fast nlm (class version)
This commit is contained in:
Anatoly Baksheev
parent
4b5bbb7752
commit
9a4265a8d0
@ -849,15 +849,15 @@ gpu::nonLocalMeans
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-------------------
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Performs pure non local means denoising without any simplification, and thus it is not fast.
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.. ocv:function:: void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_widow_size = 11, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null())
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.. ocv:function:: 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())
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:param src: Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
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:param dst: Destination imagwe.
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:param dst: Destination image.
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:param h: Filter sigma regulating filter strength for color.
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:param search_widow_size: Size of search window.
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:param search_window: Size of search window.
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:param block_size: Size of block used for computing weights.
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@ -868,7 +868,73 @@ Performs pure non local means denoising without any simplification, and thus it
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.. seealso::
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:ocv:func:`fastNlMeansDenoising`
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gpu::FastNonLocalMeansDenoising
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-------------------------------
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.. ocv:class:: gpu::FastNonLocalMeansDenoising
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class FastNonLocalMeansDenoising
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{
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public:
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//! Simple method, recommended for grayscale images (though it supports multichannel images)
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void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
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//! Processes luminance and color components separatelly
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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());
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};
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The class implements fast approximate Non Local Means Denoising algorithm.
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gpu::FastNonLocalMeansDenoising::simpleMethod()
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-------------------------------------
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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
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.. 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());
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:param src: Input 8-bit 1-channel, 2-channel or 3-channel image.
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:param dst: Output image with the same size and type as ``src`` .
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: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
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: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
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:param block_size: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
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:param stream: Stream for the asynchronous invocations.
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This function expected to be applied to grayscale images. For colored images look at ``FastNonLocalMeansDenoising::labMethod``.
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.. seealso::
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:ocv:func:`fastNlMeansDenoising`
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gpu::FastNonLocalMeansDenoising::labMethod()
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-------------------------------------
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Modification of ``FastNonLocalMeansDenoising::simpleMethod`` for color images
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.. 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());
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:param src: Input 8-bit 3-channel image.
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:param dst: Output image with the same size and type as ``src`` .
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: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
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: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
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: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
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:param block_size: Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels
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:param stream: Stream for the asynchronous invocations.
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The function converts image to CIELAB colorspace and then separately denoise L and AB components with given h parameters using ``FastNonLocalMeansDenoising::simpleMethod`` function.
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.. seealso::
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:ocv:func:`fastNlMeansDenoisingColored`
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gpu::alphaComp
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-------------------
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Composites two images using alpha opacity values contained in each image.
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@ -774,11 +774,24 @@ CV_EXPORTS void bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size,
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int borderMode = BORDER_DEFAULT, Stream& stream = Stream::Null());
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//! Brute force non-local means algorith (slow but universal)
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CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h,
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int search_widow_size = 11, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());
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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());
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//! Fast (but approximate)version of non-local means algorith similar to CPU function (running sums technique)
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CV_EXPORTS void fastNlMeansDenoising( const GpuMat& src, GpuMat& dst, float h, int search_radius = 10, int block_radius = 3, Stream& s = Stream::Null());
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class CV_EXPORTS FastNonLocalMeansDenoising
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{
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public:
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//! Simple method, recommended for grayscale images (though it supports multichannel images)
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void simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window = 21, int block_size = 7, Stream& s = Stream::Null());
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//! Processes luminance and color components separatelly
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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());
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private:
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GpuMat buffer, extended_src_buffer;
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GpuMat lab, l, ab;
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};
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struct CV_EXPORTS CannyBuf;
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@ -3,16 +3,18 @@
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using namespace std;
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using namespace testing;
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#define GPU_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::szXGA, perf::sz720p, perf::sz1080p)
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//////////////////////////////////////////////////////////////////////
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// BilateralFilter
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DEF_PARAM_TEST(Sz_Depth_Cn_KernelSz, cv::Size, MatDepth , int, int);
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DEF_PARAM_TEST(Sz_Depth_Cn_KernelSz, cv::Size, MatDepth, int, int);
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PERF_TEST_P(Sz_Depth_Cn_KernelSz, Denoising_BilateralFilter,
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Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F), GPU_CHANNELS_1_3_4, Values(3, 5, 9)))
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Combine(GPU_DENOISING_IMAGE_SIZES, Values(CV_8U, CV_32F), testing::Values(1, 3), Values(3, 5, 9)))
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{
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declare.time(30.0);
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declare.time(60.0);
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cv::Size size = GET_PARAM(0);
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int depth = GET_PARAM(1);
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@ -57,16 +59,16 @@ PERF_TEST_P(Sz_Depth_Cn_KernelSz, Denoising_BilateralFilter,
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//////////////////////////////////////////////////////////////////////
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// nonLocalMeans
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DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth , int, int, int);
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DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, int, int, int);
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PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_NonLocalMeans,
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Combine(GPU_TYPICAL_MAT_SIZES, Values<MatDepth>(CV_8U), Values(1), Values(21), Values(5, 7)))
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Combine(GPU_DENOISING_IMAGE_SIZES, Values<MatDepth>(CV_8U), Values(1, 3), Values(21), Values(5, 7)))
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{
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declare.time(30.0);
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declare.time(60.0);
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cv::Size size = GET_PARAM(0);
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int depth = GET_PARAM(1);
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int channels = GET_PARAM(2);
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int depth = GET_PARAM(1);
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int channels = GET_PARAM(2);
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int search_widow_size = GET_PARAM(3);
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int block_size = GET_PARAM(4);
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@ -101,22 +103,21 @@ PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_NonLocalMeans,
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//////////////////////////////////////////////////////////////////////
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// fastNonLocalMeans
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DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth , int, int, int);
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DEF_PARAM_TEST(Sz_Depth_WinSz_BlockSz, cv::Size, MatDepth, int, int);
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PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_FastNonLocalMeans,
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Combine(GPU_TYPICAL_MAT_SIZES, Values<MatDepth>(CV_8U), Values(1), Values(21), Values(5, 7)))
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PERF_TEST_P(Sz_Depth_WinSz_BlockSz, Denoising_FastNonLocalMeans,
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Combine(GPU_DENOISING_IMAGE_SIZES, Values<MatDepth>(CV_8U), Values(21), Values(7)))
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{
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declare.time(30.0);
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cv::Size size = GET_PARAM(0);
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int depth = GET_PARAM(1);
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int channels = GET_PARAM(2);
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declare.time(150.0);
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int search_widow_size = GET_PARAM(3);
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int block_size = GET_PARAM(4);
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float h = 10;
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int type = CV_MAKE_TYPE(depth, channels);
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cv::Size size = GET_PARAM(0);
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int depth = GET_PARAM(1);
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int search_widow_size = GET_PARAM(2);
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int block_size = GET_PARAM(3);
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float h = 10;
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int type = CV_MAKE_TYPE(depth, 1);
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cv::Mat src(size, type);
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fillRandom(src);
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@ -124,12 +125,14 @@ PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_FastNonLocalMeans,
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if (runOnGpu)
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{
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cv::gpu::GpuMat d_src(src);
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cv::gpu::GpuMat d_dst;
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cv::gpu::fastNlMeansDenoising(d_src, d_dst, h, search_widow_size/2, block_size/2);
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cv::gpu::GpuMat d_dst;
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cv::gpu::FastNonLocalMeansDenoising fnlmd;
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fnlmd.simpleMethod(d_src, d_dst, h, search_widow_size, block_size);
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TEST_CYCLE()
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{
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cv::gpu::fastNlMeansDenoising(d_src, d_dst, h, search_widow_size/2, block_size/2);
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fnlmd.simpleMethod(d_src, d_dst, h, search_widow_size, block_size);
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}
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}
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else
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@ -142,4 +145,50 @@ PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_FastNonLocalMeans,
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cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// fastNonLocalMeans (colored)
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PERF_TEST_P(Sz_Depth_WinSz_BlockSz, Denoising_FastNonLocalMeansColored,
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Combine(GPU_DENOISING_IMAGE_SIZES, Values<MatDepth>(CV_8U), Values(21), Values(7)))
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{
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declare.time(350.0);
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cv::Size size = GET_PARAM(0);
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int depth = GET_PARAM(1);
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int search_widow_size = GET_PARAM(2);
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int block_size = GET_PARAM(3);
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float h = 10;
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int type = CV_MAKE_TYPE(depth, 3);
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cv::Mat src(size, type);
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fillRandom(src);
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if (runOnGpu)
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{
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cv::gpu::GpuMat d_src(src);
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cv::gpu::GpuMat d_dst;
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cv::gpu::FastNonLocalMeansDenoising fnlmd;
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fnlmd.labMethod(d_src, d_dst, h, h, search_widow_size, block_size);
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TEST_CYCLE()
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{
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fnlmd.labMethod(d_src, d_dst, h, h, search_widow_size, block_size);
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}
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}
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else
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{
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cv::Mat dst;
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cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
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TEST_CYCLE()
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{
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cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
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}
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}
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}
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@ -97,7 +97,7 @@ namespace cv { namespace gpu { namespace device
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}
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template void copyMakeBorder_gpu<uchar, 1>(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderMode, const uchar* borderValue, cudaStream_t stream);
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//template void copyMakeBorder_gpu<uchar, 2>(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderMode, const uchar* borderValue, cudaStream_t stream);
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template void copyMakeBorder_gpu<uchar, 2>(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderMode, const uchar* borderValue, cudaStream_t stream);
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template void copyMakeBorder_gpu<uchar, 3>(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderMode, const uchar* borderValue, cudaStream_t stream);
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template void copyMakeBorder_gpu<uchar, 4>(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderMode, const uchar* borderValue, cudaStream_t stream);
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@ -68,68 +68,70 @@ namespace cv { namespace gpu { namespace device
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__device__ __forceinline__ float norm2(const float4& v) { return v.x*v.x + v.y*v.y + v.z*v.z + v.w*v.w; }
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template<typename T, typename B>
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__global__ void nlm_kernel(const PtrStepSz<T> src, PtrStep<T> dst, const B b, int search_radius, int block_radius, float h2_inv_half)
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__global__ void nlm_kernel(const PtrStep<T> src, PtrStepSz<T> dst, const B b, int search_radius, int block_radius, float noise_mult)
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{
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typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type value_type;
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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 >= src.cols || y >= src.rows)
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const int i = blockDim.y * blockIdx.y + threadIdx.y;
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const int j = blockDim.x * blockIdx.x + threadIdx.x;
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if (j >= dst.cols || i >= dst.rows)
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return;
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float block_radius2_inv = -1.f/(block_radius * block_radius);
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int bsize = search_radius + block_radius;
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int search_window = 2 * search_radius + 1;
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float minus_search_window2_inv = -1.f/(search_window * search_window);
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value_type sum1 = VecTraits<value_type>::all(0);
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float sum2 = 0.f;
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if (x - search_radius - block_radius >=0 && y - search_radius - block_radius >=0 &&
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x + search_radius + block_radius < src.cols && y + search_radius + block_radius < src.rows)
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if (j - bsize >= 0 && j + bsize < dst.cols && i - bsize >= 0 && i + bsize < dst.rows)
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{
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for(float cy = -search_radius; cy <= search_radius; ++cy)
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for(float cx = -search_radius; cx <= search_radius; ++cx)
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{
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float color2 = 0;
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for(float by = -block_radius; by <= block_radius; ++by)
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for(float bx = -block_radius; bx <= block_radius; ++bx)
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for(float y = -search_radius; y <= search_radius; ++y)
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for(float x = -search_radius; x <= search_radius; ++x)
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{
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float dist2 = 0;
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for(float ty = -block_radius; ty <= block_radius; ++ty)
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for(float tx = -block_radius; tx <= block_radius; ++tx)
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{
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value_type v1 = saturate_cast<value_type>(src(y + by, x + bx));
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value_type v2 = saturate_cast<value_type>(src(y + cy + by, x + cx + bx));
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color2 += norm2(v1 - v2);
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value_type bv = saturate_cast<value_type>(src(i + y + ty, j + x + tx));
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value_type av = saturate_cast<value_type>(src(i + ty, j + tx));
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dist2 += norm2(av - bv);
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}
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float dist2 = cx * cx + cy * cy;
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float w = __expf(color2 * h2_inv_half + dist2 * block_radius2_inv);
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float w = __expf(dist2 * noise_mult + (x * x + y * y) * minus_search_window2_inv);
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sum1 = sum1 + saturate_cast<value_type>(src(y + cy, x + cy)) * w;
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/*if (i == 255 && j == 255)
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printf("%f %f\n", w, dist2 * minus_h2_inv + (x * x + y * y) * minus_search_window2_inv);*/
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sum1 = sum1 + w * saturate_cast<value_type>(src(i + y, j + x));
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sum2 += w;
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}
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}
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else
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{
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for(float cy = -search_radius; cy <= search_radius; ++cy)
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for(float cx = -search_radius; cx <= search_radius; ++cx)
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for(float y = -search_radius; y <= search_radius; ++y)
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for(float x = -search_radius; x <= search_radius; ++x)
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{
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float color2 = 0;
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for(float by = -block_radius; by <= block_radius; ++by)
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for(float bx = -block_radius; bx <= block_radius; ++bx)
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float dist2 = 0;
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for(float ty = -block_radius; ty <= block_radius; ++ty)
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for(float tx = -block_radius; tx <= block_radius; ++tx)
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{
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value_type v1 = saturate_cast<value_type>(b.at(y + by, x + bx, src.data, src.step));
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value_type v2 = saturate_cast<value_type>(b.at(y + cy + by, x + cx + bx, src.data, src.step));
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color2 += norm2(v1 - v2);
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value_type bv = saturate_cast<value_type>(b.at(i + y + ty, j + x + tx, src));
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value_type av = saturate_cast<value_type>(b.at(i + ty, j + tx, src));
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dist2 += norm2(av - bv);
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}
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float w = __expf(dist2 * noise_mult + (x * x + y * y) * minus_search_window2_inv);
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float dist2 = cx * cx + cy * cy;
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float w = __expf(color2 * h2_inv_half + dist2 * block_radius2_inv);
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|
||||
sum1 = sum1 + saturate_cast<value_type>(b.at(y + cy, x + cy, src.data, src.step)) * w;
|
||||
sum1 = sum1 + w * saturate_cast<value_type>(b.at(i + y, j + x, src));
|
||||
sum2 += w;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
dst(y, x) = saturate_cast<T>(sum1 / sum2);
|
||||
dst(i, j) = saturate_cast<T>(sum1 / sum2);
|
||||
|
||||
}
|
||||
|
||||
@ -141,10 +143,12 @@ namespace cv { namespace gpu { namespace device
|
||||
|
||||
B<T> b(src.rows, src.cols);
|
||||
|
||||
float h2_inv_half = -0.5f/(h * h * VecTraits<T>::cn);
|
||||
|
||||
int block_window = 2 * block_radius + 1;
|
||||
float minus_h2_inv = -1.f/(h * h * VecTraits<T>::cn);
|
||||
float noise_mult = minus_h2_inv/(block_window * block_window);
|
||||
|
||||
cudaSafeCall( cudaFuncSetCacheConfig (nlm_kernel<T, B<T> >, cudaFuncCachePreferL1) );
|
||||
nlm_kernel<<<grid, block>>>((PtrStepSz<T>)src, (PtrStepSz<T>)dst, b, search_radius, block_radius, h2_inv_half);
|
||||
nlm_kernel<<<grid, block>>>((PtrStepSz<T>)src, (PtrStepSz<T>)dst, b, search_radius, block_radius, noise_mult);
|
||||
cudaSafeCall ( cudaGetLastError () );
|
||||
|
||||
if (stream == 0)
|
||||
@ -184,18 +188,13 @@ namespace cv { namespace gpu { namespace device
|
||||
__device__ __forceinline__ int calcDist(const uchar2& a, const uchar2& b) { return (a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y); }
|
||||
__device__ __forceinline__ int calcDist(const uchar3& a, const uchar3& b) { return (a.x-b.x)*(a.x-b.x) + (a.y-b.y)*(a.y-b.y) + (a.z-b.z)*(a.z-b.z); }
|
||||
|
||||
|
||||
|
||||
template <class T> struct FastNonLocalMenas
|
||||
{
|
||||
enum
|
||||
{
|
||||
CTA_SIZE = 256,
|
||||
|
||||
//TILE_COLS = 256,
|
||||
//TILE_ROWS = 32,
|
||||
|
||||
TILE_COLS = 256,
|
||||
CTA_SIZE = 128,
|
||||
|
||||
TILE_COLS = 128,
|
||||
TILE_ROWS = 32,
|
||||
|
||||
STRIDE = CTA_SIZE
|
||||
@ -203,7 +202,7 @@ namespace cv { namespace gpu { namespace device
|
||||
|
||||
struct plus
|
||||
{
|
||||
__device__ __forceinline float operator()(float v1, float v2) const { return v1 + v2; }
|
||||
__device__ __forceinline__ float operator()(float v1, float v2) const { return v1 + v2; }
|
||||
};
|
||||
|
||||
int search_radius;
|
||||
@ -219,14 +218,14 @@ namespace cv { namespace gpu { namespace device
|
||||
PtrStep<T> src;
|
||||
mutable PtrStepi buffer;
|
||||
|
||||
__device__ __forceinline__ void initSums_TileFistColumn(int i, int j, int* dist_sums, PtrStepi& col_dist_sums, PtrStepi& up_col_dist_sums) const
|
||||
__device__ __forceinline__ void initSums_BruteForce(int i, int j, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const
|
||||
{
|
||||
for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
|
||||
{
|
||||
dist_sums[index] = 0;
|
||||
|
||||
for(int tx = 0; tx < block_window; ++tx)
|
||||
col_dist_sums(tx, index) = 0;
|
||||
col_sums(tx, index) = 0;
|
||||
|
||||
int y = index / search_window;
|
||||
int x = index - y * search_window;
|
||||
@ -240,17 +239,15 @@ namespace cv { namespace gpu { namespace device
|
||||
#if 1
|
||||
for (int tx = -block_radius; tx <= block_radius; ++tx)
|
||||
{
|
||||
int col_dist_sums_tx_block_radius_index = 0;
|
||||
|
||||
int col_sum = 0;
|
||||
for (int ty = -block_radius; ty <= block_radius; ++ty)
|
||||
{
|
||||
int dist = calcDist(src(ay + ty, ax + tx), src(by + ty, bx + tx));
|
||||
|
||||
dist_sums[index] += dist;
|
||||
col_dist_sums_tx_block_radius_index += dist;
|
||||
col_sum += dist;
|
||||
}
|
||||
|
||||
col_dist_sums(tx + block_radius, index) = col_dist_sums_tx_block_radius_index;
|
||||
col_sums(tx + block_radius, index) = col_sum;
|
||||
}
|
||||
#else
|
||||
for (int ty = -block_radius; ty <= block_radius; ++ty)
|
||||
@ -259,16 +256,16 @@ namespace cv { namespace gpu { namespace device
|
||||
int dist = calcDist(src(ay + ty, ax + tx), src(by + ty, bx + tx));
|
||||
|
||||
dist_sums[index] += dist;
|
||||
col_dist_sums(tx + block_radius, index) += dist;
|
||||
col_sums(tx + block_radius, index) += dist;
|
||||
}
|
||||
#endif
|
||||
|
||||
up_col_dist_sums(j, index) = col_dist_sums(block_window - 1, index);
|
||||
up_col_sums(j, index) = col_sums(block_window - 1, index);
|
||||
}
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void shiftLeftSums_TileFirstRow(int i, int j, int first_col, int* dist_sums, PtrStepi& col_dist_sums, PtrStepi& up_col_dist_sums) const
|
||||
{
|
||||
__device__ __forceinline__ void shiftRight_FirstRow(int i, int j, int first, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const
|
||||
{
|
||||
for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
|
||||
{
|
||||
int y = index / search_window;
|
||||
@ -280,54 +277,46 @@ namespace cv { namespace gpu { namespace device
|
||||
int by = i + y - search_radius;
|
||||
int bx = j + x - search_radius + block_radius;
|
||||
|
||||
int col_dist_sum = 0;
|
||||
int col_sum = 0;
|
||||
|
||||
for (int ty = -block_radius; ty <= block_radius; ++ty)
|
||||
col_dist_sum += calcDist(src(ay + ty, ax), src(by + ty, bx));
|
||||
|
||||
int old_dist_sums = dist_sums[index];
|
||||
int old_col_sum = col_dist_sums(first_col, index);
|
||||
dist_sums[index] += col_dist_sum - old_col_sum;
|
||||
|
||||
col_sum += calcDist(src(ay + ty, ax), src(by + ty, bx));
|
||||
|
||||
col_dist_sums(first_col, index) = col_dist_sum;
|
||||
up_col_dist_sums(j, index) = col_dist_sum;
|
||||
dist_sums[index] += col_sum - col_sums(first, index);
|
||||
|
||||
col_sums(first, index) = col_sum;
|
||||
up_col_sums(j, index) = col_sum;
|
||||
}
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void shiftLeftSums_UsingUpSums(int i, int j, int first_col, int* dist_sums, PtrStepi& col_dist_sums, PtrStepi& up_col_dist_sums) const
|
||||
__device__ __forceinline__ void shiftRight_UpSums(int i, int j, int first, int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums) const
|
||||
{
|
||||
int ay = i;
|
||||
int ax = j + block_radius;
|
||||
|
||||
int start_by = i - search_radius;
|
||||
int start_bx = j - search_radius + block_radius;
|
||||
|
||||
T a_up = src(ay - block_radius - 1, ax);
|
||||
T a_down = src(ay + block_radius, ax);
|
||||
|
||||
for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
|
||||
{
|
||||
dist_sums[index] -= col_dist_sums(first_col, index);
|
||||
|
||||
{
|
||||
int y = index / search_window;
|
||||
int x = index - y * search_window;
|
||||
|
||||
int by = start_by + y;
|
||||
int bx = start_bx + x;
|
||||
int by = i + y - search_radius;
|
||||
int bx = j + x - search_radius + block_radius;
|
||||
|
||||
T b_up = src(by - block_radius - 1, bx);
|
||||
T b_down = src(by + block_radius, bx);
|
||||
|
||||
int col_dist_sums_first_col_index = up_col_dist_sums(j, index) + calcDist(a_down, b_down) - calcDist(a_up, b_up);
|
||||
|
||||
col_dist_sums(first_col, index) = col_dist_sums_first_col_index;
|
||||
dist_sums[index] += col_dist_sums_first_col_index;
|
||||
up_col_dist_sums(j, index) = col_dist_sums_first_col_index;
|
||||
int col_sum = up_col_sums(j, index) + calcDist(a_down, b_down) - calcDist(a_up, b_up);
|
||||
|
||||
dist_sums[index] += col_sum - col_sums(first, index);
|
||||
col_sums(first, index) = col_sum;
|
||||
up_col_sums(j, index) = col_sum;
|
||||
}
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void convolve_search_window(int i, int j, const int* dist_sums, PtrStepi& col_dist_sums, PtrStepi& up_col_dist_sums, T& dst) const
|
||||
__device__ __forceinline__ void convolve_window(int i, int j, const int* dist_sums, PtrStepi& col_sums, PtrStepi& up_col_sums, T& dst) const
|
||||
{
|
||||
typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type sum_type;
|
||||
|
||||
@ -336,8 +325,8 @@ namespace cv { namespace gpu { namespace device
|
||||
|
||||
float bw2_inv = 1.f/(block_window * block_window);
|
||||
|
||||
int start_x = j - search_radius;
|
||||
int start_y = i - search_radius;
|
||||
int sx = j - search_radius;
|
||||
int sy = i - search_radius;
|
||||
|
||||
for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
|
||||
{
|
||||
@ -348,7 +337,7 @@ namespace cv { namespace gpu { namespace device
|
||||
float weight = __expf(avg_dist * minus_h2_inv);
|
||||
weights_sum += weight;
|
||||
|
||||
sum = sum + weight * saturate_cast<sum_type>(src(start_y + y, start_x + x));
|
||||
sum = sum + weight * saturate_cast<sum_type>(src(sy + y, sx + x));
|
||||
}
|
||||
|
||||
volatile __shared__ float cta_buffer[CTA_SIZE];
|
||||
@ -357,21 +346,19 @@ namespace cv { namespace gpu { namespace device
|
||||
|
||||
cta_buffer[tid] = weights_sum;
|
||||
__syncthreads();
|
||||
Block::reduce<CTA_SIZE>(cta_buffer, plus());
|
||||
|
||||
if (tid == 0)
|
||||
weights_sum = cta_buffer[0];
|
||||
Block::reduce<CTA_SIZE>(cta_buffer, plus());
|
||||
weights_sum = cta_buffer[0];
|
||||
|
||||
__syncthreads();
|
||||
|
||||
|
||||
for(int n = 0; n < VecTraits<T>::cn; ++n)
|
||||
{
|
||||
cta_buffer[tid] = reinterpret_cast<float*>(&sum)[n];
|
||||
__syncthreads();
|
||||
Block::reduce<CTA_SIZE>(cta_buffer, plus());
|
||||
|
||||
if (tid == 0)
|
||||
reinterpret_cast<float*>(&sum)[n] = cta_buffer[0];
|
||||
Block::reduce<CTA_SIZE>(cta_buffer, plus());
|
||||
reinterpret_cast<float*>(&sum)[n] = cta_buffer[0];
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
@ -387,17 +374,17 @@ namespace cv { namespace gpu { namespace device
|
||||
int tex = ::min(tbx + TILE_COLS, dst.cols);
|
||||
int tey = ::min(tby + TILE_ROWS, dst.rows);
|
||||
|
||||
PtrStepi col_dist_sums;
|
||||
col_dist_sums.data = buffer.ptr(dst.cols + blockIdx.x * block_window) + blockIdx.y * search_window * search_window;
|
||||
col_dist_sums.step = buffer.step;
|
||||
PtrStepi col_sums;
|
||||
col_sums.data = buffer.ptr(dst.cols + blockIdx.x * block_window) + blockIdx.y * search_window * search_window;
|
||||
col_sums.step = buffer.step;
|
||||
|
||||
PtrStepi up_col_dist_sums;
|
||||
up_col_dist_sums.data = buffer.data + blockIdx.y * search_window * search_window;
|
||||
up_col_dist_sums.step = buffer.step;
|
||||
PtrStepi up_col_sums;
|
||||
up_col_sums.data = buffer.data + blockIdx.y * search_window * search_window;
|
||||
up_col_sums.step = buffer.step;
|
||||
|
||||
extern __shared__ int dist_sums[]; //search_window * search_window
|
||||
|
||||
int first_col = -1;
|
||||
int first = 0;
|
||||
|
||||
for (int i = tby; i < tey; ++i)
|
||||
for (int j = tbx; j < tex; ++j)
|
||||
@ -406,22 +393,22 @@ namespace cv { namespace gpu { namespace device
|
||||
|
||||
if (j == tbx)
|
||||
{
|
||||
initSums_TileFistColumn(i, j, dist_sums, col_dist_sums, up_col_dist_sums);
|
||||
first_col = 0;
|
||||
initSums_BruteForce(i, j, dist_sums, col_sums, up_col_sums);
|
||||
first = 0;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (i == tby)
|
||||
shiftLeftSums_TileFirstRow(i, j, first_col, dist_sums, col_dist_sums, up_col_dist_sums);
|
||||
shiftRight_FirstRow(i, j, first, dist_sums, col_sums, up_col_sums);
|
||||
else
|
||||
shiftLeftSums_UsingUpSums(i, j, first_col, dist_sums, col_dist_sums, up_col_dist_sums);
|
||||
shiftRight_UpSums(i, j, first, dist_sums, col_sums, up_col_sums);
|
||||
|
||||
first_col = (first_col + 1) % block_window;
|
||||
first = (first + 1) % block_window;
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
convolve_search_window(i, j, dist_sums, col_dist_sums, up_col_dist_sums, dst(i, j));
|
||||
convolve_window(i, j, dist_sums, col_sums, up_col_sums, dst(i, j));
|
||||
}
|
||||
}
|
||||
|
||||
@ -463,6 +450,55 @@ namespace cv { namespace gpu { namespace device
|
||||
template void nlm_fast_gpu<uchar>(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
|
||||
template void nlm_fast_gpu<uchar2>(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
|
||||
template void nlm_fast_gpu<uchar3>(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
|
||||
|
||||
|
||||
|
||||
__global__ void fnlm_split_kernel(const PtrStepSz<uchar3> lab, PtrStepb l, PtrStep<uchar2> ab)
|
||||
{
|
||||
int x = threadIdx.x + blockIdx.x * blockDim.x;
|
||||
int y = threadIdx.y + blockIdx.y * blockDim.y;
|
||||
|
||||
if (x < lab.cols && y < lab.rows)
|
||||
{
|
||||
uchar3 p = lab(y, x);
|
||||
ab(y,x) = make_uchar2(p.y, p.z);
|
||||
l(y,x) = p.x;
|
||||
}
|
||||
}
|
||||
|
||||
void fnlm_split_channels(const PtrStepSz<uchar3>& lab, PtrStepb l, PtrStep<uchar2> ab, cudaStream_t stream)
|
||||
{
|
||||
dim3 b(32, 8);
|
||||
dim3 g(divUp(lab.cols, b.x), divUp(lab.rows, b.y));
|
||||
|
||||
fnlm_split_kernel<<<g, b>>>(lab, l, ab);
|
||||
cudaSafeCall ( cudaGetLastError () );
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
|
||||
__global__ void fnlm_merge_kernel(const PtrStepb l, const PtrStep<uchar2> ab, PtrStepSz<uchar3> lab)
|
||||
{
|
||||
int x = threadIdx.x + blockIdx.x * blockDim.x;
|
||||
int y = threadIdx.y + blockIdx.y * blockDim.y;
|
||||
|
||||
if (x < lab.cols && y < lab.rows)
|
||||
{
|
||||
uchar2 p = ab(y, x);
|
||||
lab(y, x) = make_uchar3(l(y, x), p.x, p.y);
|
||||
}
|
||||
}
|
||||
|
||||
void fnlm_merge_channels(const PtrStepb& l, const PtrStep<uchar2>& ab, PtrStepSz<uchar3> lab, cudaStream_t stream)
|
||||
{
|
||||
dim3 b(32, 8);
|
||||
dim3 g(divUp(lab.cols, b.x), divUp(lab.rows, b.y));
|
||||
|
||||
fnlm_merge_kernel<<<g, b>>>(l, ab, lab);
|
||||
cudaSafeCall ( cudaGetLastError () );
|
||||
if (stream == 0)
|
||||
cudaSafeCall( cudaDeviceSynchronize() );
|
||||
}
|
||||
}
|
||||
}}}
|
||||
|
||||
|
||||
@ -104,7 +104,7 @@ void cv::gpu::bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, f
|
||||
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_size, int block_size, int borderMode, Stream& 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::device::imgproc::nlm_bruteforce_gpu;
|
||||
typedef void (*func_t)(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream);
|
||||
@ -121,12 +121,9 @@ void cv::gpu::nonLocalMeans(const GpuMat& src, GpuMat& dst, float h, int search_
|
||||
|
||||
int gpuBorderType;
|
||||
CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
|
||||
|
||||
int search_radius = search_window_size/2;
|
||||
int block_radius = block_size/2;
|
||||
|
||||
|
||||
dst.create(src.size(), src.type());
|
||||
func(src, dst, search_radius, block_radius, h, gpuBorderType, StreamAccessor::getStream(s));
|
||||
func(src, dst, search_window/2, block_window/2, h, gpuBorderType, StreamAccessor::getStream(s));
|
||||
}
|
||||
|
||||
|
||||
@ -143,220 +140,76 @@ namespace cv { namespace gpu { namespace device
|
||||
template<typename T>
|
||||
void nlm_fast_gpu(const PtrStepSzb& src, PtrStepSzb dst, PtrStepi buffer,
|
||||
int search_window, int block_window, float h, cudaStream_t stream);
|
||||
|
||||
}
|
||||
|
||||
void fnlm_split_channels(const PtrStepSz<uchar3>& lab, PtrStepb l, PtrStep<uchar2> ab, cudaStream_t stream);
|
||||
void fnlm_merge_channels(const PtrStepb& l, const PtrStep<uchar2>& ab, PtrStepSz<uchar3> lab, cudaStream_t stream);
|
||||
}
|
||||
}}}
|
||||
|
||||
|
||||
|
||||
//class CV_EXPORTS FastNonLocalMeansDenoising
|
||||
//{
|
||||
//public:
|
||||
// FastNonLocalMeansDenoising(float h, int search_radius, int block_radius, const Size& image_size = Size())
|
||||
// {
|
||||
// if (size.area() != 0)
|
||||
// allocate_buffers(image_size);
|
||||
// }
|
||||
|
||||
// void operator()(const GpuMat& src, GpuMat& dst);
|
||||
|
||||
//private:
|
||||
// void allocate_buffers(const Size& image_size)
|
||||
// {
|
||||
// col_dist_sums.create(block_window_, search_window_ * search_window_, CV_32S);
|
||||
// up_col_dist_sums.create(image_size.width, search_window_ * search_window_, CV_32S);
|
||||
// }
|
||||
|
||||
// int search_radius_;
|
||||
// int block_radius;
|
||||
// GpuMat col_dist_sums_;
|
||||
// GpuMat up_col_dist_sums_;
|
||||
//};
|
||||
|
||||
void cv::gpu::fastNlMeansDenoising( const GpuMat& src, GpuMat& dst, float h, int search_radius, int block_radius, Stream& s)
|
||||
void cv::gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window, int block_window, Stream& s)
|
||||
{
|
||||
dst.create(src.size(), src.type());
|
||||
CV_Assert(src.depth() == CV_8U && src.channels() < 4);
|
||||
|
||||
int border_size = search_window/2 + block_window/2;
|
||||
Size esize = src.size() + Size(border_size, border_size) * 2;
|
||||
|
||||
cv::gpu::ensureSizeIsEnough(esize, CV_8UC3, extended_src_buffer);
|
||||
GpuMat extended_src(esize, src.type(), extended_src_buffer.ptr(), extended_src_buffer.step);
|
||||
|
||||
GpuMat extended_src, src_hdr;
|
||||
int border_size = search_radius + block_radius;
|
||||
cv::gpu::copyMakeBorder(src, extended_src, border_size, border_size, border_size, border_size, cv::BORDER_DEFAULT, Scalar(), s);
|
||||
src_hdr = extended_src(Rect(Point2i(border_size, border_size), src.size()));
|
||||
GpuMat src_hdr = extended_src(Rect(Point2i(border_size, border_size), src.size()));
|
||||
|
||||
int bcols, brows;
|
||||
device::imgproc::nln_fast_get_buffer_size(src_hdr, search_window, block_window, bcols, brows);
|
||||
buffer.create(brows, bcols, CV_32S);
|
||||
|
||||
using namespace cv::gpu::device::imgproc;
|
||||
typedef void (*nlm_fast_t)(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
|
||||
static const nlm_fast_t funcs[] = { nlm_fast_gpu<uchar>, nlm_fast_gpu<uchar2>, nlm_fast_gpu<uchar3>, 0 };
|
||||
|
||||
int search_window = 2 * search_radius + 1;
|
||||
int block_window = 2 * block_radius + 1;
|
||||
|
||||
int bcols, brows;
|
||||
nln_fast_get_buffer_size(src_hdr, search_window, block_window, bcols, brows);
|
||||
|
||||
//GpuMat col_dist_sums(block_window * gx, search_window * search_window * gy, CV_32S);
|
||||
//GpuMat up_col_dist_sums(src.cols, search_window * search_window * gy, CV_32S);
|
||||
GpuMat buffer(brows, bcols, CV_32S);
|
||||
|
||||
static const nlm_fast_t funcs[] = { nlm_fast_gpu<uchar>, nlm_fast_gpu<uchar2>, nlm_fast_gpu<uchar3>, 0};
|
||||
|
||||
dst.create(src.size(), src.type());
|
||||
funcs[src.channels()-1](src_hdr, dst, buffer, search_window, block_window, h, StreamAccessor::getStream(s));
|
||||
}
|
||||
|
||||
//void cv::gpu::fastNlMeansDenoisingColored( const GpuMat& src, GpuMat& dst, float h, float hForColorComponents, int templateWindowSize, int searchWindowSize)
|
||||
//{
|
||||
// Mat src = _src.getMat();
|
||||
// _dst.create(src.size(), src.type());
|
||||
// Mat dst = _dst.getMat();
|
||||
void cv::gpu::FastNonLocalMeansDenoising::labMethod( const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window, int block_window, Stream& s)
|
||||
{
|
||||
#if (CUDA_VERSION < 5000)
|
||||
(void)src;
|
||||
(void)dst;
|
||||
(void)h_luminance;
|
||||
(void)h_color;
|
||||
(void)search_window;
|
||||
(void)block_window;
|
||||
(void)s;
|
||||
|
||||
CV_Error( CV_GpuApiCallError, "Lab method required CUDA 5.0 and higher" );
|
||||
#else
|
||||
|
||||
// if (src.type() != CV_8UC3) {
|
||||
// CV_Error(CV_StsBadArg, "Type of input image should be CV_8UC3!");
|
||||
// return;
|
||||
// }
|
||||
|
||||
// Mat src_lab;
|
||||
// cvtColor(src, src_lab, CV_LBGR2Lab);
|
||||
CV_Assert(src.type() == CV_8UC3);
|
||||
|
||||
lab.create(src.size(), src.type());
|
||||
cv::gpu::cvtColor(src, lab, CV_BGR2Lab, 0, s);
|
||||
|
||||
// Mat l(src.size(), CV_8U);
|
||||
// Mat ab(src.size(), CV_8UC2);
|
||||
// Mat l_ab[] = { l, ab };
|
||||
// int from_to[] = { 0,0, 1,1, 2,2 };
|
||||
// mixChannels(&src_lab, 1, l_ab, 2, from_to, 3);
|
||||
/*Mat t;
|
||||
cv::cvtColor(Mat(src), t, CV_BGR2Lab);
|
||||
lab.upload(t);*/
|
||||
|
||||
// fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize);
|
||||
// fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);
|
||||
l.create(src.size(), CV_8U);
|
||||
ab.create(src.size(), CV_8UC2);
|
||||
device::imgproc::fnlm_split_channels(lab, l, ab, StreamAccessor::getStream(s));
|
||||
|
||||
simpleMethod(l, l, h_luminance, search_window, block_window, s);
|
||||
simpleMethod(ab, ab, h_color, search_window, block_window, s);
|
||||
|
||||
// Mat l_ab_denoised[] = { l, ab };
|
||||
// Mat dst_lab(src.size(), src.type());
|
||||
// mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
|
||||
device::imgproc::fnlm_merge_channels(l, ab, lab, StreamAccessor::getStream(s));
|
||||
cv::gpu::cvtColor(lab, dst, CV_Lab2BGR, 0, s);
|
||||
|
||||
// cvtColor(dst_lab, dst, CV_Lab2LBGR);
|
||||
//}
|
||||
/*cv::cvtColor(Mat(lab), t, CV_Lab2BGR);
|
||||
dst.upload(t);*/
|
||||
|
||||
//static void fastNlMeansDenoisingMultiCheckPreconditions(
|
||||
// const std::vector<Mat>& srcImgs,
|
||||
// int imgToDenoiseIndex, int temporalWindowSize,
|
||||
// int templateWindowSize, int searchWindowSize)
|
||||
//{
|
||||
// int src_imgs_size = (int)srcImgs.size();
|
||||
// if (src_imgs_size == 0) {
|
||||
// CV_Error(CV_StsBadArg, "Input images vector should not be empty!");
|
||||
// }
|
||||
|
||||
// if (temporalWindowSize % 2 == 0 ||
|
||||
// searchWindowSize % 2 == 0 ||
|
||||
// templateWindowSize % 2 == 0) {
|
||||
// CV_Error(CV_StsBadArg, "All windows sizes should be odd!");
|
||||
// }
|
||||
|
||||
// int temporalWindowHalfSize = temporalWindowSize / 2;
|
||||
// if (imgToDenoiseIndex - temporalWindowHalfSize < 0 ||
|
||||
// imgToDenoiseIndex + temporalWindowHalfSize >= src_imgs_size)
|
||||
// {
|
||||
// CV_Error(CV_StsBadArg,
|
||||
// "imgToDenoiseIndex and temporalWindowSize "
|
||||
// "should be choosen corresponding srcImgs size!");
|
||||
// }
|
||||
|
||||
// for (int i = 1; i < src_imgs_size; i++) {
|
||||
// if (srcImgs[0].size() != srcImgs[i].size() || srcImgs[0].type() != srcImgs[i].type()) {
|
||||
// CV_Error(CV_StsBadArg, "Input images should have the same size and type!");
|
||||
// }
|
||||
// }
|
||||
//}
|
||||
|
||||
//void cv::fastNlMeansDenoisingMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
|
||||
// int imgToDenoiseIndex, int temporalWindowSize,
|
||||
// float h, int templateWindowSize, int searchWindowSize)
|
||||
//{
|
||||
// vector<Mat> srcImgs;
|
||||
// _srcImgs.getMatVector(srcImgs);
|
||||
|
||||
// fastNlMeansDenoisingMultiCheckPreconditions(
|
||||
// srcImgs, imgToDenoiseIndex,
|
||||
// temporalWindowSize, templateWindowSize, searchWindowSize
|
||||
// );
|
||||
// _dst.create(srcImgs[0].size(), srcImgs[0].type());
|
||||
// Mat dst = _dst.getMat();
|
||||
|
||||
// switch (srcImgs[0].type()) {
|
||||
// case CV_8U:
|
||||
// parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
|
||||
// FastNlMeansMultiDenoisingInvoker<uchar>(
|
||||
// srcImgs, imgToDenoiseIndex, temporalWindowSize,
|
||||
// dst, templateWindowSize, searchWindowSize, h));
|
||||
// break;
|
||||
// case CV_8UC2:
|
||||
// parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
|
||||
// FastNlMeansMultiDenoisingInvoker<cv::Vec2b>(
|
||||
// srcImgs, imgToDenoiseIndex, temporalWindowSize,
|
||||
// dst, templateWindowSize, searchWindowSize, h));
|
||||
// break;
|
||||
// case CV_8UC3:
|
||||
// parallel_for(cv::BlockedRange(0, srcImgs[0].rows),
|
||||
// FastNlMeansMultiDenoisingInvoker<cv::Vec3b>(
|
||||
// srcImgs, imgToDenoiseIndex, temporalWindowSize,
|
||||
// dst, templateWindowSize, searchWindowSize, h));
|
||||
// break;
|
||||
// default:
|
||||
// CV_Error(CV_StsBadArg,
|
||||
// "Unsupported matrix format! Only uchar, Vec2b, Vec3b are supported");
|
||||
// }
|
||||
//}
|
||||
|
||||
//void cv::fastNlMeansDenoisingColoredMulti( InputArrayOfArrays _srcImgs, OutputArray _dst,
|
||||
// int imgToDenoiseIndex, int temporalWindowSize,
|
||||
// float h, float hForColorComponents,
|
||||
// int templateWindowSize, int searchWindowSize)
|
||||
//{
|
||||
// vector<Mat> srcImgs;
|
||||
// _srcImgs.getMatVector(srcImgs);
|
||||
|
||||
// fastNlMeansDenoisingMultiCheckPreconditions(
|
||||
// srcImgs, imgToDenoiseIndex,
|
||||
// temporalWindowSize, templateWindowSize, searchWindowSize
|
||||
// );
|
||||
|
||||
// _dst.create(srcImgs[0].size(), srcImgs[0].type());
|
||||
// Mat dst = _dst.getMat();
|
||||
|
||||
// int src_imgs_size = (int)srcImgs.size();
|
||||
|
||||
// if (srcImgs[0].type() != CV_8UC3) {
|
||||
// CV_Error(CV_StsBadArg, "Type of input images should be CV_8UC3!");
|
||||
// return;
|
||||
// }
|
||||
|
||||
// int from_to[] = { 0,0, 1,1, 2,2 };
|
||||
|
||||
// // TODO convert only required images
|
||||
// vector<Mat> src_lab(src_imgs_size);
|
||||
// vector<Mat> l(src_imgs_size);
|
||||
// vector<Mat> ab(src_imgs_size);
|
||||
// for (int i = 0; i < src_imgs_size; i++) {
|
||||
// src_lab[i] = Mat::zeros(srcImgs[0].size(), CV_8UC3);
|
||||
// l[i] = Mat::zeros(srcImgs[0].size(), CV_8UC1);
|
||||
// ab[i] = Mat::zeros(srcImgs[0].size(), CV_8UC2);
|
||||
// cvtColor(srcImgs[i], src_lab[i], CV_LBGR2Lab);
|
||||
|
||||
// Mat l_ab[] = { l[i], ab[i] };
|
||||
// mixChannels(&src_lab[i], 1, l_ab, 2, from_to, 3);
|
||||
// }
|
||||
|
||||
// Mat dst_l;
|
||||
// Mat dst_ab;
|
||||
|
||||
// fastNlMeansDenoisingMulti(
|
||||
// l, dst_l, imgToDenoiseIndex, temporalWindowSize,
|
||||
// h, templateWindowSize, searchWindowSize);
|
||||
|
||||
// fastNlMeansDenoisingMulti(
|
||||
// ab, dst_ab, imgToDenoiseIndex, temporalWindowSize,
|
||||
// hForColorComponents, templateWindowSize, searchWindowSize);
|
||||
|
||||
// Mat l_ab_denoised[] = { dst_l, dst_ab };
|
||||
// Mat dst_lab(srcImgs[0].size(), srcImgs[0].type());
|
||||
// mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
|
||||
|
||||
// cvtColor(dst_lab, dst, CV_Lab2LBGR);
|
||||
//}
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
#endif
|
||||
|
||||
@ -329,11 +329,11 @@ void cv::gpu::copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom
|
||||
typedef void (*caller_t)(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderType, const Scalar& value, cudaStream_t stream);
|
||||
static const caller_t callers[6][4] =
|
||||
{
|
||||
{ copyMakeBorder_caller<uchar, 1> , 0/*copyMakeBorder_caller<uchar, 2>*/ , copyMakeBorder_caller<uchar, 3> , copyMakeBorder_caller<uchar, 4>},
|
||||
{ copyMakeBorder_caller<uchar, 1> , copyMakeBorder_caller<uchar, 2> , copyMakeBorder_caller<uchar, 3> , copyMakeBorder_caller<uchar, 4>},
|
||||
{0/*copyMakeBorder_caller<schar, 1>*/, 0/*copyMakeBorder_caller<schar, 2>*/ , 0/*copyMakeBorder_caller<schar, 3>*/, 0/*copyMakeBorder_caller<schar, 4>*/},
|
||||
{ copyMakeBorder_caller<ushort, 1> , 0/*copyMakeBorder_caller<ushort, 2>*/, copyMakeBorder_caller<ushort, 3> , copyMakeBorder_caller<ushort, 4>},
|
||||
{ copyMakeBorder_caller<short, 1> , 0/*copyMakeBorder_caller<short, 2>*/ , copyMakeBorder_caller<short, 3> , copyMakeBorder_caller<short, 4>},
|
||||
{0/*copyMakeBorder_caller<int, 1>*/ , 0/*copyMakeBorder_caller<int, 2>*/ , 0/*copyMakeBorder_caller<int, 3>*/ , 0/*copyMakeBorder_caller<int, 4>*/},
|
||||
{0/*copyMakeBorder_caller<int, 1>*/, 0/*copyMakeBorder_caller<int, 2>*/ , 0/*copyMakeBorder_caller<int, 3>*/, 0/*copyMakeBorder_caller<int , 4>*/},
|
||||
{ copyMakeBorder_caller<float, 1> , 0/*copyMakeBorder_caller<float, 2>*/ , copyMakeBorder_caller<float, 3> , copyMakeBorder_caller<float ,4>}
|
||||
};
|
||||
|
||||
|
||||
@ -72,9 +72,7 @@ PARAM_TEST_CASE(BilateralFilter, cv::gpu::DeviceInfo, cv::Size, MatType)
|
||||
TEST_P(BilateralFilter, Accuracy)
|
||||
{
|
||||
cv::Mat src = randomMat(size, type);
|
||||
//cv::Mat src = readImage("hog/road.png", cv::IMREAD_GRAYSCALE);
|
||||
//cv::Mat src = readImage("csstereobp/aloe-R.png", cv::IMREAD_GRAYSCALE);
|
||||
|
||||
|
||||
src.convertTo(src, type);
|
||||
cv::gpu::GpuMat dst;
|
||||
|
||||
@ -118,16 +116,16 @@ TEST_P(BruteForceNonLocalMeans, Regression)
|
||||
cv::cvtColor(bgr, gray, CV_BGR2GRAY);
|
||||
|
||||
GpuMat dbgr, dgray;
|
||||
cv::gpu::nonLocalMeans(GpuMat(bgr), dbgr, 10);
|
||||
cv::gpu::nonLocalMeans(GpuMat(gray), dgray, 10);
|
||||
cv::gpu::nonLocalMeans(GpuMat(bgr), dbgr, 20);
|
||||
cv::gpu::nonLocalMeans(GpuMat(gray), dgray, 20);
|
||||
|
||||
#if 0
|
||||
dumpImage("denoising/denoised_lena_bgr.png", cv::Mat(dbgr));
|
||||
dumpImage("denoising/denoised_lena_gray.png", cv::Mat(dgray));
|
||||
dumpImage("denoising/nlm_denoised_lena_bgr.png", cv::Mat(dbgr));
|
||||
dumpImage("denoising/nlm_denoised_lena_gray.png", cv::Mat(dgray));
|
||||
#endif
|
||||
|
||||
cv::Mat bgr_gold = readImage("denoising/denoised_lena_bgr.png", cv::IMREAD_COLOR);
|
||||
cv::Mat gray_gold = readImage("denoising/denoised_lena_gray.png", cv::IMREAD_GRAYSCALE);
|
||||
cv::Mat bgr_gold = readImage("denoising/nlm_denoised_lena_bgr.png", cv::IMREAD_COLOR);
|
||||
cv::Mat gray_gold = readImage("denoising/nlm_denoised_lena_gray.png", cv::IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(bgr_gold.empty() || gray_gold.empty());
|
||||
|
||||
EXPECT_MAT_NEAR(bgr_gold, dbgr, 1e-4);
|
||||
@ -156,27 +154,29 @@ TEST_P(FastNonLocalMeans, Regression)
|
||||
{
|
||||
using cv::gpu::GpuMat;
|
||||
|
||||
cv::Mat bgr = readImage("denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR);
|
||||
cv::Mat bgr = readImage("denoising/lena_noised_gaussian_sigma=20_multi_0.png", cv::IMREAD_COLOR);
|
||||
ASSERT_FALSE(bgr.empty());
|
||||
|
||||
cv::Mat gray;
|
||||
cv::cvtColor(bgr, gray, CV_BGR2GRAY);
|
||||
|
||||
GpuMat dbgr, dgray;
|
||||
cv::gpu::fastNlMeansDenoising(GpuMat(gray), dgray, 10);
|
||||
cv::gpu::FastNonLocalMeansDenoising fnlmd;
|
||||
|
||||
fnlmd.simpleMethod(GpuMat(gray), dgray, 20);
|
||||
fnlmd.labMethod(GpuMat(bgr), dbgr, 20, 10);
|
||||
|
||||
#if 0
|
||||
//dumpImage("denoising/fnlm_denoised_lena_bgr.png", cv::Mat(dbgr));
|
||||
dumpImage("denoising/fnlm_denoised_lena_gray.png", cv::Mat(dgray));
|
||||
//dumpImage("denoising/fnlm_denoised_lena_gray.png", cv::Mat(dgray));
|
||||
#endif
|
||||
|
||||
//cv::Mat bgr_gold = readImage("denoising/denoised_lena_bgr.png", cv::IMREAD_COLOR);
|
||||
cv::Mat bgr_gold = readImage("denoising/fnlm_denoised_lena_bgr.png", cv::IMREAD_COLOR);
|
||||
cv::Mat gray_gold = readImage("denoising/fnlm_denoised_lena_gray.png", cv::IMREAD_GRAYSCALE);
|
||||
ASSERT_FALSE(/*bgr_gold.empty() || */gray_gold.empty());
|
||||
|
||||
//EXPECT_MAT_NEAR(bgr_gold, dbgr, 1e-4);
|
||||
EXPECT_MAT_NEAR(gray_gold, dgray, 1e-4);
|
||||
ASSERT_FALSE(bgr_gold.empty() || gray_gold.empty());
|
||||
|
||||
EXPECT_MAT_NEAR(bgr_gold, dbgr, 1);
|
||||
EXPECT_MAT_NEAR(gray_gold, dgray, 1);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(GPU_Denoising, FastNonLocalMeans, ALL_DEVICES);
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user