalar = j;
if( j_scalar < roi.width )
{
const int thresh_pivot = thresh + 1;
uchar tab[256] = {0};
switch( type )
{
case THRESH_BINARY:
memset(tab, 0, thresh_pivot);
if (thresh_pivot < 256) {
memset(tab + thresh_pivot, maxval, 256 - thresh_pivot);
}
break;
case THRESH_BINARY_INV:
memset(tab, maxval, thresh_pivot);
if (thresh_pivot < 256) {
memset(tab + thresh_pivot, 0, 256 - thresh_pivot);
}
break;
case THRESH_TRUNC:
for( int i = 0; i <= thresh; i++ )
tab[i] = (uchar)i;
if (thresh_pivot < 256) {
memset(tab + thresh_pivot, thresh, 256 - thresh_pivot);
}
break;
case THRESH_TOZERO:
memset(tab, 0, thresh_pivot);
for( int i = thresh_pivot; i < 256; i++ )
tab[i] = (uchar)i;
break;
case THRESH_TOZERO_INV:
for( int i = 0; i <= thresh; i++ )
tab[i] = (uchar)i;
if (thresh_pivot < 256) {
memset(tab + thresh_pivot, 0, 256 - thresh_pivot);
}
break;
}
src = _src.ptr();
dst = _dst.ptr();
for( int i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = j_scalar;
#if CV_ENABLE_UNROLLED
for( ; j <= roi.width - 4; j += 4 )
{
uchar t0 = tab[src[j]];
uchar t1 = tab[src[j+1]];
dst[j] = t0;
dst[j+1] = t1;
t0 = tab[src[j+2]];
t1 = tab[src[j+3]];
dst[j+2] = t0;
dst[j+3] = t1;
}
#endif
for( ; j < roi.width; j++ )
dst[j] = tab[src[j]];
}
}
}
static void
thresh_16u(const Mat& _src, Mat& _dst, ushort thresh, ushort maxval, int type)
{
Size roi = _src.size();
roi.width *= _src.channels();
size_t src_step = _src.step / _src.elemSize1();
size_t dst_step = _dst.step / _dst.elemSize1();
if (_src.isContinuous() && _dst.isContinuous())
{
roi.width *= roi.height;
roi.height = 1;
src_step = dst_step = roi.width;
}
// HAVE_TEGRA_OPTIMIZATION not supported
// HAVE_IPP not supported
const ushort* src = _src.ptr<ushort>();
ushort* dst = _dst.ptr<ushort>();
#if CV_SIMD128
bool useSIMD = checkHardwareSupport(CV_CPU_SSE2) || checkHardwareSupport(CV_CPU_NEON);
if (useSIMD)
{
int i, j;
v_uint16x8 thresh_u = v_setall_u16(thresh);
v_uint16x8 maxval16 = v_setall_u16(maxval);
switch (type)
{
case THRESH_BINARY:
for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step)
{
for (j = 0; j <= roi.width - 16; j += 16)
{
v_uint16x8 v0, v1;
v0 = v_load(src + j);
v1 = v_load(src + j + 8);
v0 = thresh_u < v0;
v1 = thresh_u < v1;
v0 = v0 & maxval16;
v1 = v1 & maxval16;
v_store(dst + j, v0);
v_store(dst + j + 8, v1);
}
for (; j < roi.width; j++)
dst[j] = threshBinary<ushort>(src[j], thresh, maxval);
}
break;
case THRESH_BINARY_INV:
for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step)
{
j = 0;
for (; j <= roi.width - 16; j += 16)
{
v_uint16x8 v0, v1;
v0 = v_load(src + j);
v1 = v_load(src + j + 8);
v0 = v0 <= thresh_u;
v1 = v1 <= thresh_u;
v0 = v0 & maxval16;
v1 = v1 & maxval16;
v_store(dst + j, v0);
v_store(dst + j + 8, v1);
}
for (; j < roi.width; j++)
dst[j] = threshBinaryInv<ushort>(src[j], thresh, maxval);
}
break;
case THRESH_TRUNC:
for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step)
{
j = 0;
for (; j <= roi.width - 16; j += 16)
{
v_uint16x8 v0, v1;
v0 = v_load(src + j);
v1 = v_load(src + j + 8);
v0 = v_min(v0, thresh_u);
v1 = v_min(v1, thresh_u);
v_store(dst + j, v0);
v_store(dst + j + 8, v1);
}
for (; j < roi.width; j++)
dst[j] = threshTrunc<ushort>(src[j], thresh);
}
break;
case THRESH_TOZERO:
for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step)
{
j = 0;
for (; j <= roi.width - 16; j += 16)
{
v_uint16x8 v0, v1;
v0 = v_load(src + j);
v1 = v_load(src + j + 8);
v0 = (thresh_u < v0) & v0;
v1 = (thresh_u < v1) & v1;
v_store(dst + j, v0);
v_store(dst + j + 8, v1);
}
for (; j < roi.width; j++)
dst[j] = threshToZero<ushort>(src[j], thresh);
}
break;
case THRESH_TOZERO_INV:
for (i = 0; i < roi.height; i++, src += src_step, dst += dst_step)
{
j = 0;
for (; j <= roi.width - 16; j += 16)
{
v_uint16x8 v0, v1;
v0 = v_load(src + j);
v1 = v_load(src + j + 8);
v0 = (v0 <= thresh_u) & v0;
v1 = (v1 <= thresh_u) & v1;
v_store(dst + j, v0);
v_store(dst + j + 8, v1);
}
for (; j < roi.width; j++)
dst[j] = threshToZeroInv<ushort>(src[j], thresh);
}
break;
}
}
else
#endif
{
threshGeneric<ushort>(roi, src, src_step, dst, dst_step, thresh, maxval, type);
}
}
static void
thresh_16s( const Mat& _src, Mat& _dst, short thresh, short maxval, int type )
{
Size roi = _src.size();
roi.width *= _src.channels();
const short* src = _src.ptr<short>();
short* dst = _dst.ptr<short>();
size_t src_step = _src.step/sizeof(src[0]);
size_t dst_step = _dst.step/sizeof(dst[0]);
if( _src.isContinuous() && _dst.isContinuous() )
{
roi.width *= roi.height;
roi.height = 1;
src_step = dst_step = roi.width;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::useTegra() && tegra::thresh_16s(_src, _dst, roi.width, roi.height, thresh, maxval, type))
return;
#endif
#if defined(HAVE_IPP)
CV_IPP_CHECK()
{
IppiSize sz = { roi.width, roi.height };
CV_SUPPRESS_DEPRECATED_START
switch( type )
{
case THRESH_TRUNC:
if (_src.data == _dst.data && CV_INSTRUMENT_FUN_IPP(ippiThreshold_GT_16s_C1IR, dst, (int)dst_step*sizeof(dst[0]), sz, thresh) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
if (CV_INSTRUMENT_FUN_IPP(ippiThreshold_GT_16s_C1R, src, (int)src_step*sizeof(src[0]), dst, (int)dst_step*sizeof(dst[0]), sz, thresh) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
case THRESH_TOZERO:
if (_src.data == _dst.data && CV_INSTRUMENT_FUN_IPP(ippiThreshold_LTVal_16s_C1IR, dst, (int)dst_step*sizeof(dst[0]), sz, thresh + 1, 0) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
if (CV_INSTRUMENT_FUN_IPP(ippiThreshold_LTVal_16s_C1R, src, (int)src_step*sizeof(src[0]), dst, (int)dst_step*sizeof(dst[0]), sz, thresh + 1, 0) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
case THRESH_TOZERO_INV:
if (_src.data == _dst.data && CV_INSTRUMENT_FUN_IPP(ippiThreshold_GTVal_16s_C1IR, dst, (int)dst_step*sizeof(dst[0]), sz, thresh, 0) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
if (CV_INSTRUMENT_FUN_IPP(ippiThreshold_GTVal_16s_C1R, src, (int)src_step*sizeof(src[0]), dst, (int)dst_step*sizeof(dst[0]), sz, thresh, 0) >= 0)
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
}
CV_SUPPRESS_DEPRECATED_END
}
#endif
#if CV_SIMD128
bool useSIMD = checkHardwareSupport( CV_CPU_SSE2 ) || checkHardwareSupport( CV_CPU_NEON );
if( useSIMD )
{
int i, j;
v_int16x8 thresh8 = v_setall_s16( thresh );
v_int16x8 maxval8 = v_setall_s16( maxval );
switch( type )
{
case THRESH_BINARY:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 16; j += 16 )
{
v_int16x8 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 8 );
v0 = thresh8 < v0;
v1 = thresh8 < v1;
v0 = v0 & maxval8;
v1 = v1 & maxval8;
v_store( dst + j, v0 );
v_store( dst + j + 8, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshBinary<short>(src[j], thresh, maxval);
}
break;
case THRESH_BINARY_INV:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 16; j += 16 )
{
v_int16x8 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 8 );
v0 = v0 <= thresh8;
v1 = v1 <= thresh8;
v0 = v0 & maxval8;
v1 = v1 & maxval8;
v_store( dst + j, v0 );
v_store( dst + j + 8, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshBinaryInv<short>(src[j], thresh, maxval);
}
break;
case THRESH_TRUNC:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 16; j += 16 )
{
v_int16x8 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 8 );
v0 = v_min( v0, thresh8 );
v1 = v_min( v1, thresh8 );
v_store( dst + j, v0 );
v_store( dst + j + 8, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshTrunc<short>( src[j], thresh );
}
break;
case THRESH_TOZERO:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 16; j += 16 )
{
v_int16x8 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 8 );
v0 = ( thresh8 < v0 ) & v0;
v1 = ( thresh8 < v1 ) & v1;
v_store( dst + j, v0 );
v_store( dst + j + 8, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshToZero<short>(src[j], thresh);
}
break;
case THRESH_TOZERO_INV:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 16; j += 16 )
{
v_int16x8 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 8 );
v0 = ( v0 <= thresh8 ) & v0;
v1 = ( v1 <= thresh8 ) & v1;
v_store( dst + j, v0 );
v_store( dst + j + 8, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshToZeroInv<short>(src[j], thresh);
}
break;
default:
CV_Error( CV_StsBadArg, "" ); return;
}
}
else
#endif
{
threshGeneric<short>(roi, src, src_step, dst, dst_step, thresh, maxval, type);
}
}
static void
thresh_32f( const Mat& _src, Mat& _dst, float thresh, float maxval, int type )
{
Size roi = _src.size();
roi.width *= _src.channels();
const float* src = _src.ptr<float>();
float* dst = _dst.ptr<float>();
size_t src_step = _src.step/sizeof(src[0]);
size_t dst_step = _dst.step/sizeof(dst[0]);
if( _src.isContinuous() && _dst.isContinuous() )
{
roi.width *= roi.height;
roi.height = 1;
}
#ifdef HAVE_TEGRA_OPTIMIZATION
if (tegra::useTegra() && tegra::thresh_32f(_src, _dst, roi.width, roi.height, thresh, maxval, type))
return;
#endif
#if defined(HAVE_IPP)
CV_IPP_CHECK()
{
IppiSize sz = { roi.width, roi.height };
switch( type )
{
case THRESH_TRUNC:
if (0 <= CV_INSTRUMENT_FUN_IPP(ippiThreshold_GT_32f_C1R, src, (int)src_step*sizeof(src[0]), dst, (int)dst_step*sizeof(dst[0]), sz, thresh))
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
case THRESH_TOZERO:
if (0 <= CV_INSTRUMENT_FUN_IPP(ippiThreshold_LTVal_32f_C1R, src, (int)src_step*sizeof(src[0]), dst, (int)dst_step*sizeof(dst[0]), sz, thresh + FLT_EPSILON, 0))
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
case THRESH_TOZERO_INV:
if (0 <= CV_INSTRUMENT_FUN_IPP(ippiThreshold_GTVal_32f_C1R, src, (int)src_step*sizeof(src[0]), dst, (int)dst_step*sizeof(dst[0]), sz, thresh, 0))
{
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
setIppErrorStatus();
break;
}
}
#endif
#if CV_SIMD128
bool useSIMD = checkHardwareSupport( CV_CPU_SSE2 ) || checkHardwareSupport( CV_CPU_NEON );
if( useSIMD )
{
int i, j;
v_float32x4 thresh4 = v_setall_f32( thresh );
v_float32x4 maxval4 = v_setall_f32( maxval );
switch( type )
{
case THRESH_BINARY:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 8; j += 8 )
{
v_float32x4 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 4 );
v0 = thresh4 < v0;
v1 = thresh4 < v1;
v0 = v0 & maxval4;
v1 = v1 & maxval4;
v_store( dst + j, v0 );
v_store( dst + j + 4, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshBinary<float>(src[j], thresh, maxval);
}
break;
case THRESH_BINARY_INV:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 8; j += 8 )
{
v_float32x4 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 4 );
v0 = v0 <= thresh4;
v1 = v1 <= thresh4;
v0 = v0 & maxval4;
v1 = v1 & maxval4;
v_store( dst + j, v0 );
v_store( dst + j + 4, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshBinaryInv<float>(src[j], thresh, maxval);
}
break;
case THRESH_TRUNC:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 8; j += 8 )
{
v_float32x4 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 4 );
v0 = v_min( v0, thresh4 );
v1 = v_min( v1, thresh4 );
v_store( dst + j, v0 );
v_store( dst + j + 4, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshTrunc<float>(src[j], thresh);
}
break;
case THRESH_TOZERO:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 8; j += 8 )
{
v_float32x4 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 4 );
v0 = ( thresh4 < v0 ) & v0;
v1 = ( thresh4 < v1 ) & v1;
v_store( dst + j, v0 );
v_store( dst + j + 4, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshToZero<float>(src[j], thresh);
}
break;
case THRESH_TOZERO_INV:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 8; j += 8 )
{
v_float32x4 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 4 );
v0 = ( v0 <= thresh4 ) & v0;
v1 = ( v1 <= thresh4 ) & v1;
v_store( dst + j, v0 );
v_store( dst + j + 4, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshToZeroInv<float>(src[j], thresh);
}
break;
default:
CV_Error( CV_StsBadArg, "" ); return;
}
}
else
#endif
{
threshGeneric<float>(roi, src, src_step, dst, dst_step, thresh, maxval, type);
}
}
static void
thresh_64f(const Mat& _src, Mat& _dst, double thresh, double maxval, int type)
{
Size roi = _src.size();
roi.width *= _src.channels();
const double* src = _src.ptr<double>();
double* dst = _dst.ptr<double>();
size_t src_step = _src.step / sizeof(src[0]);
size_t dst_step = _dst.step / sizeof(dst[0]);
if (_src.isContinuous() && _dst.isContinuous())
{
roi.width *= roi.height;
roi.height = 1;
}
#if CV_SIMD128_64F
bool useSIMD = checkHardwareSupport( CV_CPU_SSE2 ) || checkHardwareSupport( CV_CPU_NEON );
if( useSIMD )
{
int i, j;
v_float64x2 thresh2 = v_setall_f64( thresh );
v_float64x2 maxval2 = v_setall_f64( maxval );
switch( type )
{
case THRESH_BINARY:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 4; j += 4 )
{
v_float64x2 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 2 );
v0 = thresh2 < v0;
v1 = thresh2 < v1;
v0 = v0 & maxval2;
v1 = v1 & maxval2;
v_store( dst + j, v0 );
v_store( dst + j + 2, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshBinary<double>(src[j], thresh, maxval);
}
break;
case THRESH_BINARY_INV:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 4; j += 4 )
{
v_float64x2 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 2 );
v0 = v0 <= thresh2;
v1 = v1 <= thresh2;
v0 = v0 & maxval2;
v1 = v1 & maxval2;
v_store( dst + j, v0 );
v_store( dst + j + 2, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshBinaryInv<double>(src[j], thresh, maxval);
}
break;
case THRESH_TRUNC:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 4; j += 4 )
{
v_float64x2 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 2 );
v0 = v_min( v0, thresh2 );
v1 = v_min( v1, thresh2 );
v_store( dst + j, v0 );
v_store( dst + j + 2, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshTrunc<double>(src[j], thresh);
}
break;
case THRESH_TOZERO:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 4; j += 4 )
{
v_float64x2 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 2 );
v0 = ( thresh2 < v0 ) & v0;
v1 = ( thresh2 < v1 ) & v1;
v_store( dst + j, v0 );
v_store( dst + j + 2, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshToZero<double>(src[j], thresh);
}
break;
case THRESH_TOZERO_INV:
for( i = 0; i < roi.height; i++, src += src_step, dst += dst_step )
{
j = 0;
for( ; j <= roi.width - 4; j += 4 )
{
v_float64x2 v0, v1;
v0 = v_load( src + j );
v1 = v_load( src + j + 2 );
v0 = ( v0 <= thresh2 ) & v0;
v1 = ( v1 <= thresh2 ) & v1;
v_store( dst + j, v0 );
v_store( dst + j + 2, v1 );
}
for( ; j < roi.width; j++ )
dst[j] = threshToZeroInv<double>(src[j], thresh);
}
break;
default:
CV_Error(CV_StsBadArg, ""); return;
}
}
else
#endif
{
threshGeneric<double>(roi, src, src_step, dst, dst_step, thresh, maxval, type);
}
}
#ifdef HAVE_IPP
static bool ipp_getThreshVal_Otsu_8u( const unsigned char* _src, int step, Size size, unsigned char &thresh)
{
CV_INSTRUMENT_REGION_IPP()
// Performance degradations
#if IPP_VERSION_X100 >= 201800
IppiSize srcSize = { size.width, size.height };
if(CV_INSTRUMENT_FUN_IPP(ippiComputeThreshold_Otsu_8u_C1R, _src, step, srcSize, &thresh) < 0)
return false;
return true;
#else
CV_UNUSED(_src); CV_UNUSED(step); CV_UNUSED(size); CV_UNUSED(thresh);
return false;
#endif
}
#endif
static double
getThreshVal_Otsu_8u( const Mat& _src )
{
Size size = _src.size();
int step = (int) _src.step;
if( _src.isContinuous() )
{
size.width *= size.height;
size.height = 1;
step = size.width;
}
#ifdef HAVE_IPP
unsigned char thresh = 0;
CV_IPP_RUN_FAST(ipp_getThreshVal_Otsu_8u(_src.ptr(), step, size, thresh), thresh);
#endif
const int N = 256;
int i, j, h[N] = {0};
for( i = 0; i < size.height; i++ )
{
const uchar* src = _src.ptr() + step*i;
j = 0;
#if CV_ENABLE_UNROLLED
for( ; j <= size.width - 4; j += 4 )
{
int v0 = src[j], v1 = src[j+1];
h[v0]++; h[v1]++;
v0 = src[j+2]; v1 = src[j+3];
h[v0]++; h[v1]++;
}
#endif
for( ; j < size.width; j++ )
h[src[j]]++;
}
double mu = 0, scale = 1./(size.width*size.height);
for( i = 0; i < N; i++ )
mu += i*(double)h[i];
mu *= scale;
double mu1 = 0, q1 = 0;
double max_sigma = 0, max_val = 0;
for( i = 0; i < N; i++ )
{
double p_i, q2, mu2, sigma;
p_i = h[i]*scale;
mu1 *= q1;
q1 += p_i;
q2 = 1. - q1;
if( std::min(q1,q2) < FLT_EPSILON || std::max(q1,q2) > 1. - FLT_EPSILON )
continue;
mu1 = (mu1 + i*p_i)/q1;
mu2 = (mu - q1*mu1)/q2;
sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2);
if( sigma > max_sigma )
{
max_sigma = sigma;
max_val = i;
}
}
return max_val;
}
static double
getThreshVal_Triangle_8u( const Mat& _src )
{
Size size = _src.size();
int step = (int) _src.step;
if( _src.isContinuous() )
{
size.width *= size.height;
size.height = 1;
step = size.width;
}
const int N = 256;
int i, j, h[N] = {0};
for( i = 0; i < size.height; i++ )
{
const uchar* src = _src.ptr() + step*i;
j = 0;
#if CV_ENABLE_UNROLLED
for( ; j <= size.width - 4; j += 4 )
{
int v0 = src[j], v1 = src[j+1];
h[v0]++; h[v1]++;
v0 = src[j+2]; v1 = src[j+3];
h[v0]++; h[v1]++;
}
#endif
for( ; j < size.width; j++ )
h[src[j]]++;
}
int left_bound = 0, right_bound = 0, max_ind = 0, max = 0;
int temp;
bool isflipped = false;
for( i = 0; i < N; i++ )
{
if( h[i] > 0 )
{
left_bound = i;
break;
}
}
if( left_bound > 0 )
left_bound--;
for( i = N-1; i > 0; i-- )
{
if( h[i] > 0 )
{
right_bound = i;
break;
}
}
if( right_bound < N-1 )
right_bound++;
for( i = 0; i < N; i++ )
{
if( h[i] > max)
{
max = h[i];
max_ind = i;
}
}
if( max_ind-left_bound < right_bound-max_ind)
{
isflipped = true;
i = 0, j = N-1;
while( i < j )
{
temp = h[i]; h[i] = h[j]; h[j] = temp;
i++; j--;
}
left_bound = N-1-right_bound;
max_ind = N-1-max_ind;
}
double thresh = left_bound;
double a, b, dist = 0, tempdist;
/*
* We do not need to compute precise distance here. Distance is maximized, so some constants can
* be omitted. This speeds up a computation a bit.
*/
a = max; b = left_bound-max_ind;
for( i = left_bound+1; i <= max_ind; i++ )
{
tempdist = a*i + b*h[i];
if( tempdist > dist)
{
dist = tempdist;
thresh = i;
}
}
thresh--;
if( isflipped )
thresh = N-1-thresh;
return thresh;
}
class ThresholdRunner : public ParallelLoopBody
{
public:
ThresholdRunner(Mat _src, Mat _dst, double _thresh, double _maxval, int _thresholdType)
{
src = _src;
dst = _dst;
thresh = _thresh;
maxval = _maxval;
thresholdType = _thresholdType;
}
void operator () (const Range& range) const CV_OVERRIDE
{
int row0 = range.start;
int row1 = range.end;
Mat srcStripe = src.rowRange(row0, row1);
Mat dstStripe = dst.rowRange(row0, row1);
CALL_HAL(threshold, cv_hal_threshold, srcStripe.data, srcStripe.step, dstStripe.data, dstStripe.step,
srcStripe.cols, srcStripe.rows, srcStripe.depth(), srcStripe.channels(),
thresh, maxval, thresholdType);
if (srcStripe.depth() == CV_8U)
{
thresh_8u( srcStripe, dstStripe, (uchar)thresh, (uchar)maxval, thresholdType );
}
else if( srcStripe.depth() == CV_16S )
{
thresh_16s( srcStripe, dstStripe, (short)thresh, (short)maxval, thresholdType );
}
else if( srcStripe.depth() == CV_16U )
{
thresh_16u( srcStripe, dstStripe, (ushort)thresh, (ushort)maxval, thresholdType );
}
else if( srcStripe.depth() == CV_32F )
{
thresh_32f( srcStripe, dstStripe, (float)thresh, (float)maxval, thresholdType );
}
else if( srcStripe.depth() == CV_64F )
{
thresh_64f(srcStripe, dstStripe, thresh, maxval, thresholdType);
}
}
private:
Mat src;
Mat dst;
double thresh;
double maxval;
int thresholdType;
};
#ifdef HAVE_OPENCL
static bool ocl_threshold( InputArray _src, OutputArray _dst, double & thresh, double maxval, int thresh_type )
{
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
kercn = ocl::predictOptimalVectorWidth(_src, _dst), ktype = CV_MAKE_TYPE(depth, kercn);
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
if ( !(thresh_type == THRESH_BINARY || thresh_type == THRESH_BINARY_INV || thresh_type == THRESH_TRUNC ||
thresh_type == THRESH_TOZERO || thresh_type == THRESH_TOZERO_INV) ||
(!doubleSupport && depth == CV_64F))
return false;
const char * const thresholdMap[] = { "THRESH_BINARY", "THRESH_BINARY_INV", "THRESH_TRUNC",
"THRESH_TOZERO", "THRESH_TOZERO_INV" };
ocl::Device dev = ocl::Device::getDefault();
int stride_size = dev.isIntel() && (dev.type() & ocl::Device::TYPE_GPU) ? 4 : 1;
ocl::Kernel k("threshold", ocl::imgproc::threshold_oclsrc,
format("-D %s -D T=%s -D T1=%s -D STRIDE_SIZE=%d%s", thresholdMap[thresh_type],
ocl::typeToStr(ktype), ocl::typeToStr(depth), stride_size,
doubleSupport ? " -D DOUBLE_SUPPORT" : ""));
if (k.empty())
return false;
UMat src = _src.getUMat();
_dst.create(src.size(), type);
UMat dst = _dst.getUMat();
if (depth <= CV_32S)
thresh = cvFloor(thresh);
const double min_vals[] = { 0, CHAR_MIN, 0, SHRT_MIN, INT_MIN, -FLT_MAX, -DBL_MAX, 0 };
double min_val = min_vals[depth];
k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst, cn, kercn),
ocl::KernelArg::Constant(Mat(1, 1, depth, Scalar::all(thresh))),
ocl::KernelArg::Constant(Mat(1, 1, depth, Scalar::all(maxval))),
ocl::KernelArg::Constant(Mat(1, 1, depth, Scalar::all(min_val))));
size_t globalsize[2] = { (size_t)dst.cols * cn / kercn, (size_t)dst.rows };
globalsize[1] = (globalsize[1] + stride_size - 1) / stride_size;
return k.run(2, globalsize, NULL, false);
}
#endif
#ifdef HAVE_OPENVX
#define IMPL_OPENVX_TOZERO 1
static bool openvx_threshold(Mat src, Mat dst, int thresh, int maxval, int type)
{
Mat a = src;
int trueVal, falseVal;
switch (type)
{
case THRESH_BINARY:
#ifndef VX_VERSION_1_1
if (maxval != 255)
return false;
#endif
trueVal = maxval;
falseVal = 0;
break;
case THRESH_TOZERO:
#if IMPL_OPENVX_TOZERO
trueVal = 255;
falseVal = 0;
if (dst.data == src.data)
{
a = Mat(src.size(), src.type());
src.copyTo(a);
}
break;
#endif
case THRESH_BINARY_INV:
#ifdef VX_VERSION_1_1
trueVal = 0;
falseVal = maxval;
break;
#endif
case THRESH_TOZERO_INV:
#ifdef VX_VERSION_1_1
#if IMPL_OPENVX_TOZERO
trueVal = 0;
falseVal = 255;
if (dst.data == src.data)
{
a = Mat(src.size(), src.type());
src.copyTo(a);
}
break;
#endif
#endif
case THRESH_TRUNC:
default:
return false;
}
try
{
ivx::Context ctx = ovx::getOpenVXContext();
ivx::Threshold thh = ivx::Threshold::createBinary(ctx, VX_TYPE_UINT8, thresh);
thh.setValueTrue(trueVal);
thh.setValueFalse(falseVal);
ivx::Image
ia = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(a.cols*a.channels(), a.rows, 1, (vx_int32)(a.step)), src.data),
ib = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(dst.cols*dst.channels(), dst.rows, 1, (vx_int32)(dst.step)), dst.data);
ivx::IVX_CHECK_STATUS(vxuThreshold(ctx, ia, thh, ib));
#if IMPL_OPENVX_TOZERO
if (type == THRESH_TOZERO || type == THRESH_TOZERO_INV)
{
ivx::Image
ic = ivx::Image::createFromHandle(ctx, VX_DF_IMAGE_U8,
ivx::Image::createAddressing(dst.cols*dst.channels(), dst.rows, 1, (vx_int32)(dst.step)), dst.data);
ivx::IVX_CHECK_STATUS(vxuAnd(ctx, ib, ia, ic));
}
#endif
}
catch (ivx::RuntimeError & e)
{
VX_DbgThrow(e.what());
}
catch (ivx::WrapperError & e)
{
VX_DbgThrow(e.what());
}
return true;
}
#endif
}
double cv::threshold( InputArray _src, OutputArray _dst, double thresh, double maxval, int type )
{
CV_INSTRUMENT_REGION()
CV_OCL_RUN_(_src.dims() <= 2 && _dst.isUMat(),
ocl_threshold(_src, _dst, thresh, maxval, type), thresh)
Mat src = _src.getMat();
int automatic_thresh = (type & ~CV_THRESH_MASK);
type &= THRESH_MASK;
CV_Assert( automatic_thresh != (CV_THRESH_OTSU | CV_THRESH_TRIANGLE) );
if( automatic_thresh == CV_THRESH_OTSU )
{
CV_Assert( src.type() == CV_8UC1 );
thresh = getThreshVal_Otsu_8u( src );
}
else if( automatic_thresh == CV_THRESH_TRIANGLE )
{
CV_Assert( src.type() == CV_8UC1 );
thresh = getThreshVal_Triangle_8u( src );
}
_dst.create( src.size(), src.type() );
Mat dst = _dst.getMat();
if( src.depth() == CV_8U )
{
int ithresh = cvFloor(thresh);
thresh = ithresh;
int imaxval = cvRound(maxval);
if( type == THRESH_TRUNC )
imaxval = ithresh;
imaxval = saturate_cast<uchar>(imaxval);
if( ithresh < 0 || ithresh >= 255 )
{
if( type == THRESH_BINARY || type == THRESH_BINARY_INV ||
((type == THRESH_TRUNC || type == THRESH_TOZERO_INV) && ithresh < 0) ||
(type == THRESH_TOZERO && ithresh >= 255) )
{
int v = type == THRESH_BINARY ? (ithresh >= 255 ? 0 : imaxval) :
type == THRESH_BINARY_INV ? (ithresh >= 255 ? imaxval : 0) :
/*type == THRESH_TRUNC ? imaxval :*/ 0;
dst.setTo(v);
}
else
src.copyTo(dst);
return thresh;
}
CV_OVX_RUN(!ovx::skipSmallImages<VX_KERNEL_THRESHOLD>(src.cols, src.rows),
openvx_threshold(src, dst, ithresh, imaxval, type), (double)ithresh)
thresh = ithresh;
maxval = imaxval;
}
else if( src.depth() == CV_16S )
{
int ithresh = cvFloor(thresh);
thresh = ithresh;
int imaxval = cvRound(maxval);
if( type == THRESH_TRUNC )
imaxval = ithresh;
imaxval = saturate_cast<short>(imaxval);
if( ithresh < SHRT_MIN || ithresh >= SHRT_MAX )
{
if( type == THRESH_BINARY || type == THRESH_BINARY_INV ||
((type == THRESH_TRUNC || type == THRESH_TOZERO_INV) && ithresh < SHRT_MIN) ||
(type == THRESH_TOZERO && ithresh >= SHRT_MAX) )
{
int v = type == THRESH_BINARY ? (ithresh >= SHRT_MAX ? 0 : imaxval) :
type == THRESH_BINARY_INV ? (ithresh >= SHRT_MAX ? imaxval : 0) :
/*type == THRESH_TRUNC ? imaxval :*/ 0;
dst.setTo(v);
}
else
src.copyTo(dst);
return thresh;
}
thresh = ithresh;
maxval = imaxval;
}
else if (src.depth() == CV_16U )
{
int ithresh = cvFloor(thresh);
thresh = ithresh;
int imaxval = cvRound(maxval);
if (type == THRESH_TRUNC)
imaxval = ithresh;
imaxval = saturate_cast<ushort>(imaxval);
int ushrt_min = 0;
if (ithresh < ushrt_min || ithresh >= (int)USHRT_MAX)
{
if (type == THRESH_BINARY || type == THRESH_BINARY_INV ||
((type == THRESH_TRUNC || type == THRESH_TOZERO_INV) && ithresh < ushrt_min) ||
(type == THRESH_TOZERO && ithresh >= (int)USHRT_MAX))
{
int v = type == THRESH_BINARY ? (ithresh >= (int)USHRT_MAX ? 0 : imaxval) :
type == THRESH_BINARY_INV ? (ithresh >= (int)USHRT_MAX ? imaxval : 0) :
/*type == THRESH_TRUNC ? imaxval :*/ 0;
dst.setTo(v);
}
else
src.copyTo(dst);
return thresh;
}
thresh = ithresh;
maxval = imaxval;
}
else if( src.depth() == CV_32F )
;
else if( src.depth() == CV_64F )
;
else
CV_Error( CV_StsUnsupportedFormat, "" );
parallel_for_(Range(0, dst.rows),
ThresholdRunner(src, dst, thresh, maxval, type),
dst.total()/(double)(1<<16));
return thresh;
}
void cv::adaptiveThreshold( InputArray _src, OutputArray _dst, double maxValue,
int method, int type, int blockSize, double delta )
{
CV_INSTRUMENT_REGION()
Mat src = _src.getMat();
CV_Assert( src.type() == CV_8UC1 );
CV_Assert( blockSize % 2 == 1 && blockSize > 1 );
Size size = src.size();
_dst.create( size, src.type() );
Mat dst = _dst.getMat();
if( maxValue < 0 )
{
dst = Scalar(0);
return;
}
CALL_HAL(adaptiveThreshold, cv_hal_adaptiveThreshold, src.data, src.step, dst.data, dst.step, src.cols, src.rows,
maxValue, method, type, blockSize, delta);
Mat mean;
if( src.data != dst.data )
mean = dst;
if (method == ADAPTIVE_THRESH_MEAN_C)
boxFilter( src, mean, src.type(), Size(blockSize, blockSize),
Point(-1,-1), true, BORDER_REPLICATE|BORDER_ISOLATED );
else if (method == ADAPTIVE_THRESH_GAUSSIAN_C)
{
Mat srcfloat,meanfloat;
src.convertTo(srcfloat,CV_32F);
meanfloat=srcfloat;
GaussianBlur(srcfloat, meanfloat, Size(blockSize, blockSize), 0, 0, BORDER_REPLICATE|BORDER_ISOLATED);
meanfloat.convertTo(mean, src.type());
}
else
CV_Error( CV_StsBadFlag, "Unknown/unsupported adaptive threshold method" );
int i, j;
uchar imaxval = saturate_cast<uchar>(maxValue);
int idelta = type == THRESH_BINARY ? cvCeil(delta) : cvFloor(delta);
uchar tab[768];
if( type == CV_THRESH_BINARY )
for( i = 0; i < 768; i++ )
tab[i] = (uchar)(i - 255 > -idelta ? imaxval : 0);
else if( type == CV_THRESH_BINARY_INV )
for( i = 0; i < 768; i++ )
tab[i] = (uchar)(i - 255 <= -idelta ? imaxval : 0);
else
CV_Error( CV_StsBadFlag, "Unknown/unsupported threshold type" );
if( src.isContinuous() && mean.isContinuous() && dst.isContinuous() )
{
size.width *= size.height;
size.height = 1;
}
for( i = 0; i < size.height; i++ )
{
const uchar* sdata = src.ptr(i);
const uchar* mdata = mean.ptr(i);
uchar* ddata = dst.ptr(i);
for( j = 0; j < size.width; j++ )
ddata[j] = tab[sdata[j] - mdata[j] + 255];
}
}
CV_IMPL double
cvThreshold( const void* srcarr, void* dstarr, double thresh, double maxval, int type )
{
cv::Mat src = cv::cvarrToMat(srcarr), dst = cv::cvarrToMat(dstarr), dst0 = dst;
CV_Assert( src.size == dst.size && src.channels() == dst.channels() &&
(src.depth() == dst.depth() || dst.depth() == CV_8U));
thresh = cv::threshold( src, dst, thresh, maxval, type );
if( dst0.data != dst.data )
dst.convertTo( dst0, dst0.depth() );
return thresh;
}
CV_IMPL void
cvAdaptiveThreshold( const void *srcIm, void *dstIm, double maxValue,
int method, int type, int blockSize, double delta )
{
cv::Mat src = cv::cvarrToMat(srcIm), dst = cv::cvarrToMat(dstIm);
CV_Assert( src.size == dst.size && src.type() == dst.type() );
cv::adaptiveThreshold( src, dst, maxValue, method, type, blockSize, delta );
}
/* End of file. */