2a6fb2867e
Made all STL usages explicit to be able automatically find all usages of particular class or function.
577 lines
21 KiB
C++
577 lines
21 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other GpuMaterials 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 bpied warranties, including, but not limited to, the bpied
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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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using namespace cv;
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using namespace cv::gpu;
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#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
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void cv::gpu::gemm(const GpuMat&, const GpuMat&, double, const GpuMat&, double, GpuMat&, int, Stream&) { throw_nogpu(); }
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void cv::gpu::transpose(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::flip(const GpuMat&, GpuMat&, int, Stream&) { throw_nogpu(); }
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void cv::gpu::LUT(const GpuMat&, const Mat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::magnitude(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::magnitudeSqr(const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::magnitude(const GpuMat&, const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::magnitudeSqr(const GpuMat&, const GpuMat&, GpuMat&, Stream&) { throw_nogpu(); }
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void cv::gpu::phase(const GpuMat&, const GpuMat&, GpuMat&, bool, Stream&) { throw_nogpu(); }
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void cv::gpu::cartToPolar(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, Stream&) { throw_nogpu(); }
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void cv::gpu::polarToCart(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, Stream&) { throw_nogpu(); }
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void cv::gpu::normalize(const GpuMat&, GpuMat&, double, double, int, int, const GpuMat&) { throw_nogpu(); }
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void cv::gpu::normalize(const GpuMat&, GpuMat&, double, double, int, int, const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
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#else /* !defined (HAVE_CUDA) */
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////////////////////////////////////////////////////////////////////////
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// gemm
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void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const GpuMat& src3, double beta, GpuMat& dst, int flags, Stream& stream)
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{
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#ifndef HAVE_CUBLAS
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(void)src1;
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(void)src2;
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(void)alpha;
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(void)src3;
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(void)beta;
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(void)dst;
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(void)flags;
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(void)stream;
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CV_Error(CV_StsNotImplemented, "The library was build without CUBLAS");
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#else
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// CUBLAS works with column-major matrices
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CV_Assert(src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2);
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CV_Assert(src2.type() == src1.type() && (src3.empty() || src3.type() == src1.type()));
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if (src1.depth() == CV_64F)
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{
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if (!deviceSupports(NATIVE_DOUBLE))
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CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
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}
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bool tr1 = (flags & GEMM_1_T) != 0;
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bool tr2 = (flags & GEMM_2_T) != 0;
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bool tr3 = (flags & GEMM_3_T) != 0;
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if (src1.type() == CV_64FC2)
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{
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if (tr1 || tr2 || tr3)
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CV_Error(CV_StsNotImplemented, "transpose operation doesn't implemented for CV_64FC2 type");
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}
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Size src1Size = tr1 ? Size(src1.rows, src1.cols) : src1.size();
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Size src2Size = tr2 ? Size(src2.rows, src2.cols) : src2.size();
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Size src3Size = tr3 ? Size(src3.rows, src3.cols) : src3.size();
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Size dstSize(src2Size.width, src1Size.height);
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CV_Assert(src1Size.width == src2Size.height);
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CV_Assert(src3.empty() || src3Size == dstSize);
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dst.create(dstSize, src1.type());
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if (beta != 0)
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{
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if (src3.empty())
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{
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if (stream)
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stream.enqueueMemSet(dst, Scalar::all(0));
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else
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dst.setTo(Scalar::all(0));
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}
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else
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{
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if (tr3)
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{
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transpose(src3, dst, stream);
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}
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else
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{
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if (stream)
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stream.enqueueCopy(src3, dst);
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else
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src3.copyTo(dst);
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}
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}
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}
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cublasHandle_t handle;
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cublasSafeCall( cublasCreate_v2(&handle) );
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cublasSafeCall( cublasSetStream_v2(handle, StreamAccessor::getStream(stream)) );
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cublasSafeCall( cublasSetPointerMode_v2(handle, CUBLAS_POINTER_MODE_HOST) );
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const float alphaf = static_cast<float>(alpha);
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const float betaf = static_cast<float>(beta);
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const cuComplex alphacf = make_cuComplex(alphaf, 0);
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const cuComplex betacf = make_cuComplex(betaf, 0);
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const cuDoubleComplex alphac = make_cuDoubleComplex(alpha, 0);
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const cuDoubleComplex betac = make_cuDoubleComplex(beta, 0);
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cublasOperation_t transa = tr2 ? CUBLAS_OP_T : CUBLAS_OP_N;
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cublasOperation_t transb = tr1 ? CUBLAS_OP_T : CUBLAS_OP_N;
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switch (src1.type())
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{
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case CV_32FC1:
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cublasSafeCall( cublasSgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
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&alphaf,
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src2.ptr<float>(), static_cast<int>(src2.step / sizeof(float)),
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src1.ptr<float>(), static_cast<int>(src1.step / sizeof(float)),
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&betaf,
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dst.ptr<float>(), static_cast<int>(dst.step / sizeof(float))) );
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break;
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case CV_64FC1:
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cublasSafeCall( cublasDgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
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&alpha,
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src2.ptr<double>(), static_cast<int>(src2.step / sizeof(double)),
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src1.ptr<double>(), static_cast<int>(src1.step / sizeof(double)),
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&beta,
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dst.ptr<double>(), static_cast<int>(dst.step / sizeof(double))) );
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break;
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case CV_32FC2:
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cublasSafeCall( cublasCgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
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&alphacf,
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src2.ptr<cuComplex>(), static_cast<int>(src2.step / sizeof(cuComplex)),
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src1.ptr<cuComplex>(), static_cast<int>(src1.step / sizeof(cuComplex)),
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&betacf,
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dst.ptr<cuComplex>(), static_cast<int>(dst.step / sizeof(cuComplex))) );
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break;
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case CV_64FC2:
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cublasSafeCall( cublasZgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
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&alphac,
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src2.ptr<cuDoubleComplex>(), static_cast<int>(src2.step / sizeof(cuDoubleComplex)),
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src1.ptr<cuDoubleComplex>(), static_cast<int>(src1.step / sizeof(cuDoubleComplex)),
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&betac,
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dst.ptr<cuDoubleComplex>(), static_cast<int>(dst.step / sizeof(cuDoubleComplex))) );
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break;
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}
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cublasSafeCall( cublasDestroy_v2(handle) );
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#endif
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}
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////////////////////////////////////////////////////////////////////////
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// transpose
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void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
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{
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CV_Assert(src.elemSize() == 1 || src.elemSize() == 4 || src.elemSize() == 8);
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dst.create( src.cols, src.rows, src.type() );
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cudaStream_t stream = StreamAccessor::getStream(s);
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if (src.elemSize() == 1)
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{
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NppStreamHandler h(stream);
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NppiSize sz;
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sz.width = src.cols;
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sz.height = src.rows;
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nppSafeCall( nppiTranspose_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
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dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
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}
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else if (src.elemSize() == 4)
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{
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NppStStreamHandler h(stream);
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NcvSize32u sz;
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sz.width = src.cols;
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sz.height = src.rows;
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ncvSafeCall( nppiStTranspose_32u_C1R(const_cast<Ncv32u*>(src.ptr<Ncv32u>()), static_cast<int>(src.step),
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dst.ptr<Ncv32u>(), static_cast<int>(dst.step), sz) );
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}
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else // if (src.elemSize() == 8)
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{
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if (!deviceSupports(NATIVE_DOUBLE))
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CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
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NppStStreamHandler h(stream);
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NcvSize32u sz;
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sz.width = src.cols;
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sz.height = src.rows;
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ncvSafeCall( nppiStTranspose_64u_C1R(const_cast<Ncv64u*>(src.ptr<Ncv64u>()), static_cast<int>(src.step),
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dst.ptr<Ncv64u>(), static_cast<int>(dst.step), sz) );
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}
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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////////////////////////////////////////////////////////////////////////
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// flip
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namespace
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{
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template<int DEPTH> struct NppTypeTraits;
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template<> struct NppTypeTraits<CV_8U> { typedef Npp8u npp_t; };
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template<> struct NppTypeTraits<CV_8S> { typedef Npp8s npp_t; };
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template<> struct NppTypeTraits<CV_16U> { typedef Npp16u npp_t; };
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template<> struct NppTypeTraits<CV_16S> { typedef Npp16s npp_t; };
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template<> struct NppTypeTraits<CV_32S> { typedef Npp32s npp_t; };
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template<> struct NppTypeTraits<CV_32F> { typedef Npp32f npp_t; };
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template<> struct NppTypeTraits<CV_64F> { typedef Npp64f npp_t; };
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template <int DEPTH> struct NppMirrorFunc
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{
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typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
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typedef NppStatus (*func_t)(const npp_t* pSrc, int nSrcStep, npp_t* pDst, int nDstStep, NppiSize oROI, NppiAxis flip);
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};
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template <int DEPTH, typename NppMirrorFunc<DEPTH>::func_t func> struct NppMirror
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{
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typedef typename NppMirrorFunc<DEPTH>::npp_t npp_t;
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static void call(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream)
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{
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NppStreamHandler h(stream);
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NppiSize sz;
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sz.width = src.cols;
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sz.height = src.rows;
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nppSafeCall( func(src.ptr<npp_t>(), static_cast<int>(src.step),
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dst.ptr<npp_t>(), static_cast<int>(dst.step), sz,
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(flipCode == 0 ? NPP_HORIZONTAL_AXIS : (flipCode > 0 ? NPP_VERTICAL_AXIS : NPP_BOTH_AXIS))) );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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};
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}
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void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
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{
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typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
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static const func_t funcs[6][4] =
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{
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{NppMirror<CV_8U, nppiMirror_8u_C1R>::call, 0, NppMirror<CV_8U, nppiMirror_8u_C3R>::call, NppMirror<CV_8U, nppiMirror_8u_C4R>::call},
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{0,0,0,0},
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{NppMirror<CV_16U, nppiMirror_16u_C1R>::call, 0, NppMirror<CV_16U, nppiMirror_16u_C3R>::call, NppMirror<CV_16U, nppiMirror_16u_C4R>::call},
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{0,0,0,0},
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{NppMirror<CV_32S, nppiMirror_32s_C1R>::call, 0, NppMirror<CV_32S, nppiMirror_32s_C3R>::call, NppMirror<CV_32S, nppiMirror_32s_C4R>::call},
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{NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call}
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};
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CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F);
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CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
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dst.create(src.size(), src.type());
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funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
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}
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////////////////////////////////////////////////////////////////////////
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// LUT
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void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& s)
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{
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class LevelsInit
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{
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public:
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Npp32s pLevels[256];
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const Npp32s* pLevels3[3];
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int nValues3[3];
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#if (CUDA_VERSION > 4020)
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GpuMat d_pLevels;
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#endif
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LevelsInit()
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{
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nValues3[0] = nValues3[1] = nValues3[2] = 256;
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for (int i = 0; i < 256; ++i)
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pLevels[i] = i;
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#if (CUDA_VERSION <= 4020)
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pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels;
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#else
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d_pLevels.upload(Mat(1, 256, CV_32S, pLevels));
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pLevels3[0] = pLevels3[1] = pLevels3[2] = d_pLevels.ptr<Npp32s>();
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#endif
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}
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};
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static LevelsInit lvls;
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int cn = src.channels();
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CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3);
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CV_Assert(lut.depth() == CV_8U && (lut.channels() == 1 || lut.channels() == cn) && lut.rows * lut.cols == 256 && lut.isContinuous());
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dst.create(src.size(), CV_MAKETYPE(lut.depth(), cn));
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NppiSize sz;
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sz.height = src.rows;
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sz.width = src.cols;
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Mat nppLut;
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lut.convertTo(nppLut, CV_32S);
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cudaStream_t stream = StreamAccessor::getStream(s);
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NppStreamHandler h(stream);
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if (src.type() == CV_8UC1)
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{
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#if (CUDA_VERSION <= 4020)
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nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
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dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppLut.ptr<Npp32s>(), lvls.pLevels, 256) );
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#else
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GpuMat d_nppLut(Mat(1, 256, CV_32S, nppLut.data));
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nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
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dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, d_nppLut.ptr<Npp32s>(), lvls.d_pLevels.ptr<Npp32s>(), 256) );
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#endif
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}
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else
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{
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const Npp32s* pValues3[3];
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Mat nppLut3[3];
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if (nppLut.channels() == 1)
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{
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#if (CUDA_VERSION <= 4020)
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pValues3[0] = pValues3[1] = pValues3[2] = nppLut.ptr<Npp32s>();
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#else
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GpuMat d_nppLut(Mat(1, 256, CV_32S, nppLut.data));
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pValues3[0] = pValues3[1] = pValues3[2] = d_nppLut.ptr<Npp32s>();
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#endif
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}
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else
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{
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cv::split(nppLut, nppLut3);
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#if (CUDA_VERSION <= 4020)
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pValues3[0] = nppLut3[0].ptr<Npp32s>();
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pValues3[1] = nppLut3[1].ptr<Npp32s>();
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pValues3[2] = nppLut3[2].ptr<Npp32s>();
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#else
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GpuMat d_nppLut0(Mat(1, 256, CV_32S, nppLut3[0].data));
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GpuMat d_nppLut1(Mat(1, 256, CV_32S, nppLut3[1].data));
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GpuMat d_nppLut2(Mat(1, 256, CV_32S, nppLut3[2].data));
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pValues3[0] = d_nppLut0.ptr<Npp32s>();
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pValues3[1] = d_nppLut1.ptr<Npp32s>();
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pValues3[2] = d_nppLut2.ptr<Npp32s>();
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#endif
|
|
}
|
|
|
|
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
|
|
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, lvls.pLevels3, lvls.nValues3) );
|
|
}
|
|
|
|
if (stream == 0)
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// NPP magnitide
|
|
|
|
namespace
|
|
{
|
|
typedef NppStatus (*nppMagnitude_t)(const Npp32fc* pSrc, int nSrcStep, Npp32f* pDst, int nDstStep, NppiSize oSizeROI);
|
|
|
|
inline void npp_magnitude(const GpuMat& src, GpuMat& dst, nppMagnitude_t func, cudaStream_t stream)
|
|
{
|
|
CV_Assert(src.type() == CV_32FC2);
|
|
|
|
dst.create(src.size(), CV_32FC1);
|
|
|
|
NppiSize sz;
|
|
sz.width = src.cols;
|
|
sz.height = src.rows;
|
|
|
|
NppStreamHandler h(stream);
|
|
|
|
nppSafeCall( func(src.ptr<Npp32fc>(), static_cast<int>(src.step), dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
|
|
|
|
if (stream == 0)
|
|
cudaSafeCall( cudaDeviceSynchronize() );
|
|
}
|
|
}
|
|
|
|
void cv::gpu::magnitude(const GpuMat& src, GpuMat& dst, Stream& stream)
|
|
{
|
|
npp_magnitude(src, dst, nppiMagnitude_32fc32f_C1R, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::magnitudeSqr(const GpuMat& src, GpuMat& dst, Stream& stream)
|
|
{
|
|
npp_magnitude(src, dst, nppiMagnitudeSqr_32fc32f_C1R, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// Polar <-> Cart
|
|
|
|
namespace cv { namespace gpu { namespace device
|
|
{
|
|
namespace mathfunc
|
|
{
|
|
void cartToPolar_gpu(PtrStepSzf x, PtrStepSzf y, PtrStepSzf mag, bool magSqr, PtrStepSzf angle, bool angleInDegrees, cudaStream_t stream);
|
|
void polarToCart_gpu(PtrStepSzf mag, PtrStepSzf angle, PtrStepSzf x, PtrStepSzf y, bool angleInDegrees, cudaStream_t stream);
|
|
}
|
|
}}}
|
|
|
|
namespace
|
|
{
|
|
inline void cartToPolar_caller(const GpuMat& x, const GpuMat& y, GpuMat* mag, bool magSqr, GpuMat* angle, bool angleInDegrees, cudaStream_t stream)
|
|
{
|
|
using namespace ::cv::gpu::device::mathfunc;
|
|
|
|
CV_Assert(x.size() == y.size() && x.type() == y.type());
|
|
CV_Assert(x.depth() == CV_32F);
|
|
|
|
if (mag)
|
|
mag->create(x.size(), x.type());
|
|
if (angle)
|
|
angle->create(x.size(), x.type());
|
|
|
|
GpuMat x1cn = x.reshape(1);
|
|
GpuMat y1cn = y.reshape(1);
|
|
GpuMat mag1cn = mag ? mag->reshape(1) : GpuMat();
|
|
GpuMat angle1cn = angle ? angle->reshape(1) : GpuMat();
|
|
|
|
cartToPolar_gpu(x1cn, y1cn, mag1cn, magSqr, angle1cn, angleInDegrees, stream);
|
|
}
|
|
|
|
inline void polarToCart_caller(const GpuMat& mag, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees, cudaStream_t stream)
|
|
{
|
|
using namespace ::cv::gpu::device::mathfunc;
|
|
|
|
CV_Assert((mag.empty() || mag.size() == angle.size()) && mag.type() == angle.type());
|
|
CV_Assert(mag.depth() == CV_32F);
|
|
|
|
x.create(mag.size(), mag.type());
|
|
y.create(mag.size(), mag.type());
|
|
|
|
GpuMat mag1cn = mag.reshape(1);
|
|
GpuMat angle1cn = angle.reshape(1);
|
|
GpuMat x1cn = x.reshape(1);
|
|
GpuMat y1cn = y.reshape(1);
|
|
|
|
polarToCart_gpu(mag1cn, angle1cn, x1cn, y1cn, angleInDegrees, stream);
|
|
}
|
|
}
|
|
|
|
void cv::gpu::magnitude(const GpuMat& x, const GpuMat& y, GpuMat& dst, Stream& stream)
|
|
{
|
|
cartToPolar_caller(x, y, &dst, false, 0, false, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::magnitudeSqr(const GpuMat& x, const GpuMat& y, GpuMat& dst, Stream& stream)
|
|
{
|
|
cartToPolar_caller(x, y, &dst, true, 0, false, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::phase(const GpuMat& x, const GpuMat& y, GpuMat& angle, bool angleInDegrees, Stream& stream)
|
|
{
|
|
cartToPolar_caller(x, y, 0, false, &angle, angleInDegrees, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::cartToPolar(const GpuMat& x, const GpuMat& y, GpuMat& mag, GpuMat& angle, bool angleInDegrees, Stream& stream)
|
|
{
|
|
cartToPolar_caller(x, y, &mag, false, &angle, angleInDegrees, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
void cv::gpu::polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees, Stream& stream)
|
|
{
|
|
polarToCart_caller(magnitude, angle, x, y, angleInDegrees, StreamAccessor::getStream(stream));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// normalize
|
|
|
|
void cv::gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int norm_type, int dtype, const GpuMat& mask)
|
|
{
|
|
GpuMat norm_buf;
|
|
GpuMat cvt_buf;
|
|
normalize(src, dst, a, b, norm_type, dtype, mask, norm_buf, cvt_buf);
|
|
}
|
|
|
|
void cv::gpu::normalize(const GpuMat& src, GpuMat& dst, double a, double b, int norm_type, int dtype, const GpuMat& mask, GpuMat& norm_buf, GpuMat& cvt_buf)
|
|
{
|
|
double scale = 1, shift = 0;
|
|
if (norm_type == NORM_MINMAX)
|
|
{
|
|
double smin = 0, smax = 0;
|
|
double dmin = std::min(a, b), dmax = std::max(a, b);
|
|
minMax(src, &smin, &smax, mask, norm_buf);
|
|
scale = (dmax - dmin) * (smax - smin > std::numeric_limits<double>::epsilon() ? 1.0 / (smax - smin) : 0.0);
|
|
shift = dmin - smin * scale;
|
|
}
|
|
else if (norm_type == NORM_L2 || norm_type == NORM_L1 || norm_type == NORM_INF)
|
|
{
|
|
scale = norm(src, norm_type, mask, norm_buf);
|
|
scale = scale > std::numeric_limits<double>::epsilon() ? a / scale : 0.0;
|
|
shift = 0;
|
|
}
|
|
else
|
|
{
|
|
CV_Error(CV_StsBadArg, "Unknown/unsupported norm type");
|
|
}
|
|
|
|
if (mask.empty())
|
|
{
|
|
src.convertTo(dst, dtype, scale, shift);
|
|
}
|
|
else
|
|
{
|
|
src.convertTo(cvt_buf, dtype, scale, shift);
|
|
cvt_buf.copyTo(dst, mask);
|
|
}
|
|
}
|
|
|
|
#endif /* !defined (HAVE_CUDA) */
|