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https://github.com/opencv/opencv.git
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used new device layer for cv::gpu::mulSpectrums
This commit is contained in:
Vladislav Vinogradov
parent
3f62e78592
commit
e820c5c65f
@ -292,95 +292,6 @@ void cv::cuda::gemm(InputArray _src1, InputArray _src2, double alpha, InputArray
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#endif
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}
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//////////////////////////////////////////////////////////////////////////////
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// mulSpectrums
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#ifdef HAVE_CUFFT
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namespace cv { namespace cuda { namespace device
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{
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void mulSpectrums(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, PtrStepSz<cufftComplex> c, cudaStream_t stream);
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void mulSpectrums_CONJ(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, PtrStepSz<cufftComplex> c, cudaStream_t stream);
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}}}
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#endif
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void cv::cuda::mulSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, bool conjB, Stream& stream)
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{
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#ifndef HAVE_CUFFT
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(void) _src1;
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(void) _src2;
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(void) _dst;
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(void) flags;
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(void) conjB;
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(void) stream;
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throw_no_cuda();
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#else
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(void) flags;
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typedef void (*Caller)(const PtrStep<cufftComplex>, const PtrStep<cufftComplex>, PtrStepSz<cufftComplex>, cudaStream_t stream);
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static Caller callers[] = { device::mulSpectrums, device::mulSpectrums_CONJ };
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GpuMat src1 = _src1.getGpuMat();
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GpuMat src2 = _src2.getGpuMat();
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CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2 );
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CV_Assert( src1.size() == src2.size() );
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_dst.create(src1.size(), CV_32FC2);
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GpuMat dst = _dst.getGpuMat();
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Caller caller = callers[(int)conjB];
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caller(src1, src2, dst, StreamAccessor::getStream(stream));
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#endif
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}
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//////////////////////////////////////////////////////////////////////////////
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// mulAndScaleSpectrums
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#ifdef HAVE_CUFFT
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namespace cv { namespace cuda { namespace device
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{
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void mulAndScaleSpectrums(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, float scale, PtrStepSz<cufftComplex> c, cudaStream_t stream);
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void mulAndScaleSpectrums_CONJ(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, float scale, PtrStepSz<cufftComplex> c, cudaStream_t stream);
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}}}
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#endif
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void cv::cuda::mulAndScaleSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, float scale, bool conjB, Stream& stream)
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{
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#ifndef HAVE_CUFFT
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(void) _src1;
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(void) _src2;
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(void) _dst;
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(void) flags;
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(void) scale;
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(void) conjB;
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(void) stream;
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throw_no_cuda();
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#else
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(void)flags;
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typedef void (*Caller)(const PtrStep<cufftComplex>, const PtrStep<cufftComplex>, float scale, PtrStepSz<cufftComplex>, cudaStream_t stream);
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static Caller callers[] = { device::mulAndScaleSpectrums, device::mulAndScaleSpectrums_CONJ };
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GpuMat src1 = _src1.getGpuMat();
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GpuMat src2 = _src2.getGpuMat();
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CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2);
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CV_Assert( src1.size() == src2.size() );
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_dst.create(src1.size(), CV_32FC2);
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GpuMat dst = _dst.getGpuMat();
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Caller caller = callers[(int)conjB];
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caller(src1, src2, scale, dst, StreamAccessor::getStream(stream));
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#endif
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}
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//////////////////////////////////////////////////////////////////////////////
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// dft
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@ -40,132 +40,126 @@
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//
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//M*/
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#if !defined CUDA_DISABLER
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#include "opencv2/opencv_modules.hpp"
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#include "cvconfig.h"
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#ifndef HAVE_OPENCV_CUDEV
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#ifdef HAVE_CUFFT
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#error "opencv_cudev is required"
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#include <cufft.h>
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#else
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#include "opencv2/core/cuda/common.hpp"
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#include "opencv2/cudaarithm.hpp"
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#include "opencv2/cudev.hpp"
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namespace cv { namespace cuda { namespace device
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using namespace cv::cudev;
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//////////////////////////////////////////////////////////////////////////////
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// mulSpectrums
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namespace
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{
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//////////////////////////////////////////////////////////////////////////
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// mulSpectrums
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__global__ void mulSpectrumsKernel(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, PtrStepSz<cufftComplex> c)
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__device__ __forceinline__ float real(const float2& val)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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return val.x;
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}
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if (x < c.cols && y < c.rows)
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__device__ __forceinline__ float imag(const float2& val)
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{
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return val.y;
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}
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__device__ __forceinline__ float2 cmul(const float2& a, const float2& b)
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{
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return make_float2((real(a) * real(b)) - (imag(a) * imag(b)),
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(real(a) * imag(b)) + (imag(a) * real(b)));
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}
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__device__ __forceinline__ float2 conj(const float2& a)
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{
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return make_float2(real(a), -imag(a));
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}
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struct comlex_mul : binary_function<float2, float2, float2>
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{
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__device__ __forceinline__ float2 operator ()(const float2& a, const float2& b) const
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{
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c.ptr(y)[x] = cuCmulf(a.ptr(y)[x], b.ptr(y)[x]);
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return cmul(a, b);
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}
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}
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};
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void mulSpectrums(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, PtrStepSz<cufftComplex> c, cudaStream_t stream)
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struct comlex_mul_conj : binary_function<float2, float2, float2>
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{
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dim3 threads(256);
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dim3 grid(divUp(c.cols, threads.x), divUp(c.rows, threads.y));
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mulSpectrumsKernel<<<grid, threads, 0, stream>>>(a, b, c);
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cudaSafeCall( cudaGetLastError() );
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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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// mulSpectrums_CONJ
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__global__ void mulSpectrumsKernel_CONJ(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, PtrStepSz<cufftComplex> c)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x < c.cols && y < c.rows)
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__device__ __forceinline__ float2 operator ()(const float2& a, const float2& b) const
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{
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c.ptr(y)[x] = cuCmulf(a.ptr(y)[x], cuConjf(b.ptr(y)[x]));
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return cmul(a, conj(b));
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}
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}
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};
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void mulSpectrums_CONJ(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, PtrStepSz<cufftComplex> c, cudaStream_t stream)
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struct comlex_mul_scale : binary_function<float2, float2, float2>
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{
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dim3 threads(256);
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dim3 grid(divUp(c.cols, threads.x), divUp(c.rows, threads.y));
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float scale;
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mulSpectrumsKernel_CONJ<<<grid, threads, 0, stream>>>(a, b, c);
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cudaSafeCall( cudaGetLastError() );
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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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// mulAndScaleSpectrums
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__global__ void mulAndScaleSpectrumsKernel(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, float scale, PtrStepSz<cufftComplex> c)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x < c.cols && y < c.rows)
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__device__ __forceinline__ float2 operator ()(const float2& a, const float2& b) const
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{
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cufftComplex v = cuCmulf(a.ptr(y)[x], b.ptr(y)[x]);
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c.ptr(y)[x] = make_cuFloatComplex(cuCrealf(v) * scale, cuCimagf(v) * scale);
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return scale * cmul(a, b);
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}
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}
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};
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void mulAndScaleSpectrums(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, float scale, PtrStepSz<cufftComplex> c, cudaStream_t stream)
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struct comlex_mul_conj_scale : binary_function<float2, float2, float2>
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{
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dim3 threads(256);
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dim3 grid(divUp(c.cols, threads.x), divUp(c.rows, threads.y));
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float scale;
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mulAndScaleSpectrumsKernel<<<grid, threads, 0, stream>>>(a, b, scale, c);
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cudaSafeCall( cudaGetLastError() );
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if (stream)
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cudaSafeCall( cudaDeviceSynchronize() );
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}
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//////////////////////////////////////////////////////////////////////////
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// mulAndScaleSpectrums_CONJ
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__global__ void mulAndScaleSpectrumsKernel_CONJ(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, float scale, PtrStepSz<cufftComplex> c)
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{
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const int x = blockIdx.x * blockDim.x + threadIdx.x;
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const int y = blockIdx.y * blockDim.y + threadIdx.y;
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if (x < c.cols && y < c.rows)
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__device__ __forceinline__ float2 operator ()(const float2& a, const float2& b) const
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{
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cufftComplex v = cuCmulf(a.ptr(y)[x], cuConjf(b.ptr(y)[x]));
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c.ptr(y)[x] = make_cuFloatComplex(cuCrealf(v) * scale, cuCimagf(v) * scale);
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return scale * cmul(a, conj(b));
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}
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}
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};
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}
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void cv::cuda::mulSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, bool conjB, Stream& stream)
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{
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(void) flags;
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void mulAndScaleSpectrums_CONJ(const PtrStep<cufftComplex> a, const PtrStep<cufftComplex> b, float scale, PtrStepSz<cufftComplex> c, cudaStream_t stream)
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GpuMat src1 = _src1.getGpuMat();
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GpuMat src2 = _src2.getGpuMat();
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CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2 );
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CV_Assert( src1.size() == src2.size() );
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_dst.create(src1.size(), CV_32FC2);
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GpuMat dst = _dst.getGpuMat();
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if (conjB)
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gridTransformBinary(globPtr<float2>(src1), globPtr<float2>(src2), globPtr<float2>(dst), comlex_mul_conj(), stream);
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else
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gridTransformBinary(globPtr<float2>(src1), globPtr<float2>(src2), globPtr<float2>(dst), comlex_mul(), stream);
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}
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void cv::cuda::mulAndScaleSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, float scale, bool conjB, Stream& stream)
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{
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(void) flags;
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GpuMat src1 = _src1.getGpuMat();
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GpuMat src2 = _src2.getGpuMat();
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CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2);
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CV_Assert( src1.size() == src2.size() );
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_dst.create(src1.size(), CV_32FC2);
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GpuMat dst = _dst.getGpuMat();
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if (conjB)
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{
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dim3 threads(256);
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dim3 grid(divUp(c.cols, threads.x), divUp(c.rows, threads.y));
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mulAndScaleSpectrumsKernel_CONJ<<<grid, threads, 0, stream>>>(a, b, scale, c);
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cudaSafeCall( cudaGetLastError() );
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if (stream == 0)
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cudaSafeCall( cudaDeviceSynchronize() );
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comlex_mul_conj_scale op;
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op.scale = scale;
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gridTransformBinary(globPtr<float2>(src1), globPtr<float2>(src2), globPtr<float2>(dst), op, stream);
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}
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}}} // namespace cv { namespace cuda { namespace cudev
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else
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{
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comlex_mul_scale op;
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op.scale = scale;
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gridTransformBinary(globPtr<float2>(src1), globPtr<float2>(src2), globPtr<float2>(dst), op, stream);
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}
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}
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#endif // HAVE_CUFFT
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#endif /* CUDA_DISABLER */
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#endif
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