//\r
//M*/\r
\r
-#if !defined CUDA_DISABLER\r
\r
#include "opencv2/gpu/device/saturate_cast.hpp"\r
#include "opencv2/gpu/device/transform.hpp"\r
# pragma clang diagnostic pop\r
#endif\r
}}} // namespace cv { namespace gpu { namespace device\r
-\r
-#endif /* CUDA_DISABLER */
\ No newline at end of file
\r
bool cv::gpu::TargetArchs::builtWith(cv::gpu::FeatureSet feature_set)\r
{\r
-#if defined HAVE_CUDA && !defined(CUDA_DISABLER)\r
+#if defined (HAVE_CUDA)\r
return ::compareToSet(CUDA_ARCH_FEATURES, feature_set, std::greater_equal<int>());\r
#else\r
(void)feature_set;\r
\r
bool cv::gpu::TargetArchs::hasPtx(int major, int minor)\r
{\r
-#if defined HAVE_CUDA && !defined(CUDA_DISABLER)\r
+#if defined (HAVE_CUDA)\r
return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor, std::equal_to<int>());\r
#else\r
(void)major;\r
\r
bool cv::gpu::TargetArchs::hasBin(int major, int minor)\r
{\r
-#if defined (HAVE_CUDA) && !defined(CUDA_DISABLER)\r
+#if defined (HAVE_CUDA)\r
return ::compareToSet(CUDA_ARCH_BIN, major * 10 + minor, std::equal_to<int>());\r
#else\r
(void)major;\r
\r
bool cv::gpu::TargetArchs::hasEqualOrLessPtx(int major, int minor)\r
{\r
-#if defined HAVE_CUDA && !defined(CUDA_DISABLER)\r
+#if defined (HAVE_CUDA)\r
return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor,\r
std::less_equal<int>());\r
#else\r
\r
bool cv::gpu::TargetArchs::hasEqualOrGreaterPtx(int major, int minor)\r
{\r
-#if defined HAVE_CUDA && !defined(CUDA_DISABLER)\r
- return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor,\r
- std::greater_equal<int>());\r
+#if defined (HAVE_CUDA)\r
+ return ::compareToSet(CUDA_ARCH_PTX, major * 10 + minor, std::greater_equal<int>());\r
#else\r
(void)major;\r
(void)minor;\r
\r
bool cv::gpu::TargetArchs::hasEqualOrGreaterBin(int major, int minor)\r
{\r
-#if defined HAVE_CUDA && !defined(CUDA_DISABLER)\r
+#if defined (HAVE_CUDA)\r
return ::compareToSet(CUDA_ARCH_BIN, major * 10 + minor,\r
std::greater_equal<int>());\r
#else\r
#endif\r
}\r
\r
-#if !defined HAVE_CUDA || defined(CUDA_DISABLER)\r
+#if !defined (HAVE_CUDA)\r
\r
#define throw_nogpu CV_Error(CV_GpuNotSupported, "The library is compiled without CUDA support")\r
\r
};\r
}\r
\r
-#if !defined HAVE_CUDA || defined(CUDA_DISABLER)\r
+#if !defined HAVE_CUDA || defined(CUDA_DISABLER_)\r
\r
namespace\r
{\r
endif()
set(the_description "GPU-accelerated Computer Vision")
-ocv_add_module(gpu opencv_imgproc opencv_calib3d opencv_objdetect opencv_video opencv_nonfree opencv_legacy)
+ocv_add_module(gpu opencv_imgproc opencv_calib3d opencv_objdetect opencv_video opencv_nonfree opencv_photo opencv_legacy)
ocv_module_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/src/cuda" "${CMAKE_CURRENT_SOURCE_DIR}/../highgui/src")
.. 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())
- :param src: Source image. Supports only CV_8UC1, CV_8UC3.
+ :param src: Source image. Supports only CV_8UC1, CV_8UC2 and CV_8UC3.
:param dst: Destination imagwe.
CV_EXPORTS void nonLocalMeans(const GpuMat& src, GpuMat& dst, float h,\r
int search_widow_size = 11, int block_size = 7, int borderMode = BORDER_DEFAULT, Stream& s = Stream::Null());\r
\r
+//! Fast (but approximate)version of non-local means algorith similar to CPU function (running sums technique)\r
+CV_EXPORTS void fastNlMeansDenoising( const GpuMat& src, GpuMat& dst, float h, int search_radius = 10, int block_radius = 3, Stream& s = Stream::Null());\r
\r
struct CV_EXPORTS CannyBuf;\r
\r
{
FAIL();
}
+}
+
+
+//////////////////////////////////////////////////////////////////////
+// fastNonLocalMeans
+
+DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth , int, int, int);
+
+PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_FastNonLocalMeans,
+ Combine(GPU_TYPICAL_MAT_SIZES, Values<MatDepth>(CV_8U), Values(1), Values(21), Values(5, 7)))
+{
+ declare.time(30.0);
+
+ cv::Size size = GET_PARAM(0);
+ int depth = GET_PARAM(1);
+ int channels = GET_PARAM(2);
+
+ int search_widow_size = GET_PARAM(3);
+ int block_size = GET_PARAM(4);
+
+ float h = 10;
+ int type = CV_MAKE_TYPE(depth, channels);
+
+ cv::Mat src(size, type);
+ fillRandom(src);
+
+ if (runOnGpu)
+ {
+ cv::gpu::GpuMat d_src(src);
+ cv::gpu::GpuMat d_dst;
+ cv::gpu::fastNlMeansDenoising(d_src, d_dst, h, search_widow_size/2, block_size/2);
+
+ TEST_CYCLE()
+ {
+ cv::gpu::fastNlMeansDenoising(d_src, d_dst, h, search_widow_size/2, block_size/2);
+ }
+ }
+ else
+ {
+ cv::Mat dst;
+ cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size);
+
+ TEST_CYCLE()
+ {
+ cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size);
+ }
+ }
}
\ No newline at end of file
#include "opencv2/video/video.hpp"\r
#include "opencv2/nonfree/nonfree.hpp"\r
#include "opencv2/legacy/legacy.hpp"\r
+#include "opencv2/photo/photo.hpp"\r
\r
#include "utility.hpp"\r
\r
return !this->empty();
}
+#endif
+
//////////////////////////////////////////////////////////////////////////////////////////////////////
+#if defined (HAVE_CUDA)
+
struct RectConvert
{
Rect operator()(const NcvRect32u& nr) const { return Rect(nr.x, nr.y, nr.width, nr.height); }
#include "opencv2/gpu/device/vec_traits.hpp"
#include "opencv2/gpu/device/vec_math.hpp"
+#include "opencv2/gpu/device/block.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
using namespace cv::gpu;
}
template void nlm_bruteforce_gpu<uchar>(const PtrStepSzb&, PtrStepSzb, int, int, float, int, cudaStream_t);
+ template void nlm_bruteforce_gpu<uchar2>(const PtrStepSzb&, PtrStepSzb, int, int, float, int, cudaStream_t);
template void nlm_bruteforce_gpu<uchar3>(const PtrStepSzb&, PtrStepSzb, int, int, float, int, cudaStream_t);
}
}}}
+//////////////////////////////////////////////////////////////////////////////////
+//// Non Local Means Denosing (fast approximate version)
+
+namespace cv { namespace gpu { namespace device
+{
+ namespace imgproc
+ {
+ __device__ __forceinline__ int calcDist(const uchar& a, const uchar& b) { return (a-b)*(a-b); }
+ __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,
+ TILE_ROWS = 32,
+
+ STRIDE = CTA_SIZE
+ };
+
+ struct plus
+ {
+ __device__ __forceinline float operator()(float v1, float v2) const { return v1 + v2; }
+ };
+
+ int search_radius;
+ int block_radius;
+
+ int search_window;
+ int block_window;
+ float minus_h2_inv;
+
+ FastNonLocalMenas(int search_window_, int block_window_, float h) : search_radius(search_window_/2), block_radius(block_window_/2),
+ search_window(search_window_), block_window(block_window_), minus_h2_inv(-1.f/(h * h * VecTraits<T>::cn)) {}
+
+ 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
+ {
+ 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;
+
+ int y = index / search_window;
+ int x = index - y * search_window;
+
+ int ay = i;
+ int ax = j;
+
+ int by = i + y - search_radius;
+ int bx = j + x - search_radius;
+
+#if 1
+ for (int tx = -block_radius; tx <= block_radius; ++tx)
+ {
+ int col_dist_sums_tx_block_radius_index = 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_dist_sums(tx + block_radius, index) = col_dist_sums_tx_block_radius_index;
+ }
+#else
+ for (int ty = -block_radius; ty <= block_radius; ++ty)
+ for (int tx = -block_radius; tx <= block_radius; ++tx)
+ {
+ 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;
+ }
+#endif
+
+ up_col_dist_sums(j, index) = col_dist_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
+ {
+ for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
+ {
+ int y = index / search_window;
+ int x = index - y * search_window;
+
+ int ay = i;
+ int ax = j + block_radius;
+
+ int by = i + y - search_radius;
+ int bx = j + x - search_radius + block_radius;
+
+ int col_dist_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_dist_sums(first_col, index) = col_dist_sum;
+ up_col_dist_sums(j, index) = col_dist_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
+ {
+ 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;
+
+ 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;
+ }
+ }
+
+ __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
+ {
+ typedef typename TypeVec<float, VecTraits<T>::cn>::vec_type sum_type;
+
+ float weights_sum = 0;
+ sum_type sum = VecTraits<sum_type>::all(0);
+
+ float bw2_inv = 1.f/(block_window * block_window);
+
+ int start_x = j - search_radius;
+ int start_y = i - search_radius;
+
+ for(int index = threadIdx.x; index < search_window * search_window; index += STRIDE)
+ {
+ int y = index / search_window;
+ int x = index - y * search_window;
+
+ float avg_dist = dist_sums[index] * bw2_inv;
+ 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));
+ }
+
+ volatile __shared__ float cta_buffer[CTA_SIZE];
+
+ int tid = threadIdx.x;
+
+ cta_buffer[tid] = weights_sum;
+ __syncthreads();
+ Block::reduce<CTA_SIZE>(cta_buffer, plus());
+
+ if (tid == 0)
+ 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];
+ __syncthreads();
+ }
+
+ if (tid == 0)
+ dst = saturate_cast<T>(sum/weights_sum);
+ }
+
+ __device__ __forceinline__ void operator()(PtrStepSz<T>& dst) const
+ {
+ int tbx = blockIdx.x * TILE_COLS;
+ int tby = blockIdx.y * TILE_ROWS;
+
+ 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 up_col_dist_sums;
+ up_col_dist_sums.data = buffer.data + blockIdx.y * search_window * search_window;
+ up_col_dist_sums.step = buffer.step;
+
+ extern __shared__ int dist_sums[]; //search_window * search_window
+
+ int first_col = -1;
+
+ for (int i = tby; i < tey; ++i)
+ for (int j = tbx; j < tex; ++j)
+ {
+ __syncthreads();
+
+ if (j == tbx)
+ {
+ initSums_TileFistColumn(i, j, dist_sums, col_dist_sums, up_col_dist_sums);
+ first_col = 0;
+ }
+ else
+ {
+ if (i == tby)
+ shiftLeftSums_TileFirstRow(i, j, first_col, dist_sums, col_dist_sums, up_col_dist_sums);
+ else
+ shiftLeftSums_UsingUpSums(i, j, first_col, dist_sums, col_dist_sums, up_col_dist_sums);
+
+ first_col = (first_col + 1) % block_window;
+ }
+
+ __syncthreads();
+
+ convolve_search_window(i, j, dist_sums, col_dist_sums, up_col_dist_sums, dst(i, j));
+ }
+ }
+
+ };
+
+ template<typename T>
+ __global__ void fast_nlm_kernel(const FastNonLocalMenas<T> fnlm, PtrStepSz<T> dst) { fnlm(dst); }
+
+ void nln_fast_get_buffer_size(const PtrStepSzb& src, int search_window, int block_window, int& buffer_cols, int& buffer_rows)
+ {
+ typedef FastNonLocalMenas<uchar> FNLM;
+ dim3 grid(divUp(src.cols, FNLM::TILE_COLS), divUp(src.rows, FNLM::TILE_ROWS));
+
+ buffer_cols = search_window * search_window * grid.y;
+ buffer_rows = src.cols + block_window * grid.x;
+ }
+
+ 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)
+ {
+ typedef FastNonLocalMenas<T> FNLM;
+ FNLM fnlm(search_window, block_window, h);
+
+ fnlm.src = (PtrStepSz<T>)src;
+ fnlm.buffer = buffer;
+
+ dim3 block(FNLM::CTA_SIZE, 1);
+ dim3 grid(divUp(src.cols, FNLM::TILE_COLS), divUp(src.rows, FNLM::TILE_ROWS));
+ int smem = search_window * search_window * sizeof(int);
+
+
+ fast_nlm_kernel<<<grid, block, smem>>>(fnlm, (PtrStepSz<T>)dst);
+ cudaSafeCall ( cudaGetLastError () );
+ if (stream == 0)
+ cudaSafeCall( cudaDeviceSynchronize() );
+ }
+
+ 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);
+ }
+}}}
+
+
#endif /* CUDA_DISABLER */
\ No newline at end of file
#endif /* !defined (HAVE_CUDA) */\r
\r
\r
-#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)\r
+#if !defined (HAVE_CUDA)\r
\r
void cv::gpu::Stream::create() { throw_nogpu(); }\r
void cv::gpu::Stream::release() { throw_nogpu(); }\r
void cv::gpu::bilateralFilter(const GpuMat&, GpuMat&, int, float, float, int, Stream&) { throw_nogpu(); }
void cv::gpu::nonLocalMeans(const GpuMat&, GpuMat&, float, int, int, int, Stream&) { throw_nogpu(); }
+void cv::gpu::fastNlMeansDenoising( const GpuMat&, GpuMat&, float, int, int, Stream&) { throw_nogpu(); }
#else
+//////////////////////////////////////////////////////////////////////////////////
+//// Non Local Means Denosing (brute force)
namespace cv { namespace gpu { namespace device
{
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);
- static const func_t funcs[4] = { nlm_bruteforce_gpu<uchar>, 0 /*nlm_bruteforce_gpu<uchar2>*/ , nlm_bruteforce_gpu<uchar3>, 0/*nlm_bruteforce_gpu<uchar4>,*/ };
+ static const func_t funcs[4] = { nlm_bruteforce_gpu<uchar>, nlm_bruteforce_gpu<uchar2>, nlm_bruteforce_gpu<uchar3>, 0/*nlm_bruteforce_gpu<uchar4>,*/ };
- CV_Assert(src.type() == CV_8U || src.type() == CV_8UC3);
+ CV_Assert(src.type() == CV_8U || src.type() == CV_8UC2 || src.type() == CV_8UC3);
const func_t func = funcs[src.channels() - 1];
CV_Assert(func != 0);
}
+//////////////////////////////////////////////////////////////////////////////////
+//// Non Local Means Denosing (fast approxinate)
+namespace cv { namespace gpu { namespace device
+{
+ namespace imgproc
+ {
+ void nln_fast_get_buffer_size(const PtrStepSzb& src, int search_window, int block_window, int& buffer_cols, int& buffer_rows);
+ 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);
+ }
+}}}
+
+
+
+//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)
+{
+ dst.create(src.size(), src.type());
+ CV_Assert(src.depth() == CV_8U && src.channels() < 4);
+
+ 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()));
+
+ 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);
+
+ 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();
+
+// 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);
+
+// 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);
+
+// fastNlMeansDenoising(l, l, h, templateWindowSize, searchWindowSize);
+// fastNlMeansDenoising(ab, ab, hForColorComponents, templateWindowSize, searchWindowSize);
+
+// Mat l_ab_denoised[] = { l, ab };
+// Mat dst_lab(src.size(), src.type());
+// mixChannels(l_ab_denoised, 2, &dst_lab, 1, from_to, 3);
+
+// cvtColor(dst_lab, dst, CV_Lab2LBGR);
+//}
+
+//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
+
+
}\r
}\r
\r
-bool cv::gpu::tryConvertToGpuBorderType(int cpuBorderType, int& gpuBorderType)\r
-{\r
- switch (cpuBorderType)\r
- {\r
- case cv::BORDER_REFLECT101:\r
- gpuBorderType = cv::gpu::BORDER_REFLECT101_GPU;\r
- return true;\r
- case cv::BORDER_REPLICATE:\r
- gpuBorderType = cv::gpu::BORDER_REPLICATE_GPU;\r
- return true;\r
- case cv::BORDER_CONSTANT:\r
- gpuBorderType = cv::gpu::BORDER_CONSTANT_GPU;\r
- return true;\r
- case cv::BORDER_REFLECT:\r
- gpuBorderType = cv::gpu::BORDER_REFLECT_GPU;\r
- return true;\r
- case cv::BORDER_WRAP:\r
- gpuBorderType = cv::gpu::BORDER_WRAP_GPU;\r
- return true;\r
- default:\r
- return false;\r
- };\r
- return false;\r
-}\r
-\r
void cv::gpu::cornerHarris(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, double k, int borderType)\r
{\r
GpuMat Dx, Dy;\r
//\r
//M*/\r
\r
-#if !defined CUDA_DISABLER\r
-\r
\r
#include <iostream>\r
#include <string>\r
debugOutputHandler = func;\r
}\r
\r
+#if !defined CUDA_DISABLER\r
+\r
\r
//==============================================================================\r
//\r
--- /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*/
+
+#ifndef __OPENCV_GPU_DEVICE_BLOCK_HPP__
+#define __OPENCV_GPU_DEVICE_BLOCK_HPP__
+
+namespace cv { namespace gpu { namespace device
+{
+ struct Block
+ {
+ static __device__ __forceinline__ unsigned int id()
+ {
+ return blockIdx.x;
+ }
+
+ static __device__ __forceinline__ unsigned int stride()
+ {
+ return blockDim.x * blockDim.y * blockDim.z;
+ }
+
+ static __device__ __forceinline__ void sync()
+ {
+ __syncthreads();
+ }
+
+ static __device__ __forceinline__ int flattenedThreadId()
+ {
+ return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
+ }
+
+ template<typename It, typename T>
+ static __device__ __forceinline__ void fill(It beg, It end, const T& value)
+ {
+ int STRIDE = stride();
+ It t = beg + flattenedThreadId();
+
+ for(; t < end; t += STRIDE)
+ *t = value;
+ }
+
+ template<typename OutIt, typename T>
+ static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
+ {
+ int STRIDE = stride();
+ int tid = flattenedThreadId();
+ value += tid;
+
+ for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE)
+ *t = value;
+ }
+
+ template<typename InIt, typename OutIt>
+ static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out)
+ {
+ int STRIDE = stride();
+ InIt t = beg + flattenedThreadId();
+ OutIt o = out + (t - beg);
+
+ for(; t < end; t += STRIDE, o += STRIDE)
+ *o = *t;
+ }
+
+ template<typename InIt, typename OutIt, class UnOp>
+ static __device__ __forceinline__ void transfrom(InIt beg, InIt end, OutIt out, UnOp op)
+ {
+ int STRIDE = stride();
+ InIt t = beg + flattenedThreadId();
+ OutIt o = out + (t - beg);
+
+ for(; t < end; t += STRIDE, o += STRIDE)
+ *o = op(*t);
+ }
+
+ template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
+ static __device__ __forceinline__ void transfrom(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
+ {
+ int STRIDE = stride();
+ InIt1 t1 = beg1 + flattenedThreadId();
+ InIt2 t2 = beg2 + flattenedThreadId();
+ OutIt o = out + (t1 - beg1);
+
+ for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE)
+ *o = op(*t1, *t2);
+ }
+
+ template<int CTA_SIZE, typename T, class BinOp>
+ static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op)
+ {
+ int tid = flattenedThreadId();
+ T val = buffer[tid];
+
+ if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
+ if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
+ if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
+ if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); }
+
+ if (tid < 32)
+ {
+ if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); }
+ if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); }
+ if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); }
+ if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); }
+ if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); }
+ if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); }
+ }
+ }
+
+ template<int CTA_SIZE, typename T, class BinOp>
+ static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op)
+ {
+ int tid = flattenedThreadId();
+ T val = buffer[tid] = init;
+ __syncthreads();
+
+ if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
+ if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
+ if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
+ if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); }
+
+ if (tid < 32)
+ {
+ if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); }
+ if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); }
+ if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); }
+ if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); }
+ if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); }
+ if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); }
+ }
+ __syncthreads();
+ return buffer[0];
+ }
+
+ template <typename T, class BinOp>
+ static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op)
+ {
+ int ftid = flattenedThreadId();
+ int sft = stride();
+
+ if (sft < n)
+ {
+ for (unsigned int i = sft + ftid; i < n; i += sft)
+ data[ftid] = op(data[ftid], data[i]);
+
+ __syncthreads();
+
+ n = sft;
+ }
+
+ while (n > 1)
+ {
+ unsigned int half = n/2;
+
+ if (ftid < half)
+ data[ftid] = op(data[ftid], data[n - ftid - 1]);
+
+ __syncthreads();
+
+ n = n - half;
+ }
+ }
+ };
+}}}
+
+#endif /* __OPENCV_GPU_DEVICE_BLOCK_HPP__ */
+
+
\r
#include "precomp.hpp"\r
\r
-/* End of file. */
\ No newline at end of file
+\r
+\r
+bool cv::gpu::tryConvertToGpuBorderType(int cpuBorderType, int& gpuBorderType)\r
+{\r
+ switch (cpuBorderType)\r
+ {\r
+ case cv::BORDER_REFLECT101:\r
+ gpuBorderType = cv::gpu::BORDER_REFLECT101_GPU;\r
+ return true;\r
+ case cv::BORDER_REPLICATE:\r
+ gpuBorderType = cv::gpu::BORDER_REPLICATE_GPU;\r
+ return true;\r
+ case cv::BORDER_CONSTANT:\r
+ gpuBorderType = cv::gpu::BORDER_CONSTANT_GPU;\r
+ return true;\r
+ case cv::BORDER_REFLECT:\r
+ gpuBorderType = cv::gpu::BORDER_REFLECT_GPU;\r
+ return true;\r
+ case cv::BORDER_WRAP:\r
+ gpuBorderType = cv::gpu::BORDER_WRAP_GPU;\r
+ return true;\r
+ default:\r
+ return false;\r
+ };\r
+ return false;\r
+}\r
+\r
+\r
+\r
+/* End of file. */\r
+\r
////////////////////////////////////////////////////////
// Brute Force Non local means
-struct NonLocalMeans: testing::TestWithParam<cv::gpu::DeviceInfo>
+struct BruteForceNonLocalMeans: testing::TestWithParam<cv::gpu::DeviceInfo>
{
cv::gpu::DeviceInfo devInfo;
}
};
-TEST_P(NonLocalMeans, Regression)
+TEST_P(BruteForceNonLocalMeans, Regression)
{
using cv::gpu::GpuMat;
EXPECT_MAT_NEAR(gray_gold, dgray, 1e-4);
}
-INSTANTIATE_TEST_CASE_P(GPU_Denoising, NonLocalMeans, ALL_DEVICES);
+INSTANTIATE_TEST_CASE_P(GPU_Denoising, BruteForceNonLocalMeans, ALL_DEVICES);
-#endif // HAVE_CUDA
\ No newline at end of file
+
+////////////////////////////////////////////////////////
+// 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());
+ }
+};
+
+TEST_P(FastNonLocalMeans, Regression)
+{
+ using cv::gpu::GpuMat;
+
+ 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);
+
+#if 0
+ //dumpImage("denoising/fnlm_denoised_lena_bgr.png", cv::Mat(dbgr));
+ 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 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);
+
+}
+
+INSTANTIATE_TEST_CASE_P(GPU_Denoising, FastNonLocalMeans, ALL_DEVICES);
+
+
+#endif // HAVE_CUDA
projects / profile / ivi / opencv.git / commitdiff