mirror of
https://github.com/opencv/opencv.git
synced 2025-01-07 19:54:18 +08:00
6dd8a9b6ad
add REDUCE_SUM2 #13879 proposal to add REDUCE_SUM2 to cv::reduce, an operation that sums up the square of elements
414 lines
13 KiB
C++
414 lines
13 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, 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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#ifndef __OPENCV_PRECOMP_H__
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#define __OPENCV_PRECOMP_H__
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#ifdef BUILD_PLUGIN
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#include "opencv2/core/utility.hpp"
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#else // BUILD_PLUGIN
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#include "opencv2/opencv_modules.hpp"
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#include "cvconfig.h"
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#include "opencv2/core/utility.hpp"
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#include "opencv2/core/core_c.h"
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#include "opencv2/core/cuda.hpp"
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#include "opencv2/core/opengl.hpp"
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#include "opencv2/core/va_intel.hpp"
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#include "opencv2/core/private.hpp"
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#include "opencv2/core/private.cuda.hpp"
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#ifdef HAVE_OPENCL
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#include "opencv2/core/ocl.hpp"
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#endif
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#include <ctype.h>
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#include <float.h>
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#include <limits.h>
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#include <math.h>
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#include <stdarg.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <algorithm>
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#include <cmath>
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#include <cstdlib>
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#include <limits>
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#include <float.h>
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#include <cstring>
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#include <cassert>
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#define USE_SSE2 (cv::checkHardwareSupport(CV_CPU_SSE2))
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#define USE_SSE4_2 (cv::checkHardwareSupport(CV_CPU_SSE4_2))
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#define USE_AVX (cv::checkHardwareSupport(CV_CPU_AVX))
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#define USE_AVX2 (cv::checkHardwareSupport(CV_CPU_AVX2))
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#include "opencv2/core/hal/hal.hpp"
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#include "opencv2/core/hal/intrin.hpp"
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#include "opencv2/core/sse_utils.hpp"
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#include "opencv2/core/neon_utils.hpp"
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#include "opencv2/core/vsx_utils.hpp"
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#include "hal_replacement.hpp"
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#define GET_OPTIMIZED(func) (func)
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namespace cv
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{
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// -128.f ... 255.f
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extern const float g_8x32fTab[];
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#define CV_8TO32F(x) cv::g_8x32fTab[(x)+128]
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extern const ushort g_8x16uSqrTab[];
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#define CV_SQR_8U(x) cv::g_8x16uSqrTab[(x)+255]
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extern const uchar g_Saturate8u[];
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#define CV_FAST_CAST_8U(t) (assert(-256 <= (t) && (t) <= 512), cv::g_Saturate8u[(t)+256])
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#define CV_MIN_8U(a,b) ((a) - CV_FAST_CAST_8U((a) - (b)))
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#define CV_MAX_8U(a,b) ((a) + CV_FAST_CAST_8U((b) - (a)))
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template<typename T1, typename T2=T1, typename T3=T1> struct OpNop
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{
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typedef T1 type1;
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typedef T2 type2;
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typedef T3 rtype;
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T3 operator ()(const T1 a) const { return saturate_cast<T3>(a); }
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};
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template<typename T1, typename T2=T1, typename T3=T1> struct OpSqr
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{
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typedef T1 type1;
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typedef T2 type2;
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typedef T3 rtype;
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T3 operator ()(const T1 a) const { return saturate_cast<T3>(a)*saturate_cast<T3>(a); }
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};
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template<typename T1, typename T2=T1, typename T3=T1> struct OpAdd
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{
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typedef T1 type1;
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typedef T2 type2;
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typedef T3 rtype;
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T3 operator ()(const T1 a, const T2 b) const { return saturate_cast<T3>(a + b); }
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};
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template<typename T1, typename T2=T1, typename T3=T1> struct OpAddSqr
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{
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typedef T1 type1;
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typedef T2 type2;
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typedef T3 rtype;
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T3 operator ()(const T1 a, const T2 b) const { return saturate_cast<T3>(a + saturate_cast<T3>(b)*saturate_cast<T3>(b)); }
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};
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template<typename T1, typename T2=T1, typename T3=T1> struct OpSub
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{
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typedef T1 type1;
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typedef T2 type2;
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typedef T3 rtype;
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T3 operator ()(const T1 a, const T2 b) const { return saturate_cast<T3>(a - b); }
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};
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template<typename T1, typename T2=T1, typename T3=T1> struct OpRSub
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{
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typedef T1 type1;
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typedef T2 type2;
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typedef T3 rtype;
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T3 operator ()(const T1 a, const T2 b) const { return saturate_cast<T3>(b - a); }
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};
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template<typename T> struct OpMin
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{
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typedef T type1;
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typedef T type2;
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typedef T rtype;
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T operator ()(const T a, const T b) const { return std::min(a, b); }
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};
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template<typename T> struct OpMax
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{
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typedef T type1;
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typedef T type2;
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typedef T rtype;
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T operator ()(const T a, const T b) const { return std::max(a, b); }
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};
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template<typename T> struct OpAbsDiff
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{
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typedef T type1;
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typedef T type2;
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typedef T rtype;
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T operator()(T a, T b) const { return a > b ? a - b : b - a; }
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};
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// specializations to prevent "-0" results
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template<> struct OpAbsDiff<float>
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{
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typedef float type1;
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typedef float type2;
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typedef float rtype;
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float operator()(float a, float b) const { return std::abs(a - b); }
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};
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template<> struct OpAbsDiff<double>
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{
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typedef double type1;
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typedef double type2;
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typedef double rtype;
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double operator()(double a, double b) const { return std::abs(a - b); }
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};
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template<typename T> struct OpAnd
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{
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typedef T type1;
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typedef T type2;
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typedef T rtype;
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T operator()( T a, T b ) const { return a & b; }
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};
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template<typename T> struct OpOr
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{
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typedef T type1;
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typedef T type2;
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typedef T rtype;
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T operator()( T a, T b ) const { return a | b; }
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};
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template<typename T> struct OpXor
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{
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typedef T type1;
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typedef T type2;
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typedef T rtype;
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T operator()( T a, T b ) const { return a ^ b; }
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};
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template<typename T> struct OpNot
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{
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typedef T type1;
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typedef T type2;
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typedef T rtype;
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T operator()( T a, T ) const { return ~a; }
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};
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template<> inline uchar OpAdd<uchar>::operator ()(uchar a, uchar b) const
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{ return CV_FAST_CAST_8U(a + b); }
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template<> inline uchar OpSub<uchar>::operator ()(uchar a, uchar b) const
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{ return CV_FAST_CAST_8U(a - b); }
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template<> inline short OpAbsDiff<short>::operator ()(short a, short b) const
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{ return saturate_cast<short>(std::abs(a - b)); }
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template<> inline schar OpAbsDiff<schar>::operator ()(schar a, schar b) const
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{ return saturate_cast<schar>(std::abs(a - b)); }
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template<> inline uchar OpMin<uchar>::operator ()(uchar a, uchar b) const { return CV_MIN_8U(a, b); }
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template<> inline uchar OpMax<uchar>::operator ()(uchar a, uchar b) const { return CV_MAX_8U(a, b); }
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typedef void (*UnaryFunc)(const uchar* src1, size_t step1,
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uchar* dst, size_t step, Size sz,
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void*);
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typedef void (*BinaryFunc)(const uchar* src1, size_t step1,
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const uchar* src2, size_t step2,
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uchar* dst, size_t step, Size sz,
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void*);
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typedef void (*BinaryFuncC)(const uchar* src1, size_t step1,
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const uchar* src2, size_t step2,
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uchar* dst, size_t step, int width, int height,
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void*);
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BinaryFunc getConvertFunc(int sdepth, int ddepth);
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BinaryFunc getConvertScaleFunc(int sdepth, int ddepth);
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BinaryFunc getCopyMaskFunc(size_t esz);
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/* default memory block for sparse array elements */
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#define CV_SPARSE_MAT_BLOCK (1<<12)
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/* initial hash table size */
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#define CV_SPARSE_HASH_SIZE0 (1<<10)
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/* maximal average node_count/hash_size ratio beyond which hash table is resized */
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#define CV_SPARSE_HASH_RATIO 3
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// There is some mess in code with vectors representation.
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// Both vector-column / vector-rows are used with dims=2 (as Mat2D always).
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// Reshape matrices if necessary (in case of vectors) and returns size with scaled width.
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Size getContinuousSize2D(Mat& m1, int widthScale=1);
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Size getContinuousSize2D(Mat& m1, Mat& m2, int widthScale=1);
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Size getContinuousSize2D(Mat& m1, Mat& m2, Mat& m3, int widthScale=1);
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void setSize( Mat& m, int _dims, const int* _sz, const size_t* _steps, bool autoSteps=false );
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void finalizeHdr(Mat& m);
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int updateContinuityFlag(int flags, int dims, const int* size, const size_t* step);
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struct NoVec
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{
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size_t operator()(const void*, const void*, void*, size_t) const { return 0; }
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};
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#define CV_SPLIT_MERGE_MAX_BLOCK_SIZE(cn) ((INT_MAX/4)/(cn)) // HAL implementation accepts 'int' len, so INT_MAX doesn't work here
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enum { BLOCK_SIZE = 1024 };
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#if defined HAVE_IPP && (IPP_VERSION_X100 >= 700)
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#define ARITHM_USE_IPP 1
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#else
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#define ARITHM_USE_IPP 0
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#endif
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inline bool checkScalar(const Mat& sc, int atype, _InputArray::KindFlag sckind, _InputArray::KindFlag akind)
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{
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if( sc.dims > 2 || !sc.isContinuous() )
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return false;
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Size sz = sc.size();
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if(sz.width != 1 && sz.height != 1)
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return false;
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int cn = CV_MAT_CN(atype);
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if( akind == _InputArray::MATX && sckind != _InputArray::MATX )
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return false;
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return sz == Size(1, 1) || sz == Size(1, cn) || sz == Size(cn, 1) ||
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(sz == Size(1, 4) && sc.type() == CV_64F && cn <= 4);
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}
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inline bool checkScalar(InputArray sc, int atype, _InputArray::KindFlag sckind, _InputArray::KindFlag akind)
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{
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if( sc.dims() > 2 || !sc.isContinuous() )
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return false;
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Size sz = sc.size();
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if(sz.width != 1 && sz.height != 1)
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return false;
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int cn = CV_MAT_CN(atype);
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if( akind == _InputArray::MATX && sckind != _InputArray::MATX )
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return false;
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return sz == Size(1, 1) || sz == Size(1, cn) || sz == Size(cn, 1) ||
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(sz == Size(1, 4) && sc.type() == CV_64F && cn <= 4);
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}
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void convertAndUnrollScalar( const Mat& sc, int buftype, uchar* scbuf, size_t blocksize );
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#ifdef CV_COLLECT_IMPL_DATA
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struct ImplCollector
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{
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ImplCollector()
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{
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useCollection = false;
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implFlags = 0;
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}
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bool useCollection; // enable/disable impl data collection
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int implFlags;
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std::vector<int> implCode;
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std::vector<String> implFun;
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cv::Mutex mutex;
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};
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#endif
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struct CoreTLSData
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{
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CoreTLSData() :
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//#ifdef HAVE_OPENCL
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oclExecutionContextInitialized(false), useOpenCL(-1),
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//#endif
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useIPP(-1),
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useIPP_NE(-1)
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#ifdef HAVE_OPENVX
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,useOpenVX(-1)
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#endif
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{}
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RNG rng;
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//#ifdef HAVE_OPENCL
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ocl::OpenCLExecutionContext oclExecutionContext;
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bool oclExecutionContextInitialized;
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int useOpenCL; // 1 - use, 0 - do not use, -1 - auto/not initialized
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//#endif
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int useIPP; // 1 - use, 0 - do not use, -1 - auto/not initialized
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int useIPP_NE; // 1 - use, 0 - do not use, -1 - auto/not initialized
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#ifdef HAVE_OPENVX
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int useOpenVX; // 1 - use, 0 - do not use, -1 - auto/not initialized
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#endif
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};
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CoreTLSData& getCoreTlsData();
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#if defined(BUILD_SHARED_LIBS)
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#if defined _WIN32 || defined WINCE
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#define CL_RUNTIME_EXPORT __declspec(dllexport)
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#elif defined __GNUC__ && __GNUC__ >= 4
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#define CL_RUNTIME_EXPORT __attribute__ ((visibility ("default")))
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#else
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#define CL_RUNTIME_EXPORT
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#endif
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#else
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#define CL_RUNTIME_EXPORT
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#endif
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extern CV_EXPORTS
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bool __termination; // skip some cleanups, because process is terminating
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// (for example, if ExitProcess() was already called)
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CV_EXPORTS
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cv::Mutex& getInitializationMutex();
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/// @brief Returns timestamp in nanoseconds since program launch
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int64 getTimestampNS();
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#define CV_SINGLETON_LAZY_INIT_(TYPE, INITIALIZER, RET_VALUE) \
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static TYPE* const instance = INITIALIZER; \
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return RET_VALUE;
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#define CV_SINGLETON_LAZY_INIT(TYPE, INITIALIZER) CV_SINGLETON_LAZY_INIT_(TYPE, INITIALIZER, instance)
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#define CV_SINGLETON_LAZY_INIT_REF(TYPE, INITIALIZER) CV_SINGLETON_LAZY_INIT_(TYPE, INITIALIZER, *instance)
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CV_EXPORTS void releaseTlsStorageThread();
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int cv_snprintf(char* buf, int len, const char* fmt, ...);
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int cv_vsnprintf(char* buf, int len, const char* fmt, va_list args);
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}
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#endif // BUILD_PLUGIN
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#endif // __OPENCV_PRECOMP_H__
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