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45958eaabc
IPP can be switched on and off on runtime; Optional implementation collector was added (switched off by default in CMake). Gathers data of implementation used in functions and report this info through performance TS; TS modifications for implementations control;
3851 lines
139 KiB
C++
3851 lines
139 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include <climits>
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#include <limits>
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#include "opencl_kernels_core.hpp"
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namespace cv
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{
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template<typename T> static inline Scalar rawToScalar(const T& v)
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{
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Scalar s;
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typedef typename DataType<T>::channel_type T1;
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int i, n = DataType<T>::channels;
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for( i = 0; i < n; i++ )
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s.val[i] = ((T1*)&v)[i];
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return s;
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}
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/****************************************************************************************\
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* sum *
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\****************************************************************************************/
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template <typename T, typename ST>
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struct Sum_SIMD
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{
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int operator () (const T *, const uchar *, ST *, int, int) const
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{
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return 0;
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}
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};
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#if CV_NEON
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template <>
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struct Sum_SIMD<uchar, int>
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{
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int operator () (const uchar * src0, const uchar * mask, int * dst, int len, int cn) const
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{
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if (mask || (cn != 1 && cn != 2 && cn != 4))
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return 0;
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int x = 0;
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uint32x4_t v_sum = vdupq_n_u32(0u);
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for ( ; x <= len - 16; x += 16)
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{
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uint8x16_t v_src = vld1q_u8(src0 + x);
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uint16x8_t v_half = vmovl_u8(vget_low_u8(v_src));
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v_sum = vaddw_u16(v_sum, vget_low_u16(v_half));
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v_sum = vaddw_u16(v_sum, vget_high_u16(v_half));
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v_half = vmovl_u8(vget_high_u8(v_src));
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v_sum = vaddw_u16(v_sum, vget_low_u16(v_half));
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v_sum = vaddw_u16(v_sum, vget_high_u16(v_half));
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}
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for ( ; x <= len - 8; x += 8)
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{
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uint16x8_t v_src = vmovl_u8(vld1_u8(src0 + x));
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v_sum = vaddw_u16(v_sum, vget_low_u16(v_src));
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v_sum = vaddw_u16(v_sum, vget_high_u16(v_src));
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}
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unsigned int CV_DECL_ALIGNED(16) ar[4];
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vst1q_u32(ar, v_sum);
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for (int i = 0; i < 4; i += cn)
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for (int j = 0; j < cn; ++j)
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dst[j] += ar[j + i];
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return x / cn;
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}
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};
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template <>
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struct Sum_SIMD<schar, int>
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{
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int operator () (const schar * src0, const uchar * mask, int * dst, int len, int cn) const
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{
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if (mask || (cn != 1 && cn != 2 && cn != 4))
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return 0;
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int x = 0;
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int32x4_t v_sum = vdupq_n_s32(0);
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for ( ; x <= len - 16; x += 16)
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{
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int8x16_t v_src = vld1q_s8(src0 + x);
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int16x8_t v_half = vmovl_s8(vget_low_s8(v_src));
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v_sum = vaddw_s16(v_sum, vget_low_s16(v_half));
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v_sum = vaddw_s16(v_sum, vget_high_s16(v_half));
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v_half = vmovl_s8(vget_high_s8(v_src));
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v_sum = vaddw_s16(v_sum, vget_low_s16(v_half));
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v_sum = vaddw_s16(v_sum, vget_high_s16(v_half));
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}
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for ( ; x <= len - 8; x += 8)
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{
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int16x8_t v_src = vmovl_s8(vld1_s8(src0 + x));
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v_sum = vaddw_s16(v_sum, vget_low_s16(v_src));
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v_sum = vaddw_s16(v_sum, vget_high_s16(v_src));
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}
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int CV_DECL_ALIGNED(16) ar[4];
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vst1q_s32(ar, v_sum);
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for (int i = 0; i < 4; i += cn)
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for (int j = 0; j < cn; ++j)
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dst[j] += ar[j + i];
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return x / cn;
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}
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};
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template <>
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struct Sum_SIMD<ushort, int>
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{
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int operator () (const ushort * src0, const uchar * mask, int * dst, int len, int cn) const
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{
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if (mask || (cn != 1 && cn != 2 && cn != 4))
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return 0;
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int x = 0;
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uint32x4_t v_sum = vdupq_n_u32(0u);
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for ( ; x <= len - 8; x += 8)
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{
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uint16x8_t v_src = vld1q_u16(src0 + x);
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v_sum = vaddw_u16(v_sum, vget_low_u16(v_src));
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v_sum = vaddw_u16(v_sum, vget_high_u16(v_src));
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}
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for ( ; x <= len - 4; x += 4)
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v_sum = vaddw_u16(v_sum, vld1_u16(src0 + x));
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unsigned int CV_DECL_ALIGNED(16) ar[4];
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vst1q_u32(ar, v_sum);
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for (int i = 0; i < 4; i += cn)
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for (int j = 0; j < cn; ++j)
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dst[j] += ar[j + i];
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return x / cn;
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}
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};
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template <>
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struct Sum_SIMD<short, int>
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{
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int operator () (const short * src0, const uchar * mask, int * dst, int len, int cn) const
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{
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if (mask || (cn != 1 && cn != 2 && cn != 4))
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return 0;
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int x = 0;
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int32x4_t v_sum = vdupq_n_s32(0u);
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for ( ; x <= len - 8; x += 8)
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{
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int16x8_t v_src = vld1q_s16(src0 + x);
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v_sum = vaddw_s16(v_sum, vget_low_s16(v_src));
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v_sum = vaddw_s16(v_sum, vget_high_s16(v_src));
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}
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for ( ; x <= len - 4; x += 4)
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v_sum = vaddw_s16(v_sum, vld1_s16(src0 + x));
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int CV_DECL_ALIGNED(16) ar[4];
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vst1q_s32(ar, v_sum);
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for (int i = 0; i < 4; i += cn)
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for (int j = 0; j < cn; ++j)
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dst[j] += ar[j + i];
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return x / cn;
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}
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};
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#endif
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template<typename T, typename ST>
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static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn )
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{
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const T* src = src0;
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if( !mask )
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{
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Sum_SIMD<T, ST> vop;
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int i = vop(src0, mask, dst, len, cn), k = cn % 4;
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src += i * cn;
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if( k == 1 )
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{
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ST s0 = dst[0];
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#if CV_ENABLE_UNROLLED
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for(; i <= len - 4; i += 4, src += cn*4 )
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s0 += src[0] + src[cn] + src[cn*2] + src[cn*3];
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#endif
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for( ; i < len; i++, src += cn )
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s0 += src[0];
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dst[0] = s0;
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}
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else if( k == 2 )
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{
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ST s0 = dst[0], s1 = dst[1];
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for( ; i < len; i++, src += cn )
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{
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s0 += src[0];
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s1 += src[1];
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}
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dst[0] = s0;
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dst[1] = s1;
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}
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else if( k == 3 )
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{
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ST s0 = dst[0], s1 = dst[1], s2 = dst[2];
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for( ; i < len; i++, src += cn )
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{
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s0 += src[0];
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s1 += src[1];
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s2 += src[2];
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}
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dst[0] = s0;
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dst[1] = s1;
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dst[2] = s2;
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}
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for( ; k < cn; k += 4 )
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{
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src = src0 + i*cn + k;
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ST s0 = dst[k], s1 = dst[k+1], s2 = dst[k+2], s3 = dst[k+3];
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for( ; i < len; i++, src += cn )
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{
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s0 += src[0]; s1 += src[1];
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s2 += src[2]; s3 += src[3];
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}
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dst[k] = s0;
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dst[k+1] = s1;
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dst[k+2] = s2;
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dst[k+3] = s3;
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}
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return len;
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}
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int i, nzm = 0;
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if( cn == 1 )
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{
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ST s = dst[0];
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for( i = 0; i < len; i++ )
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if( mask[i] )
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{
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s += src[i];
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nzm++;
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}
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dst[0] = s;
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}
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else if( cn == 3 )
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{
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ST s0 = dst[0], s1 = dst[1], s2 = dst[2];
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for( i = 0; i < len; i++, src += 3 )
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if( mask[i] )
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{
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s0 += src[0];
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s1 += src[1];
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s2 += src[2];
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nzm++;
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}
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dst[0] = s0;
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dst[1] = s1;
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dst[2] = s2;
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}
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else
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{
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for( i = 0; i < len; i++, src += cn )
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if( mask[i] )
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{
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int k = 0;
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#if CV_ENABLE_UNROLLED
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for( ; k <= cn - 4; k += 4 )
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{
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ST s0, s1;
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s0 = dst[k] + src[k];
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s1 = dst[k+1] + src[k+1];
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dst[k] = s0; dst[k+1] = s1;
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s0 = dst[k+2] + src[k+2];
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s1 = dst[k+3] + src[k+3];
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dst[k+2] = s0; dst[k+3] = s1;
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}
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#endif
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for( ; k < cn; k++ )
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dst[k] += src[k];
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nzm++;
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}
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}
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return nzm;
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}
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static int sum8u( const uchar* src, const uchar* mask, int* dst, int len, int cn )
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{ return sum_(src, mask, dst, len, cn); }
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static int sum8s( const schar* src, const uchar* mask, int* dst, int len, int cn )
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{ return sum_(src, mask, dst, len, cn); }
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static int sum16u( const ushort* src, const uchar* mask, int* dst, int len, int cn )
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{ return sum_(src, mask, dst, len, cn); }
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static int sum16s( const short* src, const uchar* mask, int* dst, int len, int cn )
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{ return sum_(src, mask, dst, len, cn); }
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static int sum32s( const int* src, const uchar* mask, double* dst, int len, int cn )
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{ return sum_(src, mask, dst, len, cn); }
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static int sum32f( const float* src, const uchar* mask, double* dst, int len, int cn )
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{ return sum_(src, mask, dst, len, cn); }
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static int sum64f( const double* src, const uchar* mask, double* dst, int len, int cn )
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{ return sum_(src, mask, dst, len, cn); }
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typedef int (*SumFunc)(const uchar*, const uchar* mask, uchar*, int, int);
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static SumFunc getSumFunc(int depth)
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{
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static SumFunc sumTab[] =
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{
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(SumFunc)GET_OPTIMIZED(sum8u), (SumFunc)sum8s,
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(SumFunc)sum16u, (SumFunc)sum16s,
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(SumFunc)sum32s,
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(SumFunc)GET_OPTIMIZED(sum32f), (SumFunc)sum64f,
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0
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};
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return sumTab[depth];
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}
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template<typename T>
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static int countNonZero_(const T* src, int len )
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{
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int i=0, nz = 0;
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#if CV_ENABLE_UNROLLED
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for(; i <= len - 4; i += 4 )
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nz += (src[i] != 0) + (src[i+1] != 0) + (src[i+2] != 0) + (src[i+3] != 0);
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#endif
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for( ; i < len; i++ )
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nz += src[i] != 0;
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return nz;
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}
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static int countNonZero8u( const uchar* src, int len )
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{
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int i=0, nz = 0;
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#if CV_SSE2
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if(USE_SSE2)//5x-6x
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{
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__m128i pattern = _mm_setzero_si128 ();
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static uchar tab[256];
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static volatile bool initialized = false;
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if( !initialized )
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{
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// we compute inverse popcount table,
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// since we pass (img[x] == 0) mask as index in the table.
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for( int j = 0; j < 256; j++ )
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{
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int val = 0;
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for( int mask = 1; mask < 256; mask += mask )
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val += (j & mask) == 0;
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tab[j] = (uchar)val;
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}
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initialized = true;
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}
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for (; i<=len-16; i+=16)
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{
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__m128i r0 = _mm_loadu_si128((const __m128i*)(src+i));
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int val = _mm_movemask_epi8(_mm_cmpeq_epi8(r0, pattern));
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nz += tab[val & 255] + tab[val >> 8];
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}
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}
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#elif CV_NEON
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int len0 = len & -16, blockSize1 = (1 << 8) - 16, blockSize0 = blockSize1 << 6;
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uint32x4_t v_nz = vdupq_n_u32(0u);
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uint8x16_t v_zero = vdupq_n_u8(0), v_1 = vdupq_n_u8(1);
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const uchar * src0 = src;
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while( i < len0 )
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{
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int blockSizei = std::min(len0 - i, blockSize0), j = 0;
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while (j < blockSizei)
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{
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int blockSizej = std::min(blockSizei - j, blockSize1), k = 0;
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uint8x16_t v_pz = v_zero;
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for( ; k <= blockSizej - 16; k += 16 )
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v_pz = vaddq_u8(v_pz, vandq_u8(vceqq_u8(vld1q_u8(src0 + k), v_zero), v_1));
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uint16x8_t v_p1 = vmovl_u8(vget_low_u8(v_pz)), v_p2 = vmovl_u8(vget_high_u8(v_pz));
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v_nz = vaddq_u32(vaddl_u16(vget_low_u16(v_p1), vget_high_u16(v_p1)), v_nz);
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v_nz = vaddq_u32(vaddl_u16(vget_low_u16(v_p2), vget_high_u16(v_p2)), v_nz);
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src0 += blockSizej;
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j += blockSizej;
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}
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i += blockSizei;
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}
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CV_DECL_ALIGNED(16) unsigned int buf[4];
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vst1q_u32(buf, v_nz);
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nz += i - saturate_cast<int>(buf[0] + buf[1] + buf[2] + buf[3]);
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#endif
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for( ; i < len; i++ )
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nz += src[i] != 0;
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return nz;
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}
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static int countNonZero16u( const ushort* src, int len )
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{
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int i = 0, nz = 0;
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#if CV_NEON
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int len0 = len & -8, blockSize1 = (1 << 15), blockSize0 = blockSize1 << 6;
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uint32x4_t v_nz = vdupq_n_u32(0u);
|
|
uint16x8_t v_zero = vdupq_n_u16(0), v_1 = vdupq_n_u16(1);
|
|
|
|
while( i < len0 )
|
|
{
|
|
int blockSizei = std::min(len0 - i, blockSize0), j = 0;
|
|
|
|
while (j < blockSizei)
|
|
{
|
|
int blockSizej = std::min(blockSizei - j, blockSize1), k = 0;
|
|
uint16x8_t v_pz = v_zero;
|
|
|
|
for( ; k <= blockSizej - 8; k += 8 )
|
|
v_pz = vaddq_u16(v_pz, vandq_u16(vceqq_u16(vld1q_u16(src + k), v_zero), v_1));
|
|
|
|
v_nz = vaddq_u32(vaddl_u16(vget_low_u16(v_pz), vget_high_u16(v_pz)), v_nz);
|
|
|
|
src += blockSizej;
|
|
j += blockSizej;
|
|
}
|
|
|
|
i += blockSizei;
|
|
}
|
|
|
|
CV_DECL_ALIGNED(16) unsigned int buf[4];
|
|
vst1q_u32(buf, v_nz);
|
|
nz += i - saturate_cast<int>(buf[0] + buf[1] + buf[2] + buf[3]);
|
|
#endif
|
|
return nz + countNonZero_(src, len - i);
|
|
}
|
|
|
|
static int countNonZero32s( const int* src, int len )
|
|
{
|
|
int i = 0, nz = 0;
|
|
#if CV_NEON
|
|
int len0 = len & -8, blockSize1 = (1 << 15), blockSize0 = blockSize1 << 6;
|
|
uint32x4_t v_nz = vdupq_n_u32(0u);
|
|
int32x4_t v_zero = vdupq_n_s32(0.0f);
|
|
uint16x8_t v_1 = vdupq_n_u16(1u), v_zerou = vdupq_n_u16(0u);
|
|
|
|
while( i < len0 )
|
|
{
|
|
int blockSizei = std::min(len0 - i, blockSize0), j = 0;
|
|
|
|
while (j < blockSizei)
|
|
{
|
|
int blockSizej = std::min(blockSizei - j, blockSize1), k = 0;
|
|
uint16x8_t v_pz = v_zerou;
|
|
|
|
for( ; k <= blockSizej - 8; k += 8 )
|
|
v_pz = vaddq_u16(v_pz, vandq_u16(vcombine_u16(vmovn_u32(vceqq_s32(vld1q_s32(src + k), v_zero)),
|
|
vmovn_u32(vceqq_s32(vld1q_s32(src + k + 4), v_zero))), v_1));
|
|
|
|
v_nz = vaddq_u32(vaddl_u16(vget_low_u16(v_pz), vget_high_u16(v_pz)), v_nz);
|
|
|
|
src += blockSizej;
|
|
j += blockSizej;
|
|
}
|
|
|
|
i += blockSizei;
|
|
}
|
|
|
|
CV_DECL_ALIGNED(16) unsigned int buf[4];
|
|
vst1q_u32(buf, v_nz);
|
|
nz += i - saturate_cast<int>(buf[0] + buf[1] + buf[2] + buf[3]);
|
|
#endif
|
|
return nz + countNonZero_(src, len - i);
|
|
}
|
|
|
|
static int countNonZero32f( const float* src, int len )
|
|
{
|
|
int i = 0, nz = 0;
|
|
#if CV_NEON
|
|
int len0 = len & -8, blockSize1 = (1 << 15), blockSize0 = blockSize1 << 6;
|
|
uint32x4_t v_nz = vdupq_n_u32(0u);
|
|
float32x4_t v_zero = vdupq_n_f32(0.0f);
|
|
uint16x8_t v_1 = vdupq_n_u16(1u), v_zerou = vdupq_n_u16(0u);
|
|
|
|
while( i < len0 )
|
|
{
|
|
int blockSizei = std::min(len0 - i, blockSize0), j = 0;
|
|
|
|
while (j < blockSizei)
|
|
{
|
|
int blockSizej = std::min(blockSizei - j, blockSize1), k = 0;
|
|
uint16x8_t v_pz = v_zerou;
|
|
|
|
for( ; k <= blockSizej - 8; k += 8 )
|
|
v_pz = vaddq_u16(v_pz, vandq_u16(vcombine_u16(vmovn_u32(vceqq_f32(vld1q_f32(src + k), v_zero)),
|
|
vmovn_u32(vceqq_f32(vld1q_f32(src + k + 4), v_zero))), v_1));
|
|
|
|
v_nz = vaddq_u32(vaddl_u16(vget_low_u16(v_pz), vget_high_u16(v_pz)), v_nz);
|
|
|
|
src += blockSizej;
|
|
j += blockSizej;
|
|
}
|
|
|
|
i += blockSizei;
|
|
}
|
|
|
|
CV_DECL_ALIGNED(16) unsigned int buf[4];
|
|
vst1q_u32(buf, v_nz);
|
|
nz += i - saturate_cast<int>(buf[0] + buf[1] + buf[2] + buf[3]);
|
|
#endif
|
|
return nz + countNonZero_(src, len - i);
|
|
}
|
|
|
|
static int countNonZero64f( const double* src, int len )
|
|
{ return countNonZero_(src, len); }
|
|
|
|
typedef int (*CountNonZeroFunc)(const uchar*, int);
|
|
|
|
static CountNonZeroFunc getCountNonZeroTab(int depth)
|
|
{
|
|
static CountNonZeroFunc countNonZeroTab[] =
|
|
{
|
|
(CountNonZeroFunc)GET_OPTIMIZED(countNonZero8u), (CountNonZeroFunc)GET_OPTIMIZED(countNonZero8u),
|
|
(CountNonZeroFunc)GET_OPTIMIZED(countNonZero16u), (CountNonZeroFunc)GET_OPTIMIZED(countNonZero16u),
|
|
(CountNonZeroFunc)GET_OPTIMIZED(countNonZero32s), (CountNonZeroFunc)GET_OPTIMIZED(countNonZero32f),
|
|
(CountNonZeroFunc)GET_OPTIMIZED(countNonZero64f), 0
|
|
};
|
|
|
|
return countNonZeroTab[depth];
|
|
}
|
|
|
|
template<typename T, typename ST, typename SQT>
|
|
static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, int len, int cn )
|
|
{
|
|
const T* src = src0;
|
|
|
|
if( !mask )
|
|
{
|
|
int i;
|
|
int k = cn % 4;
|
|
|
|
if( k == 1 )
|
|
{
|
|
ST s0 = sum[0];
|
|
SQT sq0 = sqsum[0];
|
|
for( i = 0; i < len; i++, src += cn )
|
|
{
|
|
T v = src[0];
|
|
s0 += v; sq0 += (SQT)v*v;
|
|
}
|
|
sum[0] = s0;
|
|
sqsum[0] = sq0;
|
|
}
|
|
else if( k == 2 )
|
|
{
|
|
ST s0 = sum[0], s1 = sum[1];
|
|
SQT sq0 = sqsum[0], sq1 = sqsum[1];
|
|
for( i = 0; i < len; i++, src += cn )
|
|
{
|
|
T v0 = src[0], v1 = src[1];
|
|
s0 += v0; sq0 += (SQT)v0*v0;
|
|
s1 += v1; sq1 += (SQT)v1*v1;
|
|
}
|
|
sum[0] = s0; sum[1] = s1;
|
|
sqsum[0] = sq0; sqsum[1] = sq1;
|
|
}
|
|
else if( k == 3 )
|
|
{
|
|
ST s0 = sum[0], s1 = sum[1], s2 = sum[2];
|
|
SQT sq0 = sqsum[0], sq1 = sqsum[1], sq2 = sqsum[2];
|
|
for( i = 0; i < len; i++, src += cn )
|
|
{
|
|
T v0 = src[0], v1 = src[1], v2 = src[2];
|
|
s0 += v0; sq0 += (SQT)v0*v0;
|
|
s1 += v1; sq1 += (SQT)v1*v1;
|
|
s2 += v2; sq2 += (SQT)v2*v2;
|
|
}
|
|
sum[0] = s0; sum[1] = s1; sum[2] = s2;
|
|
sqsum[0] = sq0; sqsum[1] = sq1; sqsum[2] = sq2;
|
|
}
|
|
|
|
for( ; k < cn; k += 4 )
|
|
{
|
|
src = src0 + k;
|
|
ST s0 = sum[k], s1 = sum[k+1], s2 = sum[k+2], s3 = sum[k+3];
|
|
SQT sq0 = sqsum[k], sq1 = sqsum[k+1], sq2 = sqsum[k+2], sq3 = sqsum[k+3];
|
|
for( i = 0; i < len; i++, src += cn )
|
|
{
|
|
T v0, v1;
|
|
v0 = src[0], v1 = src[1];
|
|
s0 += v0; sq0 += (SQT)v0*v0;
|
|
s1 += v1; sq1 += (SQT)v1*v1;
|
|
v0 = src[2], v1 = src[3];
|
|
s2 += v0; sq2 += (SQT)v0*v0;
|
|
s3 += v1; sq3 += (SQT)v1*v1;
|
|
}
|
|
sum[k] = s0; sum[k+1] = s1;
|
|
sum[k+2] = s2; sum[k+3] = s3;
|
|
sqsum[k] = sq0; sqsum[k+1] = sq1;
|
|
sqsum[k+2] = sq2; sqsum[k+3] = sq3;
|
|
}
|
|
return len;
|
|
}
|
|
|
|
int i, nzm = 0;
|
|
|
|
if( cn == 1 )
|
|
{
|
|
ST s0 = sum[0];
|
|
SQT sq0 = sqsum[0];
|
|
for( i = 0; i < len; i++ )
|
|
if( mask[i] )
|
|
{
|
|
T v = src[i];
|
|
s0 += v; sq0 += (SQT)v*v;
|
|
nzm++;
|
|
}
|
|
sum[0] = s0;
|
|
sqsum[0] = sq0;
|
|
}
|
|
else if( cn == 3 )
|
|
{
|
|
ST s0 = sum[0], s1 = sum[1], s2 = sum[2];
|
|
SQT sq0 = sqsum[0], sq1 = sqsum[1], sq2 = sqsum[2];
|
|
for( i = 0; i < len; i++, src += 3 )
|
|
if( mask[i] )
|
|
{
|
|
T v0 = src[0], v1 = src[1], v2 = src[2];
|
|
s0 += v0; sq0 += (SQT)v0*v0;
|
|
s1 += v1; sq1 += (SQT)v1*v1;
|
|
s2 += v2; sq2 += (SQT)v2*v2;
|
|
nzm++;
|
|
}
|
|
sum[0] = s0; sum[1] = s1; sum[2] = s2;
|
|
sqsum[0] = sq0; sqsum[1] = sq1; sqsum[2] = sq2;
|
|
}
|
|
else
|
|
{
|
|
for( i = 0; i < len; i++, src += cn )
|
|
if( mask[i] )
|
|
{
|
|
for( int k = 0; k < cn; k++ )
|
|
{
|
|
T v = src[k];
|
|
ST s = sum[k] + v;
|
|
SQT sq = sqsum[k] + (SQT)v*v;
|
|
sum[k] = s; sqsum[k] = sq;
|
|
}
|
|
nzm++;
|
|
}
|
|
}
|
|
return nzm;
|
|
}
|
|
|
|
|
|
static int sqsum8u( const uchar* src, const uchar* mask, int* sum, int* sqsum, int len, int cn )
|
|
{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
|
|
|
|
static int sqsum8s( const schar* src, const uchar* mask, int* sum, int* sqsum, int len, int cn )
|
|
{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
|
|
|
|
static int sqsum16u( const ushort* src, const uchar* mask, int* sum, double* sqsum, int len, int cn )
|
|
{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
|
|
|
|
static int sqsum16s( const short* src, const uchar* mask, int* sum, double* sqsum, int len, int cn )
|
|
{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
|
|
|
|
static int sqsum32s( const int* src, const uchar* mask, double* sum, double* sqsum, int len, int cn )
|
|
{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
|
|
|
|
static int sqsum32f( const float* src, const uchar* mask, double* sum, double* sqsum, int len, int cn )
|
|
{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
|
|
|
|
static int sqsum64f( const double* src, const uchar* mask, double* sum, double* sqsum, int len, int cn )
|
|
{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
|
|
|
|
typedef int (*SumSqrFunc)(const uchar*, const uchar* mask, uchar*, uchar*, int, int);
|
|
|
|
static SumSqrFunc getSumSqrTab(int depth)
|
|
{
|
|
static SumSqrFunc sumSqrTab[] =
|
|
{
|
|
(SumSqrFunc)GET_OPTIMIZED(sqsum8u), (SumSqrFunc)sqsum8s, (SumSqrFunc)sqsum16u, (SumSqrFunc)sqsum16s,
|
|
(SumSqrFunc)sqsum32s, (SumSqrFunc)GET_OPTIMIZED(sqsum32f), (SumSqrFunc)sqsum64f, 0
|
|
};
|
|
|
|
return sumSqrTab[depth];
|
|
}
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
template <typename T> Scalar ocl_part_sum(Mat m)
|
|
{
|
|
CV_Assert(m.rows == 1);
|
|
|
|
Scalar s = Scalar::all(0);
|
|
int cn = m.channels();
|
|
const T * const ptr = m.ptr<T>(0);
|
|
|
|
for (int x = 0, w = m.cols * cn; x < w; )
|
|
for (int c = 0; c < cn; ++c, ++x)
|
|
s[c] += ptr[x];
|
|
|
|
return s;
|
|
}
|
|
|
|
enum { OCL_OP_SUM = 0, OCL_OP_SUM_ABS = 1, OCL_OP_SUM_SQR = 2 };
|
|
|
|
static bool ocl_sum( InputArray _src, Scalar & res, int sum_op, InputArray _mask = noArray(),
|
|
InputArray _src2 = noArray(), bool calc2 = false, const Scalar & res2 = Scalar() )
|
|
{
|
|
CV_Assert(sum_op == OCL_OP_SUM || sum_op == OCL_OP_SUM_ABS || sum_op == OCL_OP_SUM_SQR);
|
|
|
|
const ocl::Device & dev = ocl::Device::getDefault();
|
|
bool doubleSupport = dev.doubleFPConfig() > 0,
|
|
haveMask = _mask.kind() != _InputArray::NONE,
|
|
haveSrc2 = _src2.kind() != _InputArray::NONE;
|
|
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
|
|
kercn = cn == 1 && !haveMask ? ocl::predictOptimalVectorWidth(_src, _src2) : 1,
|
|
mcn = std::max(cn, kercn);
|
|
CV_Assert(!haveSrc2 || _src2.type() == type);
|
|
int convert_cn = haveSrc2 ? mcn : cn;
|
|
|
|
if ( (!doubleSupport && depth == CV_64F) || cn > 4 )
|
|
return false;
|
|
|
|
int ngroups = dev.maxComputeUnits(), dbsize = ngroups * (calc2 ? 2 : 1);
|
|
size_t wgs = dev.maxWorkGroupSize();
|
|
|
|
int ddepth = std::max(sum_op == OCL_OP_SUM_SQR ? CV_32F : CV_32S, depth),
|
|
dtype = CV_MAKE_TYPE(ddepth, cn);
|
|
CV_Assert(!haveMask || _mask.type() == CV_8UC1);
|
|
|
|
int wgs2_aligned = 1;
|
|
while (wgs2_aligned < (int)wgs)
|
|
wgs2_aligned <<= 1;
|
|
wgs2_aligned >>= 1;
|
|
|
|
static const char * const opMap[3] = { "OP_SUM", "OP_SUM_ABS", "OP_SUM_SQR" };
|
|
char cvt[2][40];
|
|
String opts = format("-D srcT=%s -D srcT1=%s -D dstT=%s -D dstTK=%s -D dstT1=%s -D ddepth=%d -D cn=%d"
|
|
" -D convertToDT=%s -D %s -D WGS=%d -D WGS2_ALIGNED=%d%s%s%s%s -D kercn=%d%s%s%s -D convertFromU=%s",
|
|
ocl::typeToStr(CV_MAKE_TYPE(depth, mcn)), ocl::typeToStr(depth),
|
|
ocl::typeToStr(dtype), ocl::typeToStr(CV_MAKE_TYPE(ddepth, mcn)),
|
|
ocl::typeToStr(ddepth), ddepth, cn,
|
|
ocl::convertTypeStr(depth, ddepth, mcn, cvt[0]),
|
|
opMap[sum_op], (int)wgs, wgs2_aligned,
|
|
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
|
|
haveMask ? " -D HAVE_MASK" : "",
|
|
_src.isContinuous() ? " -D HAVE_SRC_CONT" : "",
|
|
haveMask && _mask.isContinuous() ? " -D HAVE_MASK_CONT" : "", kercn,
|
|
haveSrc2 ? " -D HAVE_SRC2" : "", calc2 ? " -D OP_CALC2" : "",
|
|
haveSrc2 && _src2.isContinuous() ? " -D HAVE_SRC2_CONT" : "",
|
|
depth <= CV_32S && ddepth == CV_32S ? ocl::convertTypeStr(CV_8U, ddepth, convert_cn, cvt[1]) : "noconvert");
|
|
|
|
ocl::Kernel k("reduce", ocl::core::reduce_oclsrc, opts);
|
|
if (k.empty())
|
|
return false;
|
|
|
|
UMat src = _src.getUMat(), src2 = _src2.getUMat(),
|
|
db(1, dbsize, dtype), mask = _mask.getUMat();
|
|
|
|
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
|
|
dbarg = ocl::KernelArg::PtrWriteOnly(db),
|
|
maskarg = ocl::KernelArg::ReadOnlyNoSize(mask),
|
|
src2arg = ocl::KernelArg::ReadOnlyNoSize(src2);
|
|
|
|
if (haveMask)
|
|
{
|
|
if (haveSrc2)
|
|
k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg, src2arg);
|
|
else
|
|
k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg);
|
|
}
|
|
else
|
|
{
|
|
if (haveSrc2)
|
|
k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, src2arg);
|
|
else
|
|
k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg);
|
|
}
|
|
|
|
size_t globalsize = ngroups * wgs;
|
|
if (k.run(1, &globalsize, &wgs, false))
|
|
{
|
|
typedef Scalar (*part_sum)(Mat m);
|
|
part_sum funcs[3] = { ocl_part_sum<int>, ocl_part_sum<float>, ocl_part_sum<double> },
|
|
func = funcs[ddepth - CV_32S];
|
|
|
|
Mat mres = db.getMat(ACCESS_READ);
|
|
if (calc2)
|
|
const_cast<Scalar &>(res2) = func(mres.colRange(ngroups, dbsize));
|
|
|
|
res = func(mres.colRange(0, ngroups));
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
#endif
|
|
|
|
}
|
|
|
|
cv::Scalar cv::sum( InputArray _src )
|
|
{
|
|
#ifdef HAVE_OPENCL
|
|
Scalar _res;
|
|
CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2,
|
|
ocl_sum(_src, _res, OCL_OP_SUM),
|
|
_res)
|
|
#endif
|
|
|
|
Mat src = _src.getMat();
|
|
int k, cn = src.channels(), depth = src.depth();
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
CV_IPP_CHECK()
|
|
{
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
|
|
if( src.dims == 2 || (src.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
|
|
{
|
|
IppiSize sz = { cols, rows };
|
|
int type = src.type();
|
|
typedef IppStatus (CV_STDCALL* ippiSumFuncHint)(const void*, int, IppiSize, double *, IppHintAlgorithm);
|
|
typedef IppStatus (CV_STDCALL* ippiSumFuncNoHint)(const void*, int, IppiSize, double *);
|
|
ippiSumFuncHint ippFuncHint =
|
|
type == CV_32FC1 ? (ippiSumFuncHint)ippiSum_32f_C1R :
|
|
type == CV_32FC3 ? (ippiSumFuncHint)ippiSum_32f_C3R :
|
|
type == CV_32FC4 ? (ippiSumFuncHint)ippiSum_32f_C4R :
|
|
0;
|
|
ippiSumFuncNoHint ippFuncNoHint =
|
|
type == CV_8UC1 ? (ippiSumFuncNoHint)ippiSum_8u_C1R :
|
|
type == CV_8UC3 ? (ippiSumFuncNoHint)ippiSum_8u_C3R :
|
|
type == CV_8UC4 ? (ippiSumFuncNoHint)ippiSum_8u_C4R :
|
|
type == CV_16UC1 ? (ippiSumFuncNoHint)ippiSum_16u_C1R :
|
|
type == CV_16UC3 ? (ippiSumFuncNoHint)ippiSum_16u_C3R :
|
|
type == CV_16UC4 ? (ippiSumFuncNoHint)ippiSum_16u_C4R :
|
|
type == CV_16SC1 ? (ippiSumFuncNoHint)ippiSum_16s_C1R :
|
|
type == CV_16SC3 ? (ippiSumFuncNoHint)ippiSum_16s_C3R :
|
|
type == CV_16SC4 ? (ippiSumFuncNoHint)ippiSum_16s_C4R :
|
|
0;
|
|
CV_Assert(!ippFuncHint || !ippFuncNoHint);
|
|
if( ippFuncHint || ippFuncNoHint )
|
|
{
|
|
Ipp64f res[4];
|
|
IppStatus ret = ippFuncHint ? ippFuncHint(src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) :
|
|
ippFuncNoHint(src.ptr(), (int)src.step[0], sz, res);
|
|
if( ret >= 0 )
|
|
{
|
|
Scalar sc;
|
|
for( int i = 0; i < cn; i++ )
|
|
sc[i] = res[i];
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return sc;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
SumFunc func = getSumFunc(depth);
|
|
|
|
CV_Assert( cn <= 4 && func != 0 );
|
|
|
|
const Mat* arrays[] = {&src, 0};
|
|
uchar* ptrs[1];
|
|
NAryMatIterator it(arrays, ptrs);
|
|
Scalar s;
|
|
int total = (int)it.size, blockSize = total, intSumBlockSize = 0;
|
|
int j, count = 0;
|
|
AutoBuffer<int> _buf;
|
|
int* buf = (int*)&s[0];
|
|
size_t esz = 0;
|
|
bool blockSum = depth < CV_32S;
|
|
|
|
if( blockSum )
|
|
{
|
|
intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15);
|
|
blockSize = std::min(blockSize, intSumBlockSize);
|
|
_buf.allocate(cn);
|
|
buf = _buf;
|
|
|
|
for( k = 0; k < cn; k++ )
|
|
buf[k] = 0;
|
|
esz = src.elemSize();
|
|
}
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
{
|
|
for( j = 0; j < total; j += blockSize )
|
|
{
|
|
int bsz = std::min(total - j, blockSize);
|
|
func( ptrs[0], 0, (uchar*)buf, bsz, cn );
|
|
count += bsz;
|
|
if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
|
|
{
|
|
for( k = 0; k < cn; k++ )
|
|
{
|
|
s[k] += buf[k];
|
|
buf[k] = 0;
|
|
}
|
|
count = 0;
|
|
}
|
|
ptrs[0] += bsz*esz;
|
|
}
|
|
}
|
|
return s;
|
|
}
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
namespace cv {
|
|
|
|
static bool ocl_countNonZero( InputArray _src, int & res )
|
|
{
|
|
int type = _src.type(), depth = CV_MAT_DEPTH(type), kercn = ocl::predictOptimalVectorWidth(_src);
|
|
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0;
|
|
|
|
if (depth == CV_64F && !doubleSupport)
|
|
return false;
|
|
|
|
int dbsize = ocl::Device::getDefault().maxComputeUnits();
|
|
size_t wgs = ocl::Device::getDefault().maxWorkGroupSize();
|
|
|
|
int wgs2_aligned = 1;
|
|
while (wgs2_aligned < (int)wgs)
|
|
wgs2_aligned <<= 1;
|
|
wgs2_aligned >>= 1;
|
|
|
|
ocl::Kernel k("reduce", ocl::core::reduce_oclsrc,
|
|
format("-D srcT=%s -D srcT1=%s -D cn=1 -D OP_COUNT_NON_ZERO"
|
|
" -D WGS=%d -D kercn=%d -D WGS2_ALIGNED=%d%s%s",
|
|
ocl::typeToStr(CV_MAKE_TYPE(depth, kercn)),
|
|
ocl::typeToStr(depth), (int)wgs, kercn,
|
|
wgs2_aligned, doubleSupport ? " -D DOUBLE_SUPPORT" : "",
|
|
_src.isContinuous() ? " -D HAVE_SRC_CONT" : ""));
|
|
if (k.empty())
|
|
return false;
|
|
|
|
UMat src = _src.getUMat(), db(1, dbsize, CV_32SC1);
|
|
k.args(ocl::KernelArg::ReadOnlyNoSize(src), src.cols, (int)src.total(),
|
|
dbsize, ocl::KernelArg::PtrWriteOnly(db));
|
|
|
|
size_t globalsize = dbsize * wgs;
|
|
if (k.run(1, &globalsize, &wgs, true))
|
|
return res = saturate_cast<int>(cv::sum(db.getMat(ACCESS_READ))[0]), true;
|
|
return false;
|
|
}
|
|
|
|
}
|
|
|
|
#endif
|
|
|
|
int cv::countNonZero( InputArray _src )
|
|
{
|
|
int type = _src.type(), cn = CV_MAT_CN(type);
|
|
CV_Assert( cn == 1 );
|
|
|
|
#ifdef HAVE_OPENCL
|
|
int res = -1;
|
|
CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2,
|
|
ocl_countNonZero(_src, res),
|
|
res)
|
|
#endif
|
|
|
|
Mat src = _src.getMat();
|
|
|
|
#if defined HAVE_IPP && !defined HAVE_IPP_ICV_ONLY && 0
|
|
CV_IPP_CHECK()
|
|
{
|
|
if (src.dims <= 2 || src.isContinuous())
|
|
{
|
|
IppiSize roiSize = { src.cols, src.rows };
|
|
Ipp32s count = 0, srcstep = (Ipp32s)src.step;
|
|
IppStatus status = (IppStatus)-1;
|
|
|
|
if (src.isContinuous())
|
|
{
|
|
roiSize.width = (Ipp32s)src.total();
|
|
roiSize.height = 1;
|
|
srcstep = (Ipp32s)src.total() * CV_ELEM_SIZE(type);
|
|
}
|
|
|
|
int depth = CV_MAT_DEPTH(type);
|
|
if (depth == CV_8U)
|
|
status = ippiCountInRange_8u_C1R((const Ipp8u *)src.data, srcstep, roiSize, &count, 0, 0);
|
|
else if (depth == CV_32F)
|
|
status = ippiCountInRange_32f_C1R((const Ipp32f *)src.data, srcstep, roiSize, &count, 0, 0);
|
|
|
|
if (status >= 0)
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return (Ipp32s)src.total() - count;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
#endif
|
|
|
|
CountNonZeroFunc func = getCountNonZeroTab(src.depth());
|
|
CV_Assert( func != 0 );
|
|
|
|
const Mat* arrays[] = {&src, 0};
|
|
uchar* ptrs[1];
|
|
NAryMatIterator it(arrays, ptrs);
|
|
int total = (int)it.size, nz = 0;
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
nz += func( ptrs[0], total );
|
|
|
|
return nz;
|
|
}
|
|
|
|
cv::Scalar cv::mean( InputArray _src, InputArray _mask )
|
|
{
|
|
Mat src = _src.getMat(), mask = _mask.getMat();
|
|
CV_Assert( mask.empty() || mask.type() == CV_8U );
|
|
|
|
int k, cn = src.channels(), depth = src.depth();
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
CV_IPP_CHECK()
|
|
{
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
|
|
if( src.dims == 2 || (src.isContinuous() && mask.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
|
|
{
|
|
IppiSize sz = { cols, rows };
|
|
int type = src.type();
|
|
if( !mask.empty() )
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC1)(const void *, int, const void *, int, IppiSize, Ipp64f *);
|
|
ippiMaskMeanFuncC1 ippFuncC1 =
|
|
type == CV_8UC1 ? (ippiMaskMeanFuncC1)ippiMean_8u_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskMeanFuncC1)ippiMean_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskMeanFuncC1)ippiMean_32f_C1MR :
|
|
0;
|
|
if( ippFuncC1 )
|
|
{
|
|
Ipp64f res;
|
|
if( ippFuncC1(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, &res) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return Scalar(res);
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC3)(const void *, int, const void *, int, IppiSize, int, Ipp64f *);
|
|
ippiMaskMeanFuncC3 ippFuncC3 =
|
|
type == CV_8UC3 ? (ippiMaskMeanFuncC3)ippiMean_8u_C3CMR :
|
|
type == CV_16UC3 ? (ippiMaskMeanFuncC3)ippiMean_16u_C3CMR :
|
|
type == CV_32FC3 ? (ippiMaskMeanFuncC3)ippiMean_32f_C3CMR :
|
|
0;
|
|
if( ippFuncC3 )
|
|
{
|
|
Ipp64f res1, res2, res3;
|
|
if( ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 1, &res1) >= 0 &&
|
|
ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 2, &res2) >= 0 &&
|
|
ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 3, &res3) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return Scalar(res1, res2, res3);
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMeanFuncHint)(const void*, int, IppiSize, double *, IppHintAlgorithm);
|
|
typedef IppStatus (CV_STDCALL* ippiMeanFuncNoHint)(const void*, int, IppiSize, double *);
|
|
ippiMeanFuncHint ippFuncHint =
|
|
type == CV_32FC1 ? (ippiMeanFuncHint)ippiMean_32f_C1R :
|
|
type == CV_32FC3 ? (ippiMeanFuncHint)ippiMean_32f_C3R :
|
|
type == CV_32FC4 ? (ippiMeanFuncHint)ippiMean_32f_C4R :
|
|
0;
|
|
ippiMeanFuncNoHint ippFuncNoHint =
|
|
type == CV_8UC1 ? (ippiMeanFuncNoHint)ippiMean_8u_C1R :
|
|
type == CV_8UC3 ? (ippiMeanFuncNoHint)ippiMean_8u_C3R :
|
|
type == CV_8UC4 ? (ippiMeanFuncNoHint)ippiMean_8u_C4R :
|
|
type == CV_16UC1 ? (ippiMeanFuncNoHint)ippiMean_16u_C1R :
|
|
type == CV_16UC3 ? (ippiMeanFuncNoHint)ippiMean_16u_C3R :
|
|
type == CV_16UC4 ? (ippiMeanFuncNoHint)ippiMean_16u_C4R :
|
|
type == CV_16SC1 ? (ippiMeanFuncNoHint)ippiMean_16s_C1R :
|
|
type == CV_16SC3 ? (ippiMeanFuncNoHint)ippiMean_16s_C3R :
|
|
type == CV_16SC4 ? (ippiMeanFuncNoHint)ippiMean_16s_C4R :
|
|
0;
|
|
// Make sure only zero or one version of the function pointer is valid
|
|
CV_Assert(!ippFuncHint || !ippFuncNoHint);
|
|
if( ippFuncHint || ippFuncNoHint )
|
|
{
|
|
Ipp64f res[4];
|
|
IppStatus ret = ippFuncHint ? ippFuncHint(src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) :
|
|
ippFuncNoHint(src.ptr(), (int)src.step[0], sz, res);
|
|
if( ret >= 0 )
|
|
{
|
|
Scalar sc;
|
|
for( int i = 0; i < cn; i++ )
|
|
sc[i] = res[i];
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return sc;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
SumFunc func = getSumFunc(depth);
|
|
|
|
CV_Assert( cn <= 4 && func != 0 );
|
|
|
|
const Mat* arrays[] = {&src, &mask, 0};
|
|
uchar* ptrs[2];
|
|
NAryMatIterator it(arrays, ptrs);
|
|
Scalar s;
|
|
int total = (int)it.size, blockSize = total, intSumBlockSize = 0;
|
|
int j, count = 0;
|
|
AutoBuffer<int> _buf;
|
|
int* buf = (int*)&s[0];
|
|
bool blockSum = depth <= CV_16S;
|
|
size_t esz = 0, nz0 = 0;
|
|
|
|
if( blockSum )
|
|
{
|
|
intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15);
|
|
blockSize = std::min(blockSize, intSumBlockSize);
|
|
_buf.allocate(cn);
|
|
buf = _buf;
|
|
|
|
for( k = 0; k < cn; k++ )
|
|
buf[k] = 0;
|
|
esz = src.elemSize();
|
|
}
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
{
|
|
for( j = 0; j < total; j += blockSize )
|
|
{
|
|
int bsz = std::min(total - j, blockSize);
|
|
int nz = func( ptrs[0], ptrs[1], (uchar*)buf, bsz, cn );
|
|
count += nz;
|
|
nz0 += nz;
|
|
if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
|
|
{
|
|
for( k = 0; k < cn; k++ )
|
|
{
|
|
s[k] += buf[k];
|
|
buf[k] = 0;
|
|
}
|
|
count = 0;
|
|
}
|
|
ptrs[0] += bsz*esz;
|
|
if( ptrs[1] )
|
|
ptrs[1] += bsz;
|
|
}
|
|
}
|
|
return s*(nz0 ? 1./nz0 : 0);
|
|
}
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
namespace cv {
|
|
|
|
static bool ocl_meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask )
|
|
{
|
|
bool haveMask = _mask.kind() != _InputArray::NONE;
|
|
int nz = haveMask ? -1 : (int)_src.total();
|
|
Scalar mean, stddev;
|
|
|
|
{
|
|
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
|
|
bool doubleSupport = ocl::Device::getDefault().doubleFPConfig() > 0,
|
|
isContinuous = _src.isContinuous(),
|
|
isMaskContinuous = _mask.isContinuous();
|
|
const ocl::Device &defDev = ocl::Device::getDefault();
|
|
int groups = defDev.maxComputeUnits();
|
|
if (defDev.isIntel())
|
|
{
|
|
static const int subSliceEUCount = 10;
|
|
groups = (groups / subSliceEUCount) * 2;
|
|
}
|
|
size_t wgs = defDev.maxWorkGroupSize();
|
|
|
|
int ddepth = std::max(CV_32S, depth), sqddepth = std::max(CV_32F, depth),
|
|
dtype = CV_MAKE_TYPE(ddepth, cn),
|
|
sqdtype = CV_MAKETYPE(sqddepth, cn);
|
|
CV_Assert(!haveMask || _mask.type() == CV_8UC1);
|
|
|
|
int wgs2_aligned = 1;
|
|
while (wgs2_aligned < (int)wgs)
|
|
wgs2_aligned <<= 1;
|
|
wgs2_aligned >>= 1;
|
|
|
|
if ( (!doubleSupport && depth == CV_64F) || cn > 4 )
|
|
return false;
|
|
|
|
char cvt[2][40];
|
|
String opts = format("-D srcT=%s -D srcT1=%s -D dstT=%s -D dstT1=%s -D sqddepth=%d"
|
|
" -D sqdstT=%s -D sqdstT1=%s -D convertToSDT=%s -D cn=%d%s%s"
|
|
" -D convertToDT=%s -D WGS=%d -D WGS2_ALIGNED=%d%s%s",
|
|
ocl::typeToStr(type), ocl::typeToStr(depth),
|
|
ocl::typeToStr(dtype), ocl::typeToStr(ddepth), sqddepth,
|
|
ocl::typeToStr(sqdtype), ocl::typeToStr(sqddepth),
|
|
ocl::convertTypeStr(depth, sqddepth, cn, cvt[0]),
|
|
cn, isContinuous ? " -D HAVE_SRC_CONT" : "",
|
|
isMaskContinuous ? " -D HAVE_MASK_CONT" : "",
|
|
ocl::convertTypeStr(depth, ddepth, cn, cvt[1]),
|
|
(int)wgs, wgs2_aligned, haveMask ? " -D HAVE_MASK" : "",
|
|
doubleSupport ? " -D DOUBLE_SUPPORT" : "");
|
|
|
|
ocl::Kernel k("meanStdDev", ocl::core::meanstddev_oclsrc, opts);
|
|
if (k.empty())
|
|
return false;
|
|
|
|
int dbsize = groups * ((haveMask ? CV_ELEM_SIZE1(CV_32S) : 0) +
|
|
CV_ELEM_SIZE(sqdtype) + CV_ELEM_SIZE(dtype));
|
|
UMat src = _src.getUMat(), db(1, dbsize, CV_8UC1), mask = _mask.getUMat();
|
|
|
|
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
|
|
dbarg = ocl::KernelArg::PtrWriteOnly(db),
|
|
maskarg = ocl::KernelArg::ReadOnlyNoSize(mask);
|
|
|
|
if (haveMask)
|
|
k.args(srcarg, src.cols, (int)src.total(), groups, dbarg, maskarg);
|
|
else
|
|
k.args(srcarg, src.cols, (int)src.total(), groups, dbarg);
|
|
|
|
size_t globalsize = groups * wgs;
|
|
if (!k.run(1, &globalsize, &wgs, false))
|
|
return false;
|
|
|
|
typedef Scalar (* part_sum)(Mat m);
|
|
part_sum funcs[3] = { ocl_part_sum<int>, ocl_part_sum<float>, ocl_part_sum<double> };
|
|
Mat dbm = db.getMat(ACCESS_READ);
|
|
|
|
mean = funcs[ddepth - CV_32S](Mat(1, groups, dtype, dbm.ptr()));
|
|
stddev = funcs[sqddepth - CV_32S](Mat(1, groups, sqdtype, dbm.ptr() + groups * CV_ELEM_SIZE(dtype)));
|
|
|
|
if (haveMask)
|
|
nz = saturate_cast<int>(funcs[0](Mat(1, groups, CV_32SC1, dbm.ptr() +
|
|
groups * (CV_ELEM_SIZE(dtype) +
|
|
CV_ELEM_SIZE(sqdtype))))[0]);
|
|
}
|
|
|
|
double total = nz != 0 ? 1.0 / nz : 0;
|
|
int k, j, cn = _src.channels();
|
|
for (int i = 0; i < cn; ++i)
|
|
{
|
|
mean[i] *= total;
|
|
stddev[i] = std::sqrt(std::max(stddev[i] * total - mean[i] * mean[i] , 0.));
|
|
}
|
|
|
|
for( j = 0; j < 2; j++ )
|
|
{
|
|
const double * const sptr = j == 0 ? &mean[0] : &stddev[0];
|
|
_OutputArray _dst = j == 0 ? _mean : _sdv;
|
|
if( !_dst.needed() )
|
|
continue;
|
|
|
|
if( !_dst.fixedSize() )
|
|
_dst.create(cn, 1, CV_64F, -1, true);
|
|
Mat dst = _dst.getMat();
|
|
int dcn = (int)dst.total();
|
|
CV_Assert( dst.type() == CV_64F && dst.isContinuous() &&
|
|
(dst.cols == 1 || dst.rows == 1) && dcn >= cn );
|
|
double* dptr = dst.ptr<double>();
|
|
for( k = 0; k < cn; k++ )
|
|
dptr[k] = sptr[k];
|
|
for( ; k < dcn; k++ )
|
|
dptr[k] = 0;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
}
|
|
|
|
#endif
|
|
|
|
void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask )
|
|
{
|
|
CV_OCL_RUN(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2,
|
|
ocl_meanStdDev(_src, _mean, _sdv, _mask))
|
|
|
|
Mat src = _src.getMat(), mask = _mask.getMat();
|
|
CV_Assert( mask.empty() || mask.type() == CV_8UC1 );
|
|
|
|
int k, cn = src.channels(), depth = src.depth();
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
CV_IPP_CHECK()
|
|
{
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
|
|
if( src.dims == 2 || (src.isContinuous() && mask.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
|
|
{
|
|
Ipp64f mean_temp[3];
|
|
Ipp64f stddev_temp[3];
|
|
Ipp64f *pmean = &mean_temp[0];
|
|
Ipp64f *pstddev = &stddev_temp[0];
|
|
Mat mean, stddev;
|
|
int dcn_mean = -1;
|
|
if( _mean.needed() )
|
|
{
|
|
if( !_mean.fixedSize() )
|
|
_mean.create(cn, 1, CV_64F, -1, true);
|
|
mean = _mean.getMat();
|
|
dcn_mean = (int)mean.total();
|
|
pmean = mean.ptr<Ipp64f>();
|
|
}
|
|
int dcn_stddev = -1;
|
|
if( _sdv.needed() )
|
|
{
|
|
if( !_sdv.fixedSize() )
|
|
_sdv.create(cn, 1, CV_64F, -1, true);
|
|
stddev = _sdv.getMat();
|
|
dcn_stddev = (int)stddev.total();
|
|
pstddev = stddev.ptr<Ipp64f>();
|
|
}
|
|
for( int c = cn; c < dcn_mean; c++ )
|
|
pmean[c] = 0;
|
|
for( int c = cn; c < dcn_stddev; c++ )
|
|
pstddev[c] = 0;
|
|
IppiSize sz = { cols, rows };
|
|
int type = src.type();
|
|
if( !mask.empty() )
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMaskMeanStdDevFuncC1)(const void *, int, const void *, int, IppiSize, Ipp64f *, Ipp64f *);
|
|
ippiMaskMeanStdDevFuncC1 ippFuncC1 =
|
|
type == CV_8UC1 ? (ippiMaskMeanStdDevFuncC1)ippiMean_StdDev_8u_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskMeanStdDevFuncC1)ippiMean_StdDev_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskMeanStdDevFuncC1)ippiMean_StdDev_32f_C1MR :
|
|
0;
|
|
if( ippFuncC1 )
|
|
{
|
|
if( ippFuncC1(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, pmean, pstddev) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
typedef IppStatus (CV_STDCALL* ippiMaskMeanStdDevFuncC3)(const void *, int, const void *, int, IppiSize, int, Ipp64f *, Ipp64f *);
|
|
ippiMaskMeanStdDevFuncC3 ippFuncC3 =
|
|
type == CV_8UC3 ? (ippiMaskMeanStdDevFuncC3)ippiMean_StdDev_8u_C3CMR :
|
|
type == CV_16UC3 ? (ippiMaskMeanStdDevFuncC3)ippiMean_StdDev_16u_C3CMR :
|
|
type == CV_32FC3 ? (ippiMaskMeanStdDevFuncC3)ippiMean_StdDev_32f_C3CMR :
|
|
0;
|
|
if( ippFuncC3 )
|
|
{
|
|
if( ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
|
|
ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
|
|
ippFuncC3(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMeanStdDevFuncC1)(const void *, int, IppiSize, Ipp64f *, Ipp64f *);
|
|
ippiMeanStdDevFuncC1 ippFuncC1 =
|
|
type == CV_8UC1 ? (ippiMeanStdDevFuncC1)ippiMean_StdDev_8u_C1R :
|
|
type == CV_16UC1 ? (ippiMeanStdDevFuncC1)ippiMean_StdDev_16u_C1R :
|
|
#if (IPP_VERSION_X100 >= 801)
|
|
type == CV_32FC1 ? (ippiMeanStdDevFuncC1)ippiMean_StdDev_32f_C1R ://Aug 2013: bug in IPP 7.1, 8.0
|
|
#endif
|
|
0;
|
|
if( ippFuncC1 )
|
|
{
|
|
if( ippFuncC1(src.ptr(), (int)src.step[0], sz, pmean, pstddev) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
typedef IppStatus (CV_STDCALL* ippiMeanStdDevFuncC3)(const void *, int, IppiSize, int, Ipp64f *, Ipp64f *);
|
|
ippiMeanStdDevFuncC3 ippFuncC3 =
|
|
type == CV_8UC3 ? (ippiMeanStdDevFuncC3)ippiMean_StdDev_8u_C3CR :
|
|
type == CV_16UC3 ? (ippiMeanStdDevFuncC3)ippiMean_StdDev_16u_C3CR :
|
|
type == CV_32FC3 ? (ippiMeanStdDevFuncC3)ippiMean_StdDev_32f_C3CR :
|
|
0;
|
|
if( ippFuncC3 )
|
|
{
|
|
if( ippFuncC3(src.ptr(), (int)src.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
|
|
ippFuncC3(src.ptr(), (int)src.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
|
|
ippFuncC3(src.ptr(), (int)src.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
|
|
SumSqrFunc func = getSumSqrTab(depth);
|
|
|
|
CV_Assert( func != 0 );
|
|
|
|
const Mat* arrays[] = {&src, &mask, 0};
|
|
uchar* ptrs[2];
|
|
NAryMatIterator it(arrays, ptrs);
|
|
int total = (int)it.size, blockSize = total, intSumBlockSize = 0;
|
|
int j, count = 0, nz0 = 0;
|
|
AutoBuffer<double> _buf(cn*4);
|
|
double *s = (double*)_buf, *sq = s + cn;
|
|
int *sbuf = (int*)s, *sqbuf = (int*)sq;
|
|
bool blockSum = depth <= CV_16S, blockSqSum = depth <= CV_8S;
|
|
size_t esz = 0;
|
|
|
|
for( k = 0; k < cn; k++ )
|
|
s[k] = sq[k] = 0;
|
|
|
|
if( blockSum )
|
|
{
|
|
intSumBlockSize = 1 << 15;
|
|
blockSize = std::min(blockSize, intSumBlockSize);
|
|
sbuf = (int*)(sq + cn);
|
|
if( blockSqSum )
|
|
sqbuf = sbuf + cn;
|
|
for( k = 0; k < cn; k++ )
|
|
sbuf[k] = sqbuf[k] = 0;
|
|
esz = src.elemSize();
|
|
}
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
{
|
|
for( j = 0; j < total; j += blockSize )
|
|
{
|
|
int bsz = std::min(total - j, blockSize);
|
|
int nz = func( ptrs[0], ptrs[1], (uchar*)sbuf, (uchar*)sqbuf, bsz, cn );
|
|
count += nz;
|
|
nz0 += nz;
|
|
if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
|
|
{
|
|
for( k = 0; k < cn; k++ )
|
|
{
|
|
s[k] += sbuf[k];
|
|
sbuf[k] = 0;
|
|
}
|
|
if( blockSqSum )
|
|
{
|
|
for( k = 0; k < cn; k++ )
|
|
{
|
|
sq[k] += sqbuf[k];
|
|
sqbuf[k] = 0;
|
|
}
|
|
}
|
|
count = 0;
|
|
}
|
|
ptrs[0] += bsz*esz;
|
|
if( ptrs[1] )
|
|
ptrs[1] += bsz;
|
|
}
|
|
}
|
|
|
|
double scale = nz0 ? 1./nz0 : 0.;
|
|
for( k = 0; k < cn; k++ )
|
|
{
|
|
s[k] *= scale;
|
|
sq[k] = std::sqrt(std::max(sq[k]*scale - s[k]*s[k], 0.));
|
|
}
|
|
|
|
for( j = 0; j < 2; j++ )
|
|
{
|
|
const double* sptr = j == 0 ? s : sq;
|
|
_OutputArray _dst = j == 0 ? _mean : _sdv;
|
|
if( !_dst.needed() )
|
|
continue;
|
|
|
|
if( !_dst.fixedSize() )
|
|
_dst.create(cn, 1, CV_64F, -1, true);
|
|
Mat dst = _dst.getMat();
|
|
int dcn = (int)dst.total();
|
|
CV_Assert( dst.type() == CV_64F && dst.isContinuous() &&
|
|
(dst.cols == 1 || dst.rows == 1) && dcn >= cn );
|
|
double* dptr = dst.ptr<double>();
|
|
for( k = 0; k < cn; k++ )
|
|
dptr[k] = sptr[k];
|
|
for( ; k < dcn; k++ )
|
|
dptr[k] = 0;
|
|
}
|
|
}
|
|
|
|
/****************************************************************************************\
|
|
* minMaxLoc *
|
|
\****************************************************************************************/
|
|
|
|
namespace cv
|
|
{
|
|
|
|
template<typename T, typename WT> static void
|
|
minMaxIdx_( const T* src, const uchar* mask, WT* _minVal, WT* _maxVal,
|
|
size_t* _minIdx, size_t* _maxIdx, int len, size_t startIdx )
|
|
{
|
|
WT minVal = *_minVal, maxVal = *_maxVal;
|
|
size_t minIdx = *_minIdx, maxIdx = *_maxIdx;
|
|
|
|
if( !mask )
|
|
{
|
|
for( int i = 0; i < len; i++ )
|
|
{
|
|
T val = src[i];
|
|
if( val < minVal )
|
|
{
|
|
minVal = val;
|
|
minIdx = startIdx + i;
|
|
}
|
|
if( val > maxVal )
|
|
{
|
|
maxVal = val;
|
|
maxIdx = startIdx + i;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < len; i++ )
|
|
{
|
|
T val = src[i];
|
|
if( mask[i] && val < minVal )
|
|
{
|
|
minVal = val;
|
|
minIdx = startIdx + i;
|
|
}
|
|
if( mask[i] && val > maxVal )
|
|
{
|
|
maxVal = val;
|
|
maxIdx = startIdx + i;
|
|
}
|
|
}
|
|
}
|
|
|
|
*_minIdx = minIdx;
|
|
*_maxIdx = maxIdx;
|
|
*_minVal = minVal;
|
|
*_maxVal = maxVal;
|
|
}
|
|
|
|
static void minMaxIdx_8u(const uchar* src, const uchar* mask, int* minval, int* maxval,
|
|
size_t* minidx, size_t* maxidx, int len, size_t startidx )
|
|
{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
|
|
|
|
static void minMaxIdx_8s(const schar* src, const uchar* mask, int* minval, int* maxval,
|
|
size_t* minidx, size_t* maxidx, int len, size_t startidx )
|
|
{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
|
|
|
|
static void minMaxIdx_16u(const ushort* src, const uchar* mask, int* minval, int* maxval,
|
|
size_t* minidx, size_t* maxidx, int len, size_t startidx )
|
|
{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
|
|
|
|
static void minMaxIdx_16s(const short* src, const uchar* mask, int* minval, int* maxval,
|
|
size_t* minidx, size_t* maxidx, int len, size_t startidx )
|
|
{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
|
|
|
|
static void minMaxIdx_32s(const int* src, const uchar* mask, int* minval, int* maxval,
|
|
size_t* minidx, size_t* maxidx, int len, size_t startidx )
|
|
{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
|
|
|
|
static void minMaxIdx_32f(const float* src, const uchar* mask, float* minval, float* maxval,
|
|
size_t* minidx, size_t* maxidx, int len, size_t startidx )
|
|
{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
|
|
|
|
static void minMaxIdx_64f(const double* src, const uchar* mask, double* minval, double* maxval,
|
|
size_t* minidx, size_t* maxidx, int len, size_t startidx )
|
|
{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
|
|
|
|
typedef void (*MinMaxIdxFunc)(const uchar*, const uchar*, int*, int*, size_t*, size_t*, int, size_t);
|
|
|
|
static MinMaxIdxFunc getMinmaxTab(int depth)
|
|
{
|
|
static MinMaxIdxFunc minmaxTab[] =
|
|
{
|
|
(MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_8u), (MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_8s),
|
|
(MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_16u), (MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_16s),
|
|
(MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_32s),
|
|
(MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_32f), (MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_64f),
|
|
0
|
|
};
|
|
|
|
return minmaxTab[depth];
|
|
}
|
|
|
|
static void ofs2idx(const Mat& a, size_t ofs, int* idx)
|
|
{
|
|
int i, d = a.dims;
|
|
if( ofs > 0 )
|
|
{
|
|
ofs--;
|
|
for( i = d-1; i >= 0; i-- )
|
|
{
|
|
int sz = a.size[i];
|
|
idx[i] = (int)(ofs % sz);
|
|
ofs /= sz;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for( i = d-1; i >= 0; i-- )
|
|
idx[i] = -1;
|
|
}
|
|
}
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
template <typename T>
|
|
void getMinMaxRes(const Mat & db, double * minVal, double * maxVal,
|
|
int* minLoc, int* maxLoc,
|
|
int groupnum, int cols, double * maxVal2)
|
|
{
|
|
uint index_max = std::numeric_limits<uint>::max();
|
|
T minval = std::numeric_limits<T>::max();
|
|
T maxval = std::numeric_limits<T>::min() > 0 ? -std::numeric_limits<T>::max() : std::numeric_limits<T>::min(), maxval2 = maxval;
|
|
uint minloc = index_max, maxloc = index_max;
|
|
|
|
int index = 0;
|
|
const T * minptr = NULL, * maxptr = NULL, * maxptr2 = NULL;
|
|
const uint * minlocptr = NULL, * maxlocptr = NULL;
|
|
if (minVal || minLoc)
|
|
{
|
|
minptr = db.ptr<T>();
|
|
index += sizeof(T) * groupnum;
|
|
}
|
|
if (maxVal || maxLoc)
|
|
{
|
|
maxptr = (const T *)(db.ptr() + index);
|
|
index += sizeof(T) * groupnum;
|
|
}
|
|
if (minLoc)
|
|
{
|
|
minlocptr = (const uint *)(db.ptr() + index);
|
|
index += sizeof(uint) * groupnum;
|
|
}
|
|
if (maxLoc)
|
|
{
|
|
maxlocptr = (const uint *)(db.ptr() + index);
|
|
index += sizeof(uint) * groupnum;
|
|
}
|
|
if (maxVal2)
|
|
maxptr2 = (const T *)(db.ptr() + index);
|
|
|
|
for (int i = 0; i < groupnum; i++)
|
|
{
|
|
if (minptr && minptr[i] <= minval)
|
|
{
|
|
if (minptr[i] == minval)
|
|
{
|
|
if (minlocptr)
|
|
minloc = std::min(minlocptr[i], minloc);
|
|
}
|
|
else
|
|
{
|
|
if (minlocptr)
|
|
minloc = minlocptr[i];
|
|
minval = minptr[i];
|
|
}
|
|
}
|
|
if (maxptr && maxptr[i] >= maxval)
|
|
{
|
|
if (maxptr[i] == maxval)
|
|
{
|
|
if (maxlocptr)
|
|
maxloc = std::min(maxlocptr[i], maxloc);
|
|
}
|
|
else
|
|
{
|
|
if (maxlocptr)
|
|
maxloc = maxlocptr[i];
|
|
maxval = maxptr[i];
|
|
}
|
|
}
|
|
if (maxptr2 && maxptr2[i] > maxval2)
|
|
maxval2 = maxptr2[i];
|
|
}
|
|
bool zero_mask = (minLoc && minloc == index_max) ||
|
|
(maxLoc && maxloc == index_max);
|
|
|
|
if (minVal)
|
|
*minVal = zero_mask ? 0 : (double)minval;
|
|
if (maxVal)
|
|
*maxVal = zero_mask ? 0 : (double)maxval;
|
|
if (maxVal2)
|
|
*maxVal2 = zero_mask ? 0 : (double)maxval2;
|
|
|
|
if (minLoc)
|
|
{
|
|
minLoc[0] = zero_mask ? -1 : minloc / cols;
|
|
minLoc[1] = zero_mask ? -1 : minloc % cols;
|
|
}
|
|
if (maxLoc)
|
|
{
|
|
maxLoc[0] = zero_mask ? -1 : maxloc / cols;
|
|
maxLoc[1] = zero_mask ? -1 : maxloc % cols;
|
|
}
|
|
}
|
|
|
|
typedef void (*getMinMaxResFunc)(const Mat & db, double * minVal, double * maxVal,
|
|
int * minLoc, int *maxLoc, int gropunum, int cols, double * maxVal2);
|
|
|
|
static bool ocl_minMaxIdx( InputArray _src, double* minVal, double* maxVal, int* minLoc, int* maxLoc, InputArray _mask,
|
|
int ddepth = -1, bool absValues = false, InputArray _src2 = noArray(), double * maxVal2 = NULL)
|
|
{
|
|
const ocl::Device & dev = ocl::Device::getDefault();
|
|
bool doubleSupport = dev.doubleFPConfig() > 0, haveMask = !_mask.empty(),
|
|
haveSrc2 = _src2.kind() != _InputArray::NONE;
|
|
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
|
|
kercn = haveMask ? cn : std::min(4, ocl::predictOptimalVectorWidth(_src, _src2));
|
|
|
|
// disabled following modes since it occasionally fails on AMD devices (e.g. A10-6800K, sep. 2014)
|
|
if ((haveMask || type == CV_32FC1) && dev.isAMD())
|
|
return false;
|
|
|
|
CV_Assert( (cn == 1 && (!haveMask || _mask.type() == CV_8U)) ||
|
|
(cn >= 1 && !minLoc && !maxLoc) );
|
|
|
|
if (ddepth < 0)
|
|
ddepth = depth;
|
|
|
|
CV_Assert(!haveSrc2 || _src2.type() == type);
|
|
|
|
if (depth == CV_32S)
|
|
return false;
|
|
|
|
if ((depth == CV_64F || ddepth == CV_64F) && !doubleSupport)
|
|
return false;
|
|
|
|
int groupnum = dev.maxComputeUnits();
|
|
size_t wgs = dev.maxWorkGroupSize();
|
|
|
|
int wgs2_aligned = 1;
|
|
while (wgs2_aligned < (int)wgs)
|
|
wgs2_aligned <<= 1;
|
|
wgs2_aligned >>= 1;
|
|
|
|
bool needMinVal = minVal || minLoc, needMinLoc = minLoc != NULL,
|
|
needMaxVal = maxVal || maxLoc, needMaxLoc = maxLoc != NULL;
|
|
|
|
// in case of mask we must know whether mask is filled with zeros or not
|
|
// so let's calculate min or max location, if it's undefined, so mask is zeros
|
|
if (!(needMaxLoc || needMinLoc) && haveMask)
|
|
{
|
|
if (needMinVal)
|
|
needMinLoc = true;
|
|
else
|
|
needMaxLoc = true;
|
|
}
|
|
|
|
char cvt[2][40];
|
|
String opts = format("-D DEPTH_%d -D srcT1=%s%s -D WGS=%d -D srcT=%s"
|
|
" -D WGS2_ALIGNED=%d%s%s%s -D kercn=%d%s%s%s%s"
|
|
" -D dstT1=%s -D dstT=%s -D convertToDT=%s%s%s%s%s -D wdepth=%d -D convertFromU=%s",
|
|
depth, ocl::typeToStr(depth), haveMask ? " -D HAVE_MASK" : "", (int)wgs,
|
|
ocl::typeToStr(CV_MAKE_TYPE(depth, kercn)), wgs2_aligned,
|
|
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
|
|
_src.isContinuous() ? " -D HAVE_SRC_CONT" : "",
|
|
_mask.isContinuous() ? " -D HAVE_MASK_CONT" : "", kercn,
|
|
needMinVal ? " -D NEED_MINVAL" : "", needMaxVal ? " -D NEED_MAXVAL" : "",
|
|
needMinLoc ? " -D NEED_MINLOC" : "", needMaxLoc ? " -D NEED_MAXLOC" : "",
|
|
ocl::typeToStr(ddepth), ocl::typeToStr(CV_MAKE_TYPE(ddepth, kercn)),
|
|
ocl::convertTypeStr(depth, ddepth, kercn, cvt[0]),
|
|
absValues ? " -D OP_ABS" : "",
|
|
haveSrc2 ? " -D HAVE_SRC2" : "", maxVal2 ? " -D OP_CALC2" : "",
|
|
haveSrc2 && _src2.isContinuous() ? " -D HAVE_SRC2_CONT" : "", ddepth,
|
|
depth <= CV_32S && ddepth == CV_32S ? ocl::convertTypeStr(CV_8U, ddepth, kercn, cvt[1]) : "noconvert");
|
|
|
|
ocl::Kernel k("minmaxloc", ocl::core::minmaxloc_oclsrc, opts);
|
|
if (k.empty())
|
|
return false;
|
|
|
|
int esz = CV_ELEM_SIZE(ddepth), esz32s = CV_ELEM_SIZE1(CV_32S),
|
|
dbsize = groupnum * ((needMinVal ? esz : 0) + (needMaxVal ? esz : 0) +
|
|
(needMinLoc ? esz32s : 0) + (needMaxLoc ? esz32s : 0) +
|
|
(maxVal2 ? esz : 0));
|
|
UMat src = _src.getUMat(), src2 = _src2.getUMat(), db(1, dbsize, CV_8UC1), mask = _mask.getUMat();
|
|
|
|
if (cn > 1 && !haveMask)
|
|
{
|
|
src = src.reshape(1);
|
|
src2 = src2.reshape(1);
|
|
}
|
|
|
|
if (haveSrc2)
|
|
{
|
|
if (!haveMask)
|
|
k.args(ocl::KernelArg::ReadOnlyNoSize(src), src.cols, (int)src.total(),
|
|
groupnum, ocl::KernelArg::PtrWriteOnly(db), ocl::KernelArg::ReadOnlyNoSize(src2));
|
|
else
|
|
k.args(ocl::KernelArg::ReadOnlyNoSize(src), src.cols, (int)src.total(),
|
|
groupnum, ocl::KernelArg::PtrWriteOnly(db), ocl::KernelArg::ReadOnlyNoSize(mask),
|
|
ocl::KernelArg::ReadOnlyNoSize(src2));
|
|
}
|
|
else
|
|
{
|
|
if (!haveMask)
|
|
k.args(ocl::KernelArg::ReadOnlyNoSize(src), src.cols, (int)src.total(),
|
|
groupnum, ocl::KernelArg::PtrWriteOnly(db));
|
|
else
|
|
k.args(ocl::KernelArg::ReadOnlyNoSize(src), src.cols, (int)src.total(),
|
|
groupnum, ocl::KernelArg::PtrWriteOnly(db), ocl::KernelArg::ReadOnlyNoSize(mask));
|
|
}
|
|
|
|
size_t globalsize = groupnum * wgs;
|
|
if (!k.run(1, &globalsize, &wgs, true))
|
|
return false;
|
|
|
|
static const getMinMaxResFunc functab[7] =
|
|
{
|
|
getMinMaxRes<uchar>,
|
|
getMinMaxRes<char>,
|
|
getMinMaxRes<ushort>,
|
|
getMinMaxRes<short>,
|
|
getMinMaxRes<int>,
|
|
getMinMaxRes<float>,
|
|
getMinMaxRes<double>
|
|
};
|
|
|
|
getMinMaxResFunc func = functab[ddepth];
|
|
|
|
int locTemp[2];
|
|
func(db.getMat(ACCESS_READ), minVal, maxVal,
|
|
needMinLoc ? minLoc ? minLoc : locTemp : minLoc,
|
|
needMaxLoc ? maxLoc ? maxLoc : locTemp : maxLoc,
|
|
groupnum, src.cols, maxVal2);
|
|
|
|
return true;
|
|
}
|
|
|
|
#endif
|
|
|
|
}
|
|
|
|
void cv::minMaxIdx(InputArray _src, double* minVal,
|
|
double* maxVal, int* minIdx, int* maxIdx,
|
|
InputArray _mask)
|
|
{
|
|
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
|
|
CV_Assert( (cn == 1 && (_mask.empty() || _mask.type() == CV_8U)) ||
|
|
(cn > 1 && _mask.empty() && !minIdx && !maxIdx) );
|
|
|
|
CV_OCL_RUN(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2 && (_mask.empty() || _src.size() == _mask.size()),
|
|
ocl_minMaxIdx(_src, minVal, maxVal, minIdx, maxIdx, _mask))
|
|
|
|
Mat src = _src.getMat(), mask = _mask.getMat();
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
CV_IPP_CHECK()
|
|
{
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
|
|
if( src.dims == 2 || (src.isContinuous() && mask.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
|
|
{
|
|
IppiSize sz = { cols * cn, rows };
|
|
|
|
if( !mask.empty() )
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMaskMinMaxIndxFuncC1)(const void *, int, const void *, int,
|
|
IppiSize, Ipp32f *, Ipp32f *, IppiPoint *, IppiPoint *);
|
|
|
|
CV_SUPPRESS_DEPRECATED_START
|
|
ippiMaskMinMaxIndxFuncC1 ippFuncC1 =
|
|
type == CV_8UC1 ? (ippiMaskMinMaxIndxFuncC1)ippiMinMaxIndx_8u_C1MR :
|
|
type == CV_8SC1 ? (ippiMaskMinMaxIndxFuncC1)ippiMinMaxIndx_8s_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskMinMaxIndxFuncC1)ippiMinMaxIndx_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskMinMaxIndxFuncC1)ippiMinMaxIndx_32f_C1MR : 0;
|
|
CV_SUPPRESS_DEPRECATED_END
|
|
|
|
if( ippFuncC1 )
|
|
{
|
|
Ipp32f min, max;
|
|
IppiPoint minp, maxp;
|
|
if( ippFuncC1(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, &min, &max, &minp, &maxp) >= 0 )
|
|
{
|
|
if( minVal )
|
|
*minVal = (double)min;
|
|
if( maxVal )
|
|
*maxVal = (double)max;
|
|
if( !minp.x && !minp.y && !maxp.x && !maxp.y && !mask.ptr()[0] )
|
|
minp.x = maxp.x = -1;
|
|
if( minIdx )
|
|
{
|
|
size_t minidx = minp.y * cols + minp.x + 1;
|
|
ofs2idx(src, minidx, minIdx);
|
|
}
|
|
if( maxIdx )
|
|
{
|
|
size_t maxidx = maxp.y * cols + maxp.x + 1;
|
|
ofs2idx(src, maxidx, maxIdx);
|
|
}
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMinMaxIndxFuncC1)(const void *, int, IppiSize, Ipp32f *, Ipp32f *, IppiPoint *, IppiPoint *);
|
|
|
|
CV_SUPPRESS_DEPRECATED_START
|
|
ippiMinMaxIndxFuncC1 ippFuncC1 =
|
|
depth == CV_8U ? (ippiMinMaxIndxFuncC1)ippiMinMaxIndx_8u_C1R :
|
|
depth == CV_8S ? (ippiMinMaxIndxFuncC1)ippiMinMaxIndx_8s_C1R :
|
|
depth == CV_16U ? (ippiMinMaxIndxFuncC1)ippiMinMaxIndx_16u_C1R :
|
|
depth == CV_32F ? (ippiMinMaxIndxFuncC1)ippiMinMaxIndx_32f_C1R : 0;
|
|
CV_SUPPRESS_DEPRECATED_END
|
|
|
|
if( ippFuncC1 )
|
|
{
|
|
Ipp32f min, max;
|
|
IppiPoint minp, maxp;
|
|
if( ippFuncC1(src.ptr(), (int)src.step[0], sz, &min, &max, &minp, &maxp) >= 0 )
|
|
{
|
|
if( minVal )
|
|
*minVal = (double)min;
|
|
if( maxVal )
|
|
*maxVal = (double)max;
|
|
if( minIdx )
|
|
{
|
|
size_t minidx = minp.y * cols + minp.x + 1;
|
|
ofs2idx(src, minidx, minIdx);
|
|
}
|
|
if( maxIdx )
|
|
{
|
|
size_t maxidx = maxp.y * cols + maxp.x + 1;
|
|
ofs2idx(src, maxidx, maxIdx);
|
|
}
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
MinMaxIdxFunc func = getMinmaxTab(depth);
|
|
CV_Assert( func != 0 );
|
|
|
|
const Mat* arrays[] = {&src, &mask, 0};
|
|
uchar* ptrs[2];
|
|
NAryMatIterator it(arrays, ptrs);
|
|
|
|
size_t minidx = 0, maxidx = 0;
|
|
int iminval = INT_MAX, imaxval = INT_MIN;
|
|
float fminval = FLT_MAX, fmaxval = -FLT_MAX;
|
|
double dminval = DBL_MAX, dmaxval = -DBL_MAX;
|
|
size_t startidx = 1;
|
|
int *minval = &iminval, *maxval = &imaxval;
|
|
int planeSize = (int)it.size*cn;
|
|
|
|
if( depth == CV_32F )
|
|
minval = (int*)&fminval, maxval = (int*)&fmaxval;
|
|
else if( depth == CV_64F )
|
|
minval = (int*)&dminval, maxval = (int*)&dmaxval;
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it, startidx += planeSize )
|
|
func( ptrs[0], ptrs[1], minval, maxval, &minidx, &maxidx, planeSize, startidx );
|
|
|
|
if( minidx == 0 )
|
|
dminval = dmaxval = 0;
|
|
else if( depth == CV_32F )
|
|
dminval = fminval, dmaxval = fmaxval;
|
|
else if( depth <= CV_32S )
|
|
dminval = iminval, dmaxval = imaxval;
|
|
|
|
if( minVal )
|
|
*minVal = dminval;
|
|
if( maxVal )
|
|
*maxVal = dmaxval;
|
|
|
|
if( minIdx )
|
|
ofs2idx(src, minidx, minIdx);
|
|
if( maxIdx )
|
|
ofs2idx(src, maxidx, maxIdx);
|
|
}
|
|
|
|
void cv::minMaxLoc( InputArray _img, double* minVal, double* maxVal,
|
|
Point* minLoc, Point* maxLoc, InputArray mask )
|
|
{
|
|
CV_Assert(_img.dims() <= 2);
|
|
|
|
minMaxIdx(_img, minVal, maxVal, (int*)minLoc, (int*)maxLoc, mask);
|
|
if( minLoc )
|
|
std::swap(minLoc->x, minLoc->y);
|
|
if( maxLoc )
|
|
std::swap(maxLoc->x, maxLoc->y);
|
|
}
|
|
|
|
/****************************************************************************************\
|
|
* norm *
|
|
\****************************************************************************************/
|
|
|
|
namespace cv
|
|
{
|
|
|
|
float normL2Sqr_(const float* a, const float* b, int n)
|
|
{
|
|
int j = 0; float d = 0.f;
|
|
#if CV_SSE
|
|
if( USE_SSE2 )
|
|
{
|
|
float CV_DECL_ALIGNED(16) buf[4];
|
|
__m128 d0 = _mm_setzero_ps(), d1 = _mm_setzero_ps();
|
|
|
|
for( ; j <= n - 8; j += 8 )
|
|
{
|
|
__m128 t0 = _mm_sub_ps(_mm_loadu_ps(a + j), _mm_loadu_ps(b + j));
|
|
__m128 t1 = _mm_sub_ps(_mm_loadu_ps(a + j + 4), _mm_loadu_ps(b + j + 4));
|
|
d0 = _mm_add_ps(d0, _mm_mul_ps(t0, t0));
|
|
d1 = _mm_add_ps(d1, _mm_mul_ps(t1, t1));
|
|
}
|
|
_mm_store_ps(buf, _mm_add_ps(d0, d1));
|
|
d = buf[0] + buf[1] + buf[2] + buf[3];
|
|
}
|
|
else
|
|
#endif
|
|
{
|
|
for( ; j <= n - 4; j += 4 )
|
|
{
|
|
float t0 = a[j] - b[j], t1 = a[j+1] - b[j+1], t2 = a[j+2] - b[j+2], t3 = a[j+3] - b[j+3];
|
|
d += t0*t0 + t1*t1 + t2*t2 + t3*t3;
|
|
}
|
|
}
|
|
|
|
for( ; j < n; j++ )
|
|
{
|
|
float t = a[j] - b[j];
|
|
d += t*t;
|
|
}
|
|
return d;
|
|
}
|
|
|
|
|
|
float normL1_(const float* a, const float* b, int n)
|
|
{
|
|
int j = 0; float d = 0.f;
|
|
#if CV_SSE
|
|
if( USE_SSE2 )
|
|
{
|
|
float CV_DECL_ALIGNED(16) buf[4];
|
|
static const int CV_DECL_ALIGNED(16) absbuf[4] = {0x7fffffff, 0x7fffffff, 0x7fffffff, 0x7fffffff};
|
|
__m128 d0 = _mm_setzero_ps(), d1 = _mm_setzero_ps();
|
|
__m128 absmask = _mm_load_ps((const float*)absbuf);
|
|
|
|
for( ; j <= n - 8; j += 8 )
|
|
{
|
|
__m128 t0 = _mm_sub_ps(_mm_loadu_ps(a + j), _mm_loadu_ps(b + j));
|
|
__m128 t1 = _mm_sub_ps(_mm_loadu_ps(a + j + 4), _mm_loadu_ps(b + j + 4));
|
|
d0 = _mm_add_ps(d0, _mm_and_ps(t0, absmask));
|
|
d1 = _mm_add_ps(d1, _mm_and_ps(t1, absmask));
|
|
}
|
|
_mm_store_ps(buf, _mm_add_ps(d0, d1));
|
|
d = buf[0] + buf[1] + buf[2] + buf[3];
|
|
}
|
|
else
|
|
#elif CV_NEON
|
|
float32x4_t v_sum = vdupq_n_f32(0.0f);
|
|
for ( ; j <= n - 4; j += 4)
|
|
v_sum = vaddq_f32(v_sum, vabdq_f32(vld1q_f32(a + j), vld1q_f32(b + j)));
|
|
|
|
float CV_DECL_ALIGNED(16) buf[4];
|
|
vst1q_f32(buf, v_sum);
|
|
d = buf[0] + buf[1] + buf[2] + buf[3];
|
|
#endif
|
|
{
|
|
for( ; j <= n - 4; j += 4 )
|
|
{
|
|
d += std::abs(a[j] - b[j]) + std::abs(a[j+1] - b[j+1]) +
|
|
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
|
|
}
|
|
}
|
|
|
|
for( ; j < n; j++ )
|
|
d += std::abs(a[j] - b[j]);
|
|
return d;
|
|
}
|
|
|
|
int normL1_(const uchar* a, const uchar* b, int n)
|
|
{
|
|
int j = 0, d = 0;
|
|
#if CV_SSE
|
|
if( USE_SSE2 )
|
|
{
|
|
__m128i d0 = _mm_setzero_si128();
|
|
|
|
for( ; j <= n - 16; j += 16 )
|
|
{
|
|
__m128i t0 = _mm_loadu_si128((const __m128i*)(a + j));
|
|
__m128i t1 = _mm_loadu_si128((const __m128i*)(b + j));
|
|
|
|
d0 = _mm_add_epi32(d0, _mm_sad_epu8(t0, t1));
|
|
}
|
|
|
|
for( ; j <= n - 4; j += 4 )
|
|
{
|
|
__m128i t0 = _mm_cvtsi32_si128(*(const int*)(a + j));
|
|
__m128i t1 = _mm_cvtsi32_si128(*(const int*)(b + j));
|
|
|
|
d0 = _mm_add_epi32(d0, _mm_sad_epu8(t0, t1));
|
|
}
|
|
d = _mm_cvtsi128_si32(_mm_add_epi32(d0, _mm_unpackhi_epi64(d0, d0)));
|
|
}
|
|
else
|
|
#elif CV_NEON
|
|
uint32x4_t v_sum = vdupq_n_u32(0.0f);
|
|
for ( ; j <= n - 16; j += 16)
|
|
{
|
|
uint8x16_t v_dst = vabdq_u8(vld1q_u8(a + j), vld1q_u8(b + j));
|
|
uint16x8_t v_low = vmovl_u8(vget_low_u8(v_dst)), v_high = vmovl_u8(vget_high_u8(v_dst));
|
|
v_sum = vaddq_u32(v_sum, vaddl_u16(vget_low_u16(v_low), vget_low_u16(v_high)));
|
|
v_sum = vaddq_u32(v_sum, vaddl_u16(vget_high_u16(v_low), vget_high_u16(v_high)));
|
|
}
|
|
|
|
uint CV_DECL_ALIGNED(16) buf[4];
|
|
vst1q_u32(buf, v_sum);
|
|
d = buf[0] + buf[1] + buf[2] + buf[3];
|
|
#endif
|
|
{
|
|
for( ; j <= n - 4; j += 4 )
|
|
{
|
|
d += std::abs(a[j] - b[j]) + std::abs(a[j+1] - b[j+1]) +
|
|
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
|
|
}
|
|
}
|
|
for( ; j < n; j++ )
|
|
d += std::abs(a[j] - b[j]);
|
|
return d;
|
|
}
|
|
|
|
static const uchar popCountTable[] =
|
|
{
|
|
0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
|
|
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
|
|
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
|
|
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
|
|
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
|
|
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
|
|
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
|
|
3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
|
|
};
|
|
|
|
static const uchar popCountTable2[] =
|
|
{
|
|
0, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
|
|
1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
|
|
1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
|
|
2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
|
|
1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
|
|
2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
|
|
1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
|
|
2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4
|
|
};
|
|
|
|
static const uchar popCountTable4[] =
|
|
{
|
|
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
|
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
|
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
|
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
|
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
|
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
|
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
|
|
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
|
|
};
|
|
|
|
static int normHamming(const uchar* a, int n)
|
|
{
|
|
int i = 0, result = 0;
|
|
#if CV_NEON
|
|
{
|
|
uint32x4_t bits = vmovq_n_u32(0);
|
|
for (; i <= n - 16; i += 16) {
|
|
uint8x16_t A_vec = vld1q_u8 (a + i);
|
|
uint8x16_t bitsSet = vcntq_u8 (A_vec);
|
|
uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
|
|
uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
|
|
bits = vaddq_u32(bits, bitSet4);
|
|
}
|
|
uint64x2_t bitSet2 = vpaddlq_u32 (bits);
|
|
result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0);
|
|
result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2);
|
|
}
|
|
#endif
|
|
for( ; i <= n - 4; i += 4 )
|
|
result += popCountTable[a[i]] + popCountTable[a[i+1]] +
|
|
popCountTable[a[i+2]] + popCountTable[a[i+3]];
|
|
for( ; i < n; i++ )
|
|
result += popCountTable[a[i]];
|
|
return result;
|
|
}
|
|
|
|
int normHamming(const uchar* a, const uchar* b, int n)
|
|
{
|
|
int i = 0, result = 0;
|
|
#if CV_NEON
|
|
{
|
|
uint32x4_t bits = vmovq_n_u32(0);
|
|
for (; i <= n - 16; i += 16) {
|
|
uint8x16_t A_vec = vld1q_u8 (a + i);
|
|
uint8x16_t B_vec = vld1q_u8 (b + i);
|
|
uint8x16_t AxorB = veorq_u8 (A_vec, B_vec);
|
|
uint8x16_t bitsSet = vcntq_u8 (AxorB);
|
|
uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
|
|
uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
|
|
bits = vaddq_u32(bits, bitSet4);
|
|
}
|
|
uint64x2_t bitSet2 = vpaddlq_u32 (bits);
|
|
result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0);
|
|
result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2);
|
|
}
|
|
#endif
|
|
for( ; i <= n - 4; i += 4 )
|
|
result += popCountTable[a[i] ^ b[i]] + popCountTable[a[i+1] ^ b[i+1]] +
|
|
popCountTable[a[i+2] ^ b[i+2]] + popCountTable[a[i+3] ^ b[i+3]];
|
|
for( ; i < n; i++ )
|
|
result += popCountTable[a[i] ^ b[i]];
|
|
return result;
|
|
}
|
|
|
|
static int normHamming(const uchar* a, int n, int cellSize)
|
|
{
|
|
if( cellSize == 1 )
|
|
return normHamming(a, n);
|
|
const uchar* tab = 0;
|
|
if( cellSize == 2 )
|
|
tab = popCountTable2;
|
|
else if( cellSize == 4 )
|
|
tab = popCountTable4;
|
|
else
|
|
CV_Error( CV_StsBadSize, "bad cell size (not 1, 2 or 4) in normHamming" );
|
|
int i = 0, result = 0;
|
|
#if CV_ENABLE_UNROLLED
|
|
for( ; i <= n - 4; i += 4 )
|
|
result += tab[a[i]] + tab[a[i+1]] + tab[a[i+2]] + tab[a[i+3]];
|
|
#endif
|
|
for( ; i < n; i++ )
|
|
result += tab[a[i]];
|
|
return result;
|
|
}
|
|
|
|
int normHamming(const uchar* a, const uchar* b, int n, int cellSize)
|
|
{
|
|
if( cellSize == 1 )
|
|
return normHamming(a, b, n);
|
|
const uchar* tab = 0;
|
|
if( cellSize == 2 )
|
|
tab = popCountTable2;
|
|
else if( cellSize == 4 )
|
|
tab = popCountTable4;
|
|
else
|
|
CV_Error( CV_StsBadSize, "bad cell size (not 1, 2 or 4) in normHamming" );
|
|
int i = 0, result = 0;
|
|
#if CV_ENABLE_UNROLLED
|
|
for( ; i <= n - 4; i += 4 )
|
|
result += tab[a[i] ^ b[i]] + tab[a[i+1] ^ b[i+1]] +
|
|
tab[a[i+2] ^ b[i+2]] + tab[a[i+3] ^ b[i+3]];
|
|
#endif
|
|
for( ; i < n; i++ )
|
|
result += tab[a[i] ^ b[i]];
|
|
return result;
|
|
}
|
|
|
|
|
|
template<typename T, typename ST> int
|
|
normInf_(const T* src, const uchar* mask, ST* _result, int len, int cn)
|
|
{
|
|
ST result = *_result;
|
|
if( !mask )
|
|
{
|
|
result = std::max(result, normInf<T, ST>(src, len*cn));
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < len; i++, src += cn )
|
|
if( mask[i] )
|
|
{
|
|
for( int k = 0; k < cn; k++ )
|
|
result = std::max(result, ST(std::abs(src[k])));
|
|
}
|
|
}
|
|
*_result = result;
|
|
return 0;
|
|
}
|
|
|
|
template<typename T, typename ST> int
|
|
normL1_(const T* src, const uchar* mask, ST* _result, int len, int cn)
|
|
{
|
|
ST result = *_result;
|
|
if( !mask )
|
|
{
|
|
result += normL1<T, ST>(src, len*cn);
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < len; i++, src += cn )
|
|
if( mask[i] )
|
|
{
|
|
for( int k = 0; k < cn; k++ )
|
|
result += std::abs(src[k]);
|
|
}
|
|
}
|
|
*_result = result;
|
|
return 0;
|
|
}
|
|
|
|
template<typename T, typename ST> int
|
|
normL2_(const T* src, const uchar* mask, ST* _result, int len, int cn)
|
|
{
|
|
ST result = *_result;
|
|
if( !mask )
|
|
{
|
|
result += normL2Sqr<T, ST>(src, len*cn);
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < len; i++, src += cn )
|
|
if( mask[i] )
|
|
{
|
|
for( int k = 0; k < cn; k++ )
|
|
{
|
|
T v = src[k];
|
|
result += (ST)v*v;
|
|
}
|
|
}
|
|
}
|
|
*_result = result;
|
|
return 0;
|
|
}
|
|
|
|
template<typename T, typename ST> int
|
|
normDiffInf_(const T* src1, const T* src2, const uchar* mask, ST* _result, int len, int cn)
|
|
{
|
|
ST result = *_result;
|
|
if( !mask )
|
|
{
|
|
result = std::max(result, normInf<T, ST>(src1, src2, len*cn));
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < len; i++, src1 += cn, src2 += cn )
|
|
if( mask[i] )
|
|
{
|
|
for( int k = 0; k < cn; k++ )
|
|
result = std::max(result, (ST)std::abs(src1[k] - src2[k]));
|
|
}
|
|
}
|
|
*_result = result;
|
|
return 0;
|
|
}
|
|
|
|
template<typename T, typename ST> int
|
|
normDiffL1_(const T* src1, const T* src2, const uchar* mask, ST* _result, int len, int cn)
|
|
{
|
|
ST result = *_result;
|
|
if( !mask )
|
|
{
|
|
result += normL1<T, ST>(src1, src2, len*cn);
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < len; i++, src1 += cn, src2 += cn )
|
|
if( mask[i] )
|
|
{
|
|
for( int k = 0; k < cn; k++ )
|
|
result += std::abs(src1[k] - src2[k]);
|
|
}
|
|
}
|
|
*_result = result;
|
|
return 0;
|
|
}
|
|
|
|
template<typename T, typename ST> int
|
|
normDiffL2_(const T* src1, const T* src2, const uchar* mask, ST* _result, int len, int cn)
|
|
{
|
|
ST result = *_result;
|
|
if( !mask )
|
|
{
|
|
result += normL2Sqr<T, ST>(src1, src2, len*cn);
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < len; i++, src1 += cn, src2 += cn )
|
|
if( mask[i] )
|
|
{
|
|
for( int k = 0; k < cn; k++ )
|
|
{
|
|
ST v = src1[k] - src2[k];
|
|
result += v*v;
|
|
}
|
|
}
|
|
}
|
|
*_result = result;
|
|
return 0;
|
|
}
|
|
|
|
|
|
#define CV_DEF_NORM_FUNC(L, suffix, type, ntype) \
|
|
static int norm##L##_##suffix(const type* src, const uchar* mask, ntype* r, int len, int cn) \
|
|
{ return norm##L##_(src, mask, r, len, cn); } \
|
|
static int normDiff##L##_##suffix(const type* src1, const type* src2, \
|
|
const uchar* mask, ntype* r, int len, int cn) \
|
|
{ return normDiff##L##_(src1, src2, mask, r, (int)len, cn); }
|
|
|
|
#define CV_DEF_NORM_ALL(suffix, type, inftype, l1type, l2type) \
|
|
CV_DEF_NORM_FUNC(Inf, suffix, type, inftype) \
|
|
CV_DEF_NORM_FUNC(L1, suffix, type, l1type) \
|
|
CV_DEF_NORM_FUNC(L2, suffix, type, l2type)
|
|
|
|
CV_DEF_NORM_ALL(8u, uchar, int, int, int)
|
|
CV_DEF_NORM_ALL(8s, schar, int, int, int)
|
|
CV_DEF_NORM_ALL(16u, ushort, int, int, double)
|
|
CV_DEF_NORM_ALL(16s, short, int, int, double)
|
|
CV_DEF_NORM_ALL(32s, int, int, double, double)
|
|
CV_DEF_NORM_ALL(32f, float, float, double, double)
|
|
CV_DEF_NORM_ALL(64f, double, double, double, double)
|
|
|
|
|
|
typedef int (*NormFunc)(const uchar*, const uchar*, uchar*, int, int);
|
|
typedef int (*NormDiffFunc)(const uchar*, const uchar*, const uchar*, uchar*, int, int);
|
|
|
|
static NormFunc getNormFunc(int normType, int depth)
|
|
{
|
|
static NormFunc normTab[3][8] =
|
|
{
|
|
{
|
|
(NormFunc)GET_OPTIMIZED(normInf_8u), (NormFunc)GET_OPTIMIZED(normInf_8s), (NormFunc)GET_OPTIMIZED(normInf_16u), (NormFunc)GET_OPTIMIZED(normInf_16s),
|
|
(NormFunc)GET_OPTIMIZED(normInf_32s), (NormFunc)GET_OPTIMIZED(normInf_32f), (NormFunc)normInf_64f, 0
|
|
},
|
|
{
|
|
(NormFunc)GET_OPTIMIZED(normL1_8u), (NormFunc)GET_OPTIMIZED(normL1_8s), (NormFunc)GET_OPTIMIZED(normL1_16u), (NormFunc)GET_OPTIMIZED(normL1_16s),
|
|
(NormFunc)GET_OPTIMIZED(normL1_32s), (NormFunc)GET_OPTIMIZED(normL1_32f), (NormFunc)normL1_64f, 0
|
|
},
|
|
{
|
|
(NormFunc)GET_OPTIMIZED(normL2_8u), (NormFunc)GET_OPTIMIZED(normL2_8s), (NormFunc)GET_OPTIMIZED(normL2_16u), (NormFunc)GET_OPTIMIZED(normL2_16s),
|
|
(NormFunc)GET_OPTIMIZED(normL2_32s), (NormFunc)GET_OPTIMIZED(normL2_32f), (NormFunc)normL2_64f, 0
|
|
}
|
|
};
|
|
|
|
return normTab[normType][depth];
|
|
}
|
|
|
|
static NormDiffFunc getNormDiffFunc(int normType, int depth)
|
|
{
|
|
static NormDiffFunc normDiffTab[3][8] =
|
|
{
|
|
{
|
|
(NormDiffFunc)GET_OPTIMIZED(normDiffInf_8u), (NormDiffFunc)normDiffInf_8s,
|
|
(NormDiffFunc)normDiffInf_16u, (NormDiffFunc)normDiffInf_16s,
|
|
(NormDiffFunc)normDiffInf_32s, (NormDiffFunc)GET_OPTIMIZED(normDiffInf_32f),
|
|
(NormDiffFunc)normDiffInf_64f, 0
|
|
},
|
|
{
|
|
(NormDiffFunc)GET_OPTIMIZED(normDiffL1_8u), (NormDiffFunc)normDiffL1_8s,
|
|
(NormDiffFunc)normDiffL1_16u, (NormDiffFunc)normDiffL1_16s,
|
|
(NormDiffFunc)normDiffL1_32s, (NormDiffFunc)GET_OPTIMIZED(normDiffL1_32f),
|
|
(NormDiffFunc)normDiffL1_64f, 0
|
|
},
|
|
{
|
|
(NormDiffFunc)GET_OPTIMIZED(normDiffL2_8u), (NormDiffFunc)normDiffL2_8s,
|
|
(NormDiffFunc)normDiffL2_16u, (NormDiffFunc)normDiffL2_16s,
|
|
(NormDiffFunc)normDiffL2_32s, (NormDiffFunc)GET_OPTIMIZED(normDiffL2_32f),
|
|
(NormDiffFunc)normDiffL2_64f, 0
|
|
}
|
|
};
|
|
|
|
return normDiffTab[normType][depth];
|
|
}
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
static bool ocl_norm( InputArray _src, int normType, InputArray _mask, double & result )
|
|
{
|
|
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,
|
|
haveMask = _mask.kind() != _InputArray::NONE;
|
|
|
|
if ( !(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR) ||
|
|
(!doubleSupport && depth == CV_64F))
|
|
return false;
|
|
|
|
UMat src = _src.getUMat();
|
|
|
|
if (normType == NORM_INF)
|
|
{
|
|
if (!ocl_minMaxIdx(_src, NULL, &result, NULL, NULL, _mask,
|
|
std::max(depth, CV_32S), depth != CV_8U && depth != CV_16U))
|
|
return false;
|
|
}
|
|
else if (normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR)
|
|
{
|
|
Scalar sc;
|
|
bool unstype = depth == CV_8U || depth == CV_16U;
|
|
|
|
if ( !ocl_sum(haveMask ? src : src.reshape(1), sc, normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
OCL_OP_SUM_SQR : (unstype ? OCL_OP_SUM : OCL_OP_SUM_ABS), _mask) )
|
|
return false;
|
|
|
|
if (!haveMask)
|
|
cn = 1;
|
|
|
|
double s = 0.0;
|
|
for (int i = 0; i < cn; ++i)
|
|
s += sc[i];
|
|
|
|
result = normType == NORM_L1 || normType == NORM_L2SQR ? s : std::sqrt(s);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
#endif
|
|
|
|
}
|
|
|
|
double cv::norm( InputArray _src, int normType, InputArray _mask )
|
|
{
|
|
normType &= NORM_TYPE_MASK;
|
|
CV_Assert( normType == NORM_INF || normType == NORM_L1 ||
|
|
normType == NORM_L2 || normType == NORM_L2SQR ||
|
|
((normType == NORM_HAMMING || normType == NORM_HAMMING2) && _src.type() == CV_8U) );
|
|
|
|
#ifdef HAVE_OPENCL
|
|
double _result = 0;
|
|
CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2,
|
|
ocl_norm(_src, normType, _mask, _result),
|
|
_result)
|
|
#endif
|
|
|
|
Mat src = _src.getMat(), mask = _mask.getMat();
|
|
int depth = src.depth(), cn = src.channels();
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
CV_IPP_CHECK()
|
|
{
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
|
|
|
|
if( (src.dims == 2 || (src.isContinuous() && mask.isContinuous()))
|
|
&& cols > 0 && (size_t)rows*cols == total_size
|
|
&& (normType == NORM_INF || normType == NORM_L1 ||
|
|
normType == NORM_L2 || normType == NORM_L2SQR) )
|
|
{
|
|
IppiSize sz = { cols, rows };
|
|
int type = src.type();
|
|
if( !mask.empty() )
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMaskNormFuncC1)(const void *, int, const void *, int, IppiSize, Ipp64f *);
|
|
ippiMaskNormFuncC1 ippFuncC1 =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiMaskNormFuncC1)ippiNorm_Inf_8u_C1MR :
|
|
type == CV_8SC1 ? (ippiMaskNormFuncC1)ippiNorm_Inf_8s_C1MR :
|
|
// type == CV_16UC1 ? (ippiMaskNormFuncC1)ippiNorm_Inf_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormFuncC1)ippiNorm_Inf_32f_C1MR :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiMaskNormFuncC1)ippiNorm_L1_8u_C1MR :
|
|
type == CV_8SC1 ? (ippiMaskNormFuncC1)ippiNorm_L1_8s_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskNormFuncC1)ippiNorm_L1_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormFuncC1)ippiNorm_L1_32f_C1MR :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiMaskNormFuncC1)ippiNorm_L2_8u_C1MR :
|
|
type == CV_8SC1 ? (ippiMaskNormFuncC1)ippiNorm_L2_8s_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskNormFuncC1)ippiNorm_L2_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormFuncC1)ippiNorm_L2_32f_C1MR :
|
|
0) : 0;
|
|
if( ippFuncC1 )
|
|
{
|
|
Ipp64f norm;
|
|
if( ippFuncC1(src.ptr(), (int)src.step[0], mask.ptr(), (int)mask.step[0], sz, &norm) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
|
|
setIppErrorStatus();
|
|
}
|
|
/*typedef IppStatus (CV_STDCALL* ippiMaskNormFuncC3)(const void *, int, const void *, int, IppiSize, int, Ipp64f *);
|
|
ippiMaskNormFuncC3 ippFuncC3 =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC3 ? (ippiMaskNormFuncC3)ippiNorm_Inf_8u_C3CMR :
|
|
type == CV_8SC3 ? (ippiMaskNormFuncC3)ippiNorm_Inf_8s_C3CMR :
|
|
type == CV_16UC3 ? (ippiMaskNormFuncC3)ippiNorm_Inf_16u_C3CMR :
|
|
type == CV_32FC3 ? (ippiMaskNormFuncC3)ippiNorm_Inf_32f_C3CMR :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC3 ? (ippiMaskNormFuncC3)ippiNorm_L1_8u_C3CMR :
|
|
type == CV_8SC3 ? (ippiMaskNormFuncC3)ippiNorm_L1_8s_C3CMR :
|
|
type == CV_16UC3 ? (ippiMaskNormFuncC3)ippiNorm_L1_16u_C3CMR :
|
|
type == CV_32FC3 ? (ippiMaskNormFuncC3)ippiNorm_L1_32f_C3CMR :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC3 ? (ippiMaskNormFuncC3)ippiNorm_L2_8u_C3CMR :
|
|
type == CV_8SC3 ? (ippiMaskNormFuncC3)ippiNorm_L2_8s_C3CMR :
|
|
type == CV_16UC3 ? (ippiMaskNormFuncC3)ippiNorm_L2_16u_C3CMR :
|
|
type == CV_32FC3 ? (ippiMaskNormFuncC3)ippiNorm_L2_32f_C3CMR :
|
|
0) : 0;
|
|
if( ippFuncC3 )
|
|
{
|
|
Ipp64f norm1, norm2, norm3;
|
|
if( ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 1, &norm1) >= 0 &&
|
|
ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 2, &norm2) >= 0 &&
|
|
ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 3, &norm3) >= 0)
|
|
{
|
|
Ipp64f norm =
|
|
normType == NORM_INF ? std::max(std::max(norm1, norm2), norm3) :
|
|
normType == NORM_L1 ? norm1 + norm2 + norm3 :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ? std::sqrt(norm1 * norm1 + norm2 * norm2 + norm3 * norm3) :
|
|
0;
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
setIppErrorStatus();
|
|
}*/
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiNormFuncHint)(const void *, int, IppiSize, Ipp64f *, IppHintAlgorithm hint);
|
|
typedef IppStatus (CV_STDCALL* ippiNormFuncNoHint)(const void *, int, IppiSize, Ipp64f *);
|
|
ippiNormFuncHint ippFuncHint =
|
|
normType == NORM_L1 ?
|
|
(type == CV_32FC1 ? (ippiNormFuncHint)ippiNorm_L1_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormFuncHint)ippiNorm_L1_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormFuncHint)ippiNorm_L1_32f_C4R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_32FC1 ? (ippiNormFuncHint)ippiNorm_L2_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormFuncHint)ippiNorm_L2_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormFuncHint)ippiNorm_L2_32f_C4R :
|
|
0) : 0;
|
|
ippiNormFuncNoHint ippFuncNoHint =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiNormFuncNoHint)ippiNorm_Inf_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormFuncNoHint)ippiNorm_Inf_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormFuncNoHint)ippiNorm_Inf_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormFuncNoHint)ippiNorm_Inf_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormFuncNoHint)ippiNorm_Inf_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormFuncNoHint)ippiNorm_Inf_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormFuncNoHint)ippiNorm_Inf_16s_C1R :
|
|
#if (IPP_VERSION_X100 >= 801)
|
|
type == CV_16SC3 ? (ippiNormFuncNoHint)ippiNorm_Inf_16s_C3R : //Aug 2013: problem in IPP 7.1, 8.0 : -32768
|
|
type == CV_16SC4 ? (ippiNormFuncNoHint)ippiNorm_Inf_16s_C4R : //Aug 2013: problem in IPP 7.1, 8.0 : -32768
|
|
#endif
|
|
type == CV_32FC1 ? (ippiNormFuncNoHint)ippiNorm_Inf_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormFuncNoHint)ippiNorm_Inf_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormFuncNoHint)ippiNorm_Inf_32f_C4R :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiNormFuncNoHint)ippiNorm_L1_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormFuncNoHint)ippiNorm_L1_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormFuncNoHint)ippiNorm_L1_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormFuncNoHint)ippiNorm_L1_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormFuncNoHint)ippiNorm_L1_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormFuncNoHint)ippiNorm_L1_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormFuncNoHint)ippiNorm_L1_16s_C1R :
|
|
type == CV_16SC3 ? (ippiNormFuncNoHint)ippiNorm_L1_16s_C3R :
|
|
type == CV_16SC4 ? (ippiNormFuncNoHint)ippiNorm_L1_16s_C4R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiNormFuncNoHint)ippiNorm_L2_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormFuncNoHint)ippiNorm_L2_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormFuncNoHint)ippiNorm_L2_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormFuncNoHint)ippiNorm_L2_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormFuncNoHint)ippiNorm_L2_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormFuncNoHint)ippiNorm_L2_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormFuncNoHint)ippiNorm_L2_16s_C1R :
|
|
type == CV_16SC3 ? (ippiNormFuncNoHint)ippiNorm_L2_16s_C3R :
|
|
type == CV_16SC4 ? (ippiNormFuncNoHint)ippiNorm_L2_16s_C4R :
|
|
0) : 0;
|
|
// Make sure only zero or one version of the function pointer is valid
|
|
CV_Assert(!ippFuncHint || !ippFuncNoHint);
|
|
if( ippFuncHint || ippFuncNoHint )
|
|
{
|
|
Ipp64f norm_array[4];
|
|
IppStatus ret = ippFuncHint ? ippFuncHint(src.ptr(), (int)src.step[0], sz, norm_array, ippAlgHintAccurate) :
|
|
ippFuncNoHint(src.ptr(), (int)src.step[0], sz, norm_array);
|
|
if( ret >= 0 )
|
|
{
|
|
Ipp64f norm = (normType == NORM_L2 || normType == NORM_L2SQR) ? norm_array[0] * norm_array[0] : norm_array[0];
|
|
for( int i = 1; i < cn; i++ )
|
|
{
|
|
norm =
|
|
normType == NORM_INF ? std::max(norm, norm_array[i]) :
|
|
normType == NORM_L1 ? norm + norm_array[i] :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ? norm + norm_array[i] * norm_array[i] :
|
|
0;
|
|
}
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return normType == NORM_L2 ? (double)std::sqrt(norm) : (double)norm;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
if( src.isContinuous() && mask.empty() )
|
|
{
|
|
size_t len = src.total()*cn;
|
|
if( len == (size_t)(int)len )
|
|
{
|
|
if( depth == CV_32F )
|
|
{
|
|
const float* data = src.ptr<float>();
|
|
|
|
if( normType == NORM_L2 )
|
|
{
|
|
double result = 0;
|
|
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
|
|
return std::sqrt(result);
|
|
}
|
|
if( normType == NORM_L2SQR )
|
|
{
|
|
double result = 0;
|
|
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
|
|
return result;
|
|
}
|
|
if( normType == NORM_L1 )
|
|
{
|
|
double result = 0;
|
|
GET_OPTIMIZED(normL1_32f)(data, 0, &result, (int)len, 1);
|
|
return result;
|
|
}
|
|
if( normType == NORM_INF )
|
|
{
|
|
float result = 0;
|
|
GET_OPTIMIZED(normInf_32f)(data, 0, &result, (int)len, 1);
|
|
return result;
|
|
}
|
|
}
|
|
if( depth == CV_8U )
|
|
{
|
|
const uchar* data = src.ptr<uchar>();
|
|
|
|
if( normType == NORM_HAMMING )
|
|
return normHamming(data, (int)len);
|
|
|
|
if( normType == NORM_HAMMING2 )
|
|
return normHamming(data, (int)len, 2);
|
|
}
|
|
}
|
|
}
|
|
|
|
CV_Assert( mask.empty() || mask.type() == CV_8U );
|
|
|
|
if( normType == NORM_HAMMING || normType == NORM_HAMMING2 )
|
|
{
|
|
if( !mask.empty() )
|
|
{
|
|
Mat temp;
|
|
bitwise_and(src, mask, temp);
|
|
return norm(temp, normType);
|
|
}
|
|
int cellSize = normType == NORM_HAMMING ? 1 : 2;
|
|
|
|
const Mat* arrays[] = {&src, 0};
|
|
uchar* ptrs[1];
|
|
NAryMatIterator it(arrays, ptrs);
|
|
int total = (int)it.size;
|
|
int result = 0;
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
result += normHamming(ptrs[0], total, cellSize);
|
|
|
|
return result;
|
|
}
|
|
|
|
NormFunc func = getNormFunc(normType >> 1, depth);
|
|
CV_Assert( func != 0 );
|
|
|
|
const Mat* arrays[] = {&src, &mask, 0};
|
|
uchar* ptrs[2];
|
|
union
|
|
{
|
|
double d;
|
|
int i;
|
|
float f;
|
|
}
|
|
result;
|
|
result.d = 0;
|
|
NAryMatIterator it(arrays, ptrs);
|
|
int j, total = (int)it.size, blockSize = total, intSumBlockSize = 0, count = 0;
|
|
bool blockSum = (normType == NORM_L1 && depth <= CV_16S) ||
|
|
((normType == NORM_L2 || normType == NORM_L2SQR) && depth <= CV_8S);
|
|
int isum = 0;
|
|
int *ibuf = &result.i;
|
|
size_t esz = 0;
|
|
|
|
if( blockSum )
|
|
{
|
|
intSumBlockSize = (normType == NORM_L1 && depth <= CV_8S ? (1 << 23) : (1 << 15))/cn;
|
|
blockSize = std::min(blockSize, intSumBlockSize);
|
|
ibuf = &isum;
|
|
esz = src.elemSize();
|
|
}
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
{
|
|
for( j = 0; j < total; j += blockSize )
|
|
{
|
|
int bsz = std::min(total - j, blockSize);
|
|
func( ptrs[0], ptrs[1], (uchar*)ibuf, bsz, cn );
|
|
count += bsz;
|
|
if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
|
|
{
|
|
result.d += isum;
|
|
isum = 0;
|
|
count = 0;
|
|
}
|
|
ptrs[0] += bsz*esz;
|
|
if( ptrs[1] )
|
|
ptrs[1] += bsz;
|
|
}
|
|
}
|
|
|
|
if( normType == NORM_INF )
|
|
{
|
|
if( depth == CV_64F )
|
|
;
|
|
else if( depth == CV_32F )
|
|
result.d = result.f;
|
|
else
|
|
result.d = result.i;
|
|
}
|
|
else if( normType == NORM_L2 )
|
|
result.d = std::sqrt(result.d);
|
|
|
|
return result.d;
|
|
}
|
|
|
|
#ifdef HAVE_OPENCL
|
|
|
|
namespace cv {
|
|
|
|
static bool ocl_norm( InputArray _src1, InputArray _src2, int normType, InputArray _mask, double & result )
|
|
{
|
|
Scalar sc1, sc2;
|
|
int type = _src1.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
|
|
bool relative = (normType & NORM_RELATIVE) != 0;
|
|
normType &= ~NORM_RELATIVE;
|
|
bool normsum = normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR;
|
|
|
|
if (normsum)
|
|
{
|
|
if (!ocl_sum(_src1, sc1, normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
OCL_OP_SUM_SQR : OCL_OP_SUM, _mask, _src2, relative, sc2))
|
|
return false;
|
|
}
|
|
else
|
|
{
|
|
if (!ocl_minMaxIdx(_src1, NULL, &sc1[0], NULL, NULL, _mask, std::max(CV_32S, depth),
|
|
false, _src2, relative ? &sc2[0] : NULL))
|
|
return false;
|
|
cn = 1;
|
|
}
|
|
|
|
double s2 = 0;
|
|
for (int i = 0; i < cn; ++i)
|
|
{
|
|
result += sc1[i];
|
|
if (relative)
|
|
s2 += sc2[i];
|
|
}
|
|
|
|
if (normType == NORM_L2)
|
|
{
|
|
result = std::sqrt(result);
|
|
if (relative)
|
|
s2 = std::sqrt(s2);
|
|
}
|
|
|
|
if (relative)
|
|
result /= (s2 + DBL_EPSILON);
|
|
|
|
return true;
|
|
}
|
|
|
|
}
|
|
|
|
#endif
|
|
|
|
double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _mask )
|
|
{
|
|
CV_Assert( _src1.sameSize(_src2) && _src1.type() == _src2.type() );
|
|
|
|
#ifdef HAVE_OPENCL
|
|
double _result = 0;
|
|
CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src1.isUMat()),
|
|
ocl_norm(_src1, _src2, normType, _mask, _result),
|
|
_result)
|
|
#endif
|
|
|
|
if( normType & CV_RELATIVE )
|
|
{
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
CV_IPP_CHECK()
|
|
{
|
|
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
|
|
|
|
normType &= NORM_TYPE_MASK;
|
|
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR ||
|
|
((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src1.type() == CV_8U) );
|
|
size_t total_size = src1.total();
|
|
int rows = src1.size[0], cols = rows ? (int)(total_size/rows) : 0;
|
|
if( (src1.dims == 2 || (src1.isContinuous() && src2.isContinuous() && mask.isContinuous()))
|
|
&& cols > 0 && (size_t)rows*cols == total_size
|
|
&& (normType == NORM_INF || normType == NORM_L1 ||
|
|
normType == NORM_L2 || normType == NORM_L2SQR) )
|
|
{
|
|
IppiSize sz = { cols, rows };
|
|
int type = src1.type();
|
|
if( !mask.empty() )
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMaskNormRelFuncC1)(const void *, int, const void *, int, const void *, int, IppiSize, Ipp64f *);
|
|
ippiMaskNormRelFuncC1 ippFuncC1 =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_Inf_8u_C1MR :
|
|
#ifndef __APPLE__
|
|
type == CV_8SC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_Inf_8s_C1MR :
|
|
#endif
|
|
type == CV_16UC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_Inf_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_Inf_32f_C1MR :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L1_8u_C1MR :
|
|
#ifndef __APPLE__
|
|
type == CV_8SC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L1_8s_C1MR :
|
|
#endif
|
|
type == CV_16UC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L1_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L1_32f_C1MR :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L2_8u_C1MR :
|
|
type == CV_8SC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L2_8s_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L2_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L2_32f_C1MR :
|
|
0) : 0;
|
|
if( ippFuncC1 )
|
|
{
|
|
Ipp64f norm;
|
|
if( ippFuncC1(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], mask.ptr(), (int)mask.step[0], sz, &norm) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiNormRelFuncNoHint)(const void *, int, const void *, int, IppiSize, Ipp64f *);
|
|
typedef IppStatus (CV_STDCALL* ippiNormRelFuncHint)(const void *, int, const void *, int, IppiSize, Ipp64f *, IppHintAlgorithm hint);
|
|
ippiNormRelFuncNoHint ippFuncNoHint =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiNormRelFuncNoHint)ippiNormRel_Inf_8u_C1R :
|
|
type == CV_16UC1 ? (ippiNormRelFuncNoHint)ippiNormRel_Inf_16u_C1R :
|
|
type == CV_16SC1 ? (ippiNormRelFuncNoHint)ippiNormRel_Inf_16s_C1R :
|
|
type == CV_32FC1 ? (ippiNormRelFuncNoHint)ippiNormRel_Inf_32f_C1R :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiNormRelFuncNoHint)ippiNormRel_L1_8u_C1R :
|
|
type == CV_16UC1 ? (ippiNormRelFuncNoHint)ippiNormRel_L1_16u_C1R :
|
|
type == CV_16SC1 ? (ippiNormRelFuncNoHint)ippiNormRel_L1_16s_C1R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiNormRelFuncNoHint)ippiNormRel_L2_8u_C1R :
|
|
type == CV_16UC1 ? (ippiNormRelFuncNoHint)ippiNormRel_L2_16u_C1R :
|
|
type == CV_16SC1 ? (ippiNormRelFuncNoHint)ippiNormRel_L2_16s_C1R :
|
|
0) : 0;
|
|
ippiNormRelFuncHint ippFuncHint =
|
|
normType == NORM_L1 ?
|
|
(type == CV_32FC1 ? (ippiNormRelFuncHint)ippiNormRel_L1_32f_C1R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_32FC1 ? (ippiNormRelFuncHint)ippiNormRel_L2_32f_C1R :
|
|
0) : 0;
|
|
if (ippFuncNoHint)
|
|
{
|
|
Ipp64f norm;
|
|
if( ippFuncNoHint(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], sz, &norm) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return (double)norm;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
if (ippFuncHint)
|
|
{
|
|
Ipp64f norm;
|
|
if( ippFuncHint(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], sz, &norm, ippAlgHintAccurate) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return (double)norm;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
return norm(_src1, _src2, normType & ~CV_RELATIVE, _mask)/(norm(_src2, normType, _mask) + DBL_EPSILON);
|
|
}
|
|
|
|
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
|
|
int depth = src1.depth(), cn = src1.channels();
|
|
|
|
normType &= 7;
|
|
CV_Assert( normType == NORM_INF || normType == NORM_L1 ||
|
|
normType == NORM_L2 || normType == NORM_L2SQR ||
|
|
((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src1.type() == CV_8U) );
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
CV_IPP_CHECK()
|
|
{
|
|
size_t total_size = src1.total();
|
|
int rows = src1.size[0], cols = rows ? (int)(total_size/rows) : 0;
|
|
if( (src1.dims == 2 || (src1.isContinuous() && src2.isContinuous() && mask.isContinuous()))
|
|
&& cols > 0 && (size_t)rows*cols == total_size
|
|
&& (normType == NORM_INF || normType == NORM_L1 ||
|
|
normType == NORM_L2 || normType == NORM_L2SQR) )
|
|
{
|
|
IppiSize sz = { cols, rows };
|
|
int type = src1.type();
|
|
if( !mask.empty() )
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMaskNormDiffFuncC1)(const void *, int, const void *, int, const void *, int, IppiSize, Ipp64f *);
|
|
ippiMaskNormDiffFuncC1 ippFuncC1 =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_Inf_8u_C1MR :
|
|
type == CV_8SC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_Inf_8s_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_Inf_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_Inf_32f_C1MR :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L1_8u_C1MR :
|
|
#ifndef __APPLE__
|
|
type == CV_8SC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L1_8s_C1MR :
|
|
#endif
|
|
type == CV_16UC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L1_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L1_32f_C1MR :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L2_8u_C1MR :
|
|
type == CV_8SC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L2_8s_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L2_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L2_32f_C1MR :
|
|
0) : 0;
|
|
if( ippFuncC1 )
|
|
{
|
|
Ipp64f norm;
|
|
if( ippFuncC1(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], mask.ptr(), (int)mask.step[0], sz, &norm) >= 0 )
|
|
{
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
#ifndef __APPLE__
|
|
typedef IppStatus (CV_STDCALL* ippiMaskNormDiffFuncC3)(const void *, int, const void *, int, const void *, int, IppiSize, int, Ipp64f *);
|
|
ippiMaskNormDiffFuncC3 ippFuncC3 =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_Inf_8u_C3CMR :
|
|
type == CV_8SC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_Inf_8s_C3CMR :
|
|
type == CV_16UC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_Inf_16u_C3CMR :
|
|
type == CV_32FC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_Inf_32f_C3CMR :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_L1_8u_C3CMR :
|
|
type == CV_8SC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_L1_8s_C3CMR :
|
|
type == CV_16UC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_L1_16u_C3CMR :
|
|
type == CV_32FC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_L1_32f_C3CMR :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_L2_8u_C3CMR :
|
|
type == CV_8SC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_L2_8s_C3CMR :
|
|
type == CV_16UC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_L2_16u_C3CMR :
|
|
type == CV_32FC3 ? (ippiMaskNormDiffFuncC3)ippiNormDiff_L2_32f_C3CMR :
|
|
0) : 0;
|
|
if( ippFuncC3 )
|
|
{
|
|
Ipp64f norm1, norm2, norm3;
|
|
if( ippFuncC3(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], mask.data, (int)mask.step[0], sz, 1, &norm1) >= 0 &&
|
|
ippFuncC3(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], mask.data, (int)mask.step[0], sz, 2, &norm2) >= 0 &&
|
|
ippFuncC3(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], mask.data, (int)mask.step[0], sz, 3, &norm3) >= 0)
|
|
{
|
|
Ipp64f norm =
|
|
normType == NORM_INF ? std::max(std::max(norm1, norm2), norm3) :
|
|
normType == NORM_L1 ? norm1 + norm2 + norm3 :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ? std::sqrt(norm1 * norm1 + norm2 * norm2 + norm3 * norm3) :
|
|
0;
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
#endif
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiNormDiffFuncHint)(const void *, int, const void *, int, IppiSize, Ipp64f *, IppHintAlgorithm hint);
|
|
typedef IppStatus (CV_STDCALL* ippiNormDiffFuncNoHint)(const void *, int, const void *, int, IppiSize, Ipp64f *);
|
|
ippiNormDiffFuncHint ippFuncHint =
|
|
normType == NORM_L1 ?
|
|
(type == CV_32FC1 ? (ippiNormDiffFuncHint)ippiNormDiff_L1_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormDiffFuncHint)ippiNormDiff_L1_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormDiffFuncHint)ippiNormDiff_L1_32f_C4R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_32FC1 ? (ippiNormDiffFuncHint)ippiNormDiff_L2_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormDiffFuncHint)ippiNormDiff_L2_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormDiffFuncHint)ippiNormDiff_L2_32f_C4R :
|
|
0) : 0;
|
|
ippiNormDiffFuncNoHint ippFuncNoHint =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_16s_C1R :
|
|
#if (IPP_VERSION_X100 >= 801)
|
|
type == CV_16SC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_16s_C3R : //Aug 2013: problem in IPP 7.1, 8.0 : -32768
|
|
type == CV_16SC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_16s_C4R : //Aug 2013: problem in IPP 7.1, 8.0 : -32768
|
|
#endif
|
|
type == CV_32FC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_Inf_32f_C4R :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_16s_C1R :
|
|
type == CV_16SC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_16s_C3R :
|
|
type == CV_16SC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L1_16s_C4R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_16s_C1R :
|
|
type == CV_16SC3 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_16s_C3R :
|
|
type == CV_16SC4 ? (ippiNormDiffFuncNoHint)ippiNormDiff_L2_16s_C4R :
|
|
0) : 0;
|
|
// Make sure only zero or one version of the function pointer is valid
|
|
CV_Assert(!ippFuncHint || !ippFuncNoHint);
|
|
if( ippFuncHint || ippFuncNoHint )
|
|
{
|
|
Ipp64f norm_array[4];
|
|
IppStatus ret = ippFuncHint ? ippFuncHint(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], sz, norm_array, ippAlgHintAccurate) :
|
|
ippFuncNoHint(src1.ptr(), (int)src1.step[0], src2.ptr(), (int)src2.step[0], sz, norm_array);
|
|
if( ret >= 0 )
|
|
{
|
|
Ipp64f norm = (normType == NORM_L2 || normType == NORM_L2SQR) ? norm_array[0] * norm_array[0] : norm_array[0];
|
|
for( int i = 1; i < src1.channels(); i++ )
|
|
{
|
|
norm =
|
|
normType == NORM_INF ? std::max(norm, norm_array[i]) :
|
|
normType == NORM_L1 ? norm + norm_array[i] :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ? norm + norm_array[i] * norm_array[i] :
|
|
0;
|
|
}
|
|
CV_IMPL_ADD(CV_IMPL_IPP);
|
|
return normType == NORM_L2 ? (double)std::sqrt(norm) : (double)norm;
|
|
}
|
|
setIppErrorStatus();
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#endif
|
|
|
|
if( src1.isContinuous() && src2.isContinuous() && mask.empty() )
|
|
{
|
|
size_t len = src1.total()*src1.channels();
|
|
if( len == (size_t)(int)len )
|
|
{
|
|
if( src1.depth() == CV_32F )
|
|
{
|
|
const float* data1 = src1.ptr<float>();
|
|
const float* data2 = src2.ptr<float>();
|
|
|
|
if( normType == NORM_L2 )
|
|
{
|
|
double result = 0;
|
|
GET_OPTIMIZED(normDiffL2_32f)(data1, data2, 0, &result, (int)len, 1);
|
|
return std::sqrt(result);
|
|
}
|
|
if( normType == NORM_L2SQR )
|
|
{
|
|
double result = 0;
|
|
GET_OPTIMIZED(normDiffL2_32f)(data1, data2, 0, &result, (int)len, 1);
|
|
return result;
|
|
}
|
|
if( normType == NORM_L1 )
|
|
{
|
|
double result = 0;
|
|
GET_OPTIMIZED(normDiffL1_32f)(data1, data2, 0, &result, (int)len, 1);
|
|
return result;
|
|
}
|
|
if( normType == NORM_INF )
|
|
{
|
|
float result = 0;
|
|
GET_OPTIMIZED(normDiffInf_32f)(data1, data2, 0, &result, (int)len, 1);
|
|
return result;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
CV_Assert( mask.empty() || mask.type() == CV_8U );
|
|
|
|
if( normType == NORM_HAMMING || normType == NORM_HAMMING2 )
|
|
{
|
|
if( !mask.empty() )
|
|
{
|
|
Mat temp;
|
|
bitwise_xor(src1, src2, temp);
|
|
bitwise_and(temp, mask, temp);
|
|
return norm(temp, normType);
|
|
}
|
|
int cellSize = normType == NORM_HAMMING ? 1 : 2;
|
|
|
|
const Mat* arrays[] = {&src1, &src2, 0};
|
|
uchar* ptrs[2];
|
|
NAryMatIterator it(arrays, ptrs);
|
|
int total = (int)it.size;
|
|
int result = 0;
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
result += normHamming(ptrs[0], ptrs[1], total, cellSize);
|
|
|
|
return result;
|
|
}
|
|
|
|
NormDiffFunc func = getNormDiffFunc(normType >> 1, depth);
|
|
CV_Assert( func != 0 );
|
|
|
|
const Mat* arrays[] = {&src1, &src2, &mask, 0};
|
|
uchar* ptrs[3];
|
|
union
|
|
{
|
|
double d;
|
|
float f;
|
|
int i;
|
|
unsigned u;
|
|
}
|
|
result;
|
|
result.d = 0;
|
|
NAryMatIterator it(arrays, ptrs);
|
|
int j, total = (int)it.size, blockSize = total, intSumBlockSize = 0, count = 0;
|
|
bool blockSum = (normType == NORM_L1 && depth <= CV_16S) ||
|
|
((normType == NORM_L2 || normType == NORM_L2SQR) && depth <= CV_8S);
|
|
unsigned isum = 0;
|
|
unsigned *ibuf = &result.u;
|
|
size_t esz = 0;
|
|
|
|
if( blockSum )
|
|
{
|
|
intSumBlockSize = normType == NORM_L1 && depth <= CV_8S ? (1 << 23) : (1 << 15);
|
|
blockSize = std::min(blockSize, intSumBlockSize);
|
|
ibuf = &isum;
|
|
esz = src1.elemSize();
|
|
}
|
|
|
|
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
|
{
|
|
for( j = 0; j < total; j += blockSize )
|
|
{
|
|
int bsz = std::min(total - j, blockSize);
|
|
func( ptrs[0], ptrs[1], ptrs[2], (uchar*)ibuf, bsz, cn );
|
|
count += bsz;
|
|
if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
|
|
{
|
|
result.d += isum;
|
|
isum = 0;
|
|
count = 0;
|
|
}
|
|
ptrs[0] += bsz*esz;
|
|
ptrs[1] += bsz*esz;
|
|
if( ptrs[2] )
|
|
ptrs[2] += bsz;
|
|
}
|
|
}
|
|
|
|
if( normType == NORM_INF )
|
|
{
|
|
if( depth == CV_64F )
|
|
;
|
|
else if( depth == CV_32F )
|
|
result.d = result.f;
|
|
else
|
|
result.d = result.u;
|
|
}
|
|
else if( normType == NORM_L2 )
|
|
result.d = std::sqrt(result.d);
|
|
|
|
return result.d;
|
|
}
|
|
|
|
|
|
///////////////////////////////////// batch distance ///////////////////////////////////////
|
|
|
|
namespace cv
|
|
{
|
|
|
|
template<typename _Tp, typename _Rt>
|
|
void batchDistL1_(const _Tp* src1, const _Tp* src2, size_t step2,
|
|
int nvecs, int len, _Rt* dist, const uchar* mask)
|
|
{
|
|
step2 /= sizeof(src2[0]);
|
|
if( !mask )
|
|
{
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = normL1<_Tp, _Rt>(src1, src2 + step2*i, len);
|
|
}
|
|
else
|
|
{
|
|
_Rt val0 = std::numeric_limits<_Rt>::max();
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = mask[i] ? normL1<_Tp, _Rt>(src1, src2 + step2*i, len) : val0;
|
|
}
|
|
}
|
|
|
|
template<typename _Tp, typename _Rt>
|
|
void batchDistL2Sqr_(const _Tp* src1, const _Tp* src2, size_t step2,
|
|
int nvecs, int len, _Rt* dist, const uchar* mask)
|
|
{
|
|
step2 /= sizeof(src2[0]);
|
|
if( !mask )
|
|
{
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len);
|
|
}
|
|
else
|
|
{
|
|
_Rt val0 = std::numeric_limits<_Rt>::max();
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = mask[i] ? normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len) : val0;
|
|
}
|
|
}
|
|
|
|
template<typename _Tp, typename _Rt>
|
|
void batchDistL2_(const _Tp* src1, const _Tp* src2, size_t step2,
|
|
int nvecs, int len, _Rt* dist, const uchar* mask)
|
|
{
|
|
step2 /= sizeof(src2[0]);
|
|
if( !mask )
|
|
{
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = std::sqrt(normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len));
|
|
}
|
|
else
|
|
{
|
|
_Rt val0 = std::numeric_limits<_Rt>::max();
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = mask[i] ? std::sqrt(normL2Sqr<_Tp, _Rt>(src1, src2 + step2*i, len)) : val0;
|
|
}
|
|
}
|
|
|
|
static void batchDistHamming(const uchar* src1, const uchar* src2, size_t step2,
|
|
int nvecs, int len, int* dist, const uchar* mask)
|
|
{
|
|
step2 /= sizeof(src2[0]);
|
|
if( !mask )
|
|
{
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = normHamming(src1, src2 + step2*i, len);
|
|
}
|
|
else
|
|
{
|
|
int val0 = INT_MAX;
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = mask[i] ? normHamming(src1, src2 + step2*i, len) : val0;
|
|
}
|
|
}
|
|
|
|
static void batchDistHamming2(const uchar* src1, const uchar* src2, size_t step2,
|
|
int nvecs, int len, int* dist, const uchar* mask)
|
|
{
|
|
step2 /= sizeof(src2[0]);
|
|
if( !mask )
|
|
{
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = normHamming(src1, src2 + step2*i, len, 2);
|
|
}
|
|
else
|
|
{
|
|
int val0 = INT_MAX;
|
|
for( int i = 0; i < nvecs; i++ )
|
|
dist[i] = mask[i] ? normHamming(src1, src2 + step2*i, len, 2) : val0;
|
|
}
|
|
}
|
|
|
|
static void batchDistL1_8u32s(const uchar* src1, const uchar* src2, size_t step2,
|
|
int nvecs, int len, int* dist, const uchar* mask)
|
|
{
|
|
batchDistL1_<uchar, int>(src1, src2, step2, nvecs, len, dist, mask);
|
|
}
|
|
|
|
static void batchDistL1_8u32f(const uchar* src1, const uchar* src2, size_t step2,
|
|
int nvecs, int len, float* dist, const uchar* mask)
|
|
{
|
|
batchDistL1_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
|
|
}
|
|
|
|
static void batchDistL2Sqr_8u32s(const uchar* src1, const uchar* src2, size_t step2,
|
|
int nvecs, int len, int* dist, const uchar* mask)
|
|
{
|
|
batchDistL2Sqr_<uchar, int>(src1, src2, step2, nvecs, len, dist, mask);
|
|
}
|
|
|
|
static void batchDistL2Sqr_8u32f(const uchar* src1, const uchar* src2, size_t step2,
|
|
int nvecs, int len, float* dist, const uchar* mask)
|
|
{
|
|
batchDistL2Sqr_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
|
|
}
|
|
|
|
static void batchDistL2_8u32f(const uchar* src1, const uchar* src2, size_t step2,
|
|
int nvecs, int len, float* dist, const uchar* mask)
|
|
{
|
|
batchDistL2_<uchar, float>(src1, src2, step2, nvecs, len, dist, mask);
|
|
}
|
|
|
|
static void batchDistL1_32f(const float* src1, const float* src2, size_t step2,
|
|
int nvecs, int len, float* dist, const uchar* mask)
|
|
{
|
|
batchDistL1_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
|
|
}
|
|
|
|
static void batchDistL2Sqr_32f(const float* src1, const float* src2, size_t step2,
|
|
int nvecs, int len, float* dist, const uchar* mask)
|
|
{
|
|
batchDistL2Sqr_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
|
|
}
|
|
|
|
static void batchDistL2_32f(const float* src1, const float* src2, size_t step2,
|
|
int nvecs, int len, float* dist, const uchar* mask)
|
|
{
|
|
batchDistL2_<float, float>(src1, src2, step2, nvecs, len, dist, mask);
|
|
}
|
|
|
|
typedef void (*BatchDistFunc)(const uchar* src1, const uchar* src2, size_t step2,
|
|
int nvecs, int len, uchar* dist, const uchar* mask);
|
|
|
|
|
|
struct BatchDistInvoker : public ParallelLoopBody
|
|
{
|
|
BatchDistInvoker( const Mat& _src1, const Mat& _src2,
|
|
Mat& _dist, Mat& _nidx, int _K,
|
|
const Mat& _mask, int _update,
|
|
BatchDistFunc _func)
|
|
{
|
|
src1 = &_src1;
|
|
src2 = &_src2;
|
|
dist = &_dist;
|
|
nidx = &_nidx;
|
|
K = _K;
|
|
mask = &_mask;
|
|
update = _update;
|
|
func = _func;
|
|
}
|
|
|
|
void operator()(const Range& range) const
|
|
{
|
|
AutoBuffer<int> buf(src2->rows);
|
|
int* bufptr = buf;
|
|
|
|
for( int i = range.start; i < range.end; i++ )
|
|
{
|
|
func(src1->ptr(i), src2->ptr(), src2->step, src2->rows, src2->cols,
|
|
K > 0 ? (uchar*)bufptr : dist->ptr(i), mask->data ? mask->ptr(i) : 0);
|
|
|
|
if( K > 0 )
|
|
{
|
|
int* nidxptr = nidx->ptr<int>(i);
|
|
// since positive float's can be compared just like int's,
|
|
// we handle both CV_32S and CV_32F cases with a single branch
|
|
int* distptr = (int*)dist->ptr(i);
|
|
|
|
int j, k;
|
|
|
|
for( j = 0; j < src2->rows; j++ )
|
|
{
|
|
int d = bufptr[j];
|
|
if( d < distptr[K-1] )
|
|
{
|
|
for( k = K-2; k >= 0 && distptr[k] > d; k-- )
|
|
{
|
|
nidxptr[k+1] = nidxptr[k];
|
|
distptr[k+1] = distptr[k];
|
|
}
|
|
nidxptr[k+1] = j + update;
|
|
distptr[k+1] = d;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
const Mat *src1;
|
|
const Mat *src2;
|
|
Mat *dist;
|
|
Mat *nidx;
|
|
const Mat *mask;
|
|
int K;
|
|
int update;
|
|
BatchDistFunc func;
|
|
};
|
|
|
|
}
|
|
|
|
void cv::batchDistance( InputArray _src1, InputArray _src2,
|
|
OutputArray _dist, int dtype, OutputArray _nidx,
|
|
int normType, int K, InputArray _mask,
|
|
int update, bool crosscheck )
|
|
{
|
|
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
|
|
int type = src1.type();
|
|
CV_Assert( type == src2.type() && src1.cols == src2.cols &&
|
|
(type == CV_32F || type == CV_8U));
|
|
CV_Assert( _nidx.needed() == (K > 0) );
|
|
|
|
if( dtype == -1 )
|
|
{
|
|
dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ? CV_32S : CV_32F;
|
|
}
|
|
CV_Assert( (type == CV_8U && dtype == CV_32S) || dtype == CV_32F);
|
|
|
|
K = std::min(K, src2.rows);
|
|
|
|
_dist.create(src1.rows, (K > 0 ? K : src2.rows), dtype);
|
|
Mat dist = _dist.getMat(), nidx;
|
|
if( _nidx.needed() )
|
|
{
|
|
_nidx.create(dist.size(), CV_32S);
|
|
nidx = _nidx.getMat();
|
|
}
|
|
|
|
if( update == 0 && K > 0 )
|
|
{
|
|
dist = Scalar::all(dtype == CV_32S ? (double)INT_MAX : (double)FLT_MAX);
|
|
nidx = Scalar::all(-1);
|
|
}
|
|
|
|
if( crosscheck )
|
|
{
|
|
CV_Assert( K == 1 && update == 0 && mask.empty() );
|
|
Mat tdist, tidx;
|
|
batchDistance(src2, src1, tdist, dtype, tidx, normType, K, mask, 0, false);
|
|
|
|
// if an idx-th element from src1 appeared to be the nearest to i-th element of src2,
|
|
// we update the minimum mutual distance between idx-th element of src1 and the whole src2 set.
|
|
// As a result, if nidx[idx] = i*, it means that idx-th element of src1 is the nearest
|
|
// to i*-th element of src2 and i*-th element of src2 is the closest to idx-th element of src1.
|
|
// If nidx[idx] = -1, it means that there is no such ideal couple for it in src2.
|
|
// This O(N) procedure is called cross-check and it helps to eliminate some false matches.
|
|
if( dtype == CV_32S )
|
|
{
|
|
for( int i = 0; i < tdist.rows; i++ )
|
|
{
|
|
int idx = tidx.at<int>(i);
|
|
int d = tdist.at<int>(i), d0 = dist.at<int>(idx);
|
|
if( d < d0 )
|
|
{
|
|
dist.at<int>(idx) = d;
|
|
nidx.at<int>(idx) = i + update;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < tdist.rows; i++ )
|
|
{
|
|
int idx = tidx.at<int>(i);
|
|
float d = tdist.at<float>(i), d0 = dist.at<float>(idx);
|
|
if( d < d0 )
|
|
{
|
|
dist.at<float>(idx) = d;
|
|
nidx.at<int>(idx) = i + update;
|
|
}
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
|
|
BatchDistFunc func = 0;
|
|
if( type == CV_8U )
|
|
{
|
|
if( normType == NORM_L1 && dtype == CV_32S )
|
|
func = (BatchDistFunc)batchDistL1_8u32s;
|
|
else if( normType == NORM_L1 && dtype == CV_32F )
|
|
func = (BatchDistFunc)batchDistL1_8u32f;
|
|
else if( normType == NORM_L2SQR && dtype == CV_32S )
|
|
func = (BatchDistFunc)batchDistL2Sqr_8u32s;
|
|
else if( normType == NORM_L2SQR && dtype == CV_32F )
|
|
func = (BatchDistFunc)batchDistL2Sqr_8u32f;
|
|
else if( normType == NORM_L2 && dtype == CV_32F )
|
|
func = (BatchDistFunc)batchDistL2_8u32f;
|
|
else if( normType == NORM_HAMMING && dtype == CV_32S )
|
|
func = (BatchDistFunc)batchDistHamming;
|
|
else if( normType == NORM_HAMMING2 && dtype == CV_32S )
|
|
func = (BatchDistFunc)batchDistHamming2;
|
|
}
|
|
else if( type == CV_32F && dtype == CV_32F )
|
|
{
|
|
if( normType == NORM_L1 )
|
|
func = (BatchDistFunc)batchDistL1_32f;
|
|
else if( normType == NORM_L2SQR )
|
|
func = (BatchDistFunc)batchDistL2Sqr_32f;
|
|
else if( normType == NORM_L2 )
|
|
func = (BatchDistFunc)batchDistL2_32f;
|
|
}
|
|
|
|
if( func == 0 )
|
|
CV_Error_(CV_StsUnsupportedFormat,
|
|
("The combination of type=%d, dtype=%d and normType=%d is not supported",
|
|
type, dtype, normType));
|
|
|
|
parallel_for_(Range(0, src1.rows),
|
|
BatchDistInvoker(src1, src2, dist, nidx, K, mask, update, func));
|
|
}
|
|
|
|
|
|
void cv::findNonZero( InputArray _src, OutputArray _idx )
|
|
{
|
|
Mat src = _src.getMat();
|
|
CV_Assert( src.type() == CV_8UC1 );
|
|
int n = countNonZero(src);
|
|
if( _idx.kind() == _InputArray::MAT && !_idx.getMatRef().isContinuous() )
|
|
_idx.release();
|
|
_idx.create(n, 1, CV_32SC2);
|
|
Mat idx = _idx.getMat();
|
|
CV_Assert(idx.isContinuous());
|
|
Point* idx_ptr = idx.ptr<Point>();
|
|
|
|
for( int i = 0; i < src.rows; i++ )
|
|
{
|
|
const uchar* bin_ptr = src.ptr(i);
|
|
for( int j = 0; j < src.cols; j++ )
|
|
if( bin_ptr[j] )
|
|
*idx_ptr++ = Point(j, i);
|
|
}
|
|
}
|
|
|
|
double cv::PSNR(InputArray _src1, InputArray _src2)
|
|
{
|
|
CV_Assert( _src1.depth() == CV_8U );
|
|
double diff = std::sqrt(norm(_src1, _src2, NORM_L2SQR)/(_src1.total()*_src1.channels()));
|
|
return 20*log10(255./(diff+DBL_EPSILON));
|
|
}
|
|
|
|
|
|
CV_IMPL CvScalar cvSum( const CvArr* srcarr )
|
|
{
|
|
cv::Scalar sum = cv::sum(cv::cvarrToMat(srcarr, false, true, 1));
|
|
if( CV_IS_IMAGE(srcarr) )
|
|
{
|
|
int coi = cvGetImageCOI((IplImage*)srcarr);
|
|
if( coi )
|
|
{
|
|
CV_Assert( 0 < coi && coi <= 4 );
|
|
sum = cv::Scalar(sum[coi-1]);
|
|
}
|
|
}
|
|
return sum;
|
|
}
|
|
|
|
CV_IMPL int cvCountNonZero( const CvArr* imgarr )
|
|
{
|
|
cv::Mat img = cv::cvarrToMat(imgarr, false, true, 1);
|
|
if( img.channels() > 1 )
|
|
cv::extractImageCOI(imgarr, img);
|
|
return countNonZero(img);
|
|
}
|
|
|
|
|
|
CV_IMPL CvScalar
|
|
cvAvg( const void* imgarr, const void* maskarr )
|
|
{
|
|
cv::Mat img = cv::cvarrToMat(imgarr, false, true, 1);
|
|
cv::Scalar mean = !maskarr ? cv::mean(img) : cv::mean(img, cv::cvarrToMat(maskarr));
|
|
if( CV_IS_IMAGE(imgarr) )
|
|
{
|
|
int coi = cvGetImageCOI((IplImage*)imgarr);
|
|
if( coi )
|
|
{
|
|
CV_Assert( 0 < coi && coi <= 4 );
|
|
mean = cv::Scalar(mean[coi-1]);
|
|
}
|
|
}
|
|
return mean;
|
|
}
|
|
|
|
|
|
CV_IMPL void
|
|
cvAvgSdv( const CvArr* imgarr, CvScalar* _mean, CvScalar* _sdv, const void* maskarr )
|
|
{
|
|
cv::Scalar mean, sdv;
|
|
|
|
cv::Mat mask;
|
|
if( maskarr )
|
|
mask = cv::cvarrToMat(maskarr);
|
|
|
|
cv::meanStdDev(cv::cvarrToMat(imgarr, false, true, 1), mean, sdv, mask );
|
|
|
|
if( CV_IS_IMAGE(imgarr) )
|
|
{
|
|
int coi = cvGetImageCOI((IplImage*)imgarr);
|
|
if( coi )
|
|
{
|
|
CV_Assert( 0 < coi && coi <= 4 );
|
|
mean = cv::Scalar(mean[coi-1]);
|
|
sdv = cv::Scalar(sdv[coi-1]);
|
|
}
|
|
}
|
|
|
|
if( _mean )
|
|
*(cv::Scalar*)_mean = mean;
|
|
if( _sdv )
|
|
*(cv::Scalar*)_sdv = sdv;
|
|
}
|
|
|
|
|
|
CV_IMPL void
|
|
cvMinMaxLoc( const void* imgarr, double* _minVal, double* _maxVal,
|
|
CvPoint* _minLoc, CvPoint* _maxLoc, const void* maskarr )
|
|
{
|
|
cv::Mat mask, img = cv::cvarrToMat(imgarr, false, true, 1);
|
|
if( maskarr )
|
|
mask = cv::cvarrToMat(maskarr);
|
|
if( img.channels() > 1 )
|
|
cv::extractImageCOI(imgarr, img);
|
|
|
|
cv::minMaxLoc( img, _minVal, _maxVal,
|
|
(cv::Point*)_minLoc, (cv::Point*)_maxLoc, mask );
|
|
}
|
|
|
|
|
|
CV_IMPL double
|
|
cvNorm( const void* imgA, const void* imgB, int normType, const void* maskarr )
|
|
{
|
|
cv::Mat a, mask;
|
|
if( !imgA )
|
|
{
|
|
imgA = imgB;
|
|
imgB = 0;
|
|
}
|
|
|
|
a = cv::cvarrToMat(imgA, false, true, 1);
|
|
if( maskarr )
|
|
mask = cv::cvarrToMat(maskarr);
|
|
|
|
if( a.channels() > 1 && CV_IS_IMAGE(imgA) && cvGetImageCOI((const IplImage*)imgA) > 0 )
|
|
cv::extractImageCOI(imgA, a);
|
|
|
|
if( !imgB )
|
|
return !maskarr ? cv::norm(a, normType) : cv::norm(a, normType, mask);
|
|
|
|
cv::Mat b = cv::cvarrToMat(imgB, false, true, 1);
|
|
if( b.channels() > 1 && CV_IS_IMAGE(imgB) && cvGetImageCOI((const IplImage*)imgB) > 0 )
|
|
cv::extractImageCOI(imgB, b);
|
|
|
|
return !maskarr ? cv::norm(a, b, normType) : cv::norm(a, b, normType, mask);
|
|
}
|