mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
187 lines
4.8 KiB
187 lines
4.8 KiB
#include "perf_precomp.hpp" |
|
|
|
using namespace std; |
|
using namespace testing; |
|
|
|
#define GPU_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::sz720p) |
|
|
|
////////////////////////////////////////////////////////////////////// |
|
// BilateralFilter |
|
|
|
DEF_PARAM_TEST(Sz_Depth_Cn_KernelSz, cv::Size, MatDepth, MatCn, int); |
|
|
|
PERF_TEST_P(Sz_Depth_Cn_KernelSz, Denoising_BilateralFilter, |
|
Combine(GPU_DENOISING_IMAGE_SIZES, |
|
Values(CV_8U, CV_32F), |
|
GPU_CHANNELS_1_3, |
|
Values(3, 5, 9))) |
|
{ |
|
declare.time(60.0); |
|
|
|
const cv::Size size = GET_PARAM(0); |
|
const int depth = GET_PARAM(1); |
|
const int channels = GET_PARAM(2); |
|
const int kernel_size = GET_PARAM(3); |
|
|
|
const float sigma_color = 7; |
|
const float sigma_spatial = 5; |
|
const int borderMode = cv::BORDER_REFLECT101; |
|
|
|
const int type = CV_MAKE_TYPE(depth, channels); |
|
|
|
cv::Mat src(size, type); |
|
declare.in(src, WARMUP_RNG); |
|
|
|
if (PERF_RUN_GPU()) |
|
{ |
|
const cv::gpu::GpuMat d_src(src); |
|
cv::gpu::GpuMat dst; |
|
|
|
TEST_CYCLE() cv::gpu::bilateralFilter(d_src, dst, kernel_size, sigma_color, sigma_spatial, borderMode); |
|
|
|
GPU_SANITY_CHECK(dst); |
|
} |
|
else |
|
{ |
|
cv::Mat dst; |
|
|
|
TEST_CYCLE() cv::bilateralFilter(src, dst, kernel_size, sigma_color, sigma_spatial, borderMode); |
|
|
|
CPU_SANITY_CHECK(dst); |
|
} |
|
} |
|
|
|
////////////////////////////////////////////////////////////////////// |
|
// nonLocalMeans |
|
|
|
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int); |
|
|
|
PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_NonLocalMeans, |
|
Combine(GPU_DENOISING_IMAGE_SIZES, |
|
Values<MatDepth>(CV_8U), |
|
GPU_CHANNELS_1_3, |
|
Values(21), |
|
Values(5))) |
|
{ |
|
declare.time(60.0); |
|
|
|
const cv::Size size = GET_PARAM(0); |
|
const int depth = GET_PARAM(1); |
|
const int channels = GET_PARAM(2); |
|
const int search_widow_size = GET_PARAM(3); |
|
const int block_size = GET_PARAM(4); |
|
|
|
const float h = 10; |
|
const int borderMode = cv::BORDER_REFLECT101; |
|
|
|
const int type = CV_MAKE_TYPE(depth, channels); |
|
|
|
cv::Mat src(size, type); |
|
declare.in(src, WARMUP_RNG); |
|
|
|
if (PERF_RUN_GPU()) |
|
{ |
|
const cv::gpu::GpuMat d_src(src); |
|
cv::gpu::GpuMat dst; |
|
|
|
TEST_CYCLE() cv::gpu::nonLocalMeans(d_src, dst, h, search_widow_size, block_size, borderMode); |
|
|
|
GPU_SANITY_CHECK(dst); |
|
} |
|
else |
|
{ |
|
FAIL_NO_CPU(); |
|
} |
|
} |
|
|
|
|
|
////////////////////////////////////////////////////////////////////// |
|
// fastNonLocalMeans |
|
|
|
DEF_PARAM_TEST(Sz_Depth_Cn_WinSz_BlockSz, cv::Size, MatDepth, MatCn, int, int); |
|
|
|
PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_FastNonLocalMeans, |
|
Combine(GPU_DENOISING_IMAGE_SIZES, |
|
Values<MatDepth>(CV_8U), |
|
GPU_CHANNELS_1_3, |
|
Values(21), |
|
Values(7))) |
|
{ |
|
declare.time(60.0); |
|
|
|
const cv::Size size = GET_PARAM(0); |
|
const int depth = GET_PARAM(1); |
|
const int search_widow_size = GET_PARAM(2); |
|
const int block_size = GET_PARAM(3); |
|
|
|
const float h = 10; |
|
const int type = CV_MAKE_TYPE(depth, 1); |
|
|
|
cv::Mat src(size, type); |
|
declare.in(src, WARMUP_RNG); |
|
|
|
if (PERF_RUN_GPU()) |
|
{ |
|
cv::gpu::FastNonLocalMeansDenoising fnlmd; |
|
|
|
const cv::gpu::GpuMat d_src(src); |
|
cv::gpu::GpuMat dst; |
|
|
|
TEST_CYCLE() fnlmd.simpleMethod(d_src, dst, h, search_widow_size, block_size); |
|
|
|
GPU_SANITY_CHECK(dst); |
|
} |
|
else |
|
{ |
|
cv::Mat dst; |
|
|
|
TEST_CYCLE() cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size); |
|
|
|
CPU_SANITY_CHECK(dst); |
|
} |
|
} |
|
|
|
////////////////////////////////////////////////////////////////////// |
|
// fastNonLocalMeans (colored) |
|
|
|
DEF_PARAM_TEST(Sz_Depth_WinSz_BlockSz, cv::Size, MatDepth, int, int); |
|
|
|
PERF_TEST_P(Sz_Depth_WinSz_BlockSz, Denoising_FastNonLocalMeansColored, |
|
Combine(GPU_DENOISING_IMAGE_SIZES, |
|
Values<MatDepth>(CV_8U), |
|
Values(21), |
|
Values(7))) |
|
{ |
|
declare.time(60.0); |
|
|
|
const cv::Size size = GET_PARAM(0); |
|
const int depth = GET_PARAM(1); |
|
const int search_widow_size = GET_PARAM(2); |
|
const int block_size = GET_PARAM(3); |
|
|
|
const float h = 10; |
|
const int type = CV_MAKE_TYPE(depth, 3); |
|
|
|
cv::Mat src(size, type); |
|
declare.in(src, WARMUP_RNG); |
|
|
|
if (PERF_RUN_GPU()) |
|
{ |
|
cv::gpu::FastNonLocalMeansDenoising fnlmd; |
|
|
|
const cv::gpu::GpuMat d_src(src); |
|
cv::gpu::GpuMat dst; |
|
|
|
TEST_CYCLE() fnlmd.labMethod(d_src, dst, h, h, search_widow_size, block_size); |
|
|
|
GPU_SANITY_CHECK(dst); |
|
} |
|
else |
|
{ |
|
cv::Mat dst; |
|
|
|
TEST_CYCLE() cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size); |
|
|
|
CPU_SANITY_CHECK(dst); |
|
} |
|
}
|
|
|