2018-02-06 20:02:51 +08:00
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#include "precomp.hpp"
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#include "opencl_kernels_core.hpp"
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#include "convert.hpp"
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/****************************************************************************************\
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* convertScale[Abs] *
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\****************************************************************************************/
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namespace cv
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{
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2018-09-07 00:36:59 +08:00
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template<typename _Ts, typename _Td> inline void
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cvtabs_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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Size size, float a, float b )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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#if CV_SIMD
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v_float32 va = vx_setall_f32(a), vb = vx_setall_f32(b);
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const int VECSZ = v_float32::nlanes*2;
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2018-02-06 20:02:51 +08:00
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#endif
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2018-09-07 00:36:59 +08:00
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sstep /= sizeof(src[0]);
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dstep /= sizeof(dst[0]);
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2018-02-06 20:02:51 +08:00
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2018-09-07 00:36:59 +08:00
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for( int i = 0; i < size.height; i++, src += sstep, dst += dstep )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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int j = 0;
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#if CV_SIMD
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for( ; j < size.width; j += VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j > size.width - VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j == 0 || src == (_Ts*)dst )
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break;
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j = size.width - VECSZ;
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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v_float32 v0, v1;
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vx_load_pair_as(src + j, v0, v1);
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v0 = v_fma(v0, va, vb);
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v1 = v_fma(v1, va, vb);
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v_store_pair_as(dst + j, v_abs(v0), v_abs(v1));
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2018-02-06 20:02:51 +08:00
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}
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#endif
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2018-09-07 00:36:59 +08:00
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for( ; j < size.width; j++ )
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dst[j] = saturate_cast<_Td>(std::abs(src[j]*a + b));
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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}
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2018-02-06 20:02:51 +08:00
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2018-09-07 00:36:59 +08:00
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// variant for convrsions 16f <-> ... w/o unrolling
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template<typename _Ts, typename _Td> inline void
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cvtabs1_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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Size size, float a, float b )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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#if CV_SIMD
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v_float32 va = vx_setall_f32(a), vb = vx_setall_f32(b);
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const int VECSZ = v_float32::nlanes*2;
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2018-02-06 20:02:51 +08:00
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#endif
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2018-09-07 00:36:59 +08:00
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sstep /= sizeof(src[0]);
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dstep /= sizeof(dst[0]);
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2018-02-06 20:02:51 +08:00
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2018-09-07 00:36:59 +08:00
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for( int i = 0; i < size.height; i++, src += sstep, dst += dstep )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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int j = 0;
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#if CV_SIMD
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for( ; j < size.width; j += VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j > size.width - VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j == 0 || src == (_Ts*)dst )
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break;
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j = size.width - VECSZ;
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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v_float32 v0;
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vx_load_as(src + j, v0);
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v0 = v_fma(v0, va, vb);
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v_store_as(dst + j, v_abs(v0));
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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#endif
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for( ; j < size.width; j++ )
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dst[j] = saturate_cast<_Td>(src[j]*a + b);
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2018-02-06 20:02:51 +08:00
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}
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}
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2018-09-07 00:36:59 +08:00
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template<typename _Ts, typename _Td> inline void
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cvt_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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Size size, float a, float b )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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#if CV_SIMD
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v_float32 va = vx_setall_f32(a), vb = vx_setall_f32(b);
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const int VECSZ = v_float32::nlanes*2;
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2018-02-06 20:02:51 +08:00
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#endif
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2018-09-07 00:36:59 +08:00
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sstep /= sizeof(src[0]);
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dstep /= sizeof(dst[0]);
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2018-02-06 20:02:51 +08:00
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2018-09-07 00:36:59 +08:00
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for( int i = 0; i < size.height; i++, src += sstep, dst += dstep )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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int j = 0;
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#if CV_SIMD
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for( ; j < size.width; j += VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j > size.width - VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j == 0 || src == (_Ts*)dst )
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break;
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j = size.width - VECSZ;
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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v_float32 v0, v1;
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vx_load_pair_as(src + j, v0, v1);
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v0 = v_fma(v0, va, vb);
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v1 = v_fma(v1, va, vb);
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v_store_pair_as(dst + j, v0, v1);
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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#endif
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for( ; j < size.width; j++ )
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dst[j] = saturate_cast<_Td>(src[j]*a + b);
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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}
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2018-02-06 20:02:51 +08:00
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2018-09-07 00:36:59 +08:00
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// variant for convrsions 16f <-> ... w/o unrolling
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template<typename _Ts, typename _Td> inline void
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cvt1_32f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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Size size, float a, float b )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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#if CV_SIMD
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v_float32 va = vx_setall_f32(a), vb = vx_setall_f32(b);
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const int VECSZ = v_float32::nlanes;
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2018-02-06 20:02:51 +08:00
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#endif
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sstep /= sizeof(src[0]);
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dstep /= sizeof(dst[0]);
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2018-09-07 00:36:59 +08:00
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for( int i = 0; i < size.height; i++, src += sstep, dst += dstep )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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int j = 0;
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#if CV_SIMD
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for( ; j < size.width; j += VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j > size.width - VECSZ )
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{
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if( j == 0 || src == (_Ts*)dst )
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break;
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j = size.width - VECSZ;
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}
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v_float32 v0;
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vx_load_as(src + j, v0);
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v0 = v_fma(v0, va, vb);
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v_store_as(dst + j, v0);
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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#endif
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for( ; j < size.width; j++ )
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dst[j] = saturate_cast<_Td>(src[j]*a + b);
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2018-02-06 20:02:51 +08:00
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}
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}
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2018-09-07 00:36:59 +08:00
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template<typename _Ts, typename _Td> inline void
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cvt_64f( const _Ts* src, size_t sstep, _Td* dst, size_t dstep,
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Size size, double a, double b )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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#if CV_SIMD_64F
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v_float64 va = vx_setall_f64(a), vb = vx_setall_f64(b);
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const int VECSZ = v_float64::nlanes*2;
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#endif
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2018-02-06 20:02:51 +08:00
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sstep /= sizeof(src[0]);
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dstep /= sizeof(dst[0]);
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2018-09-07 00:36:59 +08:00
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for( int i = 0; i < size.height; i++, src += sstep, dst += dstep )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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int j = 0;
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#if CV_SIMD_64F
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for( ; j < size.width; j += VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j > size.width - VECSZ )
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2018-02-06 20:02:51 +08:00
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{
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2018-09-07 00:36:59 +08:00
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if( j == 0 || src == (_Ts*)dst )
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break;
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j = size.width - VECSZ;
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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v_float64 v0, v1;
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vx_load_pair_as(src + j, v0, v1);
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v0 = v_fma(v0, va, vb);
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v1 = v_fma(v1, va, vb);
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v_store_pair_as(dst + j, v0, v1);
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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#endif
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for( ; j < size.width; j++ )
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dst[j] = saturate_cast<_Td>(src[j]*a + b);
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2018-02-06 20:02:51 +08:00
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}
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}
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//==================================================================================================
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2018-09-07 00:36:59 +08:00
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#define DEF_CVT_SCALE_ABS_FUNC(suffix, cvt, stype, dtype, wtype) \
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2018-02-06 20:02:51 +08:00
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static void cvtScaleAbs##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
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2018-09-07 00:36:59 +08:00
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dtype* dst, size_t dstep, Size size, double* scale) \
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2018-02-06 20:02:51 +08:00
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{ \
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2018-09-07 00:36:59 +08:00
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cvt(src, sstep, dst, dstep, size, (wtype)scale[0], (wtype)scale[1]); \
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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#define DEF_CVT_SCALE_FUNC(suffix, cvt, stype, dtype, wtype) \
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2018-02-06 20:02:51 +08:00
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static void cvtScale##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
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2018-09-07 00:36:59 +08:00
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dtype* dst, size_t dstep, Size size, double* scale) \
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2018-02-06 20:02:51 +08:00
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{ \
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2018-09-07 00:36:59 +08:00
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cvt(src, sstep, dst, dstep, size, (wtype)scale[0], (wtype)scale[1]); \
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2018-02-06 20:02:51 +08:00
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}
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2018-09-07 00:36:59 +08:00
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DEF_CVT_SCALE_ABS_FUNC(8u, cvtabs_32f, uchar, uchar, float)
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DEF_CVT_SCALE_ABS_FUNC(8s8u, cvtabs_32f, schar, uchar, float)
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DEF_CVT_SCALE_ABS_FUNC(16u8u, cvtabs_32f, ushort, uchar, float)
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DEF_CVT_SCALE_ABS_FUNC(16s8u, cvtabs_32f, short, uchar, float)
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DEF_CVT_SCALE_ABS_FUNC(32s8u, cvtabs_32f, int, uchar, float)
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DEF_CVT_SCALE_ABS_FUNC(32f8u, cvtabs_32f, float, uchar, float)
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DEF_CVT_SCALE_ABS_FUNC(64f8u, cvtabs_32f, double, uchar, float)
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DEF_CVT_SCALE_FUNC(8u, cvt_32f, uchar, uchar, float)
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DEF_CVT_SCALE_FUNC(8s8u, cvt_32f, schar, uchar, float)
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DEF_CVT_SCALE_FUNC(16u8u, cvt_32f, ushort, uchar, float)
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DEF_CVT_SCALE_FUNC(16s8u, cvt_32f, short, uchar, float)
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DEF_CVT_SCALE_FUNC(32s8u, cvt_32f, int, uchar, float)
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DEF_CVT_SCALE_FUNC(32f8u, cvt_32f, float, uchar, float)
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DEF_CVT_SCALE_FUNC(64f8u, cvt_32f, double, uchar, float)
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//DEF_CVT_SCALE_FUNC(16f8u, cvt_32f, float16_t, uchar, float)
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DEF_CVT_SCALE_FUNC(8u8s, cvt_32f, uchar, schar, float)
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DEF_CVT_SCALE_FUNC(8s, cvt_32f, schar, schar, float)
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DEF_CVT_SCALE_FUNC(16u8s, cvt_32f, ushort, schar, float)
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DEF_CVT_SCALE_FUNC(16s8s, cvt_32f, short, schar, float)
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DEF_CVT_SCALE_FUNC(32s8s, cvt_32f, int, schar, float)
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DEF_CVT_SCALE_FUNC(32f8s, cvt_32f, float, schar, float)
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DEF_CVT_SCALE_FUNC(64f8s, cvt_32f, double, schar, float)
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//DEF_CVT_SCALE_FUNC(16f8s, cvt_32f, float16_t, schar, float)
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DEF_CVT_SCALE_FUNC(8u16u, cvt_32f, uchar, ushort, float)
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DEF_CVT_SCALE_FUNC(8s16u, cvt_32f, schar, ushort, float)
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DEF_CVT_SCALE_FUNC(16u, cvt_32f, ushort, ushort, float)
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DEF_CVT_SCALE_FUNC(16s16u, cvt_32f, short, ushort, float)
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DEF_CVT_SCALE_FUNC(32s16u, cvt_32f, int, ushort, float)
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DEF_CVT_SCALE_FUNC(32f16u, cvt_32f, float, ushort, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(64f16u, cvt_32f, double, ushort, float)
|
|
|
|
|
//DEF_CVT_SCALE_FUNC(16f16u, cvt1_32f, float16_t, ushort, float)
|
|
|
|
|
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8u16s, cvt_32f, uchar, short, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8s16s, cvt_32f, schar, short, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16u16s, cvt_32f, ushort, short, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16s, cvt_32f, short, short, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32s16s, cvt_32f, int, short, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32f16s, cvt_32f, float, short, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(64f16s, cvt_32f, double, short, float)
|
|
|
|
|
//DEF_CVT_SCALE_FUNC(16f16s, cvt1_32f, float16_t, short, float)
|
|
|
|
|
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8u32s, cvt_32f, uchar, int, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8s32s, cvt_32f, schar, int, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16u32s, cvt_32f, ushort, int, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16s32s, cvt_32f, short, int, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32s, cvt_64f, int, int, double)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32f32s, cvt_32f, float, int, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(64f32s, cvt_64f, double, int, double)
|
|
|
|
|
//DEF_CVT_SCALE_FUNC(16f32s, cvt1_32f, float16_t, int, float)
|
|
|
|
|
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8u32f, cvt_32f, uchar, float, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8s32f, cvt_32f, schar, float, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16u32f, cvt_32f, ushort, float, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16s32f, cvt_32f, short, float, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32s32f, cvt_32f, int, float, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32f, cvt_32f, float, float, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(64f32f, cvt_64f, double, float, double)
|
|
|
|
|
//DEF_CVT_SCALE_FUNC(16f32f, cvt1_32f, float16_t, float, float)
|
|
|
|
|
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8u64f, cvt_64f, uchar, double, double)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8s64f, cvt_64f, schar, double, double)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16u64f, cvt_64f, ushort, double, double)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16s64f, cvt_64f, short, double, double)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32s64f, cvt_64f, int, double, double)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32f64f, cvt_64f, float, double, double)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(64f, cvt_64f, double, double, double)
|
|
|
|
|
//DEF_CVT_SCALE_FUNC(16f64f, cvt_64f, float16_t, double, double)
|
|
|
|
|
|
|
|
|
|
/*DEF_CVT_SCALE_FUNC(8u16f, cvt1_32f, uchar, float16_t, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(8s16f, cvt1_32f, schar, float16_t, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16u16f, cvt1_32f, ushort, float16_t, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16s16f, cvt1_32f, short, float16_t, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32s16f, cvt1_32f, int, float16_t, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(32f16f, cvt1_32f, float, float16_t, float)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(64f16f, cvt_64f, double, float16_t, double)
|
|
|
|
|
DEF_CVT_SCALE_FUNC(16f, cvt1_32f, float16_t, float16_t, float)*/
|
2018-02-06 20:02:51 +08:00
|
|
|
|
|
|
|
|
static BinaryFunc getCvtScaleAbsFunc(int depth)
|
|
|
|
|
{
|
|
|
|
|
static BinaryFunc cvtScaleAbsTab[] =
|
|
|
|
|
{
|
|
|
|
|
(BinaryFunc)cvtScaleAbs8u, (BinaryFunc)cvtScaleAbs8s8u, (BinaryFunc)cvtScaleAbs16u8u,
|
|
|
|
|
(BinaryFunc)cvtScaleAbs16s8u, (BinaryFunc)cvtScaleAbs32s8u, (BinaryFunc)cvtScaleAbs32f8u,
|
|
|
|
|
(BinaryFunc)cvtScaleAbs64f8u, 0
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
return cvtScaleAbsTab[depth];
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
BinaryFunc getConvertScaleFunc(int sdepth, int ddepth)
|
|
|
|
|
{
|
|
|
|
|
static BinaryFunc cvtScaleTab[][8] =
|
|
|
|
|
{
|
|
|
|
|
{
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale8u), (BinaryFunc)GET_OPTIMIZED(cvtScale8s8u), (BinaryFunc)GET_OPTIMIZED(cvtScale16u8u),
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale16s8u), (BinaryFunc)GET_OPTIMIZED(cvtScale32s8u), (BinaryFunc)GET_OPTIMIZED(cvtScale32f8u),
|
2018-09-07 00:36:59 +08:00
|
|
|
(BinaryFunc)cvtScale64f8u, 0 //(BinaryFunc)cvtScale16f8u
|
2018-02-06 20:02:51 +08:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale8u8s), (BinaryFunc)GET_OPTIMIZED(cvtScale8s), (BinaryFunc)GET_OPTIMIZED(cvtScale16u8s),
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale16s8s), (BinaryFunc)GET_OPTIMIZED(cvtScale32s8s), (BinaryFunc)GET_OPTIMIZED(cvtScale32f8s),
|
2018-09-07 00:36:59 +08:00
|
|
|
(BinaryFunc)cvtScale64f8s, 0 //(BinaryFunc)cvtScale16f8s
|
2018-02-06 20:02:51 +08:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale8u16u), (BinaryFunc)GET_OPTIMIZED(cvtScale8s16u), (BinaryFunc)GET_OPTIMIZED(cvtScale16u),
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale16s16u), (BinaryFunc)GET_OPTIMIZED(cvtScale32s16u), (BinaryFunc)GET_OPTIMIZED(cvtScale32f16u),
|
2018-09-07 00:36:59 +08:00
|
|
|
(BinaryFunc)cvtScale64f16u, 0 //(BinaryFunc)cvtScale16f16u
|
2018-02-06 20:02:51 +08:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale8u16s), (BinaryFunc)GET_OPTIMIZED(cvtScale8s16s), (BinaryFunc)GET_OPTIMIZED(cvtScale16u16s),
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale16s), (BinaryFunc)GET_OPTIMIZED(cvtScale32s16s), (BinaryFunc)GET_OPTIMIZED(cvtScale32f16s),
|
2018-09-07 00:36:59 +08:00
|
|
|
(BinaryFunc)cvtScale64f16s, 0 //(BinaryFunc)cvtScale16f16s
|
2018-02-06 20:02:51 +08:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale8u32s), (BinaryFunc)GET_OPTIMIZED(cvtScale8s32s), (BinaryFunc)GET_OPTIMIZED(cvtScale16u32s),
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale16s32s), (BinaryFunc)GET_OPTIMIZED(cvtScale32s), (BinaryFunc)GET_OPTIMIZED(cvtScale32f32s),
|
2018-09-07 00:36:59 +08:00
|
|
|
(BinaryFunc)cvtScale64f32s, 0 //(BinaryFunc)cvtScale16f32s
|
2018-02-06 20:02:51 +08:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale8u32f), (BinaryFunc)GET_OPTIMIZED(cvtScale8s32f), (BinaryFunc)GET_OPTIMIZED(cvtScale16u32f),
|
|
|
|
|
(BinaryFunc)GET_OPTIMIZED(cvtScale16s32f), (BinaryFunc)GET_OPTIMIZED(cvtScale32s32f), (BinaryFunc)GET_OPTIMIZED(cvtScale32f),
|
2018-09-07 00:36:59 +08:00
|
|
|
(BinaryFunc)cvtScale64f32f, 0 //(BinaryFunc)cvtScale16f32f
|
2018-02-06 20:02:51 +08:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
(BinaryFunc)cvtScale8u64f, (BinaryFunc)cvtScale8s64f, (BinaryFunc)cvtScale16u64f,
|
|
|
|
|
(BinaryFunc)cvtScale16s64f, (BinaryFunc)cvtScale32s64f, (BinaryFunc)cvtScale32f64f,
|
2018-09-07 00:36:59 +08:00
|
|
|
(BinaryFunc)cvtScale64f, 0 //(BinaryFunc)cvtScale16f64f
|
2018-02-06 20:02:51 +08:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
0, 0, 0, 0, 0, 0, 0, 0
|
2018-09-07 00:36:59 +08:00
|
|
|
/*(BinaryFunc)cvtScale8u16f, (BinaryFunc)cvtScale8s16f, (BinaryFunc)cvtScale16u16f,
|
|
|
|
|
(BinaryFunc)cvtScale16s16f, (BinaryFunc)cvtScale32s16f, (BinaryFunc)cvtScale32f16f,
|
|
|
|
|
(BinaryFunc)cvtScale64f16f, (BinaryFunc)cvtScale16f*/
|
|
|
|
|
},
|
2018-02-06 20:02:51 +08:00
|
|
|
};
|
|
|
|
|
|
|
|
|
|
return cvtScaleTab[CV_MAT_DEPTH(ddepth)][CV_MAT_DEPTH(sdepth)];
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
|
|
|
|
|
|
static bool ocl_convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, double beta )
|
|
|
|
|
{
|
|
|
|
|
const ocl::Device & d = ocl::Device::getDefault();
|
|
|
|
|
|
|
|
|
|
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
|
|
|
|
|
bool doubleSupport = d.doubleFPConfig() > 0;
|
|
|
|
|
if (!doubleSupport && depth == CV_64F)
|
|
|
|
|
return false;
|
|
|
|
|
|
|
|
|
|
_dst.create(_src.size(), CV_8UC(cn));
|
|
|
|
|
int kercn = 1;
|
|
|
|
|
if (d.isIntel())
|
|
|
|
|
{
|
|
|
|
|
static const int vectorWidths[] = {4, 4, 4, 4, 4, 4, 4, -1};
|
|
|
|
|
kercn = ocl::checkOptimalVectorWidth( vectorWidths, _src, _dst,
|
|
|
|
|
noArray(), noArray(), noArray(),
|
|
|
|
|
noArray(), noArray(), noArray(),
|
|
|
|
|
noArray(), ocl::OCL_VECTOR_MAX);
|
|
|
|
|
}
|
|
|
|
|
else
|
|
|
|
|
kercn = ocl::predictOptimalVectorWidthMax(_src, _dst);
|
|
|
|
|
|
|
|
|
|
int rowsPerWI = d.isIntel() ? 4 : 1;
|
|
|
|
|
char cvt[2][50];
|
|
|
|
|
int wdepth = std::max(depth, CV_32F);
|
|
|
|
|
String build_opt = format("-D OP_CONVERT_SCALE_ABS -D UNARY_OP -D dstT=%s -D srcT1=%s"
|
|
|
|
|
" -D workT=%s -D wdepth=%d -D convertToWT1=%s -D convertToDT=%s"
|
|
|
|
|
" -D workT1=%s -D rowsPerWI=%d%s",
|
|
|
|
|
ocl::typeToStr(CV_8UC(kercn)),
|
|
|
|
|
ocl::typeToStr(CV_MAKE_TYPE(depth, kercn)),
|
|
|
|
|
ocl::typeToStr(CV_MAKE_TYPE(wdepth, kercn)), wdepth,
|
|
|
|
|
ocl::convertTypeStr(depth, wdepth, kercn, cvt[0]),
|
|
|
|
|
ocl::convertTypeStr(wdepth, CV_8U, kercn, cvt[1]),
|
|
|
|
|
ocl::typeToStr(wdepth), rowsPerWI,
|
|
|
|
|
doubleSupport ? " -D DOUBLE_SUPPORT" : "");
|
|
|
|
|
ocl::Kernel k("KF", ocl::core::arithm_oclsrc, build_opt);
|
|
|
|
|
if (k.empty())
|
|
|
|
|
return false;
|
|
|
|
|
|
|
|
|
|
UMat src = _src.getUMat();
|
|
|
|
|
UMat dst = _dst.getUMat();
|
|
|
|
|
|
|
|
|
|
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
|
|
|
|
|
dstarg = ocl::KernelArg::WriteOnly(dst, cn, kercn);
|
|
|
|
|
|
|
|
|
|
if (wdepth == CV_32F)
|
|
|
|
|
k.args(srcarg, dstarg, (float)alpha, (float)beta);
|
|
|
|
|
else if (wdepth == CV_64F)
|
|
|
|
|
k.args(srcarg, dstarg, alpha, beta);
|
|
|
|
|
|
|
|
|
|
size_t globalsize[2] = { (size_t)src.cols * cn / kercn, ((size_t)src.rows + rowsPerWI - 1) / rowsPerWI };
|
|
|
|
|
return k.run(2, globalsize, NULL, false);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
#endif
|
|
|
|
|
|
|
|
|
|
} //cv::
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
void cv::convertScaleAbs( InputArray _src, OutputArray _dst, double alpha, double beta )
|
|
|
|
|
{
|
2018-09-14 05:35:26 +08:00
|
|
|
CV_INSTRUMENT_REGION();
|
2018-02-06 20:02:51 +08:00
|
|
|
|
|
|
|
|
CV_OCL_RUN(_src.dims() <= 2 && _dst.isUMat(),
|
|
|
|
|
ocl_convertScaleAbs(_src, _dst, alpha, beta))
|
|
|
|
|
|
|
|
|
|
Mat src = _src.getMat();
|
|
|
|
|
int cn = src.channels();
|
|
|
|
|
double scale[] = {alpha, beta};
|
|
|
|
|
_dst.create( src.dims, src.size, CV_8UC(cn) );
|
|
|
|
|
Mat dst = _dst.getMat();
|
|
|
|
|
BinaryFunc func = getCvtScaleAbsFunc(src.depth());
|
|
|
|
|
CV_Assert( func != 0 );
|
|
|
|
|
|
|
|
|
|
if( src.dims <= 2 )
|
|
|
|
|
{
|
|
|
|
|
Size sz = getContinuousSize(src, dst, cn);
|
|
|
|
|
func( src.ptr(), src.step, 0, 0, dst.ptr(), dst.step, sz, scale );
|
|
|
|
|
}
|
|
|
|
|
else
|
|
|
|
|
{
|
|
|
|
|
const Mat* arrays[] = {&src, &dst, 0};
|
2018-09-04 21:44:47 +08:00
|
|
|
uchar* ptrs[2] = {};
|
2018-02-06 20:02:51 +08:00
|
|
|
NAryMatIterator it(arrays, ptrs);
|
|
|
|
|
Size sz((int)it.size*cn, 1);
|
|
|
|
|
|
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
|
|
|
func( ptrs[0], 0, 0, 0, ptrs[1], 0, sz, scale );
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
//==================================================================================================
|
|
|
|
|
|
|
|
|
|
namespace cv {
|
|
|
|
|
|
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
|
|
|
|
|
|
static bool ocl_normalize( InputArray _src, InputOutputArray _dst, InputArray _mask, int dtype,
|
|
|
|
|
double scale, double delta )
|
|
|
|
|
{
|
|
|
|
|
UMat src = _src.getUMat();
|
|
|
|
|
|
|
|
|
|
if( _mask.empty() )
|
|
|
|
|
src.convertTo( _dst, dtype, scale, delta );
|
|
|
|
|
else if (src.channels() <= 4)
|
|
|
|
|
{
|
|
|
|
|
const ocl::Device & dev = ocl::Device::getDefault();
|
|
|
|
|
|
|
|
|
|
int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype),
|
|
|
|
|
ddepth = CV_MAT_DEPTH(dtype), wdepth = std::max(CV_32F, std::max(sdepth, ddepth)),
|
|
|
|
|
rowsPerWI = dev.isIntel() ? 4 : 1;
|
|
|
|
|
|
|
|
|
|
float fscale = static_cast<float>(scale), fdelta = static_cast<float>(delta);
|
|
|
|
|
bool haveScale = std::fabs(scale - 1) > DBL_EPSILON,
|
|
|
|
|
haveZeroScale = !(std::fabs(scale) > DBL_EPSILON),
|
|
|
|
|
haveDelta = std::fabs(delta) > DBL_EPSILON,
|
|
|
|
|
doubleSupport = dev.doubleFPConfig() > 0;
|
|
|
|
|
|
|
|
|
|
if (!haveScale && !haveDelta && stype == dtype)
|
|
|
|
|
{
|
|
|
|
|
_src.copyTo(_dst, _mask);
|
|
|
|
|
return true;
|
|
|
|
|
}
|
|
|
|
|
if (haveZeroScale)
|
|
|
|
|
{
|
|
|
|
|
_dst.setTo(Scalar(delta), _mask);
|
|
|
|
|
return true;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
if ((sdepth == CV_64F || ddepth == CV_64F) && !doubleSupport)
|
|
|
|
|
return false;
|
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|
|
|
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|
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|
char cvt[2][40];
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|
|
|
|
String opts = format("-D srcT=%s -D dstT=%s -D convertToWT=%s -D cn=%d -D rowsPerWI=%d"
|
|
|
|
|
" -D convertToDT=%s -D workT=%s%s%s%s -D srcT1=%s -D dstT1=%s",
|
|
|
|
|
ocl::typeToStr(stype), ocl::typeToStr(dtype),
|
|
|
|
|
ocl::convertTypeStr(sdepth, wdepth, cn, cvt[0]), cn,
|
|
|
|
|
rowsPerWI, ocl::convertTypeStr(wdepth, ddepth, cn, cvt[1]),
|
|
|
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|
ocl::typeToStr(CV_MAKE_TYPE(wdepth, cn)),
|
|
|
|
|
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
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|
|
|
|
haveScale ? " -D HAVE_SCALE" : "",
|
|
|
|
|
haveDelta ? " -D HAVE_DELTA" : "",
|
|
|
|
|
ocl::typeToStr(sdepth), ocl::typeToStr(ddepth));
|
|
|
|
|
|
|
|
|
|
ocl::Kernel k("normalizek", ocl::core::normalize_oclsrc, opts);
|
|
|
|
|
if (k.empty())
|
|
|
|
|
return false;
|
|
|
|
|
|
|
|
|
|
UMat mask = _mask.getUMat(), dst = _dst.getUMat();
|
|
|
|
|
|
|
|
|
|
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
|
|
|
|
|
maskarg = ocl::KernelArg::ReadOnlyNoSize(mask),
|
|
|
|
|
dstarg = ocl::KernelArg::ReadWrite(dst);
|
|
|
|
|
|
|
|
|
|
if (haveScale)
|
|
|
|
|
{
|
|
|
|
|
if (haveDelta)
|
|
|
|
|
k.args(srcarg, maskarg, dstarg, fscale, fdelta);
|
|
|
|
|
else
|
|
|
|
|
k.args(srcarg, maskarg, dstarg, fscale);
|
|
|
|
|
}
|
|
|
|
|
else
|
|
|
|
|
{
|
|
|
|
|
if (haveDelta)
|
|
|
|
|
k.args(srcarg, maskarg, dstarg, fdelta);
|
|
|
|
|
else
|
|
|
|
|
k.args(srcarg, maskarg, dstarg);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
size_t globalsize[2] = { (size_t)src.cols, ((size_t)src.rows + rowsPerWI - 1) / rowsPerWI };
|
|
|
|
|
return k.run(2, globalsize, NULL, false);
|
|
|
|
|
}
|
|
|
|
|
else
|
|
|
|
|
{
|
|
|
|
|
UMat temp;
|
|
|
|
|
src.convertTo( temp, dtype, scale, delta );
|
|
|
|
|
temp.copyTo( _dst, _mask );
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
return true;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
#endif
|
|
|
|
|
|
|
|
|
|
} // cv::
|
|
|
|
|
|
|
|
|
|
void cv::normalize( InputArray _src, InputOutputArray _dst, double a, double b,
|
|
|
|
|
int norm_type, int rtype, InputArray _mask )
|
|
|
|
|
{
|
2018-09-14 05:35:26 +08:00
|
|
|
CV_INSTRUMENT_REGION();
|
2018-02-06 20:02:51 +08:00
|
|
|
|
|
|
|
|
double scale = 1, shift = 0;
|
2018-04-28 19:14:10 +08:00
|
|
|
int type = _src.type(), depth = CV_MAT_DEPTH(type);
|
|
|
|
|
|
|
|
|
|
if( rtype < 0 )
|
|
|
|
|
rtype = _dst.fixedType() ? _dst.depth() : depth;
|
|
|
|
|
|
2018-02-06 20:02:51 +08:00
|
|
|
if( norm_type == CV_MINMAX )
|
|
|
|
|
{
|
|
|
|
|
double smin = 0, smax = 0;
|
|
|
|
|
double dmin = MIN( a, b ), dmax = MAX( a, b );
|
|
|
|
|
minMaxIdx( _src, &smin, &smax, 0, 0, _mask );
|
|
|
|
|
scale = (dmax - dmin)*(smax - smin > DBL_EPSILON ? 1./(smax - smin) : 0);
|
2018-04-28 19:14:10 +08:00
|
|
|
if( rtype == CV_32F )
|
|
|
|
|
{
|
|
|
|
|
scale = (float)scale;
|
|
|
|
|
shift = (float)dmin - (float)(smin*scale);
|
|
|
|
|
}
|
|
|
|
|
else
|
|
|
|
|
shift = dmin - smin*scale;
|
2018-02-06 20:02:51 +08:00
|
|
|
}
|
|
|
|
|
else if( norm_type == CV_L2 || norm_type == CV_L1 || norm_type == CV_C )
|
|
|
|
|
{
|
|
|
|
|
scale = norm( _src, norm_type, _mask );
|
|
|
|
|
scale = scale > DBL_EPSILON ? a/scale : 0.;
|
|
|
|
|
shift = 0;
|
|
|
|
|
}
|
|
|
|
|
else
|
|
|
|
|
CV_Error( CV_StsBadArg, "Unknown/unsupported norm type" );
|
|
|
|
|
|
|
|
|
|
CV_OCL_RUN(_dst.isUMat(),
|
|
|
|
|
ocl_normalize(_src, _dst, _mask, rtype, scale, shift))
|
|
|
|
|
|
|
|
|
|
Mat src = _src.getMat();
|
|
|
|
|
if( _mask.empty() )
|
|
|
|
|
src.convertTo( _dst, rtype, scale, shift );
|
|
|
|
|
else
|
|
|
|
|
{
|
|
|
|
|
Mat temp;
|
|
|
|
|
src.convertTo( temp, rtype, scale, shift );
|
|
|
|
|
temp.copyTo( _dst, _mask );
|
|
|
|
|
}
|
|
|
|
|
}
|
