switched to Input/Output Array in transpose/flip operations
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@ -170,17 +170,17 @@ CV_EXPORTS void merge(const std::vector<GpuMat>& src, OutputArray dst, Stream& s
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CV_EXPORTS void split(InputArray src, GpuMat* dst, Stream& stream = Stream::Null());
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CV_EXPORTS void split(InputArray src, std::vector<GpuMat>& dst, Stream& stream = Stream::Null());
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//! implements generalized matrix product algorithm GEMM from BLAS
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CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
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const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null());
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//! transposes the matrix
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//! supports matrix with element size = 1, 4 and 8 bytes (CV_8UC1, CV_8UC4, CV_16UC2, CV_32FC1, etc)
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CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst, Stream& stream = Stream::Null());
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CV_EXPORTS void transpose(InputArray src1, OutputArray dst, Stream& stream = Stream::Null());
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//! reverses the order of the rows, columns or both in a matrix
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//! supports 1, 3 and 4 channels images with CV_8U, CV_16U, CV_32S or CV_32F depth
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CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode, Stream& stream = Stream::Null());
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CV_EXPORTS void flip(InputArray src, OutputArray dst, int flipCode, Stream& stream = Stream::Null());
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//! implements generalized matrix product algorithm GEMM from BLAS
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CV_EXPORTS void gemm(const GpuMat& src1, const GpuMat& src2, double alpha,
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const GpuMat& src3, double beta, GpuMat& dst, int flags = 0, Stream& stream = Stream::Null());
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//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
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//! destination array will have the depth type as lut and the same channels number as source
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@ -53,9 +53,9 @@ void cv::gpu::merge(const std::vector<GpuMat>&, OutputArray, Stream&) { throw_no
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void cv::gpu::split(InputArray, GpuMat*, Stream&) { throw_no_cuda(); }
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void cv::gpu::split(InputArray, std::vector<GpuMat>&, Stream&) { throw_no_cuda(); }
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void cv::gpu::transpose(const GpuMat&, GpuMat&, Stream&) { throw_no_cuda(); }
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void cv::gpu::transpose(InputArray, OutputArray, Stream&) { throw_no_cuda(); }
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void cv::gpu::flip(const GpuMat&, GpuMat&, int, Stream&) { throw_no_cuda(); }
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void cv::gpu::flip(InputArray, OutputArray, int, Stream&) { throw_no_cuda(); }
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void cv::gpu::LUT(const GpuMat&, const Mat&, GpuMat&, Stream&) { throw_no_cuda(); }
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@ -182,13 +182,16 @@ namespace arithm
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template <typename T> void transpose(PtrStepSz<T> src, PtrStepSz<T> dst, cudaStream_t stream);
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}
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void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
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void cv::gpu::transpose(InputArray _src, OutputArray _dst, Stream& _stream)
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{
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GpuMat src = _src.getGpuMat();
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CV_Assert( src.elemSize() == 1 || src.elemSize() == 4 || src.elemSize() == 8 );
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dst.create( src.cols, src.rows, src.type() );
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_dst.create( src.cols, src.rows, src.type() );
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GpuMat dst = _dst.getGpuMat();
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cudaStream_t stream = StreamAccessor::getStream(s);
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cudaStream_t stream = StreamAccessor::getStream(_stream);
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if (src.elemSize() == 1)
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{
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@ -260,7 +263,7 @@ namespace
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};
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}
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void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
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void cv::gpu::flip(InputArray _src, OutputArray _dst, int flipCode, Stream& stream)
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{
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typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
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static const func_t funcs[6][4] =
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@ -273,10 +276,13 @@ void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
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{NppMirror<CV_32F, nppiMirror_32f_C1R>::call, 0, NppMirror<CV_32F, nppiMirror_32f_C3R>::call, NppMirror<CV_32F, nppiMirror_32f_C4R>::call}
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};
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GpuMat src = _src.getGpuMat();
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CV_Assert(src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S || src.depth() == CV_32F);
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CV_Assert(src.channels() == 1 || src.channels() == 3 || src.channels() == 4);
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dst.create(src.size(), src.type());
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_dst.create(src.size(), src.type());
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GpuMat dst = _dst.getGpuMat();
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funcs[src.depth()][src.channels() - 1](src, dst, flipCode, StreamAccessor::getStream(stream));
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}
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@ -130,12 +130,12 @@ void Worker::operator()(int device_id) const
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rng.fill(src, RNG::UNIFORM, 0, 1);
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// CPU works
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transpose(src, dst);
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cv::transpose(src, dst);
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// GPU works
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GpuMat d_src(src);
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GpuMat d_dst;
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transpose(d_src, d_dst);
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gpu::transpose(d_src, d_dst);
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// Check results
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bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
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@ -87,12 +87,12 @@ void Worker::operator()(int device_id) const
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rng.fill(src, RNG::UNIFORM, 0, 1);
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// CPU works
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transpose(src, dst);
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cv::transpose(src, dst);
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// GPU works
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GpuMat d_src(src);
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GpuMat d_dst;
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transpose(d_src, d_dst);
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gpu::transpose(d_src, d_dst);
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// Check results
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bool passed = norm(dst - Mat(d_dst), NORM_INF) < 1e-3;
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