generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

refactored gpu perf tests and fixed sanity tests

Browse Source
This commit is contained in:
Vladislav Vinogradov
2013-02-26 13:49:35 +04:00
parent 0d12f451be
commit a138e12a26
18 changed files with 2366 additions and 2754 deletions
Download Patch File Download Diff File Expand all files Collapse all files

172
modules/gpu/perf/perf_denoising.cpp
View File

@@ -3,8 +3,7 @@
using namespace std;
using namespace testing;
#define GPU_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::szXGA, perf::sz720p, perf::sz1080p)
#define GPU_DENOISING_IMAGE_SIZES testing::Values(perf::szVGA, perf::sz720p)
//////////////////////////////////////////////////////////////////////
// BilateralFilter
@@ -12,96 +11,86 @@ using namespace testing;
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)))
Combine(GPU_DENOISING_IMAGE_SIZES,
Values(CV_8U, CV_32F),
GPU_CHANNELS_1_3,
Values(3, 5, 9)))
{
declare.time(60.0);
cv::Size size = GET_PARAM(0);
int depth = GET_PARAM(1);
int channels = GET_PARAM(2);
int kernel_size = GET_PARAM(3);
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);
float sigma_color = 7;
float sigma_spatial = 5;
int borderMode = cv::BORDER_REFLECT101;
const float sigma_color = 7;
const float sigma_spatial = 5;
const int borderMode = cv::BORDER_REFLECT101;
int type = CV_MAKE_TYPE(depth, channels);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
fillRandom(src);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
if (PERF_RUN_GPU())
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::bilateralFilter(d_src, d_dst, kernel_size, sigma_color, sigma_spatial, borderMode);
TEST_CYCLE() cv::gpu::bilateralFilter(d_src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
TEST_CYCLE()
{
cv::gpu::bilateralFilter(d_src, d_dst, kernel_size, sigma_color, sigma_spatial, borderMode);
}
GPU_SANITY_CHECK(d_dst);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
cv::bilateralFilter(src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
TEST_CYCLE()
{
cv::bilateralFilter(src, dst, kernel_size, sigma_color, sigma_spatial, borderMode);
}
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, 7)))
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
GPU_CHANNELS_1_3,
Values(21),
Values(5)))
{
declare.time(60.0);
cv::Size size = GET_PARAM(0);
int depth = GET_PARAM(1);
int channels = GET_PARAM(2);
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);
int search_widow_size = GET_PARAM(3);
int block_size = GET_PARAM(4);
const float h = 10;
const int borderMode = cv::BORDER_REFLECT101;
float h = 10;
int borderMode = cv::BORDER_REFLECT101;
int type = CV_MAKE_TYPE(depth, channels);
const int type = CV_MAKE_TYPE(depth, channels);
cv::Mat src(size, type);
fillRandom(src);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
cv::gpu::nonLocalMeans(d_src, d_dst, h, search_widow_size, block_size, borderMode);
TEST_CYCLE() cv::gpu::nonLocalMeans(d_src, dst, h, search_widow_size, block_size, borderMode);
TEST_CYCLE()
{
cv::gpu::nonLocalMeans(d_src, d_dst, h, search_widow_size, block_size, borderMode);
}
GPU_SANITY_CHECK(d_dst);
GPU_SANITY_CHECK(dst);
}
else
{
FAIL() << "No such CPU implementation analogy";
FAIL_NO_CPU();
}
}
@@ -112,46 +101,41 @@ PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_NonLocalMeans,
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)))
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
GPU_CHANNELS_1_3,
Values(21),
Values(7)))
{
declare.time(150.0);
declare.time(60.0);
cv::Size size = GET_PARAM(0);
int depth = GET_PARAM(1);
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);
int search_widow_size = GET_PARAM(2);
int block_size = GET_PARAM(3);
float h = 10;
int type = CV_MAKE_TYPE(depth, 1);
const float h = 10;
const int type = CV_MAKE_TYPE(depth, 1);
cv::Mat src(size, type);
fillRandom(src);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::FastNonLocalMeansDenoising fnlmd;
fnlmd.simpleMethod(d_src, d_dst, h, search_widow_size, block_size);
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE()
{
fnlmd.simpleMethod(d_src, d_dst, h, search_widow_size, block_size);
}
TEST_CYCLE() fnlmd.simpleMethod(d_src, dst, h, search_widow_size, block_size);
GPU_SANITY_CHECK(d_dst);
GPU_SANITY_CHECK(dst);
}
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);
}
TEST_CYCLE() cv::fastNlMeansDenoising(src, dst, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
}
@@ -163,47 +147,41 @@ PERF_TEST_P(Sz_Depth_Cn_WinSz_BlockSz, Denoising_FastNonLocalMeans,
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)))
Combine(GPU_DENOISING_IMAGE_SIZES,
Values<MatDepth>(CV_8U),
Values(21),
Values(7)))
{
declare.time(350.0);
declare.time(60.0);
cv::Size size = GET_PARAM(0);
int depth = GET_PARAM(1);
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);
int search_widow_size = GET_PARAM(2);
int block_size = GET_PARAM(3);
float h = 10;
int type = CV_MAKE_TYPE(depth, 3);
const float h = 10;
const int type = CV_MAKE_TYPE(depth, 3);
cv::Mat src(size, type);
fillRandom(src);
declare.in(src, WARMUP_RNG);
if (PERF_RUN_GPU())
{
cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat d_dst;
cv::gpu::FastNonLocalMeansDenoising fnlmd;
fnlmd.labMethod(d_src, d_dst, h, h, search_widow_size, block_size);
const cv::gpu::GpuMat d_src(src);
cv::gpu::GpuMat dst;
TEST_CYCLE()
{
fnlmd.labMethod(d_src, d_dst, h, h, search_widow_size, block_size);
}
TEST_CYCLE() fnlmd.labMethod(d_src, dst, h, h, search_widow_size, block_size);
GPU_SANITY_CHECK(d_dst);
GPU_SANITY_CHECK(dst);
}
else
{
cv::Mat dst;
cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
TEST_CYCLE()
{
cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
}
TEST_CYCLE() cv::fastNlMeansDenoisingColored(src, dst, h, h, block_size, search_widow_size);
CPU_SANITY_CHECK(dst);
}
}
}