mirror of
https://github.com/opencv/opencv.git
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e845184843
* #1538 from StevenPuttemans:bugfix_3283 * #1545 from alalek:ocl_test_fix_rng * #1551 from alalek:cmake_install_win * #1570 from ilya-lavrenov:ipp_warn_fix * #1573 from alalek:perf_simple_strategy * #1574 from alalek:svm_workaround * #1576 from alalek:ocl_fix_cl_double * #1577 from ilya-lavrenov:ocl_setto_opencl12 * #1578 from asmorkalov:android_fd_cp_fix * #1579 from ilya-lavrenov:ocl_norm * #1582 from sperrholz:ocl-arithm-additions * #1586 from ilya-lavrenov:ocl_setto_win_fix * #1589 from ilya-lavrenov:pr1582_fix * #1591 from alalek:ocl_remove_cl_hpp_h * #1592 from alalek:ocl_program_cache_update * #1593 from ilya-lavrenov:ocl_war_on_double * #1594 from ilya-lavrenov:ocl_perf * #1595 from alalek:cl_code_cleanup * #1596 from alalek:test_fix_run_py * #1597 from alalek:ocl_fix_cleanup * #1598 from alalek:ocl_fix_build_mac * #1599 from ilya-lavrenov:ocl_mac_kernel_warnings * #1601 from ilya-lavrenov:ocl_fix_tvl1_and_sparse * #1602 from alalek:ocl_test_dump_info * #1603 from ilya-lavrenov:ocl_disable_svm_noblas * #1605 from alalek:ocl_fixes * #1606 from ilya-lavrenov:ocl_imgproc * #1607 from ilya-lavrenov:ocl_fft_cleanup * #1608 from alalek:fix_warn_upd_haar * #1609 from ilya-lavrenov:ocl_some_optimization * #1610 from alalek:ocl_fix_perf_kalman * #1612 from alalek:ocl_fix_string_info * #1614 from ilya-lavrenov:ocl_svm_misprint * #1616 from ilya-lavrenov:ocl_cvtColor * #1617 from ilya-lavrenov:ocl_info * #1622 from a0byte:2.4 * #1625 from ilya-lavrenov:to_string Conflicts: cmake/OpenCVConfig.cmake cmake/OpenCVDetectPython.cmake cmake/OpenCVGenConfig.cmake modules/core/CMakeLists.txt modules/nonfree/src/surf.ocl.cpp modules/ocl/include/opencv2/ocl/ocl.hpp modules/ocl/include/opencv2/ocl/private/util.hpp modules/ocl/perf/main.cpp modules/ocl/src/arithm.cpp modules/ocl/src/cl_operations.cpp modules/ocl/src/cl_programcache.cpp modules/ocl/src/color.cpp modules/ocl/src/fft.cpp modules/ocl/src/filtering.cpp modules/ocl/src/gemm.cpp modules/ocl/src/haar.cpp modules/ocl/src/imgproc.cpp modules/ocl/src/matrix_operations.cpp modules/ocl/src/pyrlk.cpp modules/ocl/src/split_merge.cpp modules/ocl/src/svm.cpp modules/ocl/test/main.cpp modules/ocl/test/test_fft.cpp modules/ocl/test/test_moments.cpp modules/ocl/test/test_objdetect.cpp modules/ocl/test/test_optflow.cpp modules/ocl/test/utility.hpp modules/python/CMakeLists.txt modules/ts/include/opencv2/ts.hpp modules/ts/src/ts_perf.cpp samples/android/face-detection/jni/DetectionBasedTracker_jni.cpp
2724 lines
98 KiB
C++
2724 lines
98 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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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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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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int i=0;
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int k = cn % 4;
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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 = 0; 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 = 0; 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 + 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 = 0; 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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#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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{ return countNonZero_(src, len); }
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static int countNonZero32s( const int* src, int len )
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{ return countNonZero_(src, len); }
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static int countNonZero32f( const float* src, int len )
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{ return countNonZero_(src, len); }
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static int countNonZero64f( const double* src, int len )
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{ return countNonZero_(src, len); }
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typedef int (*CountNonZeroFunc)(const uchar*, int);
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static CountNonZeroFunc getCountNonZeroTab(int depth)
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{
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static CountNonZeroFunc countNonZeroTab[] =
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{
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(CountNonZeroFunc)GET_OPTIMIZED(countNonZero8u), (CountNonZeroFunc)GET_OPTIMIZED(countNonZero8u),
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(CountNonZeroFunc)GET_OPTIMIZED(countNonZero16u), (CountNonZeroFunc)GET_OPTIMIZED(countNonZero16u),
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(CountNonZeroFunc)GET_OPTIMIZED(countNonZero32s), (CountNonZeroFunc)GET_OPTIMIZED(countNonZero32f),
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(CountNonZeroFunc)GET_OPTIMIZED(countNonZero64f), 0
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};
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return countNonZeroTab[depth];
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}
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template<typename T, typename ST, typename SQT>
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static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, 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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int i;
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int k = cn % 4;
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if( k == 1 )
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{
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ST s0 = sum[0];
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SQT sq0 = sqsum[0];
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for( i = 0; i < len; i++, src += cn )
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{
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T v = src[0];
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s0 += v; sq0 += (SQT)v*v;
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}
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sum[0] = s0;
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sqsum[0] = sq0;
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}
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else if( k == 2 )
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{
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ST s0 = sum[0], s1 = sum[1];
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SQT sq0 = sqsum[0], sq1 = sqsum[1];
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for( i = 0; i < len; i++, src += cn )
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{
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T v0 = src[0], v1 = src[1];
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s0 += v0; sq0 += (SQT)v0*v0;
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s1 += v1; sq1 += (SQT)v1*v1;
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}
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sum[0] = s0; sum[1] = s1;
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sqsum[0] = sq0; sqsum[1] = sq1;
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}
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else if( k == 3 )
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{
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ST s0 = sum[0], s1 = sum[1], s2 = sum[2];
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SQT sq0 = sqsum[0], sq1 = sqsum[1], sq2 = sqsum[2];
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for( i = 0; i < len; i++, src += cn )
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{
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T v0 = src[0], v1 = src[1], v2 = src[2];
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s0 += v0; sq0 += (SQT)v0*v0;
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s1 += v1; sq1 += (SQT)v1*v1;
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s2 += v2; sq2 += (SQT)v2*v2;
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}
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sum[0] = s0; sum[1] = s1; sum[2] = s2;
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sqsum[0] = sq0; sqsum[1] = sq1; sqsum[2] = sq2;
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}
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for( ; k < cn; k += 4 )
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{
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src = src0 + k;
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ST s0 = sum[k], s1 = sum[k+1], s2 = sum[k+2], s3 = sum[k+3];
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SQT sq0 = sqsum[k], sq1 = sqsum[k+1], sq2 = sqsum[k+2], sq3 = sqsum[k+3];
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for( i = 0; i < len; i++, src += cn )
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{
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T v0, v1;
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v0 = src[0], v1 = src[1];
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s0 += v0; sq0 += (SQT)v0*v0;
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s1 += v1; sq1 += (SQT)v1*v1;
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v0 = src[2], v1 = src[3];
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s2 += v0; sq2 += (SQT)v0*v0;
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s3 += v1; sq3 += (SQT)v1*v1;
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}
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sum[k] = s0; sum[k+1] = s1;
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sum[k+2] = s2; sum[k+3] = s3;
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sqsum[k] = sq0; sqsum[k+1] = sq1;
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sqsum[k+2] = sq2; sqsum[k+3] = sq3;
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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 s0 = sum[0];
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SQT sq0 = sqsum[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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T v = src[i];
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s0 += v; sq0 += (SQT)v*v;
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nzm++;
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}
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sum[0] = s0;
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sqsum[0] = sq0;
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}
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else if( cn == 3 )
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{
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ST s0 = sum[0], s1 = sum[1], s2 = sum[2];
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SQT sq0 = sqsum[0], sq1 = sqsum[1], sq2 = sqsum[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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T v0 = src[0], v1 = src[1], v2 = src[2];
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s0 += v0; sq0 += (SQT)v0*v0;
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s1 += v1; sq1 += (SQT)v1*v1;
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s2 += v2; sq2 += (SQT)v2*v2;
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nzm++;
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}
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sum[0] = s0; sum[1] = s1; sum[2] = s2;
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sqsum[0] = sq0; sqsum[1] = sq1; sqsum[2] = sq2;
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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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for( int k = 0; k < cn; k++ )
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{
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T v = src[k];
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ST s = sum[k] + v;
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SQT sq = sqsum[k] + (SQT)v*v;
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sum[k] = s; sqsum[k] = sq;
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}
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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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|
|
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static int sqsum8u( const uchar* src, const uchar* mask, int* sum, int* sqsum, int len, int cn )
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{ return sumsqr_(src, mask, sum, sqsum, len, cn); }
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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];
|
|
}
|
|
|
|
}
|
|
|
|
cv::Scalar cv::sum( InputArray _src )
|
|
{
|
|
Mat src = _src.getMat();
|
|
int k, cn = src.channels(), depth = src.depth();
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = (int)(total_size/rows);
|
|
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* ippiSumFunc)(const void*, int, IppiSize, double *, int);
|
|
ippiSumFunc ippFunc =
|
|
type == CV_8UC1 ? (ippiSumFunc)ippiSum_8u_C1R :
|
|
type == CV_8UC3 ? (ippiSumFunc)ippiSum_8u_C3R :
|
|
type == CV_8UC4 ? (ippiSumFunc)ippiSum_8u_C4R :
|
|
type == CV_16UC1 ? (ippiSumFunc)ippiSum_16u_C1R :
|
|
type == CV_16UC3 ? (ippiSumFunc)ippiSum_16u_C3R :
|
|
type == CV_16UC4 ? (ippiSumFunc)ippiSum_16u_C4R :
|
|
type == CV_16SC1 ? (ippiSumFunc)ippiSum_16s_C1R :
|
|
type == CV_16SC3 ? (ippiSumFunc)ippiSum_16s_C3R :
|
|
type == CV_16SC4 ? (ippiSumFunc)ippiSum_16s_C4R :
|
|
type == CV_32FC1 ? (ippiSumFunc)ippiSum_32f_C1R :
|
|
type == CV_32FC3 ? (ippiSumFunc)ippiSum_32f_C3R :
|
|
type == CV_32FC4 ? (ippiSumFunc)ippiSum_32f_C4R :
|
|
0;
|
|
if( ippFunc )
|
|
{
|
|
Ipp64f res[4];
|
|
if( ippFunc(src.data, (int)src.step[0], sz, res, ippAlgHintAccurate) >= 0 )
|
|
{
|
|
Scalar sc;
|
|
for( int i = 0; i < cn; i++ )
|
|
{
|
|
sc[i] = res[i];
|
|
}
|
|
return sc;
|
|
}
|
|
}
|
|
}
|
|
#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;
|
|
}
|
|
|
|
int cv::countNonZero( InputArray _src )
|
|
{
|
|
Mat src = _src.getMat();
|
|
CountNonZeroFunc func = getCountNonZeroTab(src.depth());
|
|
|
|
CV_Assert( src.channels() == 1 && 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)
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = (int)(total_size/rows);
|
|
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, 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.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, &res) >= 0 )
|
|
{
|
|
return Scalar(res);
|
|
}
|
|
}
|
|
typedef IppStatus (CV_STDCALL* ippiMaskMeanFuncC3)(const void *, int, 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.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 1, &res1) >= 0 &&
|
|
ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 2, &res2) >= 0 &&
|
|
ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 3, &res3) >= 0 )
|
|
{
|
|
return Scalar(res1, res2, res3);
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMeanFunc)(const void*, int, IppiSize, double *, int);
|
|
ippiMeanFunc ippFunc =
|
|
type == CV_8UC1 ? (ippiMeanFunc)ippiMean_8u_C1R :
|
|
type == CV_8UC3 ? (ippiMeanFunc)ippiMean_8u_C3R :
|
|
type == CV_8UC4 ? (ippiMeanFunc)ippiMean_8u_C4R :
|
|
type == CV_16UC1 ? (ippiMeanFunc)ippiMean_16u_C1R :
|
|
type == CV_16UC3 ? (ippiMeanFunc)ippiMean_16u_C3R :
|
|
type == CV_16UC4 ? (ippiMeanFunc)ippiMean_16u_C4R :
|
|
type == CV_16SC1 ? (ippiMeanFunc)ippiMean_16s_C1R :
|
|
type == CV_16SC3 ? (ippiMeanFunc)ippiMean_16s_C3R :
|
|
type == CV_16SC4 ? (ippiMeanFunc)ippiMean_16s_C4R :
|
|
type == CV_32FC1 ? (ippiMeanFunc)ippiMean_32f_C1R :
|
|
type == CV_32FC3 ? (ippiMeanFunc)ippiMean_32f_C3R :
|
|
type == CV_32FC4 ? (ippiMeanFunc)ippiMean_32f_C4R :
|
|
0;
|
|
if( ippFunc )
|
|
{
|
|
Ipp64f res[4];
|
|
if( ippFunc(src.data, (int)src.step[0], sz, res, ippAlgHintAccurate) >= 0 )
|
|
{
|
|
Scalar sc;
|
|
for( int i = 0; i < cn; i++ )
|
|
{
|
|
sc[i] = res[i];
|
|
}
|
|
return sc;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#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);
|
|
}
|
|
|
|
|
|
void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, 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)
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = (int)(total_size/rows);
|
|
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 = (Ipp64f *)mean.data;
|
|
}
|
|
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 = (Ipp64f *)stddev.data;
|
|
}
|
|
for( int k = cn; k < dcn_mean; k++ )
|
|
pmean[k] = 0;
|
|
for( int k = cn; k < dcn_stddev; k++ )
|
|
pstddev[k] = 0;
|
|
IppiSize sz = { cols, rows };
|
|
int type = src.type();
|
|
if( !mask.empty() )
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMaskMeanStdDevFuncC1)(const void *, int, 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.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, pmean, pstddev) >= 0 )
|
|
return;
|
|
}
|
|
typedef IppStatus (CV_STDCALL* ippiMaskMeanStdDevFuncC3)(const void *, int, 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.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
|
|
ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
|
|
ippFuncC3(src.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
|
|
return;
|
|
}
|
|
}
|
|
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 :
|
|
//type == CV_32FC1 ? (ippiMeanStdDevFuncC1)ippiMean_StdDev_32f_C1R ://Aug 2013: bug in IPP 7.1, 8.0
|
|
0;
|
|
if( ippFuncC1 )
|
|
{
|
|
if( ippFuncC1(src.data, (int)src.step[0], sz, pmean, pstddev) >= 0 )
|
|
return;
|
|
}
|
|
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.data, (int)src.step[0], sz, 1, &pmean[0], &pstddev[0]) >= 0 &&
|
|
ippFuncC3(src.data, (int)src.step[0], sz, 2, &pmean[1], &pstddev[1]) >= 0 &&
|
|
ippFuncC3(src.data, (int)src.step[0], sz, 3, &pmean[2], &pstddev[2]) >= 0 )
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
#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;
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
void cv::minMaxIdx(InputArray _src, double* minVal,
|
|
double* maxVal, int* minIdx, int* maxIdx,
|
|
InputArray _mask)
|
|
{
|
|
Mat src = _src.getMat(), mask = _mask.getMat();
|
|
int depth = src.depth(), cn = src.channels();
|
|
|
|
CV_Assert( (cn == 1 && (mask.empty() || mask.type() == CV_8U)) ||
|
|
(cn >= 1 && mask.empty() && !minIdx && !maxIdx) );
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = (int)(total_size/rows);
|
|
if( cn == 1 && ( 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* ippiMaskMinMaxIndxFuncC1)(const void *, int, const void *, int, IppiSize, Ipp32f *, Ipp32f *, IppiPoint *, IppiPoint *);
|
|
ippiMaskMinMaxIndxFuncC1 ippFuncC1 =
|
|
type == CV_8UC1 ? (ippiMaskMinMaxIndxFuncC1)ippiMinMaxIndx_8u_C1MR :
|
|
type == CV_16UC1 ? (ippiMaskMinMaxIndxFuncC1)ippiMinMaxIndx_16u_C1MR :
|
|
type == CV_32FC1 ? (ippiMaskMinMaxIndxFuncC1)ippiMinMaxIndx_32f_C1MR :
|
|
0;
|
|
if( ippFuncC1 )
|
|
{
|
|
Ipp32f min, max;
|
|
IppiPoint minp, maxp;
|
|
if( ippFuncC1(src.data, (int)src.step[0], mask.data, (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.data[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);
|
|
}
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiMinMaxIndxFuncC1)(const void *, int, IppiSize, Ipp32f *, Ipp32f *, IppiPoint *, IppiPoint *);
|
|
ippiMinMaxIndxFuncC1 ippFuncC1 =
|
|
type == CV_8UC1 ? (ippiMinMaxIndxFuncC1)ippiMinMaxIndx_8u_C1R :
|
|
type == CV_16UC1 ? (ippiMinMaxIndxFuncC1)ippiMinMaxIndx_16u_C1R :
|
|
type == CV_32FC1 ? (ippiMinMaxIndxFuncC1)ippiMinMaxIndx_32f_C1R :
|
|
0;
|
|
if( ippFuncC1 )
|
|
{
|
|
Ipp32f min, max;
|
|
IppiPoint minp, maxp;
|
|
if( ippFuncC1(src.data, (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);
|
|
}
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#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 )
|
|
{
|
|
Mat img = _img.getMat();
|
|
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
|
|
#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
|
|
#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];
|
|
}
|
|
|
|
}
|
|
|
|
double cv::norm( InputArray _src, int normType, InputArray _mask )
|
|
{
|
|
Mat src = _src.getMat(), mask = _mask.getMat();
|
|
int depth = src.depth(), cn = src.channels();
|
|
|
|
normType &= 7;
|
|
CV_Assert( normType == NORM_INF || normType == NORM_L1 ||
|
|
normType == NORM_L2 || normType == NORM_L2SQR ||
|
|
((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src.type() == CV_8U) );
|
|
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
size_t total_size = src.total();
|
|
int rows = src.size[0], cols = (int)(total_size/rows);
|
|
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.data, (int)src.step[0], mask.data, (int)mask.step[0], sz, &norm) >= 0 )
|
|
{
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
}
|
|
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;
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiNormFunc)(const void *, int, IppiSize, Ipp64f *, IppHintAlgorithm hint);
|
|
ippiNormFunc ippFunc =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiNormFunc)ippiNorm_Inf_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormFunc)ippiNorm_Inf_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormFunc)ippiNorm_Inf_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormFunc)ippiNorm_Inf_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormFunc)ippiNorm_Inf_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormFunc)ippiNorm_Inf_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormFunc)ippiNorm_Inf_16s_C1R :
|
|
//type == CV_16SC3 ? (ippiNormFunc)ippiNorm_Inf_16s_C3R : //Aug 2013: problem in IPP 7.1, 8.0 : -32768
|
|
//type == CV_16SC4 ? (ippiNormFunc)ippiNorm_Inf_16s_C4R : //Aug 2013: problem in IPP 7.1, 8.0 : -32768
|
|
type == CV_32FC1 ? (ippiNormFunc)ippiNorm_Inf_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormFunc)ippiNorm_Inf_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormFunc)ippiNorm_Inf_32f_C4R :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiNormFunc)ippiNorm_L1_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormFunc)ippiNorm_L1_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormFunc)ippiNorm_L1_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormFunc)ippiNorm_L1_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormFunc)ippiNorm_L1_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormFunc)ippiNorm_L1_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormFunc)ippiNorm_L1_16s_C1R :
|
|
type == CV_16SC3 ? (ippiNormFunc)ippiNorm_L1_16s_C3R :
|
|
type == CV_16SC4 ? (ippiNormFunc)ippiNorm_L1_16s_C4R :
|
|
type == CV_32FC1 ? (ippiNormFunc)ippiNorm_L1_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormFunc)ippiNorm_L1_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormFunc)ippiNorm_L1_32f_C4R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiNormFunc)ippiNorm_L2_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormFunc)ippiNorm_L2_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormFunc)ippiNorm_L2_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormFunc)ippiNorm_L2_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormFunc)ippiNorm_L2_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormFunc)ippiNorm_L2_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormFunc)ippiNorm_L2_16s_C1R :
|
|
type == CV_16SC3 ? (ippiNormFunc)ippiNorm_L2_16s_C3R :
|
|
type == CV_16SC4 ? (ippiNormFunc)ippiNorm_L2_16s_C4R :
|
|
type == CV_32FC1 ? (ippiNormFunc)ippiNorm_L2_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormFunc)ippiNorm_L2_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormFunc)ippiNorm_L2_32f_C4R :
|
|
0) : 0;
|
|
if( ippFunc )
|
|
{
|
|
Ipp64f norm_array[4];
|
|
if( ippFunc(src.data, (int)src.step[0], sz, norm_array, ippAlgHintAccurate) >= 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;
|
|
}
|
|
return normType == NORM_L2 ? (double)std::sqrt(norm) : (double)norm;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#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;
|
|
}
|
|
|
|
|
|
double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _mask )
|
|
{
|
|
if( normType & CV_RELATIVE )
|
|
{
|
|
#if defined (HAVE_IPP) && (IPP_VERSION_MAJOR >= 7)
|
|
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
|
|
|
|
CV_Assert( src1.size == src2.size && src1.type() == src2.type() );
|
|
|
|
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) );
|
|
size_t total_size = src1.total();
|
|
int rows = src1.size[0], cols = (int)(total_size/rows);
|
|
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 :
|
|
type == CV_8SC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_Inf_8s_C1MR :
|
|
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 :
|
|
type == CV_8SC1 ? (ippiMaskNormRelFuncC1)ippiNormRel_L1_8s_C1MR :
|
|
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.data, (int)src1.step[0], src2.data, (int)src2.step[0], mask.data, (int)mask.step[0], sz, &norm) >= 0 )
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiNormRelFunc)(const void *, int, const void *, int, IppiSize, Ipp64f *, IppHintAlgorithm hint);
|
|
ippiNormRelFunc ippFunc =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiNormRelFunc)ippiNormRel_Inf_8u_C1R :
|
|
type == CV_16UC1 ? (ippiNormRelFunc)ippiNormRel_Inf_16u_C1R :
|
|
type == CV_16SC1 ? (ippiNormRelFunc)ippiNormRel_Inf_16s_C1R :
|
|
type == CV_32FC1 ? (ippiNormRelFunc)ippiNormRel_Inf_32f_C1R :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiNormRelFunc)ippiNormRel_L1_8u_C1R :
|
|
type == CV_16UC1 ? (ippiNormRelFunc)ippiNormRel_L1_16u_C1R :
|
|
type == CV_16SC1 ? (ippiNormRelFunc)ippiNormRel_L1_16s_C1R :
|
|
type == CV_32FC1 ? (ippiNormRelFunc)ippiNormRel_L1_32f_C1R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiNormRelFunc)ippiNormRel_L2_8u_C1R :
|
|
type == CV_16UC1 ? (ippiNormRelFunc)ippiNormRel_L2_16u_C1R :
|
|
type == CV_16SC1 ? (ippiNormRelFunc)ippiNormRel_L2_16s_C1R :
|
|
type == CV_32FC1 ? (ippiNormRelFunc)ippiNormRel_L2_32f_C1R :
|
|
0) : 0;
|
|
if( ippFunc )
|
|
{
|
|
Ipp64f norm;
|
|
if( ippFunc(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], sz, &norm, ippAlgHintAccurate) >= 0 )
|
|
return (double)norm;
|
|
}
|
|
}
|
|
}
|
|
#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();
|
|
|
|
CV_Assert( src1.size == src2.size && src1.type() == src2.type() );
|
|
|
|
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)
|
|
size_t total_size = src1.total();
|
|
int rows = src1.size[0], cols = (int)(total_size/rows);
|
|
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 :
|
|
type == CV_8SC1 ? (ippiMaskNormDiffFuncC1)ippiNormDiff_L1_8s_C1MR :
|
|
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.data, (int)src1.step[0], src2.data, (int)src2.step[0], mask.data, (int)mask.step[0], sz, &norm) >= 0 )
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
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;
|
|
return normType == NORM_L2SQR ? (double)(norm * norm) : (double)norm;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
typedef IppStatus (CV_STDCALL* ippiNormDiffFunc)(const void *, int, const void *, int, IppiSize, Ipp64f *, IppHintAlgorithm hint);
|
|
ippiNormDiffFunc ippFunc =
|
|
normType == NORM_INF ?
|
|
(type == CV_8UC1 ? (ippiNormDiffFunc)ippiNormDiff_Inf_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormDiffFunc)ippiNormDiff_Inf_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormDiffFunc)ippiNormDiff_Inf_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormDiffFunc)ippiNormDiff_Inf_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormDiffFunc)ippiNormDiff_Inf_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormDiffFunc)ippiNormDiff_Inf_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormDiffFunc)ippiNormDiff_Inf_16s_C1R :
|
|
//type == CV_16SC3 ? (ippiNormDiffFunc)ippiNormDiff_Inf_16s_C3R : //Aug 2013: problem in IPP 7.1, 8.0 : -32768
|
|
//type == CV_16SC4 ? (ippiNormDiffFunc)ippiNormDiff_Inf_16s_C4R : //Aug 2013: problem in IPP 7.1, 8.0 : -32768
|
|
type == CV_32FC1 ? (ippiNormDiffFunc)ippiNormDiff_Inf_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormDiffFunc)ippiNormDiff_Inf_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormDiffFunc)ippiNormDiff_Inf_32f_C4R :
|
|
0) :
|
|
normType == NORM_L1 ?
|
|
(type == CV_8UC1 ? (ippiNormDiffFunc)ippiNormDiff_L1_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormDiffFunc)ippiNormDiff_L1_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormDiffFunc)ippiNormDiff_L1_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormDiffFunc)ippiNormDiff_L1_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormDiffFunc)ippiNormDiff_L1_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormDiffFunc)ippiNormDiff_L1_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormDiffFunc)ippiNormDiff_L1_16s_C1R :
|
|
type == CV_16SC3 ? (ippiNormDiffFunc)ippiNormDiff_L1_16s_C3R :
|
|
type == CV_16SC4 ? (ippiNormDiffFunc)ippiNormDiff_L1_16s_C4R :
|
|
type == CV_32FC1 ? (ippiNormDiffFunc)ippiNormDiff_L1_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormDiffFunc)ippiNormDiff_L1_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormDiffFunc)ippiNormDiff_L1_32f_C4R :
|
|
0) :
|
|
normType == NORM_L2 || normType == NORM_L2SQR ?
|
|
(type == CV_8UC1 ? (ippiNormDiffFunc)ippiNormDiff_L2_8u_C1R :
|
|
type == CV_8UC3 ? (ippiNormDiffFunc)ippiNormDiff_L2_8u_C3R :
|
|
type == CV_8UC4 ? (ippiNormDiffFunc)ippiNormDiff_L2_8u_C4R :
|
|
type == CV_16UC1 ? (ippiNormDiffFunc)ippiNormDiff_L2_16u_C1R :
|
|
type == CV_16UC3 ? (ippiNormDiffFunc)ippiNormDiff_L2_16u_C3R :
|
|
type == CV_16UC4 ? (ippiNormDiffFunc)ippiNormDiff_L2_16u_C4R :
|
|
type == CV_16SC1 ? (ippiNormDiffFunc)ippiNormDiff_L2_16s_C1R :
|
|
type == CV_16SC3 ? (ippiNormDiffFunc)ippiNormDiff_L2_16s_C3R :
|
|
type == CV_16SC4 ? (ippiNormDiffFunc)ippiNormDiff_L2_16s_C4R :
|
|
type == CV_32FC1 ? (ippiNormDiffFunc)ippiNormDiff_L2_32f_C1R :
|
|
type == CV_32FC3 ? (ippiNormDiffFunc)ippiNormDiff_L2_32f_C3R :
|
|
type == CV_32FC4 ? (ippiNormDiffFunc)ippiNormDiff_L2_32f_C4R :
|
|
0) : 0;
|
|
if( ippFunc )
|
|
{
|
|
Ipp64f norm_array[4];
|
|
if( ippFunc(src1.data, (int)src1.step[0], src2.data, (int)src2.step[0], sz, norm_array, ippAlgHintAccurate) >= 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;
|
|
}
|
|
return normType == NORM_L2 ? (double)std::sqrt(norm) : (double)norm;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#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);
|
|
}
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typedef void (*BatchDistFunc)(const uchar* src1, const uchar* src2, size_t step2,
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int nvecs, int len, uchar* dist, const uchar* mask);
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struct BatchDistInvoker : public ParallelLoopBody
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{
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BatchDistInvoker( const Mat& _src1, const Mat& _src2,
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Mat& _dist, Mat& _nidx, int _K,
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const Mat& _mask, int _update,
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BatchDistFunc _func)
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{
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src1 = &_src1;
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src2 = &_src2;
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dist = &_dist;
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nidx = &_nidx;
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K = _K;
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mask = &_mask;
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update = _update;
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func = _func;
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}
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void operator()(const Range& range) const
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{
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AutoBuffer<int> buf(src2->rows);
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int* bufptr = buf;
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for( int i = range.start; i < range.end; i++ )
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{
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func(src1->ptr(i), src2->ptr(), src2->step, src2->rows, src2->cols,
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K > 0 ? (uchar*)bufptr : dist->ptr(i), mask->data ? mask->ptr(i) : 0);
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if( K > 0 )
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{
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int* nidxptr = nidx->ptr<int>(i);
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// since positive float's can be compared just like int's,
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// we handle both CV_32S and CV_32F cases with a single branch
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int* distptr = (int*)dist->ptr(i);
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int j, k;
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for( j = 0; j < src2->rows; j++ )
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{
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int d = bufptr[j];
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if( d < distptr[K-1] )
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{
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for( k = K-2; k >= 0 && distptr[k] > d; k-- )
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{
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nidxptr[k+1] = nidxptr[k];
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distptr[k+1] = distptr[k];
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}
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nidxptr[k+1] = j + update;
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distptr[k+1] = d;
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}
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}
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}
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}
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}
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const Mat *src1;
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const Mat *src2;
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Mat *dist;
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Mat *nidx;
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const Mat *mask;
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int K;
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int update;
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BatchDistFunc func;
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};
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}
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void cv::batchDistance( InputArray _src1, InputArray _src2,
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OutputArray _dist, int dtype, OutputArray _nidx,
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int normType, int K, InputArray _mask,
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int update, bool crosscheck )
|
|
{
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|
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
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int type = src1.type();
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CV_Assert( type == src2.type() && src1.cols == src2.cols &&
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|
(type == CV_32F || type == CV_8U));
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|
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 = (Point*)idx.data;
|
|
|
|
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)
|
|
{
|
|
Mat src1 = _src1.getMat(), src2 = _src2.getMat();
|
|
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);
|
|
}
|