/*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 static inline T threshBinary(const T& src, const T& thresh, const T& maxval) { return src > thresh ? maxval : 0; } template static inline T threshBinaryInv(const T& src, const T& thresh, const T& maxval) { return src <= thresh ? maxval : 0; } template static inline T threshTrunc(const T& src, const T& thresh) { return std::min(src, thresh); } template static inline T threshToZero(const T& src, const T& thresh) { return src > thresh ? src : 0; } template static inline T threshToZeroInv(const T& src, const T& thresh) { return src <= thresh ? src : 0; } template 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(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(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(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(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(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* dst = _dst.ptr(); #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(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(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(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(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(src[j], thresh); } break; } } else #endif { threshGeneric(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* dst = _dst.ptr(); 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(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(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( 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(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(src[j], thresh); } break; default: CV_Error( CV_StsBadArg, "" ); return; } } else #endif { threshGeneric(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* dst = _dst.ptr(); 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(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(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(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(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(src[j], thresh); } break; default: CV_Error( CV_StsBadArg, "" ); return; } } else #endif { threshGeneric(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* dst = _dst.ptr(); 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(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(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(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(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(src[j], thresh); } break; default: CV_Error(CV_StsBadArg, ""); return; } } else #endif { threshGeneric(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(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(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(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(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(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. */