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