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Add RISC-V HAL implementation for cv::norm and cv::normalize #26804 This patch implements `cv::norm` with norm types `NORM_INF/NORM_L1/NORM_L2/NORM_L2SQR` and `Mat::convertTo` function in RVV_HAL using native intrinsic, optimizing the performance for `cv::norm(src)`, `cv::norm(src1, src2)`, and `cv::normalize(src)` with data types `8UC1/8UC4/32FC1`. `cv::normalize` also calls `minMaxIdx`, #26789 implements RVV_HAL for this. Tested on MUSE-PI for both gcc 14.2 and clang 20.0. ``` $ opencv_test_core --gtest_filter="*Norm*" $ opencv_perf_core --gtest_filter="*norm*" --perf_min_samples=300 --perf_force_samples=300 ``` The head of the perf table is shown below since the table is too long. View the full perf table here: [hal_rvv_norm.pdf](https://github.com/user-attachments/files/18468255/hal_rvv_norm.pdf) <img width="1304" alt="Untitled" src="https://github.com/user-attachments/assets/3550b671-6d96-4db3-8b5b-d4cb241da650" /> ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake
414 lines
13 KiB
C++
414 lines
13 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html
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#include "precomp.hpp"
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#include "opencl_kernels_core.hpp"
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#include "convert.simd.hpp"
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#include "convert.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL=AVX2,...,BASELINE based on CMakeLists.txt content
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namespace cv {
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namespace hal {
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void cvt16f32f(const hfloat* src, float* dst, int len)
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{
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CV_INSTRUMENT_REGION();
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CV_CPU_DISPATCH(cvt16f32f, (src, dst, len),
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CV_CPU_DISPATCH_MODES_ALL);
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}
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void cvt32f16f(const float* src, hfloat* dst, int len)
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{
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CV_INSTRUMENT_REGION();
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CV_CPU_DISPATCH(cvt32f16f, (src, dst, len),
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CV_CPU_DISPATCH_MODES_ALL);
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}
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void addRNGBias32f(float* arr, const float* scaleBiasPairs, int len)
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{
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CV_INSTRUMENT_REGION();
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CV_CPU_DISPATCH(addRNGBias32f, (arr, scaleBiasPairs, len),
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CV_CPU_DISPATCH_MODES_ALL);
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}
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void addRNGBias64f(double* arr, const double* scaleBiasPairs, int len)
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{
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CV_INSTRUMENT_REGION();
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CV_CPU_DISPATCH(addRNGBias64f, (arr, scaleBiasPairs, len),
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CV_CPU_DISPATCH_MODES_ALL);
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}
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} // namespace
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/* [TODO] Recover IPP calls
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#if defined(HAVE_IPP)
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#define DEF_CVT_FUNC_F(suffix, stype, dtype, ippFavor) \
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static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
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dtype* dst, size_t dstep, Size size, double*) \
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{ \
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CV_IPP_RUN(src && dst, CV_INSTRUMENT_FUN_IPP(ippiConvert_##ippFavor, src, (int)sstep, dst, (int)dstep, ippiSize(size.width, size.height)) >= 0) \
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cvt_(src, sstep, dst, dstep, size); \
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}
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#define DEF_CVT_FUNC_F2(suffix, stype, dtype, ippFavor) \
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static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
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dtype* dst, size_t dstep, Size size, double*) \
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{ \
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CV_IPP_RUN(src && dst, CV_INSTRUMENT_FUN_IPP(ippiConvert_##ippFavor, src, (int)sstep, dst, (int)dstep, ippiSize(size.width, size.height), ippRndFinancial, 0) >= 0) \
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cvt_(src, sstep, dst, dstep, size); \
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}
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#else
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#define DEF_CVT_FUNC_F(suffix, stype, dtype, ippFavor) \
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static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
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dtype* dst, size_t dstep, Size size, double*) \
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{ \
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cvt_(src, sstep, dst, dstep, size); \
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}
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#define DEF_CVT_FUNC_F2 DEF_CVT_FUNC_F
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#endif
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#define DEF_CVT_FUNC(suffix, stype, dtype) \
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static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
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dtype* dst, size_t dstep, Size size, double*) \
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{ \
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cvt_(src, sstep, dst, dstep, size); \
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}
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#define DEF_CPY_FUNC(suffix, stype) \
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static void cvt##suffix( const stype* src, size_t sstep, const uchar*, size_t, \
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stype* dst, size_t dstep, Size size, double*) \
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{ \
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cpy_(src, sstep, dst, dstep, size); \
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}
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DEF_CPY_FUNC(8u, uchar)
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DEF_CVT_FUNC_F(8s8u, schar, uchar, 8s8u_C1Rs)
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DEF_CVT_FUNC_F(16u8u, ushort, uchar, 16u8u_C1R)
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DEF_CVT_FUNC_F(16s8u, short, uchar, 16s8u_C1R)
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DEF_CVT_FUNC_F(32s8u, int, uchar, 32s8u_C1R)
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DEF_CVT_FUNC_F2(32f8u, float, uchar, 32f8u_C1RSfs)
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DEF_CVT_FUNC(64f8u, double, uchar)
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DEF_CVT_FUNC_F2(8u8s, uchar, schar, 8u8s_C1RSfs)
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DEF_CVT_FUNC_F2(16u8s, ushort, schar, 16u8s_C1RSfs)
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DEF_CVT_FUNC_F2(16s8s, short, schar, 16s8s_C1RSfs)
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DEF_CVT_FUNC_F(32s8s, int, schar, 32s8s_C1R)
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DEF_CVT_FUNC_F2(32f8s, float, schar, 32f8s_C1RSfs)
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DEF_CVT_FUNC(64f8s, double, schar)
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DEF_CVT_FUNC_F(8u16u, uchar, ushort, 8u16u_C1R)
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DEF_CVT_FUNC_F(8s16u, schar, ushort, 8s16u_C1Rs)
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DEF_CPY_FUNC(16u, ushort)
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DEF_CVT_FUNC_F(16s16u, short, ushort, 16s16u_C1Rs)
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DEF_CVT_FUNC_F2(32s16u, int, ushort, 32s16u_C1RSfs)
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DEF_CVT_FUNC_F2(32f16u, float, ushort, 32f16u_C1RSfs)
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DEF_CVT_FUNC(64f16u, double, ushort)
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DEF_CVT_FUNC_F(8u16s, uchar, short, 8u16s_C1R)
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DEF_CVT_FUNC_F(8s16s, schar, short, 8s16s_C1R)
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DEF_CVT_FUNC_F2(16u16s, ushort, short, 16u16s_C1RSfs)
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DEF_CVT_FUNC_F2(32s16s, int, short, 32s16s_C1RSfs)
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DEF_CVT_FUNC(32f16s, float, short)
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DEF_CVT_FUNC(64f16s, double, short)
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DEF_CVT_FUNC_F(8u32s, uchar, int, 8u32s_C1R)
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DEF_CVT_FUNC_F(8s32s, schar, int, 8s32s_C1R)
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DEF_CVT_FUNC_F(16u32s, ushort, int, 16u32s_C1R)
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DEF_CVT_FUNC_F(16s32s, short, int, 16s32s_C1R)
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DEF_CPY_FUNC(32s, int)
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DEF_CVT_FUNC_F2(32f32s, float, int, 32f32s_C1RSfs)
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DEF_CVT_FUNC(64f32s, double, int)
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DEF_CVT_FUNC_F(8u32f, uchar, float, 8u32f_C1R)
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DEF_CVT_FUNC_F(8s32f, schar, float, 8s32f_C1R)
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DEF_CVT_FUNC_F(16u32f, ushort, float, 16u32f_C1R)
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DEF_CVT_FUNC_F(16s32f, short, float, 16s32f_C1R)
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DEF_CVT_FUNC_F(32s32f, int, float, 32s32f_C1R)
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DEF_CVT_FUNC(64f32f, double, float)
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DEF_CVT_FUNC(8u64f, uchar, double)
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DEF_CVT_FUNC(8s64f, schar, double)
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DEF_CVT_FUNC(16u64f, ushort, double)
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DEF_CVT_FUNC(16s64f, short, double)
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DEF_CVT_FUNC(32s64f, int, double)
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DEF_CVT_FUNC(32f64f, float, double)
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DEF_CPY_FUNC(64s, int64)
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*/
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BinaryFunc getConvertFunc(int sdepth, int ddepth)
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{
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CV_INSTRUMENT_REGION();
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CV_CPU_DISPATCH(getConvertFunc, (sdepth, ddepth),
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CV_CPU_DISPATCH_MODES_ALL);
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}
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#ifdef HAVE_OPENCL
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static bool ocl_convertFp16( InputArray _src, OutputArray _dst, int sdepth, int ddepth )
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{
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int type = _src.type(), cn = CV_MAT_CN(type);
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_dst.createSameSize( _src, CV_MAKETYPE(ddepth, cn) );
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int kercn = 1;
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int rowsPerWI = 1;
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String build_opt = format("-D HALF_SUPPORT -D srcT=%s -D dstT=%s -D rowsPerWI=%d%s",
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sdepth == CV_32F ? "float" : "half",
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sdepth == CV_32F ? "half" : "float",
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rowsPerWI,
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sdepth == CV_32F ? " -D FLOAT_TO_HALF " : "");
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ocl::Kernel k(sdepth == CV_32F ? "convertFp16_FP32_to_FP16" : "convertFp16_FP16_to_FP32", ocl::core::halfconvert_oclsrc, build_opt);
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if (k.empty())
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return false;
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UMat src = _src.getUMat();
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UMat dst = _dst.getUMat();
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ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
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dstarg = ocl::KernelArg::WriteOnly(dst, cn, kercn);
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k.args(srcarg, dstarg);
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size_t globalsize[2] = { (size_t)src.cols * cn / kercn, ((size_t)src.rows + rowsPerWI - 1) / rowsPerWI };
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return k.run(2, globalsize, NULL, false);
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}
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static bool ocl_convertTo(InputArray src_, OutputArray dst_, int ddepth, bool noScale, double alpha, double beta)
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{
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CV_INSTRUMENT_REGION();
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CV_Assert(ddepth >= 0);
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int stype = src_.type();
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int sdepth = CV_MAT_DEPTH(stype);
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int cn = CV_MAT_CN(stype);
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int dtype = CV_MAKETYPE(ddepth, cn);
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int wdepth = (sdepth == CV_64F) ? CV_64F : CV_32F;
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bool needDouble = sdepth == CV_64F || ddepth == CV_64F;
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bool doubleCheck = true;
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if (needDouble)
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{
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doubleCheck = ocl::Device::getDefault().hasFP64();
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}
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bool halfCheck = true;
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bool needHalf = sdepth == CV_16F || ddepth == CV_16F;
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if (needHalf)
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{
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halfCheck = ocl::Device::getDefault().hasFP16();
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}
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if (!doubleCheck)
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return false;
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if (!halfCheck)
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return false;
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const int rowsPerWI = 4;
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char cvt[2][50];
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ocl::Kernel k("convertTo", ocl::core::convert_oclsrc,
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format("-D srcT=%s -D WT=%s -D dstT=%s -D convertToWT=%s -D convertToDT=%s -D rowsPerWI=%d%s%s%s",
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ocl::typeToStr(sdepth), ocl::typeToStr(wdepth), ocl::typeToStr(ddepth),
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ocl::convertTypeStr(sdepth, wdepth, 1, cvt[0], sizeof(cvt[0])),
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ocl::convertTypeStr(wdepth, ddepth, 1, cvt[1], sizeof(cvt[1])),
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rowsPerWI,
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needDouble ? " -D DOUBLE_SUPPORT" : "",
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needHalf ? " -D HALF_SUPPORT" : "",
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noScale ? " -D NO_SCALE" : ""
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)
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);
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if (k.empty())
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return false;
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UMat src = src_.getUMat();
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dst_.createSameSize(src_, dtype);
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UMat dst = dst_.getUMat();
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float alphaf = (float)alpha, betaf = (float)beta;
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if (noScale)
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k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst, cn));
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else if (wdepth == CV_32F)
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k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst, cn), alphaf, betaf);
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else
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k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst, cn), alpha, beta);
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size_t globalsize[2] = {
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(size_t)dst.cols * cn,
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divUp((size_t)dst.rows, rowsPerWI)
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};
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if (!k.run(2, globalsize, NULL, false))
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return false;
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CV_IMPL_ADD(CV_IMPL_OCL);
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return true;
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}
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#endif
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void Mat::convertTo(OutputArray dst, int type_, double alpha, double beta) const
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{
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CV_INSTRUMENT_REGION();
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if (empty())
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{
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dst.release();
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return;
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}
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int stype = type();
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int sdepth = CV_MAT_DEPTH(stype);
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int ddepth = sdepth;
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if (type_ >= 0)
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ddepth = CV_MAT_DEPTH(type_);
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else
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ddepth = dst.fixedType() ? dst.depth() : sdepth;
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bool noScale = std::fabs(alpha - 1) < DBL_EPSILON && std::fabs(beta) < DBL_EPSILON;
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if (sdepth == ddepth && noScale)
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{
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copyTo(dst);
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return;
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}
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CV_OCL_RUN(dims <= 2 && dst.isUMat(),
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ocl_convertTo(*this, dst, ddepth, noScale, alpha, beta))
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int cn = channels();
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int dtype = CV_MAKETYPE(ddepth, cn);
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Mat src = *this;
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dst.create(dims, size, dtype);
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Mat dstMat = dst.getMat();
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if( dims <= 2 )
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{
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CALL_HAL(convertScale, cv_hal_convertScale, src.data, src.step, dstMat.data, dstMat.step, src.cols * cn, src.rows, sdepth, ddepth, alpha, beta);
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}
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else if( src.isContinuous() && dstMat.isContinuous() )
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{
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CALL_HAL(convertScale, cv_hal_convertScale, src.data, 0, dstMat.data, 0, (int)src.total() * cn, 1, sdepth, ddepth, alpha, beta);
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}
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BinaryFunc func = noScale ? getConvertFunc(sdepth, ddepth) : getConvertScaleFunc(sdepth, ddepth);
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double scale[] = {alpha, beta};
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CV_Assert( func != 0 );
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if( dims <= 2 )
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{
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Size sz = getContinuousSize2D(src, dstMat, cn);
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func(src.data, src.step, 0, 0, dstMat.data, dstMat.step, sz, scale);
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}
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else
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{
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const Mat* arrays[] = {&src, &dstMat, 0};
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uchar* ptrs[2] = {};
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NAryMatIterator it(arrays, ptrs);
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Size sz((int)(it.size*cn), 1);
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for( size_t i = 0; i < it.nplanes; i++, ++it )
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func(ptrs[0], 1, 0, 0, ptrs[1], 1, sz, scale);
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}
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}
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void UMat::convertTo(OutputArray dst, int type_, double alpha, double beta) const
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{
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CV_INSTRUMENT_REGION();
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if (empty())
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{
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dst.release();
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return;
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}
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#ifdef HAVE_OPENCL
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int stype = type();
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int sdepth = CV_MAT_DEPTH(stype);
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int ddepth = sdepth;
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if (type_ >= 0)
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ddepth = CV_MAT_DEPTH(type_);
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else
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ddepth = dst.fixedType() ? dst.depth() : sdepth;
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bool noScale = std::fabs(alpha - 1) < DBL_EPSILON && std::fabs(beta) < DBL_EPSILON;
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if (sdepth == ddepth && noScale)
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{
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copyTo(dst);
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return;
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}
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CV_OCL_RUN(dims <= 2,
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ocl_convertTo(*this, dst, ddepth, noScale, alpha, beta))
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#endif // HAVE_OPENCL
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UMat src = *this; // Fake reference to itself.
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// Resolves issue 8693 in case of src == dst.
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Mat m = getMat(ACCESS_READ);
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m.convertTo(dst, type_, alpha, beta);
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(void)src;
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}
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//==================================================================================================
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void convertFp16(InputArray _src, OutputArray _dst)
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{
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CV_INSTRUMENT_REGION();
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int sdepth = _src.depth(), ddepth = 0;
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BinaryFunc func = 0;
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switch( sdepth )
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{
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case CV_32F:
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if(_dst.fixedType())
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{
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ddepth = _dst.depth();
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CV_Assert(ddepth == CV_16S || ddepth == CV_16F);
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CV_Assert(_dst.channels() == _src.channels());
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}
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else
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ddepth = CV_16S;
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func = getConvertFunc(CV_32F, CV_16F);
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break;
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case CV_16S:
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case CV_16F:
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ddepth = CV_32F;
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func = getConvertFunc(CV_16F, CV_32F);
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break;
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default:
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CV_Error(Error::StsUnsupportedFormat, "Unsupported input depth");
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return;
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}
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CV_OCL_RUN(_src.dims() <= 2 && _dst.isUMat(),
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ocl_convertFp16(_src, _dst, sdepth, ddepth))
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Mat src = _src.getMat();
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int type = CV_MAKETYPE(ddepth, src.channels());
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_dst.create( src.dims, src.size, type );
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Mat dst = _dst.getMat();
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int cn = src.channels();
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CV_Assert( func != 0 );
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if( src.dims <= 2 )
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{
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Size sz = getContinuousSize2D(src, dst, cn);
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func( src.data, src.step, 0, 0, dst.data, dst.step, sz, 0);
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}
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else
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{
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const Mat* arrays[] = {&src, &dst, 0};
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uchar* ptrs[2] = {};
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NAryMatIterator it(arrays, ptrs);
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Size sz((int)(it.size*cn), 1);
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for( size_t i = 0; i < it.nplanes; i++, ++it )
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func(ptrs[0], 0, 0, 0, ptrs[1], 0, sz, 0);
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}
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}
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} // namespace cv
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