ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef -Wmissing-declarations -Wshadow -Wunused-parameter)
-ocv_define_module(gpuimgproc opencv_imgproc opencv_gpuarithm opencv_gpufilters OPTIONAL opencv_photo)
+ocv_define_module(gpuimgproc opencv_imgproc opencv_gpuarithm opencv_gpufilters)
.. seealso::
- :ocv:func:`bilateralFilter`,
-
-
-gpu::nonLocalMeans
--------------------
-Performs pure non local means denoising without any simplification, and thus it is not fast.
-
-.. ocv:function:: void gpu::nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null())
-
- :param src: Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
-
- :param dst: Destination image.
-
- :param h: Filter sigma regulating filter strength for color.
-
- :param search_window: Size of search window.
-
- :param block_size: Size of block used for computing weights.
-
- :param borderMode: Border type. See :ocv:func:`borderInterpolate` for details. ``BORDER_REFLECT101`` , ``BORDER_REPLICATE`` , ``BORDER_CONSTANT`` , ``BORDER_REFLECT`` and ``BORDER_WRAP`` are supported for now.
-
- :param stream: Stream for the asynchronous version.
-
-.. seealso::
-
- :ocv:func:`fastNlMeansDenoising`
-
-
-
-gpu::FastNonLocalMeansDenoising
--------------------------------
-.. ocv:class:: gpu::FastNonLocalMeansDenoising
-
- ::
-
- class FastNonLocalMeansDenoising
- {
- public:
- //! Simple method, recommended for grayscale images (though it supports multichannel images)
- void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
- //! Processes luminance and color components separatelly
- void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
- };
-
-The class implements fast approximate Non Local Means Denoising algorithm.
-
-
-
-gpu::FastNonLocalMeansDenoising::simpleMethod()
------------------------------------------------
-Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising with several computational optimizations. Noise expected to be a gaussian white noise
-
-.. ocv:function:: void gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
-
- :param src: Input 8-bit 1-channel, 2-channel or 3-channel image.
-
- :param dst: Output image with the same size and type as ``src`` .
-
- :param h: Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
-
- :param search_window: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels
-
- :param block_size: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
-
- :param stream: Stream for the asynchronous invocations.
-
-This function expected to be applied to grayscale images. For colored images look at ``FastNonLocalMeansDenoising::labMethod``.
-
-.. seealso::
-
- :ocv:func:`fastNlMeansDenoising`
-
-
-
-gpu::FastNonLocalMeansDenoising::labMethod()
---------------------------------------------
-Modification of ``FastNonLocalMeansDenoising::simpleMethod`` for color images
-
-.. ocv:function:: void gpu::FastNonLocalMeansDenoising::labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
-
- :param src: Input 8-bit 3-channel image.
-
- :param dst: Output image with the same size and type as ``src`` .
-
- :param h_luminance: Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
-
- :param float: The same as h but for color components. For most images value equals 10 will be enought to remove colored noise and do not distort colors
-
- :param search_window: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels
-
- :param block_size: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
-
- :param stream: Stream for the asynchronous invocations.
-
-The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``FastNonLocalMeansDenoising::simpleMethod`` function.
-
-.. seealso::
-
- :ocv:func:`fastNlMeansDenoisingColored`
+ :ocv:func:`bilateralFilter`
CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial,
int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());
-//! Brute force non-local means algorith (slow but universal)
-CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());
-
-//! Fast (but approximate)version of non-local means algorith similar to CPU function (running sums technique)
-class CV_EXPORTS FastNonLocalMeansDenoising
-{
-public:
- //! Simple method, recommended for grayscale images (though it supports multichannel images)
- void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
-
- //! Processes luminance and color components separatelly
- void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
-
-private:
-
- GpuMat buffer, extended_src_buffer;
- GpuMat lab, l, ab;
-};
-
struct CV_EXPORTS CannyBuf
{
void create(const Size& image_size, int apperture_size = 3);
CPU_SANITY_CHECK(positions);
}
}
+
+//////////////////////////////////////////////////////////////////////
+// BilateralFilter
+
+DEF_PARAM_TEST(Sz_Depth_Cn_KernelSz, cv::Size, MatDepth, MatCn, int);
+
+PERF_TEST_P(Sz_Depth_Cn_KernelSz, BilateralFilter,
+ Combine(GPU_TYPICAL_MAT_SIZES,
+ Values(CV_8U, CV_32F),
+ GPU_CHANNELS_1_3,
+ Values(3, 5, 9)))
+{
+ declare.time(60.0);
+
+ const cv::Size size = GET_PARAM(0);
+ const int depth = GET_PARAM(1);
+ const int channels = GET_PARAM(2);
+ const int kernel_size = GET_PARAM(3);
+
+ const float sigma_color = 7;
+ const float sigma_spatial = 5;
+ const int borderMode = cv::BORDER_REFLECT101;
+
+ const int type = CV_MAKE_TYPE(depth, channels);
+
+ cv::Mat src(size, type);
+ declare.in(src, WARMUP_RNG);
+
+ if (PERF_RUN_GPU())
+ {
+ const cv::gpu::GpuMat d_src(src);
+ cv::gpu::GpuMat dst;
+
+ TEST_CYCLE() cv::gpu::bilateralFilter(d_src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
+
+ GPU_SANITY_CHECK(dst);
+ }
+ else
+ {
+ cv::Mat dst;
+
+ TEST_CYCLE() cv::bilateralFilter(src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
+
+ CPU_SANITY_CHECK(dst);
+ }
+}
#include "opencv2/gpuimgproc.hpp"
#include "opencv2/imgproc.hpp"
-#include "opencv2/photo.hpp"
-
#ifdef GTEST_CREATE_SHARED_LIBRARY
#error no modules except ts should have GTEST_CREATE_SHARED_LIBRARY defined
#endif
--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of the copyright holders may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "precomp.hpp"
+
+using namespace cv;
+using namespace cv::gpu;
+
+#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
+
+void cv::gpu::bilateralFilter(const GpuMat&, GpuMat&, int, float, float, int, Stream&) { throw_no_cuda(); }
+
+#else
+
+namespace cv { namespace gpu { namespace cudev
+{
+ namespace imgproc
+ {
+ template<typename T>
+ void bilateral_filter_gpu(const PtrStepSzb& src, PtrStepSzb dst, int kernel_size, float sigma_spatial, float sigma_color, int borderMode, cudaStream_t stream);
+ }
+}}}
+
+void cv::gpu::bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode, Stream& s)
+{
+ using cv::gpu::cudev::imgproc::bilateral_filter_gpu;
+
+ typedef void (*func_t)(const PtrStepSzb& src, PtrStepSzb dst, int kernel_size, float sigma_spatial, float sigma_color, int borderMode, cudaStream_t s);
+
+ static const func_t funcs[6][4] =
+ {
+ {bilateral_filter_gpu<uchar> , 0 /*bilateral_filter_gpu<uchar2>*/ , bilateral_filter_gpu<uchar3> , bilateral_filter_gpu<uchar4> },
+ {0 /*bilateral_filter_gpu<schar>*/, 0 /*bilateral_filter_gpu<schar2>*/ , 0 /*bilateral_filter_gpu<schar3>*/, 0 /*bilateral_filter_gpu<schar4>*/},
+ {bilateral_filter_gpu<ushort> , 0 /*bilateral_filter_gpu<ushort2>*/, bilateral_filter_gpu<ushort3> , bilateral_filter_gpu<ushort4> },
+ {bilateral_filter_gpu<short> , 0 /*bilateral_filter_gpu<short2>*/ , bilateral_filter_gpu<short3> , bilateral_filter_gpu<short4> },
+ {0 /*bilateral_filter_gpu<int>*/ , 0 /*bilateral_filter_gpu<int2>*/ , 0 /*bilateral_filter_gpu<int3>*/ , 0 /*bilateral_filter_gpu<int4>*/ },
+ {bilateral_filter_gpu<float> , 0 /*bilateral_filter_gpu<float2>*/ , bilateral_filter_gpu<float3> , bilateral_filter_gpu<float4> }
+ };
+
+ sigma_color = (sigma_color <= 0 ) ? 1 : sigma_color;
+ sigma_spatial = (sigma_spatial <= 0 ) ? 1 : sigma_spatial;
+
+
+ int radius = (kernel_size <= 0) ? cvRound(sigma_spatial*1.5) : kernel_size/2;
+ kernel_size = std::max(radius, 1)*2 + 1;
+
+ CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
+ const func_t func = funcs[src.depth()][src.channels() - 1];
+ CV_Assert(func != 0);
+
+ CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);
+
+ int gpuBorderType;
+ CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
+
+ dst.create(src.size(), src.type());
+ func(src, dst, kernel_size, sigma_spatial, sigma_color, gpuBorderType, StreamAccessor::getStream(s));
+}
+
+#endif
EXPECT_MAT_NEAR(dst_gold, dst, src.depth() == CV_32F ? 1e-3 : 1.0);
}
-INSTANTIATE_TEST_CASE_P(GPU_Denoising, BilateralFilter, testing::Combine(
+INSTANTIATE_TEST_CASE_P(GPU_ImgProc, BilateralFilter, testing::Combine(
ALL_DEVICES,
testing::Values(cv::Size(128, 128), cv::Size(113, 113), cv::Size(639, 481)),
testing::Values(MatType(CV_8UC1), MatType(CV_8UC3), MatType(CV_32FC1), MatType(CV_32FC3))
set(the_description "Computational Photography")
-ocv_define_module(photo opencv_imgproc)
+ocv_define_module(photo opencv_imgproc OPTIONAL opencv_gpuimgproc)
The function converts images to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``fastNlMeansDenoisingMulti`` function.
+
+
+gpu::nonLocalMeans
+-------------------
+Performs pure non local means denoising without any simplification, and thus it is not fast.
+
+.. ocv:function:: void gpu::nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null())
+
+ :param src: Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
+
+ :param dst: Destination image.
+
+ :param h: Filter sigma regulating filter strength for color.
+
+ :param search_window: Size of search window.
+
+ :param block_size: Size of block used for computing weights.
+
+ :param borderMode: Border type. See :ocv:func:`borderInterpolate` for details. ``BORDER_REFLECT101`` , ``BORDER_REPLICATE`` , ``BORDER_CONSTANT`` , ``BORDER_REFLECT`` and ``BORDER_WRAP`` are supported for now.
+
+ :param stream: Stream for the asynchronous version.
+
+.. seealso::
+
+ :ocv:func:`fastNlMeansDenoising`
+
+
+
+gpu::FastNonLocalMeansDenoising
+-------------------------------
+.. ocv:class:: gpu::FastNonLocalMeansDenoising
+
+ ::
+
+ class FastNonLocalMeansDenoising
+ {
+ public:
+ //! Simple method, recommended for grayscale images (though it supports multichannel images)
+ void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
+ //! Processes luminance and color components separatelly
+ void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
+ };
+
+The class implements fast approximate Non Local Means Denoising algorithm.
+
+
+
+gpu::FastNonLocalMeansDenoising::simpleMethod()
+-----------------------------------------------
+Perform image denoising using Non-local Means Denoising algorithm http://www.ipol.im/pub/algo/bcm_non_local_means_denoising with several computational optimizations. Noise expected to be a gaussian white noise
+
+.. ocv:function:: void gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
+
+ :param src: Input 8-bit 1-channel, 2-channel or 3-channel image.
+
+ :param dst: Output image with the same size and type as ``src`` .
+
+ :param h: Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
+
+ :param search_window: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels
+
+ :param block_size: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
+
+ :param stream: Stream for the asynchronous invocations.
+
+This function expected to be applied to grayscale images. For colored images look at ``FastNonLocalMeansDenoising::labMethod``.
+
+.. seealso::
+
+ :ocv:func:`fastNlMeansDenoising`
+
+
+
+gpu::FastNonLocalMeansDenoising::labMethod()
+--------------------------------------------
+Modification of ``FastNonLocalMeansDenoising::simpleMethod`` for color images
+
+.. ocv:function:: void gpu::FastNonLocalMeansDenoising::labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null())
+
+ :param src: Input 8-bit 3-channel image.
+
+ :param dst: Output image with the same size and type as ``src`` .
+
+ :param h_luminance: Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details but also preserves some noise
+
+ :param float: The same as h but for color components. For most images value equals 10 will be enought to remove colored noise and do not distort colors
+
+ :param search_window: Size in pixels of the window that is used to compute weighted average for given pixel. Should be odd. Affect performance linearly: greater search_window - greater denoising time. Recommended value 21 pixels
+
+ :param block_size: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
+
+ :param stream: Stream for the asynchronous invocations.
+
+The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``FastNonLocalMeansDenoising::simpleMethod`` function.
+
+.. seealso::
+
+ :ocv:func:`fastNlMeansDenoisingColored`
+
--- /dev/null
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+// By downloading, copying, installing or using the software you agree to this license.
+// If you do not agree to this license, do not download, install,
+// copy or use the software.
+//
+//
+// License Agreement
+// For Open Source Computer Vision Library
+//
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+// * Redistribution's of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistribution's in binary form must reproduce the above copyright notice,
+// this list of conditions and the following disclaimer in the documentation
+// and/or other materials provided with the distribution.
+//
+// * The name of the copyright holders may not be used to endorse or promote products
+// derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#ifndef __OPENCV_PHOTO_GPU_HPP__
+#define __OPENCV_PHOTO_GPU_HPP__
+
+#include "opencv2/core/gpumat.hpp"
+
+namespace cv { namespace gpu {
+
+//! Brute force non-local means algorith (slow but universal)
+CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());
+
+//! Fast (but approximate)version of non-local means algorith similar to CPU function (running sums technique)
+class CV_EXPORTS FastNonLocalMeansDenoising
+{
+public:
+ //! Simple method, recommended for grayscale images (though it supports multichannel images)
+ void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
+
+ //! Processes luminance and color components separatelly
+ void labMethod(const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
+
+private:
+
+ GpuMat buffer, extended_src_buffer;
+ GpuMat lab, l, ab;
+};
+
+}} // namespace cv { namespace gpu {
+
+#endif /* __OPENCV_PHOTO_GPU_HPP__ */
#include "perf_precomp.hpp"
+#include "opencv2/photo/gpu.hpp"
+#include "opencv2/ts/gpu_perf.hpp"
+
using namespace std;
using namespace testing;
using namespace perf;
#define GPU_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::sz720p)
//////////////////////////////////////////////////////////////////////
-// BilateralFilter
-
-DEF_PARAM_TEST(Sz_Depth_Cn_KernelSz, cv::Size, MatDepth, MatCn, int);
-
-PERF_TEST_P(Sz_Depth_Cn_KernelSz, Denoising_BilateralFilter,
- Combine(GPU_DENOISING_IMAGE_SIZES,
- Values(CV_8U, CV_32F),
- GPU_CHANNELS_1_3,
- Values(3, 5, 9)))
-{
- declare.time(60.0);
-
- const cv::Size size = GET_PARAM(0);
- const int depth = GET_PARAM(1);
- const int channels = GET_PARAM(2);
- const int kernel_size = GET_PARAM(3);
-
- const float sigma_color = 7;
- const float sigma_spatial = 5;
- const int borderMode = cv::BORDER_REFLECT101;
-
- const int type = CV_MAKE_TYPE(depth, channels);
-
- cv::Mat src(size, type);
- declare.in(src, WARMUP_RNG);
-
- if (PERF_RUN_GPU())
- {
- const cv::gpu::GpuMat d_src(src);
- cv::gpu::GpuMat dst;
-
- TEST_CYCLE() cv::gpu::bilateralFilter(d_src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
-
- GPU_SANITY_CHECK(dst);
- }
- else
- {
- cv::Mat dst;
-
- TEST_CYCLE() cv::bilateralFilter(src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
-
- CPU_SANITY_CHECK(dst);
- }
-}
-
-//////////////////////////////////////////////////////////////////////
// nonLocalMeans
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int);
#include "precomp.hpp"
+#include "opencv2/photo/gpu.hpp"
+#include "opencv2/core/gpu_private.hpp"
+#include "opencv2/gpuarithm.hpp"
+#include "opencv2/gpuimgproc.hpp"
+
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
-void cv::gpu::bilateralFilter(const GpuMat&, GpuMat&, int, float, float, int, Stream&) { throw_no_cuda(); }
void cv::gpu::nonLocalMeans(const GpuMat&, GpuMat&, float, int, int, int, Stream&) { throw_no_cuda(); }
-
void cv::gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat&, GpuMat&, float, int, int, Stream&) { throw_no_cuda(); }
void cv::gpu::FastNonLocalMeansDenoising::labMethod( const GpuMat&, GpuMat&, float, float, int, int, Stream&) { throw_no_cuda(); }
-
#else
//////////////////////////////////////////////////////////////////////////////////
namespace imgproc
{
template<typename T>
- void bilateral_filter_gpu(const PtrStepSzb& src, PtrStepSzb dst, int kernel_size, float sigma_spatial, float sigma_color, int borderMode, cudaStream_t stream);
-
- template<typename T>
void nlm_bruteforce_gpu(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream);
}
}}}
-void cv::gpu::bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode, Stream& s)
-{
- using cv::gpu::cudev::imgproc::bilateral_filter_gpu;
-
- typedef void (*func_t)(const PtrStepSzb& src, PtrStepSzb dst, int kernel_size, float sigma_spatial, float sigma_color, int borderMode, cudaStream_t s);
-
- static const func_t funcs[6][4] =
- {
- {bilateral_filter_gpu<uchar> , 0 /*bilateral_filter_gpu<uchar2>*/ , bilateral_filter_gpu<uchar3> , bilateral_filter_gpu<uchar4> },
- {0 /*bilateral_filter_gpu<schar>*/, 0 /*bilateral_filter_gpu<schar2>*/ , 0 /*bilateral_filter_gpu<schar3>*/, 0 /*bilateral_filter_gpu<schar4>*/},
- {bilateral_filter_gpu<ushort> , 0 /*bilateral_filter_gpu<ushort2>*/, bilateral_filter_gpu<ushort3> , bilateral_filter_gpu<ushort4> },
- {bilateral_filter_gpu<short> , 0 /*bilateral_filter_gpu<short2>*/ , bilateral_filter_gpu<short3> , bilateral_filter_gpu<short4> },
- {0 /*bilateral_filter_gpu<int>*/ , 0 /*bilateral_filter_gpu<int2>*/ , 0 /*bilateral_filter_gpu<int3>*/ , 0 /*bilateral_filter_gpu<int4>*/ },
- {bilateral_filter_gpu<float> , 0 /*bilateral_filter_gpu<float2>*/ , bilateral_filter_gpu<float3> , bilateral_filter_gpu<float4> }
- };
-
- sigma_color = (sigma_color <= 0 ) ? 1 : sigma_color;
- sigma_spatial = (sigma_spatial <= 0 ) ? 1 : sigma_spatial;
-
-
- int radius = (kernel_size <= 0) ? cvRound(sigma_spatial*1.5) : kernel_size/2;
- kernel_size = std::max(radius, 1)*2 + 1;
-
- CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
- const func_t func = funcs[src.depth()][src.channels() - 1];
- CV_Assert(func != 0);
-
- CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);
-
- int gpuBorderType;
- CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
-
- dst.create(src.size(), src.type());
- func(src, dst, kernel_size, sigma_spatial, sigma_color, gpuBorderType, StreamAccessor::getStream(s));
-}
-
void cv::gpu::nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_window, int block_window, int borderMode, Stream& s)
{
using cv::gpu::cudev::imgproc::nlm_bruteforce_gpu;
func(src, dst, search_window/2, block_window/2, h, gpuBorderType, StreamAccessor::getStream(s));
}
-
-//////////////////////////////////////////////////////////////////////////////////
-//// Non Local Means Denosing (fast approxinate)
-
-
namespace cv { namespace gpu { namespace cudev
{
namespace imgproc
#include "test_precomp.hpp"
+#include "opencv2/photo/gpu.hpp"
+#include "opencv2/ts/gpu_test.hpp"
+
#ifdef HAVE_CUDA
using namespace cvtest;
-
////////////////////////////////////////////////////////
// Brute Force Non local means
-struct BruteForceNonLocalMeans: testing::TestWithParam<cv::gpu::DeviceInfo>
-{
- cv::gpu::DeviceInfo devInfo;
-
- virtual void SetUp()
- {
- devInfo = GetParam();
- cv::gpu::setDevice(devInfo.deviceID());
- }
-};
-
-GPU_TEST_P(BruteForceNonLocalMeans, Regression)
+TEST(BruteForceNonLocalMeans, Regression)
{
using cv::gpu::GpuMat;
EXPECT_MAT_NEAR(gray_gold, dgray, 1e-4);
}
-INSTANTIATE_TEST_CASE_P(GPU_Denoising, BruteForceNonLocalMeans, ALL_DEVICES);
-
////////////////////////////////////////////////////////
// Fast Force Non local means
-struct FastNonLocalMeans: testing::TestWithParam<cv::gpu::DeviceInfo>
-{
- cv::gpu::DeviceInfo devInfo;
-
- virtual void SetUp()
- {
- devInfo = GetParam();
- cv::gpu::setDevice(devInfo.deviceID());
- }
-};
-
-GPU_TEST_P(FastNonLocalMeans, Regression)
+TEST(FastNonLocalMeans, Regression)
{
using cv::gpu::GpuMat;
EXPECT_MAT_NEAR(gray_gold, dgray, 1);
}
-INSTANTIATE_TEST_CASE_P(GPU_Denoising, FastNonLocalMeans, ALL_DEVICES);
-
#endif // HAVE_CUDA
projects / profile / ivi / opencv.git / commitdiff