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244 lines
8.1 KiB
Plaintext
244 lines
8.1 KiB
Plaintext
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "opencv2/opencv_modules.hpp"
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#ifndef HAVE_OPENCV_CUDEV
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#error "opencv_cudev is required"
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#else
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#include "opencv2/cudev.hpp"
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using namespace cv::cudev;
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void minMaxMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int op);
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void minMaxScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat&, double, Stream& stream, int op);
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///////////////////////////////////////////////////////////////////////
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/// minMaxMat
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namespace
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{
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template <template <typename> class Op, typename T>
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void minMaxMat_v1(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
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{
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gridTransformBinary(globPtr<T>(src1), globPtr<T>(src2), globPtr<T>(dst), Op<T>(), stream);
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}
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struct MinOp2 : binary_function<uint, uint, uint>
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{
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__device__ __forceinline__ uint operator ()(uint a, uint b) const
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{
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return vmin2(a, b);
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}
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};
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struct MaxOp2 : binary_function<uint, uint, uint>
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{
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__device__ __forceinline__ uint operator ()(uint a, uint b) const
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{
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return vmax2(a, b);
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}
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};
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template <class Op2>
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void minMaxMat_v2(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
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{
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const int vcols = src1.cols >> 1;
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GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
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GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
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GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
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gridTransformBinary(src1_, src2_, dst_, Op2(), stream);
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}
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struct MinOp4 : binary_function<uint, uint, uint>
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{
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__device__ __forceinline__ uint operator ()(uint a, uint b) const
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{
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return vmin4(a, b);
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}
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};
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struct MaxOp4 : binary_function<uint, uint, uint>
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{
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__device__ __forceinline__ uint operator ()(uint a, uint b) const
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{
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return vmax4(a, b);
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}
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};
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template <class Op4>
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void minMaxMat_v4(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
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{
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const int vcols = src1.cols >> 2;
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GlobPtrSz<uint> src1_ = globPtr((uint*) src1.data, src1.step, src1.rows, vcols);
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GlobPtrSz<uint> src2_ = globPtr((uint*) src2.data, src2.step, src1.rows, vcols);
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GlobPtrSz<uint> dst_ = globPtr((uint*) dst.data, dst.step, src1.rows, vcols);
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gridTransformBinary(src1_, src2_, dst_, Op4(), stream);
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}
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}
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void minMaxMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat&, double, Stream& stream, int op)
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{
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typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream);
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static const func_t funcs_v1[2][7] =
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{
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{
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minMaxMat_v1<minimum, uchar>,
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minMaxMat_v1<minimum, schar>,
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minMaxMat_v1<minimum, ushort>,
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minMaxMat_v1<minimum, short>,
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minMaxMat_v1<minimum, int>,
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minMaxMat_v1<minimum, float>,
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minMaxMat_v1<minimum, double>
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},
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{
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minMaxMat_v1<maximum, uchar>,
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minMaxMat_v1<maximum, schar>,
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minMaxMat_v1<maximum, ushort>,
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minMaxMat_v1<maximum, short>,
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minMaxMat_v1<maximum, int>,
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minMaxMat_v1<maximum, float>,
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minMaxMat_v1<maximum, double>
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}
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};
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static const func_t funcs_v2[2] =
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{
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minMaxMat_v2<MinOp2>, minMaxMat_v2<MaxOp2>
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};
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static const func_t funcs_v4[2] =
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{
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minMaxMat_v4<MinOp4>, minMaxMat_v4<MaxOp4>
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};
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const int depth = src1.depth();
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CV_DbgAssert( depth <= CV_64F );
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GpuMat src1_ = src1.reshape(1);
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GpuMat src2_ = src2.reshape(1);
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GpuMat dst_ = dst.reshape(1);
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if (depth == CV_8U || depth == CV_16U)
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{
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const intptr_t src1ptr = reinterpret_cast<intptr_t>(src1_.data);
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const intptr_t src2ptr = reinterpret_cast<intptr_t>(src2_.data);
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const intptr_t dstptr = reinterpret_cast<intptr_t>(dst_.data);
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const bool isAllAligned = (src1ptr & 31) == 0 && (src2ptr & 31) == 0 && (dstptr & 31) == 0;
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if (isAllAligned)
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{
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if (depth == CV_8U && (src1_.cols & 3) == 0)
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{
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funcs_v4[op](src1_, src2_, dst_, stream);
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return;
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}
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else if (depth == CV_16U && (src1_.cols & 1) == 0)
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{
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funcs_v2[op](src1_, src2_, dst_, stream);
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return;
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}
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}
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}
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const func_t func = funcs_v1[op][depth];
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func(src1_, src2_, dst_, stream);
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}
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///////////////////////////////////////////////////////////////////////
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/// minMaxScalar
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namespace
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{
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template <template <typename> class Op, typename T>
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void minMaxScalar(const GpuMat& src, double value, GpuMat& dst, Stream& stream)
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{
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gridTransformUnary(globPtr<T>(src), globPtr<T>(dst), bind2nd(Op<T>(), cv::saturate_cast<T>(value)), stream);
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}
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}
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void minMaxScalar(const GpuMat& src, cv::Scalar value, bool, GpuMat& dst, const GpuMat&, double, Stream& stream, int op)
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{
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typedef void (*func_t)(const GpuMat& src, double value, GpuMat& dst, Stream& stream);
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static const func_t funcs[2][7] =
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{
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{
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minMaxScalar<minimum, uchar>,
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minMaxScalar<minimum, schar>,
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minMaxScalar<minimum, ushort>,
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minMaxScalar<minimum, short>,
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minMaxScalar<minimum, int>,
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minMaxScalar<minimum, float>,
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minMaxScalar<minimum, double>
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},
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{
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minMaxScalar<maximum, uchar>,
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minMaxScalar<maximum, schar>,
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minMaxScalar<maximum, ushort>,
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minMaxScalar<maximum, short>,
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minMaxScalar<maximum, int>,
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minMaxScalar<maximum, float>,
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minMaxScalar<maximum, double>
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}
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};
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const int depth = src.depth();
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CV_DbgAssert( depth <= CV_64F );
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CV_DbgAssert( src.channels() == 1 );
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funcs[op][depth](src, value[0], dst, stream);
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}
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#endif
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