Alexey Spizhevoy
14 years ago
parent
6891a60149
commit
0465b89e7e
6 changed files with 1048 additions and 915 deletions
@ -0,0 +1,348 @@ |
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/*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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|
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#include "opencv2/gpu/device/vecmath.hpp" |
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#include "transform.hpp" |
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#include "internal_shared.hpp" |
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using namespace cv::gpu; |
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using namespace cv::gpu::device; |
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namespace cv { namespace gpu { namespace mathfunc |
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{ |
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////////////////////////////////////////////////////////////////////////////////////// |
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// Compare |
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template <typename T1, typename T2> |
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struct NotEqual |
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{ |
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__device__ uchar operator()(const T1& src1, const T2& src2) |
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{ |
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return static_cast<uchar>(static_cast<int>(src1 != src2) * 255); |
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} |
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}; |
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|
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template <typename T1, typename T2> |
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inline void compare_ne(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst) |
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{ |
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NotEqual<T1, T2> op; |
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transform(static_cast< DevMem2D_<T1> >(src1), static_cast< DevMem2D_<T2> >(src2), dst, op, 0); |
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} |
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void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst) |
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{ |
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compare_ne<uint, uint>(src1, src2, dst); |
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} |
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void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst) |
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{ |
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compare_ne<float, float>(src1, src2, dst); |
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} |
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|
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////////////////////////////////////////////////////////////////////////// |
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// Unary bitwise logical matrix operations |
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enum { UN_OP_NOT }; |
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template <typename T, int opid> |
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struct UnOp; |
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template <typename T> |
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struct UnOp<T, UN_OP_NOT> |
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{ |
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static __device__ T call(T v) { return ~v; } |
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}; |
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|
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template <int opid> |
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__global__ void bitwiseUnOpKernel(int rows, int width, const PtrStep src, PtrStep dst) |
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{ |
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const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4; |
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const int y = blockDim.y * blockIdx.y + threadIdx.y; |
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|
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if (y < rows) |
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{ |
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uchar* dst_ptr = dst.ptr(y) + x; |
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const uchar* src_ptr = src.ptr(y) + x; |
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if (x + sizeof(uint) - 1 < width) |
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{ |
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*(uint*)dst_ptr = UnOp<uint, opid>::call(*(uint*)src_ptr); |
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} |
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else |
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{ |
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const uchar* src_end = src.ptr(y) + width; |
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while (src_ptr < src_end) |
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{ |
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*dst_ptr++ = UnOp<uchar, opid>::call(*src_ptr++); |
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} |
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} |
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} |
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} |
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template <int opid> |
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void bitwiseUnOp(int rows, int width, const PtrStep src, PtrStep dst, |
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cudaStream_t stream) |
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{ |
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dim3 threads(16, 16); |
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dim3 grid(divUp(width, threads.x * sizeof(uint)), |
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divUp(rows, threads.y)); |
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bitwiseUnOpKernel<opid><<<grid, threads>>>(rows, width, src, dst); |
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if (stream == 0) |
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cudaSafeCall(cudaThreadSynchronize()); |
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} |
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template <typename T, int opid> |
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__global__ void bitwiseUnOpKernel(int rows, int cols, int cn, const PtrStep src, |
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const PtrStep mask, PtrStep dst) |
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{ |
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const int x = blockDim.x * blockIdx.x + threadIdx.x; |
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const int y = blockDim.y * blockIdx.y + threadIdx.y; |
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if (x < cols && y < rows && mask.ptr(y)[x / cn]) |
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{ |
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T* dst_row = (T*)dst.ptr(y); |
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const T* src_row = (const T*)src.ptr(y); |
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dst_row[x] = UnOp<T, opid>::call(src_row[x]); |
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} |
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} |
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template <typename T, int opid> |
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void bitwiseUnOp(int rows, int cols, int cn, const PtrStep src, |
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const PtrStep mask, PtrStep dst, cudaStream_t stream) |
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{ |
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dim3 threads(16, 16); |
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dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y)); |
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bitwiseUnOpKernel<T, opid><<<grid, threads>>>(rows, cols, cn, src, mask, dst); |
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if (stream == 0) |
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cudaSafeCall(cudaThreadSynchronize()); |
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} |
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void bitwiseNotCaller(int rows, int cols, int elem_size1, int cn, |
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const PtrStep src, PtrStep dst, cudaStream_t stream) |
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{ |
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bitwiseUnOp<UN_OP_NOT>(rows, cols * elem_size1 * cn, src, dst, stream); |
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} |
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template <typename T> |
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void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStep src, |
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const PtrStep mask, PtrStep dst, cudaStream_t stream) |
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{ |
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bitwiseUnOp<T, UN_OP_NOT>(rows, cols * cn, cn, src, mask, dst, stream); |
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} |
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template void bitwiseMaskNotCaller<uchar>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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template void bitwiseMaskNotCaller<ushort>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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template void bitwiseMaskNotCaller<uint>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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////////////////////////////////////////////////////////////////////////// |
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// Binary bitwise logical matrix operations |
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enum { BIN_OP_OR, BIN_OP_AND, BIN_OP_XOR }; |
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template <typename T, int opid> |
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struct BinOp; |
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template <typename T> |
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struct BinOp<T, BIN_OP_OR> |
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{ |
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static __device__ T call(T a, T b) { return a | b; } |
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}; |
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template <typename T> |
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struct BinOp<T, BIN_OP_AND> |
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{ |
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static __device__ T call(T a, T b) { return a & b; } |
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}; |
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template <typename T> |
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struct BinOp<T, BIN_OP_XOR> |
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{ |
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static __device__ T call(T a, T b) { return a ^ b; } |
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}; |
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template <int opid> |
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__global__ void bitwiseBinOpKernel(int rows, int width, const PtrStep src1, |
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const PtrStep src2, PtrStep dst) |
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{ |
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const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4; |
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const int y = blockDim.y * blockIdx.y + threadIdx.y; |
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if (y < rows) |
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{ |
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uchar* dst_ptr = dst.ptr(y) + x; |
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const uchar* src1_ptr = src1.ptr(y) + x; |
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const uchar* src2_ptr = src2.ptr(y) + x; |
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if (x + sizeof(uint) - 1 < width) |
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{ |
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*(uint*)dst_ptr = BinOp<uint, opid>::call(*(uint*)src1_ptr, *(uint*)src2_ptr); |
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} |
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else |
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{ |
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const uchar* src1_end = src1.ptr(y) + width; |
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while (src1_ptr < src1_end) |
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{ |
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*dst_ptr++ = BinOp<uchar, opid>::call(*src1_ptr++, *src2_ptr++); |
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} |
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} |
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} |
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} |
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template <int opid> |
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void bitwiseBinOp(int rows, int width, const PtrStep src1, const PtrStep src2, |
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PtrStep dst, cudaStream_t stream) |
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{ |
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dim3 threads(16, 16); |
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dim3 grid(divUp(width, threads.x * sizeof(uint)), divUp(rows, threads.y)); |
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bitwiseBinOpKernel<opid><<<grid, threads>>>(rows, width, src1, src2, dst); |
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if (stream == 0) |
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cudaSafeCall(cudaThreadSynchronize()); |
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} |
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template <typename T, int opid> |
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__global__ void bitwiseBinOpKernel( |
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int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, |
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const PtrStep mask, PtrStep dst) |
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{ |
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const int x = blockDim.x * blockIdx.x + threadIdx.x; |
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const int y = blockDim.y * blockIdx.y + threadIdx.y; |
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if (x < cols && y < rows && mask.ptr(y)[x / cn]) |
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{ |
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T* dst_row = (T*)dst.ptr(y); |
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const T* src1_row = (const T*)src1.ptr(y); |
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const T* src2_row = (const T*)src2.ptr(y); |
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dst_row[x] = BinOp<T, opid>::call(src1_row[x], src2_row[x]); |
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} |
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} |
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template <typename T, int opid> |
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void bitwiseBinOp(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, |
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const PtrStep mask, PtrStep dst, cudaStream_t stream) |
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{ |
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dim3 threads(16, 16); |
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dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y)); |
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bitwiseBinOpKernel<T, opid><<<grid, threads>>>(rows, cols, cn, src1, src2, mask, dst); |
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if (stream == 0) |
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cudaSafeCall(cudaThreadSynchronize()); |
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} |
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void bitwiseOrCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, |
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const PtrStep src2, PtrStep dst, cudaStream_t stream) |
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{ |
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bitwiseBinOp<BIN_OP_OR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream); |
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} |
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template <typename T> |
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void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, |
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const PtrStep mask, PtrStep dst, cudaStream_t stream) |
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{ |
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bitwiseBinOp<T, BIN_OP_OR>(rows, cols * cn, cn, src1, src2, mask, dst, stream); |
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} |
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template void bitwiseMaskOrCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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template void bitwiseMaskOrCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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template void bitwiseMaskOrCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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void bitwiseAndCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, |
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const PtrStep src2, PtrStep dst, cudaStream_t stream) |
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{ |
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bitwiseBinOp<BIN_OP_AND>(rows, cols * elem_size1 * cn, src1, src2, dst, stream); |
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} |
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template <typename T> |
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void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, |
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const PtrStep mask, PtrStep dst, cudaStream_t stream) |
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{ |
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bitwiseBinOp<T, BIN_OP_AND>(rows, cols * cn, cn, src1, src2, mask, dst, stream); |
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} |
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template void bitwiseMaskAndCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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template void bitwiseMaskAndCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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template void bitwiseMaskAndCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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void bitwiseXorCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, |
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const PtrStep src2, PtrStep dst, cudaStream_t stream) |
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{ |
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bitwiseBinOp<BIN_OP_XOR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream); |
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} |
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template <typename T> |
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void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, |
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const PtrStep mask, PtrStep dst, cudaStream_t stream) |
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{ |
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bitwiseBinOp<T, BIN_OP_XOR>(rows, cols * cn, cn, src1, src2, mask, dst, stream); |
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} |
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template void bitwiseMaskXorCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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template void bitwiseMaskXorCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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template void bitwiseMaskXorCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
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}}} |
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@ -1,16 +1,609 @@ |
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/*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.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// 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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// 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:
|
||||
//
|
||||
// * 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,
|
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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,
|
||||
// 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
|
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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) |
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void cv::gpu::add(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::add(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::subtract(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::subtract(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::multiply(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::multiply(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::divide(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::divide(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::absdiff(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::absdiff(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); } |
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void cv::gpu::compare(const GpuMat&, const GpuMat&, GpuMat&, int) { throw_nogpu(); } |
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void cv::gpu::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); } |
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void cv::gpu::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); } |
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void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); } |
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void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); } |
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void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); } |
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void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); } |
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void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); } |
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void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); } |
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cv::gpu::GpuMat cv::gpu::operator ~ (const GpuMat&) { throw_nogpu(); return GpuMat(); } |
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cv::gpu::GpuMat cv::gpu::operator | (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); } |
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cv::gpu::GpuMat cv::gpu::operator & (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); } |
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cv::gpu::GpuMat cv::gpu::operator ^ (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); } |
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#else |
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//////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////// Unary per-element operations /////////////////////////////////////////
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// operation(GpuMat src, GpuMat dst)
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////////////////////////////////////////////////////////////////////////
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// Basic arithmetical operations (add subtract multiply divide)
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namespace |
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{ |
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typedef NppStatus (*npp_arithm_8u_t)(const Npp8u* pSrc1, int nSrc1Step, const Npp8u* pSrc2, int nSrc2Step, Npp8u* pDst, int nDstStep, |
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NppiSize oSizeROI, int nScaleFactor); |
||||
typedef NppStatus (*npp_arithm_32s_t)(const Npp32s* pSrc1, int nSrc1Step, const Npp32s* pSrc2, int nSrc2Step, Npp32s* pDst, |
||||
int nDstStep, NppiSize oSizeROI); |
||||
typedef NppStatus (*npp_arithm_32f_t)(const Npp32f* pSrc1, int nSrc1Step, const Npp32f* pSrc2, int nSrc2Step, Npp32f* pDst, |
||||
int nDstStep, NppiSize oSizeROI); |
||||
|
||||
void nppArithmCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, |
||||
npp_arithm_8u_t npp_func_8uc1, npp_arithm_8u_t npp_func_8uc4, |
||||
npp_arithm_32s_t npp_func_32sc1, npp_arithm_32f_t npp_func_32fc1) |
||||
{ |
||||
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
|
||||
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1); |
||||
|
||||
//////////////////////////////////////////////////////////////////////////////////////////////////
|
||||
////////////////////////// Binary per-element operations ////////////////////////////////////////
|
||||
// operation(GpuMat src1, GpuMat src2, GpuMat dst)
|
||||
dst.create( src1.size(), src1.type() ); |
||||
|
||||
NppiSize sz; |
||||
sz.width = src1.cols; |
||||
sz.height = src1.rows; |
||||
|
||||
switch (src1.type()) |
||||
{ |
||||
case CV_8UC1: |
||||
nppSafeCall( npp_func_8uc1(src1.ptr<Npp8u>(), src1.step, |
||||
src2.ptr<Npp8u>(), src2.step, |
||||
dst.ptr<Npp8u>(), dst.step, sz, 0) ); |
||||
break; |
||||
case CV_8UC4: |
||||
nppSafeCall( npp_func_8uc4(src1.ptr<Npp8u>(), src1.step, |
||||
src2.ptr<Npp8u>(), src2.step, |
||||
dst.ptr<Npp8u>(), dst.step, sz, 0) ); |
||||
break; |
||||
case CV_32SC1: |
||||
nppSafeCall( npp_func_32sc1(src1.ptr<Npp32s>(), src1.step, |
||||
src2.ptr<Npp32s>(), src2.step, |
||||
dst.ptr<Npp32s>(), dst.step, sz) ); |
||||
break; |
||||
case CV_32FC1: |
||||
nppSafeCall( npp_func_32fc1(src1.ptr<Npp32f>(), src1.step, |
||||
src2.ptr<Npp32f>(), src2.step, |
||||
dst.ptr<Npp32f>(), dst.step, sz) ); |
||||
break; |
||||
default: |
||||
CV_Assert(!"Unsupported source type"); |
||||
} |
||||
} |
||||
|
||||
template<int SCN> struct NppArithmScalarFunc; |
||||
template<> struct NppArithmScalarFunc<1> |
||||
{ |
||||
typedef NppStatus (*func_ptr)(const Npp32f *pSrc, int nSrcStep, Npp32f nValue, Npp32f *pDst, |
||||
int nDstStep, NppiSize oSizeROI); |
||||
}; |
||||
template<> struct NppArithmScalarFunc<2> |
||||
{ |
||||
typedef NppStatus (*func_ptr)(const Npp32fc *pSrc, int nSrcStep, Npp32fc nValue, Npp32fc *pDst, |
||||
int nDstStep, NppiSize oSizeROI); |
||||
}; |
||||
|
||||
template<int SCN, typename NppArithmScalarFunc<SCN>::func_ptr func> struct NppArithmScalar; |
||||
template<typename NppArithmScalarFunc<1>::func_ptr func> struct NppArithmScalar<1, func> |
||||
{ |
||||
static void calc(const GpuMat& src, const Scalar& sc, GpuMat& dst) |
||||
{ |
||||
dst.create(src.size(), src.type()); |
||||
|
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
|
||||
nppSafeCall( func(src.ptr<Npp32f>(), src.step, (Npp32f)sc[0], dst.ptr<Npp32f>(), dst.step, sz) ); |
||||
} |
||||
}; |
||||
template<typename NppArithmScalarFunc<2>::func_ptr func> struct NppArithmScalar<2, func> |
||||
{ |
||||
static void calc(const GpuMat& src, const Scalar& sc, GpuMat& dst) |
||||
{ |
||||
dst.create(src.size(), src.type()); |
||||
|
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
|
||||
Npp32fc nValue; |
||||
nValue.re = (Npp32f)sc[0]; |
||||
nValue.im = (Npp32f)sc[1]; |
||||
|
||||
nppSafeCall( func(src.ptr<Npp32fc>(), src.step, nValue, dst.ptr<Npp32fc>(), dst.step, sz) ); |
||||
} |
||||
}; |
||||
} |
||||
|
||||
void cv::gpu::add(const GpuMat& src1, const GpuMat& src2, GpuMat& dst) |
||||
{ |
||||
nppArithmCaller(src1, src2, dst, nppiAdd_8u_C1RSfs, nppiAdd_8u_C4RSfs, nppiAdd_32s_C1R, nppiAdd_32f_C1R); |
||||
} |
||||
|
||||
void cv::gpu::subtract(const GpuMat& src1, const GpuMat& src2, GpuMat& dst) |
||||
{ |
||||
nppArithmCaller(src2, src1, dst, nppiSub_8u_C1RSfs, nppiSub_8u_C4RSfs, nppiSub_32s_C1R, nppiSub_32f_C1R); |
||||
} |
||||
|
||||
void cv::gpu::multiply(const GpuMat& src1, const GpuMat& src2, GpuMat& dst) |
||||
{ |
||||
nppArithmCaller(src1, src2, dst, nppiMul_8u_C1RSfs, nppiMul_8u_C4RSfs, nppiMul_32s_C1R, nppiMul_32f_C1R); |
||||
} |
||||
|
||||
void cv::gpu::divide(const GpuMat& src1, const GpuMat& src2, GpuMat& dst) |
||||
{ |
||||
nppArithmCaller(src2, src1, dst, nppiDiv_8u_C1RSfs, nppiDiv_8u_C4RSfs, nppiDiv_32s_C1R, nppiDiv_32f_C1R); |
||||
} |
||||
|
||||
void cv::gpu::add(const GpuMat& src, const Scalar& sc, GpuMat& dst) |
||||
{ |
||||
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst); |
||||
static const caller_t callers[] = {0, NppArithmScalar<1, nppiAddC_32f_C1R>::calc, NppArithmScalar<2, nppiAddC_32fc_C1R>::calc}; |
||||
|
||||
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2); |
||||
|
||||
callers[src.channels()](src, sc, dst); |
||||
} |
||||
|
||||
void cv::gpu::subtract(const GpuMat& src, const Scalar& sc, GpuMat& dst) |
||||
{ |
||||
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst); |
||||
static const caller_t callers[] = {0, NppArithmScalar<1, nppiSubC_32f_C1R>::calc, NppArithmScalar<2, nppiSubC_32fc_C1R>::calc}; |
||||
|
||||
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2); |
||||
|
||||
callers[src.channels()](src, sc, dst); |
||||
} |
||||
|
||||
void cv::gpu::multiply(const GpuMat& src, const Scalar& sc, GpuMat& dst) |
||||
{ |
||||
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst); |
||||
static const caller_t callers[] = {0, NppArithmScalar<1, nppiMulC_32f_C1R>::calc, NppArithmScalar<2, nppiMulC_32fc_C1R>::calc}; |
||||
|
||||
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2); |
||||
|
||||
callers[src.channels()](src, sc, dst); |
||||
} |
||||
|
||||
void cv::gpu::divide(const GpuMat& src, const Scalar& sc, GpuMat& dst) |
||||
{ |
||||
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst); |
||||
static const caller_t callers[] = {0, NppArithmScalar<1, nppiDivC_32f_C1R>::calc, NppArithmScalar<2, nppiDivC_32fc_C1R>::calc}; |
||||
|
||||
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2); |
||||
|
||||
callers[src.channels()](src, sc, dst); |
||||
} |
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
// Absolute difference
|
||||
|
||||
void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst) |
||||
{ |
||||
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
|
||||
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1); |
||||
|
||||
dst.create( src1.size(), src1.type() ); |
||||
|
||||
NppiSize sz; |
||||
sz.width = src1.cols; |
||||
sz.height = src1.rows; |
||||
|
||||
switch (src1.type()) |
||||
{ |
||||
case CV_8UC1: |
||||
nppSafeCall( nppiAbsDiff_8u_C1R(src1.ptr<Npp8u>(), src1.step, |
||||
src2.ptr<Npp8u>(), src2.step, |
||||
dst.ptr<Npp8u>(), dst.step, sz) ); |
||||
break; |
||||
case CV_8UC4: |
||||
nppSafeCall( nppiAbsDiff_8u_C4R(src1.ptr<Npp8u>(), src1.step, |
||||
src2.ptr<Npp8u>(), src2.step, |
||||
dst.ptr<Npp8u>(), dst.step, sz) ); |
||||
break; |
||||
case CV_32SC1: |
||||
nppSafeCall( nppiAbsDiff_32s_C1R(src1.ptr<Npp32s>(), src1.step, |
||||
src2.ptr<Npp32s>(), src2.step, |
||||
dst.ptr<Npp32s>(), dst.step, sz) ); |
||||
break; |
||||
case CV_32FC1: |
||||
nppSafeCall( nppiAbsDiff_32f_C1R(src1.ptr<Npp32f>(), src1.step, |
||||
src2.ptr<Npp32f>(), src2.step, |
||||
dst.ptr<Npp32f>(), dst.step, sz) ); |
||||
break; |
||||
default: |
||||
CV_Assert(!"Unsupported source type"); |
||||
} |
||||
} |
||||
|
||||
void cv::gpu::absdiff(const GpuMat& src, const Scalar& s, GpuMat& dst) |
||||
{ |
||||
CV_Assert(src.type() == CV_32FC1); |
||||
|
||||
dst.create( src.size(), src.type() ); |
||||
|
||||
NppiSize sz; |
||||
sz.width = src.cols; |
||||
sz.height = src.rows; |
||||
|
||||
nppSafeCall( nppiAbsDiffC_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz, (Npp32f)s[0]) ); |
||||
} |
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
// Comparison of two matrixes
|
||||
|
||||
namespace cv { namespace gpu { namespace mathfunc |
||||
{ |
||||
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst); |
||||
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst); |
||||
}}} |
||||
|
||||
void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int cmpop) |
||||
{ |
||||
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
|
||||
CV_Assert(src1.type() == CV_8UC4 || src1.type() == CV_32FC1); |
||||
|
||||
dst.create( src1.size(), CV_8UC1 ); |
||||
|
||||
static const NppCmpOp nppCmpOp[] = { NPP_CMP_EQ, NPP_CMP_GREATER, NPP_CMP_GREATER_EQ, NPP_CMP_LESS, NPP_CMP_LESS_EQ }; |
||||
|
||||
NppiSize sz; |
||||
sz.width = src1.cols; |
||||
sz.height = src1.rows; |
||||
|
||||
if (src1.type() == CV_8UC4) |
||||
{ |
||||
if (cmpop != CMP_NE) |
||||
{ |
||||
nppSafeCall( nppiCompare_8u_C4R(src1.ptr<Npp8u>(), src1.step, |
||||
src2.ptr<Npp8u>(), src2.step, |
||||
dst.ptr<Npp8u>(), dst.step, sz, nppCmpOp[cmpop]) ); |
||||
} |
||||
else |
||||
{ |
||||
mathfunc::compare_ne_8uc4(src1, src2, dst); |
||||
} |
||||
} |
||||
else |
||||
{ |
||||
if (cmpop != CMP_NE) |
||||
{ |
||||
nppSafeCall( nppiCompare_32f_C1R(src1.ptr<Npp32f>(), src1.step, |
||||
src2.ptr<Npp32f>(), src2.step, |
||||
dst.ptr<Npp8u>(), dst.step, sz, nppCmpOp[cmpop]) ); |
||||
} |
||||
else |
||||
{ |
||||
mathfunc::compare_ne_32f(src1, src2, dst); |
||||
} |
||||
} |
||||
} |
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
// Unary bitwise logical operations
|
||||
|
||||
namespace cv { namespace gpu { namespace mathfunc |
||||
{ |
||||
void bitwiseNotCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src, PtrStep dst, cudaStream_t stream); |
||||
|
||||
template <typename T> |
||||
void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStep src, const PtrStep mask, PtrStep dst, cudaStream_t stream); |
||||
}}} |
||||
|
||||
namespace |
||||
{ |
||||
void bitwiseNotCaller(const GpuMat& src, GpuMat& dst, cudaStream_t stream) |
||||
{ |
||||
dst.create(src.size(), src.type()); |
||||
|
||||
cv::gpu::mathfunc::bitwiseNotCaller(src.rows, src.cols, src.elemSize1(),
|
||||
dst.channels(), src, dst, stream); |
||||
} |
||||
|
||||
|
||||
void bitwiseNotCaller(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream) |
||||
{ |
||||
using namespace cv::gpu; |
||||
|
||||
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
||||
static Caller callers[] = {mathfunc::bitwiseMaskNotCaller<unsigned char>, mathfunc::bitwiseMaskNotCaller<unsigned char>,
|
||||
mathfunc::bitwiseMaskNotCaller<unsigned short>, mathfunc::bitwiseMaskNotCaller<unsigned short>, |
||||
mathfunc::bitwiseMaskNotCaller<unsigned int>, mathfunc::bitwiseMaskNotCaller<unsigned int>, |
||||
mathfunc::bitwiseMaskNotCaller<unsigned int>}; |
||||
|
||||
CV_Assert(mask.type() == CV_8U && mask.size() == src.size()); |
||||
dst.create(src.size(), src.type()); |
||||
|
||||
Caller caller = callers[src.depth()]; |
||||
CV_Assert(caller); |
||||
|
||||
int cn = src.depth() != CV_64F ? src.channels() : src.channels() * (sizeof(double) / sizeof(unsigned int)); |
||||
caller(src.rows, src.cols, cn, src, mask, dst, stream); |
||||
} |
||||
|
||||
} |
||||
|
||||
|
||||
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask) |
||||
{ |
||||
if (mask.empty()) |
||||
::bitwiseNotCaller(src, dst, 0); |
||||
else |
||||
::bitwiseNotCaller(src, dst, mask, 0); |
||||
} |
||||
|
||||
|
||||
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream) |
||||
{ |
||||
if (mask.empty()) |
||||
::bitwiseNotCaller(src, dst, StreamAccessor::getStream(stream)); |
||||
else |
||||
::bitwiseNotCaller(src, dst, mask, StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
|
||||
cv::gpu::GpuMat cv::gpu::operator ~ (const GpuMat& src) |
||||
{ |
||||
GpuMat dst; |
||||
bitwise_not(src, dst); |
||||
return dst; |
||||
} |
||||
|
||||
|
||||
//////////////////////////////////////////////////////////////////////////////
|
||||
// Binary bitwise logical operations
|
||||
|
||||
namespace cv { namespace gpu { namespace mathfunc |
||||
{ |
||||
void bitwiseOrCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream); |
||||
|
||||
template <typename T> |
||||
void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream); |
||||
|
||||
void bitwiseAndCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream); |
||||
|
||||
template <typename T> |
||||
void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream); |
||||
|
||||
void bitwiseXorCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream); |
||||
|
||||
template <typename T> |
||||
void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream); |
||||
}}} |
||||
|
||||
|
||||
namespace |
||||
{ |
||||
void bitwiseOrCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream) |
||||
{ |
||||
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
dst.create(src1.size(), src1.type()); |
||||
|
||||
cv::gpu::mathfunc::bitwiseOrCaller(dst.rows, dst.cols, dst.elemSize1(),
|
||||
dst.channels(), src1, src2, dst, stream); |
||||
} |
||||
|
||||
|
||||
void bitwiseOrCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream) |
||||
{ |
||||
using namespace cv::gpu; |
||||
|
||||
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
||||
static Caller callers[] = {mathfunc::bitwiseMaskOrCaller<unsigned char>, mathfunc::bitwiseMaskOrCaller<unsigned char>,
|
||||
mathfunc::bitwiseMaskOrCaller<unsigned short>, mathfunc::bitwiseMaskOrCaller<unsigned short>, |
||||
mathfunc::bitwiseMaskOrCaller<unsigned int>, mathfunc::bitwiseMaskOrCaller<unsigned int>, |
||||
mathfunc::bitwiseMaskOrCaller<unsigned int>}; |
||||
|
||||
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
dst.create(src1.size(), src1.type()); |
||||
|
||||
Caller caller = callers[src1.depth()]; |
||||
CV_Assert(caller); |
||||
|
||||
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int)); |
||||
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream); |
||||
} |
||||
|
||||
|
||||
void bitwiseAndCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream) |
||||
{ |
||||
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
dst.create(src1.size(), src1.type()); |
||||
|
||||
cv::gpu::mathfunc::bitwiseAndCaller(dst.rows, dst.cols, dst.elemSize1(),
|
||||
dst.channels(), src1, src2, dst, stream); |
||||
} |
||||
|
||||
|
||||
void bitwiseAndCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream) |
||||
{ |
||||
using namespace cv::gpu; |
||||
|
||||
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
||||
static Caller callers[] = {mathfunc::bitwiseMaskAndCaller<unsigned char>, mathfunc::bitwiseMaskAndCaller<unsigned char>,
|
||||
mathfunc::bitwiseMaskAndCaller<unsigned short>, mathfunc::bitwiseMaskAndCaller<unsigned short>, |
||||
mathfunc::bitwiseMaskAndCaller<unsigned int>, mathfunc::bitwiseMaskAndCaller<unsigned int>, |
||||
mathfunc::bitwiseMaskAndCaller<unsigned int>}; |
||||
|
||||
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
dst.create(src1.size(), src1.type()); |
||||
|
||||
Caller caller = callers[src1.depth()]; |
||||
CV_Assert(caller); |
||||
|
||||
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int)); |
||||
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream); |
||||
} |
||||
|
||||
|
||||
void bitwiseXorCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream) |
||||
{ |
||||
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
dst.create(src1.size(), src1.type()); |
||||
|
||||
cv::gpu::mathfunc::bitwiseXorCaller(dst.rows, dst.cols, dst.elemSize1(),
|
||||
dst.channels(), src1, src2, dst, stream); |
||||
} |
||||
|
||||
|
||||
void bitwiseXorCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream) |
||||
{ |
||||
using namespace cv::gpu; |
||||
|
||||
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t); |
||||
static Caller callers[] = {mathfunc::bitwiseMaskXorCaller<unsigned char>, mathfunc::bitwiseMaskXorCaller<unsigned char>,
|
||||
mathfunc::bitwiseMaskXorCaller<unsigned short>, mathfunc::bitwiseMaskXorCaller<unsigned short>, |
||||
mathfunc::bitwiseMaskXorCaller<unsigned int>, mathfunc::bitwiseMaskXorCaller<unsigned int>, |
||||
mathfunc::bitwiseMaskXorCaller<unsigned int>}; |
||||
|
||||
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type()); |
||||
dst.create(src1.size(), src1.type()); |
||||
|
||||
Caller caller = callers[src1.depth()]; |
||||
CV_Assert(caller); |
||||
|
||||
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int)); |
||||
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream); |
||||
} |
||||
} |
||||
|
||||
|
||||
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask) |
||||
{ |
||||
if (mask.empty()) |
||||
::bitwiseOrCaller(src1, src2, dst, 0); |
||||
else |
||||
::bitwiseOrCaller(src1, src2, dst, mask, 0); |
||||
} |
||||
|
||||
|
||||
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream) |
||||
{ |
||||
if (mask.empty()) |
||||
::bitwiseOrCaller(src1, src2, dst, StreamAccessor::getStream(stream)); |
||||
else |
||||
::bitwiseOrCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
|
||||
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask) |
||||
{ |
||||
if (mask.empty()) |
||||
::bitwiseAndCaller(src1, src2, dst, 0); |
||||
else |
||||
::bitwiseAndCaller(src1, src2, dst, mask, 0); |
||||
} |
||||
|
||||
|
||||
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream) |
||||
{ |
||||
if (mask.empty()) |
||||
::bitwiseAndCaller(src1, src2, dst, StreamAccessor::getStream(stream)); |
||||
else |
||||
::bitwiseAndCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
|
||||
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask) |
||||
{ |
||||
if (mask.empty()) |
||||
::bitwiseXorCaller(src1, src2, dst, 0); |
||||
else |
||||
::bitwiseXorCaller(src1, src2, dst, mask, 0); |
||||
} |
||||
|
||||
|
||||
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream) |
||||
{ |
||||
if (mask.empty()) |
||||
::bitwiseXorCaller(src1, src2, dst, StreamAccessor::getStream(stream)); |
||||
else |
||||
::bitwiseXorCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream)); |
||||
} |
||||
|
||||
|
||||
cv::gpu::GpuMat cv::gpu::operator | (const GpuMat& src1, const GpuMat& src2) |
||||
{ |
||||
GpuMat dst; |
||||
bitwise_or(src1, src2, dst); |
||||
return dst; |
||||
} |
||||
|
||||
|
||||
cv::gpu::GpuMat cv::gpu::operator & (const GpuMat& src1, const GpuMat& src2) |
||||
{ |
||||
GpuMat dst; |
||||
bitwise_and(src1, src2, dst); |
||||
return dst; |
||||
} |
||||
|
||||
|
||||
cv::gpu::GpuMat cv::gpu::operator ^ (const GpuMat& src1, const GpuMat& src2) |
||||
{ |
||||
GpuMat dst; |
||||
bitwise_xor(src1, src2, dst); |
||||
return dst; |
||||
} |
||||
|
||||
#endif |
||||
Loading…
Reference in new issue