updated image for StereoConstantSpaceBP regression test

updated gpu tests for CornerHarris and CornerMinEigen
moved direct convolution implementation to gpu::filter2D, gpu::convolve now use only DFT-based algorithm (Bug #1639)

diff --git a/modules/gpu/doc/image_filtering.rst b/modules/gpu/doc/image_filtering.rst
index 7b21fc3..2e674cf 100644 (file)
--- a/modules/gpu/doc/image_filtering.rst
+++ b/modules/gpu/doc/image_filtering.rst
@@ -395,7 +395,7 @@ Applies the non-separable 2D linear filter to an image.
 
 .. ocv:function:: void gpu::filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor=Point(-1,-1), Stream& stream = Stream::Null())
 
-    :param src: Source image.  ``CV_8UC1``  and  ``CV_8UC4``  source types are supported.
+    :param src: Source image.  ``CV_8UC1`` , ``CV_8UC4`` and ``CV_32FC1``  source types are supported.
 
     :param dst: Destination image. The size and the number of channels is the same as  ``src`` .
 

diff --git a/modules/gpu/perf/perf_filters.cpp b/modules/gpu/perf/perf_filters.cpp
index f6ba4a9..9322557 100644 (file)
--- a/modules/gpu/perf/perf_filters.cpp
+++ b/modules/gpu/perf/perf_filters.cpp
@@ -102,8 +102,8 @@ GPU_PERF_TEST(LinearFilter, cv::gpu::DeviceInfo, cv::Size, perf::MatType, int)
 INSTANTIATE_TEST_CASE_P(Filter, LinearFilter, testing::Combine(\r
                         ALL_DEVICES, \r
                         GPU_TYPICAL_MAT_SIZES, \r
-                        testing::Values(CV_8UC1, CV_8UC4),\r
-                        testing::Values(3, 5)));\r
+                        testing::Values(CV_8UC1, CV_8UC4, CV_32FC1),\r
+                        testing::Values(3, 5, 7, 9)));\r
 \r
 //////////////////////////////////////////////////////////////////////\r
 // SeparableLinearFilter\r

diff --git a/modules/gpu/perf/perf_imgproc.cpp b/modules/gpu/perf/perf_imgproc.cpp
index 1418d09..8c31d83 100644 (file)
--- a/modules/gpu/perf/perf_imgproc.cpp
+++ b/modules/gpu/perf/perf_imgproc.cpp
@@ -727,11 +727,12 @@ INSTANTIATE_TEST_CASE_P(ImgProc, Dft, testing::Combine(
 //////////////////////////////////////////////////////////////////////\r
 // Convolve\r
 \r
-GPU_PERF_TEST(Convolve, cv::gpu::DeviceInfo, cv::Size, int)\r
+GPU_PERF_TEST(Convolve, cv::gpu::DeviceInfo, cv::Size, int, bool)\r
 {\r
     cv::gpu::DeviceInfo devInfo = GET_PARAM(0);\r
     cv::Size size = GET_PARAM(1);\r
     int templ_size = GET_PARAM(2);\r
+    bool ccorr = GET_PARAM(3);\r
 \r
     cv::gpu::setDevice(devInfo.deviceID());\r
 \r
@@ -748,14 +749,15 @@ GPU_PERF_TEST(Convolve, cv::gpu::DeviceInfo, cv::Size, int)
 \r
     TEST_CYCLE()\r
     {\r
-        cv::gpu::convolve(image, templ, dst, false, buf);\r
+        cv::gpu::convolve(image, templ, dst, ccorr, buf);\r
     }\r
 }\r
 \r
 INSTANTIATE_TEST_CASE_P(ImgProc, Convolve, testing::Combine(\r
                         ALL_DEVICES, \r
                         GPU_TYPICAL_MAT_SIZES,\r
-                        testing::Values(3, 9, 27, 32, 64)));\r
+                        testing::Values(3, 9, 27, 32, 64),\r
+                        testing::Bool()));\r
 \r
 //////////////////////////////////////////////////////////////////////\r
 // PyrDown\r

diff --git a/modules/gpu/src/cuda/imgproc.cu b/modules/gpu/src/cuda/imgproc.cu
index 72053ad..1a302f4 100644 (file)
--- a/modules/gpu/src/cuda/imgproc.cu
+++ b/modules/gpu/src/cuda/imgproc.cu
@@ -904,79 +904,49 @@ namespace cv { namespace gpu { namespace device
                 cudaSafeCall(cudaDeviceSynchronize());\r
         }\r
 \r
-\r
         //////////////////////////////////////////////////////////////////////////\r
-        // convolve\r
+        // filter2D\r
 \r
-        #define CONVOLVE_MAX_KERNEL_SIZE 17\r
+        #define FILTER2D_MAX_KERNEL_SIZE 16\r
 \r
-        __constant__ float c_convolveKernel[CONVOLVE_MAX_KERNEL_SIZE * CONVOLVE_MAX_KERNEL_SIZE];\r
+        __constant__ float c_filter2DKernel[FILTER2D_MAX_KERNEL_SIZE * FILTER2D_MAX_KERNEL_SIZE];\r
 \r
-        __global__ void convolve(const DevMem2Df src, PtrStepf dst, int kWidth, int kHeight)\r
-        {\r
-            __shared__ float smem[16 + 2 * 8][16 + 2 * 8];\r
+        texture<float, cudaTextureType2D, cudaReadModeElementType> filter2DTex(0, cudaFilterModePoint, cudaAddressModeBorder);\r
 \r
+        __global__ void filter2D(int ofsX, int ofsY, DevMem2Df dst, const int kWidth, const int kHeight, const int anchorX, const int anchorY)\r
+        {\r
             const int x = blockIdx.x * blockDim.x + threadIdx.x;\r
             const int y = blockIdx.y * blockDim.y + threadIdx.y;\r
 \r
-            // x | x 0 | 0\r
-            // -----------\r
-            // x | x 0 | 0\r
-            // 0 | 0 0 | 0\r
-            // -----------\r
-            // 0 | 0 0 | 0\r
-            smem[threadIdx.y][threadIdx.x] = src.ptr(::min(::max(y - 8, 0), src.rows - 1))[::min(::max(x - 8, 0), src.cols - 1)];\r
-\r
-            // 0 | 0 x | x\r
-            // -----------\r
-            // 0 | 0 x | x\r
-            // 0 | 0 0 | 0\r
-            // -----------\r
-            // 0 | 0 0 | 0\r
-            smem[threadIdx.y][threadIdx.x + 16] = src.ptr(::min(::max(y - 8, 0), src.rows - 1))[::min(x + 8, src.cols - 1)];\r
-\r
-            // 0 | 0 0 | 0\r
-            // -----------\r
-            // 0 | 0 0 | 0\r
-            // x | x 0 | 0\r
-            // -----------\r
-            // x | x 0 | 0\r
-            smem[threadIdx.y + 16][threadIdx.x] = src.ptr(::min(y + 8, src.rows - 1))[::min(::max(x - 8, 0), src.cols - 1)];\r
-\r
-            // 0 | 0 0 | 0\r
-            // -----------\r
-            // 0 | 0 0 | 0\r
-            // 0 | 0 x | x\r
-            // -----------\r
-            // 0 | 0 x | x\r
-            smem[threadIdx.y + 16][threadIdx.x + 16] = src.ptr(::min(y + 8, src.rows - 1))[::min(x + 8, src.cols - 1)];\r
-\r
-            __syncthreads();\r
-\r
-            if (x < src.cols && y < src.rows)\r
-            {\r
-                float res = 0;\r
+            if (x >= dst.cols || y >= dst.rows)\r
+                return;\r
 \r
-                for (int i = 0; i < kHeight; ++i)\r
-                {\r
-                    for (int j = 0; j < kWidth; ++j)\r
-                    {\r
-                        res += smem[threadIdx.y + 8 - kHeight / 2 + i][threadIdx.x + 8 - kWidth / 2 + j] * c_convolveKernel[i * kWidth + j];\r
-                    }\r
-                }\r
+            float res = 0;\r
+\r
+            const int baseX = ofsX + x - anchorX;\r
+            const int baseY = ofsY + y - anchorY;\r
+\r
+            int kInd = 0;\r
 \r
-                dst.ptr(y)[x] = res;\r
+            for (int i = 0; i < kHeight; ++i)\r
+            {\r
+                for (int j = 0; j < kWidth; ++j)\r
+                    res += tex2D(filter2DTex, baseX + j, baseY + i) * c_filter2DKernel[kInd++];\r
             }\r
+\r
+            dst.ptr(y)[x] = res;\r
         }\r
 \r
-        void convolve_gpu(const DevMem2Df& src, const PtrStepf& dst, int kWidth, int kHeight, float* kernel, cudaStream_t stream)\r
+        void filter2D_gpu(DevMem2Df src, int ofsX, int ofsY, DevMem2Df dst, int kWidth, int kHeight, int anchorX, int anchorY, float* kernel, cudaStream_t stream)\r
         {\r
-            cudaSafeCall(cudaMemcpyToSymbol(c_convolveKernel, kernel, kWidth * kHeight * sizeof(float), 0, cudaMemcpyDeviceToDevice) );\r
+            cudaSafeCall(cudaMemcpyToSymbol(c_filter2DKernel, kernel, kWidth * kHeight * sizeof(float), 0, cudaMemcpyDeviceToDevice) );\r
 \r
             const dim3 block(16, 16);\r
-            const dim3 grid(divUp(src.cols, block.x), divUp(src.rows, block.y));\r
+            const dim3 grid(divUp(dst.cols, block.x), divUp(dst.rows, block.y));\r
+\r
+            bindTexture(&filter2DTex, src);\r
 \r
-            convolve<<<grid, block, 0, stream>>>(src, dst, kWidth, kHeight);\r
+            filter2D<<<grid, block, 0, stream>>>(ofsX, ofsY, dst, kWidth, kHeight, anchorX, anchorY);\r
             cudaSafeCall(cudaGetLastError());\r
 \r
             if (stream == 0)\r

diff --git a/modules/gpu/src/filtering.cpp b/modules/gpu/src/filtering.cpp
index e9977c6..7af32c1 100644 (file)
--- a/modules/gpu/src/filtering.cpp
+++ b/modules/gpu/src/filtering.cpp
@@ -659,6 +659,14 @@ void cv::gpu::morphologyEx(const GpuMat& src, GpuMat& dst, int op, const Mat& ke
 ////////////////////////////////////////////////////////////////////////////////////////////////////\r
 // Linear Filter\r
 \r
+namespace cv { namespace gpu { namespace device \r
+{\r
+    namespace imgproc\r
+    {\r
+        void filter2D_gpu(DevMem2Df src, int ofsX, int ofsY, DevMem2Df dst, int kWidth, int kHeight, int anchorX, int anchorY, float* kernel, cudaStream_t stream);\r
+    }\r
+}}}\r
+\r
 namespace\r
 {\r
     typedef NppStatus (*nppFilter2D_t)(const Npp8u * pSrc, Npp32s nSrcStep, Npp8u * pDst, Npp32s nDstStep, NppiSize oSizeROI, \r
@@ -696,20 +704,56 @@ namespace
         Npp32s nDivisor;\r
         nppFilter2D_t func;\r
     };\r
+\r
+    struct GpuLinearFilter : public BaseFilter_GPU\r
+    {\r
+        GpuLinearFilter(Size ksize_, Point anchor_, const GpuMat& kernel_) : \r
+            BaseFilter_GPU(ksize_, anchor_), kernel(kernel_) {}\r
+            \r
+        virtual void operator()(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null())\r
+        {\r
+            using namespace cv::gpu::device::imgproc;\r
+\r
+            Point ofs;\r
+            Size wholeSize;\r
+            src.locateROI(wholeSize, ofs);\r
+            GpuMat srcWhole(wholeSize, src.type(), src.datastart);\r
+\r
+            filter2D_gpu(srcWhole, ofs.x, ofs.y, dst, ksize.width, ksize.height, anchor.x, anchor.y, kernel.ptr<float>(), StreamAccessor::getStream(stream));\r
+        }\r
+\r
+        GpuMat kernel;\r
+    };\r
 }\r
 \r
 Ptr<BaseFilter_GPU> cv::gpu::getLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Size& ksize, Point anchor)\r
 {\r
-    static const nppFilter2D_t cppFilter2D_callers[] = {0, nppiFilter_8u_C1R, 0, 0, nppiFilter_8u_C4R};\r
+    CV_Assert(srcType == CV_8UC1 || srcType == CV_8UC4 || srcType == CV_32FC1);\r
+    CV_Assert(dstType == srcType);\r
+\r
+    if (srcType == CV_32FC1)\r
+    {\r
+        CV_Assert(ksize.width * ksize.height <= 16 * 16);\r
+\r
+        GpuMat gpu_krnl;\r
+        normalizeKernel(kernel, gpu_krnl, CV_32F);\r
 \r
-    CV_Assert((srcType == CV_8UC1 || srcType == CV_8UC4) && dstType == srcType);\r
+        normalizeAnchor(anchor, ksize);\r
+\r
+        return Ptr<BaseFilter_GPU>(new GpuLinearFilter(ksize, anchor, gpu_krnl));\r
+    }\r
+    else\r
+    {\r
+        static const nppFilter2D_t cppFilter2D_callers[] = {0, nppiFilter_8u_C1R, 0, 0, nppiFilter_8u_C4R};\r
     \r
-    GpuMat gpu_krnl;\r
-    int nDivisor;\r
-    normalizeKernel(kernel, gpu_krnl, CV_32S, &nDivisor, true);\r
-    normalizeAnchor(anchor, ksize);\r
+        GpuMat gpu_krnl;\r
+        int nDivisor;\r
+        normalizeKernel(kernel, gpu_krnl, CV_32S, &nDivisor, true);\r
 \r
-    return Ptr<BaseFilter_GPU>(new NPPLinearFilter(ksize, anchor, gpu_krnl, nDivisor, cppFilter2D_callers[CV_MAT_CN(srcType)]));\r
+        normalizeAnchor(anchor, ksize);\r
+\r
+        return Ptr<BaseFilter_GPU>(new NPPLinearFilter(ksize, anchor, gpu_krnl, nDivisor, cppFilter2D_callers[CV_MAT_CN(srcType)]));\r
+    }    \r
 }    \r
 \r
 Ptr<FilterEngine_GPU> cv::gpu::createLinearFilter_GPU(int srcType, int dstType, const Mat& kernel, const Point& anchor)\r
@@ -729,7 +773,8 @@ void cv::gpu::filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& ke
     dst.create(src.size(), CV_MAKETYPE(ddepth, src.channels()));\r
 \r
     Ptr<FilterEngine_GPU> f = createLinearFilter_GPU(src.type(), dst.type(), kernel, anchor);\r
-    f->apply(src, dst, Rect(0, 0, -1, -1), stream);\r
+\r
+    f->apply(src, dst, src.type() == CV_32FC1 ? Rect(0, 0, src.cols, src.rows) : Rect(0, 0, -1, -1), stream);\r
 }\r
 \r
 ////////////////////////////////////////////////////////////////////////////////////////////////////\r

diff --git a/modules/gpu/src/imgproc.cpp b/modules/gpu/src/imgproc.cpp
index 205c5b2..7cb6317 100644 (file)
--- a/modules/gpu/src/imgproc.cpp
+++ b/modules/gpu/src/imgproc.cpp
@@ -1673,137 +1673,82 @@ void cv::gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result,
     convolve(image, templ, result, ccorr, buf);\r
 }\r
 \r
-namespace cv { namespace gpu { namespace device \r
-{\r
-    namespace imgproc\r
-    {\r
-        void convolve_gpu(const DevMem2Df& src, const PtrStepf& dst, int kWidth, int kHeight, float* kernel, cudaStream_t stream);\r
-    }\r
-}}}\r
-\r
 void cv::gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream)\r
 {\r
     using namespace ::cv::gpu::device::imgproc;\r
 \r
 #ifndef HAVE_CUFFT\r
-\r
-    CV_Assert(image.type() == CV_32F);\r
-    CV_Assert(templ.type() == CV_32F);\r
-    CV_Assert(templ.cols <= 17 && templ.rows <= 17);\r
-    \r
-    result.create(image.size(), CV_32F);\r
-\r
-    GpuMat& contKernel = buf.templ_block;\r
-\r
-    if (templ.isContinuous())\r
-        contKernel = templ;\r
-    else\r
-    {\r
-        contKernel = createContinuous(templ.size(), templ.type());\r
-\r
-        if (stream)\r
-            stream.enqueueCopy(templ, contKernel);\r
-        else\r
-            templ.copyTo(contKernel);\r
-    }\r
-\r
-    convolve_gpu(image, result, templ.cols, templ.rows, contKernel.ptr<float>(), StreamAccessor::getStream(stream));\r
-\r
+    throw_nogpu();\r
 #else\r
-\r
     StaticAssert<sizeof(float) == sizeof(cufftReal)>::check();\r
     StaticAssert<sizeof(float) * 2 == sizeof(cufftComplex)>::check();\r
 \r
     CV_Assert(image.type() == CV_32F);\r
     CV_Assert(templ.type() == CV_32F);\r
 \r
-    if (templ.cols < 13 && templ.rows < 13)\r
-    {\r
-        result.create(image.size(), CV_32F);\r
+    buf.create(image.size(), templ.size());\r
+    result.create(buf.result_size, CV_32F);\r
 \r
-        GpuMat& contKernel = buf.templ_block;\r
+    Size& block_size = buf.block_size;\r
+    Size& dft_size = buf.dft_size;\r
 \r
-        if (templ.isContinuous())\r
-            contKernel = templ;\r
-        else\r
-        {\r
-            contKernel = createContinuous(templ.size(), templ.type());\r
+    GpuMat& image_block = buf.image_block;\r
+    GpuMat& templ_block = buf.templ_block;\r
+    GpuMat& result_data = buf.result_data;\r
 \r
-            if (stream)\r
-                stream.enqueueCopy(templ, contKernel);\r
-            else\r
-                templ.copyTo(contKernel);\r
-        }\r
-\r
-        convolve_gpu(image, result, templ.cols, templ.rows, contKernel.ptr<float>(), StreamAccessor::getStream(stream));\r
-    }\r
-    else\r
-    {\r
-        buf.create(image.size(), templ.size());\r
-        result.create(buf.result_size, CV_32F);\r
+    GpuMat& image_spect = buf.image_spect;\r
+    GpuMat& templ_spect = buf.templ_spect;\r
+    GpuMat& result_spect = buf.result_spect;\r
 \r
-        Size& block_size = buf.block_size;\r
-        Size& dft_size = buf.dft_size;\r
+    cufftHandle planR2C, planC2R;\r
+    cufftSafeCall(cufftPlan2d(&planC2R, dft_size.height, dft_size.width, CUFFT_C2R));\r
+    cufftSafeCall(cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C));\r
 \r
-        GpuMat& image_block = buf.image_block;\r
-        GpuMat& templ_block = buf.templ_block;\r
-        GpuMat& result_data = buf.result_data;\r
+    cufftSafeCall( cufftSetStream(planR2C, StreamAccessor::getStream(stream)) );\r
+    cufftSafeCall( cufftSetStream(planC2R, StreamAccessor::getStream(stream)) );\r
 \r
-        GpuMat& image_spect = buf.image_spect;\r
-        GpuMat& templ_spect = buf.templ_spect;\r
-        GpuMat& result_spect = buf.result_spect;\r
+    GpuMat templ_roi(templ.size(), CV_32F, templ.data, templ.step);\r
+    copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0, \r
+                   templ_block.cols - templ_roi.cols, 0, Scalar(), stream);\r
 \r
-        cufftHandle planR2C, planC2R;\r
-        cufftSafeCall(cufftPlan2d(&planC2R, dft_size.height, dft_size.width, CUFFT_C2R));\r
-        cufftSafeCall(cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C));\r
+    cufftSafeCall(cufftExecR2C(planR2C, templ_block.ptr<cufftReal>(), \r
+                               templ_spect.ptr<cufftComplex>()));\r
 \r
-        cufftSafeCall( cufftSetStream(planR2C, StreamAccessor::getStream(stream)) );\r
-        cufftSafeCall( cufftSetStream(planC2R, StreamAccessor::getStream(stream)) );\r
-\r
-        GpuMat templ_roi(templ.size(), CV_32F, templ.data, templ.step);\r
-        copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0, \r
-                       templ_block.cols - templ_roi.cols, 0, Scalar(), stream);\r
-\r
-        cufftSafeCall(cufftExecR2C(planR2C, templ_block.ptr<cufftReal>(), \r
-                                   templ_spect.ptr<cufftComplex>()));\r
-\r
-        // Process all blocks of the result matrix\r
-        for (int y = 0; y < result.rows; y += block_size.height)\r
+    // Process all blocks of the result matrix\r
+    for (int y = 0; y < result.rows; y += block_size.height)\r
+    {\r
+        for (int x = 0; x < result.cols; x += block_size.width)\r
         {\r
-            for (int x = 0; x < result.cols; x += block_size.width)\r
-            {\r
-                Size image_roi_size(std::min(x + dft_size.width, image.cols) - x,\r
-                                    std::min(y + dft_size.height, image.rows) - y);\r
-                GpuMat image_roi(image_roi_size, CV_32F, (void*)(image.ptr<float>(y) + x), \r
-                                 image.step);\r
-                copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows,\r
-                               0, image_block.cols - image_roi.cols, 0, Scalar(), stream);\r
-\r
-                cufftSafeCall(cufftExecR2C(planR2C, image_block.ptr<cufftReal>(), \r
-                                           image_spect.ptr<cufftComplex>()));\r
-                mulAndScaleSpectrums(image_spect, templ_spect, result_spect, 0,\r
-                                     1.f / dft_size.area(), ccorr, stream);\r
-                cufftSafeCall(cufftExecC2R(planC2R, result_spect.ptr<cufftComplex>(), \r
-                                           result_data.ptr<cufftReal>()));\r
-\r
-                Size result_roi_size(std::min(x + block_size.width, result.cols) - x,\r
-                                     std::min(y + block_size.height, result.rows) - y);\r
-                GpuMat result_roi(result_roi_size, result.type(), \r
-                                  (void*)(result.ptr<float>(y) + x), result.step);\r
-                GpuMat result_block(result_roi_size, result_data.type(), \r
-                                    result_data.ptr(), result_data.step);\r
-\r
-                if (stream)\r
-                    stream.enqueueCopy(result_block, result_roi);\r
-                else\r
-                    result_block.copyTo(result_roi);\r
-            }\r
-        }\r
+            Size image_roi_size(std::min(x + dft_size.width, image.cols) - x,\r
+                                std::min(y + dft_size.height, image.rows) - y);\r
+            GpuMat image_roi(image_roi_size, CV_32F, (void*)(image.ptr<float>(y) + x), \r
+                             image.step);\r
+            copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows,\r
+                           0, image_block.cols - image_roi.cols, 0, Scalar(), stream);\r
+\r
+            cufftSafeCall(cufftExecR2C(planR2C, image_block.ptr<cufftReal>(), \r
+                                       image_spect.ptr<cufftComplex>()));\r
+            mulAndScaleSpectrums(image_spect, templ_spect, result_spect, 0,\r
+                                 1.f / dft_size.area(), ccorr, stream);\r
+            cufftSafeCall(cufftExecC2R(planC2R, result_spect.ptr<cufftComplex>(), \r
+                                       result_data.ptr<cufftReal>()));\r
+\r
+            Size result_roi_size(std::min(x + block_size.width, result.cols) - x,\r
+                                 std::min(y + block_size.height, result.rows) - y);\r
+            GpuMat result_roi(result_roi_size, result.type(), \r
+                              (void*)(result.ptr<float>(y) + x), result.step);\r
+            GpuMat result_block(result_roi_size, result_data.type(), \r
+                                result_data.ptr(), result_data.step);\r
 \r
-        cufftSafeCall(cufftDestroy(planR2C));\r
-        cufftSafeCall(cufftDestroy(planC2R));\r
+            if (stream)\r
+                stream.enqueueCopy(result_block, result_roi);\r
+            else\r
+                result_block.copyTo(result_roi);\r
+        }\r
     }\r
 \r
+    cufftSafeCall(cufftDestroy(planR2C));\r
+    cufftSafeCall(cufftDestroy(planC2R));\r
 #endif\r
 }\r
 \r

diff --git a/modules/gpu/test/test_filters.cpp b/modules/gpu/test/test_filters.cpp
index d5c668f..58b473a 100644 (file)
--- a/modules/gpu/test/test_filters.cpp
+++ b/modules/gpu/test/test_filters.cpp
@@ -629,4 +629,94 @@ INSTANTIATE_TEST_CASE_P(Filter, MorphEx, Combine(
                         Values((int)cv::MORPH_OPEN, (int)cv::MORPH_CLOSE, (int)cv::MORPH_GRADIENT, (int)cv::MORPH_TOPHAT, (int)cv::MORPH_BLACKHAT),\r
                         USE_ROI));\r
 \r
+/////////////////////////////////////////////////////////////////////////////////////////////////\r
+// filter2D\r
+\r
+PARAM_TEST_CASE(Filter2D, cv::gpu::DeviceInfo, int, UseRoi)\r
+{\r
+    cv::gpu::DeviceInfo devInfo;\r
+    int ksize;\r
+    bool useRoi;\r
+\r
+    cv::Mat img;\r
+    cv::Mat kernel;\r
+    \r
+    virtual void SetUp()\r
+    {\r
+        devInfo = GET_PARAM(0);\r
+        ksize = GET_PARAM(1);\r
+        useRoi = GET_PARAM(2);\r
+\r
+        cv::gpu::setDevice(devInfo.deviceID());\r
+        \r
+        img = readImage("stereobp/aloe-L.png");\r
+        ASSERT_FALSE(img.empty());\r
+\r
+        kernel = cv::Mat::ones(ksize, ksize, CV_32FC1);\r
+    }\r
+};\r
+\r
+TEST_P(Filter2D, Rgba)\r
+{\r
+    cv::Mat src;\r
+    cv::cvtColor(img, src, CV_BGR2BGRA);\r
+\r
+    cv::Mat dst_gold;\r
+    cv::filter2D(src, dst_gold, -1, kernel, cv::Point(-1, -1), 0, cv::BORDER_CONSTANT);\r
+\r
+    cv::Mat dst;\r
+\r
+    cv::gpu::GpuMat dev_dst;\r
+\r
+    cv::gpu::filter2D(loadMat(src, useRoi), dev_dst, -1, kernel);\r
+\r
+    dev_dst.download(dst);\r
+\r
+    EXPECT_MAT_NEAR_KSIZE(dst_gold, dst, ksize, 0.0);\r
+}\r
+\r
+TEST_P(Filter2D, Gray)\r
+{\r
+    cv::Mat src;\r
+    cv::cvtColor(img, src, CV_BGR2GRAY);\r
+\r
+    cv::Mat dst_gold;\r
+    cv::filter2D(src, dst_gold, -1, kernel, cv::Point(-1, -1), 0, cv::BORDER_CONSTANT);\r
+\r
+    cv::Mat dst;\r
+\r
+    cv::gpu::GpuMat dev_dst;\r
+\r
+    cv::gpu::filter2D(loadMat(src, useRoi), dev_dst, -1, kernel);\r
+\r
+    dev_dst.download(dst);\r
+\r
+    EXPECT_MAT_NEAR_KSIZE(dst_gold, dst, ksize, 0.0);\r
+}\r
+\r
+TEST_P(Filter2D, 32FC1)\r
+{\r
+    cv::Mat src;\r
+    cv::cvtColor(img, src, CV_BGR2GRAY);\r
+    src.convertTo(src, CV_32F, 1.0 / 255.0);\r
+\r
+    cv::Mat dst_gold;\r
+    cv::filter2D(src, dst_gold, -1, kernel, cv::Point(-1, -1), 0, cv::BORDER_CONSTANT);\r
+\r
+    cv::Mat dst;\r
+\r
+    cv::gpu::GpuMat dev_dst;\r
+\r
+    cv::gpu::filter2D(loadMat(src, useRoi), dev_dst, -1, kernel);\r
+\r
+    dev_dst.download(dst);\r
+\r
+    EXPECT_MAT_NEAR_KSIZE(dst_gold, dst, ksize, 1e-3);\r
+}\r
+\r
+INSTANTIATE_TEST_CASE_P(Filter, Filter2D, Combine(\r
+                        ALL_DEVICES,\r
+                        Values(3, 5, 7, 11, 13, 15),\r
+                        USE_ROI));\r
+\r
 #endif // HAVE_CUDA\r

diff --git a/modules/gpu/test/test_imgproc.cpp b/modules/gpu/test/test_imgproc.cpp
index f25cb36..018a3e6 100644 (file)
--- a/modules/gpu/test/test_imgproc.cpp
+++ b/modules/gpu/test/test_imgproc.cpp
@@ -2573,36 +2573,36 @@ INSTANTIATE_TEST_CASE_P(ImgProc, EqualizeHist, ALL_DEVICES);
 ///////////////////////////////////////////////////////////////////////////////////////////////////////\r
 // cornerHarris\r
 \r
-PARAM_TEST_CASE(CornerHarris, cv::gpu::DeviceInfo, MatType, Border)\r
+PARAM_TEST_CASE(CornerHarris, cv::gpu::DeviceInfo, MatType, Border, int, int)\r
 {\r
     cv::gpu::DeviceInfo devInfo;\r
     int type;\r
     int borderType;\r
+    int blockSize;\r
+    int apertureSize;\r
 \r
     cv::Mat src;\r
-    int blockSize;\r
-    int apertureSize;        \r
     double k;\r
 \r
     cv::Mat dst_gold;\r
-    \r
+\r
     virtual void SetUp()\r
     {\r
         devInfo = GET_PARAM(0);\r
         type = GET_PARAM(1);\r
         borderType = GET_PARAM(2);\r
+        blockSize = GET_PARAM(3);\r
+        apertureSize = GET_PARAM(4); \r
 \r
         cv::gpu::setDevice(devInfo.deviceID());\r
-    \r
+\r
         cv::RNG& rng = TS::ptr()->get_rng();\r
-        \r
+\r
         cv::Mat img = readImage("stereobm/aloe-L.png", CV_LOAD_IMAGE_GRAYSCALE);\r
         ASSERT_FALSE(img.empty());\r
-        \r
+\r
         img.convertTo(src, type, type == CV_32F ? 1.0 / 255.0 : 1.0);\r
-        \r
-        blockSize = 1 + rng.next() % 5;\r
-        apertureSize = 1 + 2 * (rng.next() % 4);        \r
+\r
         k = rng.uniform(0.1, 0.9);\r
 \r
         cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType);\r
@@ -2612,7 +2612,7 @@ PARAM_TEST_CASE(CornerHarris, cv::gpu::DeviceInfo, MatType, Border)
 TEST_P(CornerHarris, Accuracy)\r
 {\r
     cv::Mat dst;\r
-    \r
+\r
     cv::gpu::GpuMat dev_dst;\r
 \r
     cv::gpu::cornerHarris(loadMat(src), dev_dst, blockSize, apertureSize, k, borderType);\r
@@ -2625,21 +2625,23 @@ TEST_P(CornerHarris, Accuracy)
 INSTANTIATE_TEST_CASE_P(ImgProc, CornerHarris, Combine(\r
                         ALL_DEVICES, \r
                         Values(CV_8UC1, CV_32FC1), \r
-                        Values((int) cv::BORDER_REFLECT101, (int) cv::BORDER_REPLICATE, (int) cv::BORDER_REFLECT)));\r
+                        Values((int) cv::BORDER_REFLECT101, (int) cv::BORDER_REPLICATE, (int) cv::BORDER_REFLECT),\r
+                        Values(3, 5, 7),\r
+                        Values(0, 3, 5, 7)));\r
 \r
 ///////////////////////////////////////////////////////////////////////////////////////////////////////\r
 // cornerMinEigen\r
 \r
-PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, Border)\r
+PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, Border, int, int)\r
 {\r
     cv::gpu::DeviceInfo devInfo;\r
     int type;\r
     int borderType;\r
-\r
-    cv::Mat src;\r
     int blockSize;\r
     int apertureSize;\r
 \r
+    cv::Mat src;\r
+\r
     cv::Mat dst_gold;\r
     \r
     virtual void SetUp()\r
@@ -2647,18 +2649,17 @@ PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, Border)
         devInfo = GET_PARAM(0);\r
         type = GET_PARAM(1);\r
         borderType = GET_PARAM(2);\r
+        blockSize = GET_PARAM(3);\r
+        apertureSize = GET_PARAM(4); \r
+\r
+        cv::gpu::setDevice(devInfo.deviceID());\r
 \r
-        cv::gpu::setDevice(devInfo.deviceID());        \r
-    \r
         cv::RNG& rng = TS::ptr()->get_rng();\r
-        \r
+\r
         cv::Mat img = readImage("stereobm/aloe-L.png", CV_LOAD_IMAGE_GRAYSCALE);\r
         ASSERT_FALSE(img.empty());\r
 \r
         img.convertTo(src, type, type == CV_32F ? 1.0 / 255.0 : 1.0);\r
-        \r
-        blockSize = 1 + rng.next() % 5;\r
-        apertureSize = 1 + 2 * (rng.next() % 4);\r
 \r
         cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType);\r
     }\r
@@ -2667,7 +2668,7 @@ PARAM_TEST_CASE(CornerMinEigen, cv::gpu::DeviceInfo, MatType, Border)
 TEST_P(CornerMinEigen, Accuracy)\r
 {\r
     cv::Mat dst;\r
-    \r
+\r
     cv::gpu::GpuMat dev_dst;\r
 \r
     cv::gpu::cornerMinEigenVal(loadMat(src), dev_dst, blockSize, apertureSize, borderType);\r
@@ -2680,7 +2681,9 @@ TEST_P(CornerMinEigen, Accuracy)
 INSTANTIATE_TEST_CASE_P(ImgProc, CornerMinEigen, Combine(\r
                         ALL_DEVICES, \r
                         Values(CV_8UC1, CV_32FC1), \r
-                        Values((int) cv::BORDER_REFLECT101, (int) cv::BORDER_REPLICATE, (int) cv::BORDER_REFLECT)));\r
+                        Values((int) cv::BORDER_REFLECT101, (int) cv::BORDER_REPLICATE, (int) cv::BORDER_REFLECT),\r
+                        Values(3, 5, 7),\r
+                        Values(0, 3, 5, 7)));\r
 \r
 ////////////////////////////////////////////////////////////////////////\r
 // ColumnSum\r
@@ -3641,12 +3644,54 @@ INSTANTIATE_TEST_CASE_P(ImgProc, Canny, testing::Combine(
 ////////////////////////////////////////////////////////\r
 // convolve\r
 \r
-PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, int)\r
+namespace\r
+{\r
+    void convolveDFT(const cv::Mat& A, const cv::Mat& B, cv::Mat& C, bool ccorr = false)\r
+    {\r
+        // reallocate the output array if needed\r
+        C.create(std::abs(A.rows - B.rows) + 1, std::abs(A.cols - B.cols) + 1, A.type());\r
+        Size dftSize;\r
+\r
+        // compute the size of DFT transform\r
+        dftSize.width = cv::getOptimalDFTSize(A.cols + B.cols - 1);\r
+        dftSize.height = cv::getOptimalDFTSize(A.rows + B.rows - 1);\r
+\r
+        // allocate temporary buffers and initialize them with 0\92s\r
+        cv::Mat tempA(dftSize, A.type(), cv::Scalar::all(0));\r
+        cv::Mat tempB(dftSize, B.type(), cv::Scalar::all(0));\r
+\r
+        // copy A and B to the top-left corners of tempA and tempB, respectively\r
+        cv::Mat roiA(tempA, cv::Rect(0, 0, A.cols, A.rows));\r
+        A.copyTo(roiA);\r
+        cv::Mat roiB(tempB, cv::Rect(0, 0, B.cols, B.rows));\r
+        B.copyTo(roiB);\r
+\r
+        // now transform the padded A & B in-place;\r
+        // use "nonzeroRows" hint for faster processing\r
+        cv::dft(tempA, tempA, 0, A.rows);\r
+        cv::dft(tempB, tempB, 0, B.rows);\r
+\r
+        // multiply the spectrums;\r
+        // the function handles packed spectrum representations well\r
+        cv::mulSpectrums(tempA, tempB, tempA, 0, ccorr);\r
+\r
+        // transform the product back from the frequency domain.\r
+        // Even though all the result rows will be non-zero,\r
+        // you need only the first C.rows of them, and thus you\r
+        // pass nonzeroRows == C.rows\r
+        cv::dft(tempA, tempA, cv::DFT_INVERSE + cv::DFT_SCALE, C.rows);\r
+\r
+        // now copy the result back to C.\r
+        tempA(cv::Rect(0, 0, C.cols, C.rows)).copyTo(C);\r
+    }\r
+}\r
+\r
+PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, int, bool)\r
 {    \r
     cv::gpu::DeviceInfo devInfo;\r
     int ksize;\r
+    bool ccorr;\r
     \r
-    cv::Size size;    \r
     cv::Mat src;\r
     cv::Mat kernel;\r
     \r
@@ -3656,36 +3701,38 @@ PARAM_TEST_CASE(Convolve, cv::gpu::DeviceInfo, int)
     {\r
         devInfo = GET_PARAM(0);\r
         ksize = GET_PARAM(1);\r
+        ccorr = GET_PARAM(2);\r
 \r
         cv::gpu::setDevice(devInfo.deviceID());\r
         \r
         cv::RNG& rng = TS::ptr()->get_rng();\r
 \r
-        size = cv::Size(rng.uniform(100, 200), rng.uniform(100, 200));\r
+        cv::Size size(rng.uniform(200, 400), rng.uniform(200, 400));\r
 \r
-        src = randomMat(rng, size, CV_32FC1, 0.0, 255.0, false);\r
+        src = randomMat(rng, size, CV_32FC1, 0.0, 100.0, false);\r
         kernel = randomMat(rng, cv::Size(ksize, ksize), CV_32FC1, 0.0, 1.0, false);\r
         \r
-        cv::filter2D(src, dst_gold, CV_32F, kernel, cv::Point(-1, -1), 0, cv::BORDER_REPLICATE);\r
+        convolveDFT(src, kernel, dst_gold, ccorr);\r
     }\r
 };\r
 \r
 TEST_P(Convolve, Accuracy)\r
-{    \r
+{\r
     cv::Mat dst;\r
 \r
     cv::gpu::GpuMat d_dst;\r
 \r
-    cv::gpu::convolve(loadMat(src), loadMat(kernel), d_dst);\r
+    cv::gpu::convolve(loadMat(src), loadMat(kernel), d_dst, ccorr);\r
 \r
     d_dst.download(dst);\r
 \r
-    EXPECT_MAT_NEAR(dst, dst_gold, 1e-2);\r
+    EXPECT_MAT_NEAR(dst, dst_gold, 1e-1);\r
 }\r
 \r
 \r
 INSTANTIATE_TEST_CASE_P(ImgProc, Convolve, Combine(\r
                         ALL_DEVICES, \r
-                        Values(3, 5, 7, 9, 11)));\r
+                        Values(3, 7, 11, 17, 19, 23, 45),\r
+                        Bool()));\r
 \r
 #endif // HAVE_CUDA\r