mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 14:29:49 +08:00
1811 lines
46 KiB
C++
1811 lines
46 KiB
C++
#include "perf_precomp.hpp"
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using namespace std;
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using namespace testing;
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namespace {
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//////////////////////////////////////////////////////////////////////
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// Remap
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enum{HALF_SIZE=0, UPSIDE_DOWN, REFLECTION_X, REFLECTION_BOTH};
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CV_ENUM(RemapMode, HALF_SIZE, UPSIDE_DOWN, REFLECTION_X, REFLECTION_BOTH);
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#define ALL_REMAP_MODES ValuesIn(RemapMode::all())
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void generateMap(cv::Mat& map_x, cv::Mat& map_y, int remapMode)
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{
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for (int j = 0; j < map_x.rows; ++j)
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{
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for (int i = 0; i < map_x.cols; ++i)
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{
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switch (remapMode)
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{
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case HALF_SIZE:
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if (i > map_x.cols*0.25 && i < map_x.cols*0.75 && j > map_x.rows*0.25 && j < map_x.rows*0.75)
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{
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map_x.at<float>(j,i) = 2.f * (i - map_x.cols * 0.25f) + 0.5f;
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map_y.at<float>(j,i) = 2.f * (j - map_x.rows * 0.25f) + 0.5f;
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}
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else
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{
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map_x.at<float>(j,i) = 0.f;
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map_y.at<float>(j,i) = 0.f;
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}
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break;
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case UPSIDE_DOWN:
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map_x.at<float>(j,i) = static_cast<float>(i);
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map_y.at<float>(j,i) = static_cast<float>(map_x.rows - j);
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break;
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case REFLECTION_X:
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map_x.at<float>(j,i) = static_cast<float>(map_x.cols - i);
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map_y.at<float>(j,i) = static_cast<float>(j);
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break;
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case REFLECTION_BOTH:
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map_x.at<float>(j,i) = static_cast<float>(map_x.cols - i);
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map_y.at<float>(j,i) = static_cast<float>(map_x.rows - j);
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break;
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} // end of switch
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}
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}
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}
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DEF_PARAM_TEST(Sz_Depth_Cn_Inter_Border_Mode, cv::Size, MatDepth, int, Interpolation, BorderMode, RemapMode);
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PERF_TEST_P(Sz_Depth_Cn_Inter_Border_Mode, ImgProc_Remap, Combine(
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GPU_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F),
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GPU_CHANNELS_1_3_4,
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Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
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ALL_BORDER_MODES,
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ALL_REMAP_MODES))
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{
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declare.time(20.0);
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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 interpolation = GET_PARAM(3);
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int borderMode = GET_PARAM(4);
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int remapMode = GET_PARAM(5);
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int type = CV_MAKE_TYPE(depth, channels);
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cv::Mat src(size, type);
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fillRandom(src);
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cv::Mat xmap(size, CV_32FC1);
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cv::Mat ymap(size, CV_32FC1);
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generateMap(xmap, ymap, remapMode);
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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_xmap(xmap);
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cv::gpu::GpuMat d_ymap(ymap);
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cv::gpu::GpuMat d_dst;
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cv::gpu::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
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TEST_CYCLE()
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{
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cv::gpu::remap(d_src, d_dst, d_xmap, d_ymap, interpolation, borderMode);
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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::remap(src, dst, xmap, ymap, interpolation, borderMode);
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TEST_CYCLE()
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{
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cv::remap(src, dst, xmap, ymap, interpolation, borderMode);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// Resize
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DEF_PARAM_TEST(Sz_Depth_Cn_Inter_Scale, cv::Size, MatDepth, int, Interpolation, double);
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PERF_TEST_P(Sz_Depth_Cn_Inter_Scale, ImgProc_Resize, Combine(
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GPU_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F),
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GPU_CHANNELS_1_3_4,
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ALL_INTERPOLATIONS,
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Values(0.5, 0.3, 2.0)))
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{
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declare.time(20.0);
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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 interpolation = GET_PARAM(3);
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double f = GET_PARAM(4);
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int type = CV_MAKE_TYPE(depth, channels);
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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::resize(d_src, d_dst, cv::Size(), f, f, interpolation);
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TEST_CYCLE()
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{
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cv::gpu::resize(d_src, d_dst, cv::Size(), f, f, interpolation);
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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::resize(src, dst, cv::Size(), f, f, interpolation);
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TEST_CYCLE()
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{
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cv::resize(src, dst, cv::Size(), f, f, interpolation);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// ResizeArea
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DEF_PARAM_TEST(Sz_Depth_Cn_Scale, cv::Size, MatDepth, int, double);
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PERF_TEST_P(Sz_Depth_Cn_Scale, ImgProc_ResizeArea, Combine(
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GPU_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F),
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GPU_CHANNELS_1_3_4,
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Values(0.2, 0.1, 0.05)))
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{
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declare.time(1.0);
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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 interpolation = cv::INTER_AREA;
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double f = GET_PARAM(3);
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int type = CV_MAKE_TYPE(depth, channels);
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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::resize(d_src, d_dst, cv::Size(), f, f, interpolation);
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TEST_CYCLE()
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{
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cv::gpu::resize(d_src, d_dst, cv::Size(), f, f, interpolation);
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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::resize(src, dst, cv::Size(), f, f, interpolation);
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TEST_CYCLE()
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{
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cv::resize(src, dst, cv::Size(), f, f, interpolation);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// WarpAffine
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DEF_PARAM_TEST(Sz_Depth_Cn_Inter_Border, cv::Size, MatDepth, int, Interpolation, BorderMode);
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PERF_TEST_P(Sz_Depth_Cn_Inter_Border, ImgProc_WarpAffine, Combine(
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GPU_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F),
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GPU_CHANNELS_1_3_4,
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Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
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ALL_BORDER_MODES))
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{
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declare.time(20.0);
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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 interpolation = GET_PARAM(3);
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int borderMode = GET_PARAM(4);
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int type = CV_MAKE_TYPE(depth, channels);
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cv::Mat src(size, type);
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fillRandom(src);
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const double aplha = CV_PI / 4;
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double mat[2][3] = { {std::cos(aplha), -std::sin(aplha), src.cols / 2},
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{std::sin(aplha), std::cos(aplha), 0}};
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cv::Mat M(2, 3, CV_64F, (void*) mat);
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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::warpAffine(d_src, d_dst, M, size, interpolation, borderMode);
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TEST_CYCLE()
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{
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cv::gpu::warpAffine(d_src, d_dst, M, size, interpolation, borderMode);
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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::warpAffine(src, dst, M, size, interpolation, borderMode);
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TEST_CYCLE()
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{
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cv::warpAffine(src, dst, M, size, interpolation, borderMode);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// WarpPerspective
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PERF_TEST_P(Sz_Depth_Cn_Inter_Border, ImgProc_WarpPerspective, Combine(
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GPU_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F),
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GPU_CHANNELS_1_3_4,
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Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC)),
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ALL_BORDER_MODES))
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{
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declare.time(20.0);
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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 interpolation = GET_PARAM(3);
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int borderMode = GET_PARAM(4);
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int type = CV_MAKE_TYPE(depth, channels);
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cv::Mat src(size, type);
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fillRandom(src);
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const double aplha = CV_PI / 4;
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double mat[3][3] = { {std::cos(aplha), -std::sin(aplha), src.cols / 2},
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{std::sin(aplha), std::cos(aplha), 0},
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{0.0, 0.0, 1.0}};
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cv::Mat M(3, 3, CV_64F, (void*) mat);
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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::warpPerspective(d_src, d_dst, M, size, interpolation, borderMode);
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TEST_CYCLE()
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{
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cv::gpu::warpPerspective(d_src, d_dst, M, size, interpolation, borderMode);
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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::warpPerspective(src, dst, M, size, interpolation, borderMode);
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TEST_CYCLE()
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{
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cv::warpPerspective(src, dst, M, size, interpolation, borderMode);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// CopyMakeBorder
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DEF_PARAM_TEST(Sz_Depth_Cn_Border, cv::Size, MatDepth, int, BorderMode);
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PERF_TEST_P(Sz_Depth_Cn_Border, ImgProc_CopyMakeBorder, Combine(
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GPU_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F),
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GPU_CHANNELS_1_3_4,
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ALL_BORDER_MODES))
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{
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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 borderMode = GET_PARAM(3);
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int type = CV_MAKE_TYPE(depth, channels);
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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::copyMakeBorder(d_src, d_dst, 5, 5, 5, 5, borderMode);
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TEST_CYCLE()
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{
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cv::gpu::copyMakeBorder(d_src, d_dst, 5, 5, 5, 5, borderMode);
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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::copyMakeBorder(src, dst, 5, 5, 5, 5, borderMode);
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TEST_CYCLE()
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{
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cv::copyMakeBorder(src, dst, 5, 5, 5, 5, borderMode);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// Threshold
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CV_ENUM(ThreshOp, cv::THRESH_BINARY, cv::THRESH_BINARY_INV, cv::THRESH_TRUNC, cv::THRESH_TOZERO, cv::THRESH_TOZERO_INV)
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#define ALL_THRESH_OPS ValuesIn(ThreshOp::all())
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DEF_PARAM_TEST(Sz_Depth_Op, cv::Size, MatDepth, ThreshOp);
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PERF_TEST_P(Sz_Depth_Op, ImgProc_Threshold, Combine(
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GPU_TYPICAL_MAT_SIZES,
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Values(CV_8U, CV_16U, CV_32F, CV_64F),
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ALL_THRESH_OPS))
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{
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cv::Size size = GET_PARAM(0);
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int depth = GET_PARAM(1);
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int threshOp = GET_PARAM(2);
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cv::Mat src(size, depth);
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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::threshold(d_src, d_dst, 100.0, 255.0, threshOp);
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TEST_CYCLE()
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{
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cv::gpu::threshold(d_src, d_dst, 100.0, 255.0, threshOp);
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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::threshold(src, dst, 100.0, 255.0, threshOp);
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TEST_CYCLE()
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{
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cv::threshold(src, dst, 100.0, 255.0, threshOp);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// Integral
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PERF_TEST_P(Sz, ImgProc_Integral, GPU_TYPICAL_MAT_SIZES)
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{
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cv::Size size = GetParam();
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cv::Mat src(size, CV_8UC1);
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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::GpuMat d_buf;
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cv::gpu::integralBuffered(d_src, d_dst, d_buf);
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TEST_CYCLE()
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{
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cv::gpu::integralBuffered(d_src, d_dst, d_buf);
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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::integral(src, dst);
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TEST_CYCLE()
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{
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cv::integral(src, dst);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// IntegralSqr
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PERF_TEST_P(Sz, ImgProc_IntegralSqr, GPU_TYPICAL_MAT_SIZES)
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{
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cv::Size size = GetParam();
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cv::Mat src(size, CV_8UC1);
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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::sqrIntegral(d_src, d_dst);
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TEST_CYCLE()
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{
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cv::gpu::sqrIntegral(d_src, d_dst);
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}
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}
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else
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{
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FAIL();
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}
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}
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//////////////////////////////////////////////////////////////////////
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// HistEvenC1
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PERF_TEST_P(Sz_Depth, ImgProc_HistEvenC1, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_16S)))
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{
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cv::Size size = GET_PARAM(0);
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int depth = GET_PARAM(1);
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cv::Mat src(size, depth);
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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_hist;
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cv::gpu::GpuMat d_buf;
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cv::gpu::histEven(d_src, d_hist, d_buf, 30, 0, 180);
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TEST_CYCLE()
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{
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cv::gpu::histEven(d_src, d_hist, d_buf, 30, 0, 180);
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}
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}
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else
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{
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int hbins = 30;
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float hranges[] = {0.0f, 180.0f};
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int histSize[] = {hbins};
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const float* ranges[] = {hranges};
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int channels[] = {0};
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cv::Mat hist;
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cv::calcHist(&src, 1, channels, cv::Mat(), hist, 1, histSize, ranges);
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TEST_CYCLE()
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{
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cv::calcHist(&src, 1, channels, cv::Mat(), hist, 1, histSize, ranges);
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}
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}
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}
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//////////////////////////////////////////////////////////////////////
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// HistEvenC4
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PERF_TEST_P(Sz_Depth, ImgProc_HistEvenC4, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_16S)))
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{
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cv::Size size = GET_PARAM(0);
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int depth = GET_PARAM(1);
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cv::Mat src(size, CV_MAKE_TYPE(depth, 4));
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fillRandom(src);
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int histSize[] = {30, 30, 30, 30};
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int lowerLevel[] = {0, 0, 0, 0};
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int upperLevel[] = {180, 180, 180, 180};
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if (runOnGpu)
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|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_hist[4];
|
|
cv::gpu::GpuMat d_buf;
|
|
|
|
cv::gpu::histEven(d_src, d_hist, d_buf, histSize, lowerLevel, upperLevel);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::histEven(d_src, d_hist, d_buf, histSize, lowerLevel, upperLevel);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// CalcHist
|
|
|
|
PERF_TEST_P(Sz, ImgProc_CalcHist, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
cv::Size size = GetParam();
|
|
|
|
cv::Mat src(size, CV_8UC1);
|
|
fillRandom(src);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_hist;
|
|
cv::gpu::GpuMat d_buf;
|
|
|
|
cv::gpu::calcHist(d_src, d_hist, d_buf);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::calcHist(d_src, d_hist, d_buf);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// EqualizeHist
|
|
|
|
PERF_TEST_P(Sz, ImgProc_EqualizeHist, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
cv::Size size = GetParam();
|
|
|
|
cv::Mat src(size, CV_8UC1);
|
|
fillRandom(src);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_dst;
|
|
cv::gpu::GpuMat d_hist;
|
|
cv::gpu::GpuMat d_buf;
|
|
|
|
cv::gpu::equalizeHist(d_src, d_dst, d_hist, d_buf);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::equalizeHist(d_src, d_dst, d_hist, d_buf);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::equalizeHist(src, dst);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::equalizeHist(src, dst);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// ColumnSum
|
|
|
|
PERF_TEST_P(Sz, ImgProc_ColumnSum, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
cv::Size size = GetParam();
|
|
|
|
cv::Mat src(size, CV_32FC1);
|
|
fillRandom(src);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::columnSum(d_src, d_dst);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::columnSum(d_src, d_dst);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// Canny
|
|
|
|
DEF_PARAM_TEST(Image_AppertureSz_L2gradient, string, int, bool);
|
|
|
|
PERF_TEST_P(Image_AppertureSz_L2gradient, ImgProc_Canny, Combine(
|
|
Values("perf/800x600.png", "perf/1280x1024.png", "perf/1680x1050.png"),
|
|
Values(3, 5),
|
|
Bool()))
|
|
{
|
|
string fileName = GET_PARAM(0);
|
|
int apperture_size = GET_PARAM(1);
|
|
bool useL2gradient = GET_PARAM(2);
|
|
|
|
cv::Mat image = readImage(fileName, cv::IMREAD_GRAYSCALE);
|
|
ASSERT_FALSE(image.empty());
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_image(image);
|
|
cv::gpu::GpuMat d_dst;
|
|
cv::gpu::CannyBuf d_buf;
|
|
|
|
cv::gpu::Canny(d_image, d_buf, d_dst, 50.0, 100.0, apperture_size, useL2gradient);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::Canny(d_image, d_buf, d_dst, 50.0, 100.0, apperture_size, useL2gradient);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::Canny(image, dst, 50.0, 100.0, apperture_size, useL2gradient);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::Canny(image, dst, 50.0, 100.0, apperture_size, useL2gradient);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// MeanShiftFiltering
|
|
|
|
DEF_PARAM_TEST_1(Image, string);
|
|
|
|
PERF_TEST_P(Image, ImgProc_MeanShiftFiltering, Values<string>("gpu/meanshift/cones.png"))
|
|
{
|
|
declare.time(15.0);
|
|
|
|
cv::Mat img = readImage(GetParam());
|
|
ASSERT_FALSE(img.empty());
|
|
|
|
cv::Mat rgba;
|
|
cv::cvtColor(img, rgba, cv::COLOR_BGR2BGRA);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(rgba);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::meanShiftFiltering(d_src, d_dst, 50, 50);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::meanShiftFiltering(d_src, d_dst, 50, 50);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::pyrMeanShiftFiltering(img, dst, 50, 50);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::pyrMeanShiftFiltering(img, dst, 50, 50);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// MeanShiftProc
|
|
|
|
PERF_TEST_P(Image, ImgProc_MeanShiftProc, Values<string>("gpu/meanshift/cones.png"))
|
|
{
|
|
declare.time(5.0);
|
|
|
|
cv::Mat img = readImage(GetParam());
|
|
ASSERT_FALSE(img.empty());
|
|
|
|
cv::Mat rgba;
|
|
cv::cvtColor(img, rgba, cv::COLOR_BGR2BGRA);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(rgba);
|
|
cv::gpu::GpuMat d_dstr;
|
|
cv::gpu::GpuMat d_dstsp;
|
|
|
|
cv::gpu::meanShiftProc(d_src, d_dstr, d_dstsp, 50, 50);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::meanShiftProc(d_src, d_dstr, d_dstsp, 50, 50);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// MeanShiftSegmentation
|
|
|
|
PERF_TEST_P(Image, ImgProc_MeanShiftSegmentation, Values<string>("gpu/meanshift/cones.png"))
|
|
{
|
|
declare.time(5.0);
|
|
|
|
cv::Mat img = readImage(GetParam());
|
|
ASSERT_FALSE(img.empty());
|
|
|
|
cv::Mat rgba;
|
|
cv::cvtColor(img, rgba, cv::COLOR_BGR2BGRA);
|
|
|
|
cv::Mat dst;
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(rgba);
|
|
|
|
cv::gpu::meanShiftSegmentation(d_src, dst, 10, 10, 20);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::meanShiftSegmentation(d_src, dst, 10, 10, 20);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// BlendLinear
|
|
|
|
PERF_TEST_P(Sz_Depth_Cn, ImgProc_BlendLinear, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U, CV_32F), GPU_CHANNELS_1_3_4))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int depth = GET_PARAM(1);
|
|
int channels = GET_PARAM(2);
|
|
|
|
int type = CV_MAKE_TYPE(depth, channels);
|
|
|
|
cv::Mat img1(size, type);
|
|
fillRandom(img1);
|
|
|
|
cv::Mat img2(size, type);
|
|
fillRandom(img2);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_img1(img1);
|
|
cv::gpu::GpuMat d_img2(img2);
|
|
cv::gpu::GpuMat d_weights1(size, CV_32FC1, cv::Scalar::all(0.5));
|
|
cv::gpu::GpuMat d_weights2(size, CV_32FC1, cv::Scalar::all(0.5));
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::blendLinear(d_img1, d_img2, d_weights1, d_weights2, d_dst);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::blendLinear(d_img1, d_img2, d_weights1, d_weights2, d_dst);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// Convolve
|
|
|
|
DEF_PARAM_TEST(Sz_KernelSz_Ccorr, cv::Size, int, bool);
|
|
|
|
PERF_TEST_P(Sz_KernelSz_Ccorr, ImgProc_Convolve, Combine(GPU_TYPICAL_MAT_SIZES, Values(17, 27, 32, 64), Bool()))
|
|
{
|
|
declare.time(10.0);
|
|
|
|
cv::Size size = GET_PARAM(0);
|
|
int templ_size = GET_PARAM(1);
|
|
bool ccorr = GET_PARAM(2);
|
|
|
|
cv::Mat image(size, CV_32FC1);
|
|
image.setTo(1.0);
|
|
|
|
cv::Mat templ(templ_size, templ_size, CV_32FC1);
|
|
templ.setTo(1.0);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_image = cv::gpu::createContinuous(size, CV_32FC1);
|
|
d_image.upload(image);
|
|
|
|
cv::gpu::GpuMat d_templ = cv::gpu::createContinuous(templ_size, templ_size, CV_32FC1);
|
|
d_templ.upload(templ);
|
|
|
|
cv::gpu::GpuMat d_dst;
|
|
cv::gpu::ConvolveBuf d_buf;
|
|
|
|
cv::gpu::convolve(d_image, d_templ, d_dst, ccorr, d_buf);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::convolve(d_image, d_templ, d_dst, ccorr, d_buf);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
ASSERT_FALSE(ccorr);
|
|
|
|
cv::Mat dst;
|
|
|
|
cv::filter2D(image, dst, image.depth(), templ);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::filter2D(image, dst, image.depth(), templ);
|
|
}
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// MatchTemplate8U
|
|
|
|
CV_ENUM(TemplateMethod, cv::TM_SQDIFF, cv::TM_SQDIFF_NORMED, cv::TM_CCORR, cv::TM_CCORR_NORMED, cv::TM_CCOEFF, cv::TM_CCOEFF_NORMED)
|
|
#define ALL_TEMPLATE_METHODS ValuesIn(TemplateMethod::all())
|
|
|
|
DEF_PARAM_TEST(Sz_TemplateSz_Cn_Method, cv::Size, cv::Size, int, TemplateMethod);
|
|
|
|
PERF_TEST_P(Sz_TemplateSz_Cn_Method, ImgProc_MatchTemplate8U, Combine(
|
|
GPU_TYPICAL_MAT_SIZES,
|
|
Values(cv::Size(5, 5), cv::Size(16, 16), cv::Size(30, 30)),
|
|
GPU_CHANNELS_1_3_4,
|
|
ALL_TEMPLATE_METHODS))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
cv::Size templ_size = GET_PARAM(1);
|
|
int cn = GET_PARAM(2);
|
|
int method = GET_PARAM(3);
|
|
|
|
cv::Mat image(size, CV_MAKE_TYPE(CV_8U, cn));
|
|
fillRandom(image);
|
|
|
|
cv::Mat templ(templ_size, CV_MAKE_TYPE(CV_8U, cn));
|
|
fillRandom(templ);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_image(image);
|
|
cv::gpu::GpuMat d_templ(templ);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::matchTemplate(d_image, d_templ, d_dst, method);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::matchTemplate(d_image, d_templ, d_dst, method);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::matchTemplate(image, templ, dst, method);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::matchTemplate(image, templ, dst, method);
|
|
}
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// MatchTemplate32F
|
|
|
|
PERF_TEST_P(Sz_TemplateSz_Cn_Method, ImgProc_MatchTemplate32F, Combine(
|
|
GPU_TYPICAL_MAT_SIZES,
|
|
Values(cv::Size(5, 5), cv::Size(16, 16), cv::Size(30, 30)),
|
|
GPU_CHANNELS_1_3_4,
|
|
Values(TemplateMethod(cv::TM_SQDIFF), TemplateMethod(cv::TM_CCORR))))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
cv::Size templ_size = GET_PARAM(1);
|
|
int cn = GET_PARAM(2);
|
|
int method = GET_PARAM(3);
|
|
|
|
cv::Mat image(size, CV_MAKE_TYPE(CV_32F, cn));
|
|
fillRandom(image);
|
|
|
|
cv::Mat templ(templ_size, CV_MAKE_TYPE(CV_32F, cn));
|
|
fillRandom(templ);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_image(image);
|
|
cv::gpu::GpuMat d_templ(templ);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::matchTemplate(d_image, d_templ, d_dst, method);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::matchTemplate(d_image, d_templ, d_dst, method);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::matchTemplate(image, templ, dst, method);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::matchTemplate(image, templ, dst, method);
|
|
}
|
|
}
|
|
};
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// MulSpectrums
|
|
|
|
CV_FLAGS(DftFlags, 0, cv::DFT_INVERSE, cv::DFT_SCALE, cv::DFT_ROWS, cv::DFT_COMPLEX_OUTPUT, cv::DFT_REAL_OUTPUT)
|
|
|
|
DEF_PARAM_TEST(Sz_Flags, cv::Size, DftFlags);
|
|
|
|
PERF_TEST_P(Sz_Flags, ImgProc_MulSpectrums, Combine(
|
|
GPU_TYPICAL_MAT_SIZES,
|
|
Values(0, DftFlags(cv::DFT_ROWS))))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int flag = GET_PARAM(1);
|
|
|
|
cv::Mat a(size, CV_32FC2);
|
|
fillRandom(a, 0, 100);
|
|
|
|
cv::Mat b(size, CV_32FC2);
|
|
fillRandom(b, 0, 100);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_a(a);
|
|
cv::gpu::GpuMat d_b(b);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::mulSpectrums(d_a, d_b, d_dst, flag);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::mulSpectrums(d_a, d_b, d_dst, flag);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::mulSpectrums(a, b, dst, flag);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::mulSpectrums(a, b, dst, flag);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// MulAndScaleSpectrums
|
|
|
|
PERF_TEST_P(Sz, ImgProc_MulAndScaleSpectrums, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
cv::Size size = GetParam();
|
|
|
|
float scale = 1.f / size.area();
|
|
|
|
cv::Mat src1(size, CV_32FC2);
|
|
fillRandom(src1, 0, 100);
|
|
|
|
cv::Mat src2(size, CV_32FC2);
|
|
fillRandom(src2, 0, 100);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src1(src1);
|
|
cv::gpu::GpuMat d_src2(src2);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::mulAndScaleSpectrums(d_src1, d_src2, d_dst, cv::DFT_ROWS, scale, false);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::mulAndScaleSpectrums(d_src1, d_src2, d_dst, cv::DFT_ROWS, scale, false);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// Dft
|
|
|
|
PERF_TEST_P(Sz_Flags, ImgProc_Dft, Combine(
|
|
GPU_TYPICAL_MAT_SIZES,
|
|
Values(0, DftFlags(cv::DFT_ROWS), DftFlags(cv::DFT_INVERSE))))
|
|
{
|
|
declare.time(10.0);
|
|
|
|
cv::Size size = GET_PARAM(0);
|
|
int flag = GET_PARAM(1);
|
|
|
|
cv::Mat src(size, CV_32FC2);
|
|
fillRandom(src, 0, 100);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::dft(d_src, d_dst, size, flag);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::dft(d_src, d_dst, size, flag);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::dft(src, dst, flag);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::dft(src, dst, flag);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// CornerHarris
|
|
|
|
DEF_PARAM_TEST(Image_Type_Border_BlockSz_ApertureSz, string, MatType, BorderMode, int, int);
|
|
|
|
PERF_TEST_P(Image_Type_Border_BlockSz_ApertureSz, ImgProc_CornerHarris, Combine(
|
|
Values<string>("gpu/stereobm/aloe-L.png"),
|
|
Values(CV_8UC1, CV_32FC1),
|
|
Values(BorderMode(cv::BORDER_REFLECT101), BorderMode(cv::BORDER_REPLICATE), BorderMode(cv::BORDER_REFLECT)),
|
|
Values(3, 5, 7),
|
|
Values(0, 3, 5, 7)))
|
|
{
|
|
string fileName = GET_PARAM(0);
|
|
int type = GET_PARAM(1);
|
|
int borderMode = GET_PARAM(2);
|
|
int blockSize = GET_PARAM(3);
|
|
int apertureSize = GET_PARAM(4);
|
|
|
|
cv::Mat img = readImage(fileName, cv::IMREAD_GRAYSCALE);
|
|
ASSERT_FALSE(img.empty());
|
|
img.convertTo(img, type, type == CV_32F ? 1.0 / 255.0 : 1.0);
|
|
|
|
double k = 0.5;
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_img(img);
|
|
cv::gpu::GpuMat d_dst;
|
|
cv::gpu::GpuMat d_Dx;
|
|
cv::gpu::GpuMat d_Dy;
|
|
cv::gpu::GpuMat d_buf;
|
|
|
|
cv::gpu::cornerHarris(d_img, d_dst, d_Dx, d_Dy, d_buf, blockSize, apertureSize, k, borderMode);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::cornerHarris(d_img, d_dst, d_Dx, d_Dy, d_buf, blockSize, apertureSize, k, borderMode);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::cornerHarris(img, dst, blockSize, apertureSize, k, borderMode);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::cornerHarris(img, dst, blockSize, apertureSize, k, borderMode);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// CornerMinEigenVal
|
|
|
|
PERF_TEST_P(Image_Type_Border_BlockSz_ApertureSz, ImgProc_CornerMinEigenVal, Combine(
|
|
Values<string>("gpu/stereobm/aloe-L.png"),
|
|
Values(CV_8UC1, CV_32FC1),
|
|
Values(BorderMode(cv::BORDER_REFLECT101), BorderMode(cv::BORDER_REPLICATE), BorderMode(cv::BORDER_REFLECT)),
|
|
Values(3, 5, 7),
|
|
Values(0, 3, 5, 7)))
|
|
{
|
|
string fileName = GET_PARAM(0);
|
|
int type = GET_PARAM(1);
|
|
int borderMode = GET_PARAM(2);
|
|
int blockSize = GET_PARAM(3);
|
|
int apertureSize = GET_PARAM(4);
|
|
|
|
cv::Mat img = readImage(fileName, cv::IMREAD_GRAYSCALE);
|
|
ASSERT_FALSE(img.empty());
|
|
|
|
img.convertTo(img, type, type == CV_32F ? 1.0 / 255.0 : 1.0);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_img(img);
|
|
cv::gpu::GpuMat d_dst;
|
|
cv::gpu::GpuMat d_Dx;
|
|
cv::gpu::GpuMat d_Dy;
|
|
cv::gpu::GpuMat d_buf;
|
|
|
|
cv::gpu::cornerMinEigenVal(d_img, d_dst, d_Dx, d_Dy, d_buf, blockSize, apertureSize, borderMode);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::cornerMinEigenVal(d_img, d_dst, d_Dx, d_Dy, d_buf, blockSize, apertureSize, borderMode);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::cornerMinEigenVal(img, dst, blockSize, apertureSize, borderMode);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::cornerMinEigenVal(img, dst, blockSize, apertureSize, borderMode);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// BuildWarpPlaneMaps
|
|
|
|
PERF_TEST_P(Sz, ImgProc_BuildWarpPlaneMaps, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
cv::Size size = GetParam();
|
|
|
|
cv::Mat K = cv::Mat::eye(3, 3, CV_32FC1);
|
|
cv::Mat R = cv::Mat::ones(3, 3, CV_32FC1);
|
|
cv::Mat T = cv::Mat::zeros(1, 3, CV_32F);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_map_x;
|
|
cv::gpu::GpuMat d_map_y;
|
|
|
|
cv::gpu::buildWarpPlaneMaps(size, cv::Rect(0, 0, size.width, size.height), K, R, T, 1.0, d_map_x, d_map_y);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::buildWarpPlaneMaps(size, cv::Rect(0, 0, size.width, size.height), K, R, T, 1.0, d_map_x, d_map_y);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// BuildWarpCylindricalMaps
|
|
|
|
PERF_TEST_P(Sz, ImgProc_BuildWarpCylindricalMaps, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
cv::Size size = GetParam();
|
|
|
|
cv::Mat K = cv::Mat::eye(3, 3, CV_32FC1);
|
|
cv::Mat R = cv::Mat::ones(3, 3, CV_32FC1);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_map_x;
|
|
cv::gpu::GpuMat d_map_y;
|
|
|
|
cv::gpu::buildWarpCylindricalMaps(size, cv::Rect(0, 0, size.width, size.height), K, R, 1.0, d_map_x, d_map_y);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::buildWarpCylindricalMaps(size, cv::Rect(0, 0, size.width, size.height), K, R, 1.0, d_map_x, d_map_y);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// BuildWarpSphericalMaps
|
|
|
|
PERF_TEST_P(Sz, ImgProc_BuildWarpSphericalMaps, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
cv::Size size = GetParam();
|
|
|
|
cv::Mat K = cv::Mat::eye(3, 3, CV_32FC1);
|
|
cv::Mat R = cv::Mat::ones(3, 3, CV_32FC1);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_map_x;
|
|
cv::gpu::GpuMat d_map_y;
|
|
|
|
cv::gpu::buildWarpSphericalMaps(size, cv::Rect(0, 0, size.width, size.height), K, R, 1.0, d_map_x, d_map_y);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::buildWarpSphericalMaps(size, cv::Rect(0, 0, size.width, size.height), K, R, 1.0, d_map_x, d_map_y);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// Rotate
|
|
|
|
DEF_PARAM_TEST(Sz_Depth_Cn_Inter, cv::Size, MatDepth, int, Interpolation);
|
|
|
|
PERF_TEST_P(Sz_Depth_Cn_Inter, ImgProc_Rotate, Combine(
|
|
GPU_TYPICAL_MAT_SIZES,
|
|
Values(CV_8U, CV_16U, CV_32F),
|
|
GPU_CHANNELS_1_3_4,
|
|
Values(Interpolation(cv::INTER_NEAREST), Interpolation(cv::INTER_LINEAR), Interpolation(cv::INTER_CUBIC))))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int depth = GET_PARAM(1);
|
|
int channels = GET_PARAM(2);
|
|
int interpolation = GET_PARAM(3);
|
|
|
|
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::rotate(d_src, d_dst, size, 30.0, 0, 0, interpolation);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::rotate(d_src, d_dst, size, 30.0, 0, 0, interpolation);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// PyrDown
|
|
|
|
PERF_TEST_P(Sz_Depth_Cn, ImgProc_PyrDown, Combine(
|
|
GPU_TYPICAL_MAT_SIZES,
|
|
Values(CV_8U, CV_16U, CV_32F),
|
|
GPU_CHANNELS_1_3_4))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int depth = GET_PARAM(1);
|
|
int channels = GET_PARAM(2);
|
|
|
|
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::pyrDown(d_src, d_dst);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::pyrDown(d_src, d_dst);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::pyrDown(src, dst);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::pyrDown(src, dst);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// PyrUp
|
|
|
|
PERF_TEST_P(Sz_Depth_Cn, ImgProc_PyrUp, Combine(
|
|
GPU_TYPICAL_MAT_SIZES,
|
|
Values(CV_8U, CV_16U, CV_32F),
|
|
GPU_CHANNELS_1_3_4))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int depth = GET_PARAM(1);
|
|
int channels = GET_PARAM(2);
|
|
|
|
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::pyrUp(d_src, d_dst);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::pyrUp(d_src, d_dst);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::pyrUp(src, dst);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::pyrUp(src, dst);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// CvtColor
|
|
|
|
DEF_PARAM_TEST(Sz_Depth_Code, cv::Size, MatDepth, CvtColorInfo);
|
|
|
|
PERF_TEST_P(Sz_Depth_Code, ImgProc_CvtColor, Combine(
|
|
GPU_TYPICAL_MAT_SIZES,
|
|
Values(CV_8U, CV_16U, CV_32F),
|
|
Values(CvtColorInfo(4, 4, cv::COLOR_RGBA2BGRA),
|
|
CvtColorInfo(4, 1, cv::COLOR_BGRA2GRAY),
|
|
CvtColorInfo(1, 4, cv::COLOR_GRAY2BGRA),
|
|
CvtColorInfo(3, 3, cv::COLOR_BGR2XYZ),
|
|
CvtColorInfo(3, 3, cv::COLOR_XYZ2BGR),
|
|
CvtColorInfo(3, 3, cv::COLOR_BGR2YCrCb),
|
|
CvtColorInfo(3, 3, cv::COLOR_YCrCb2BGR),
|
|
CvtColorInfo(3, 3, cv::COLOR_BGR2YUV),
|
|
CvtColorInfo(3, 3, cv::COLOR_YUV2BGR),
|
|
CvtColorInfo(3, 3, cv::COLOR_BGR2HSV),
|
|
CvtColorInfo(3, 3, cv::COLOR_HSV2BGR),
|
|
CvtColorInfo(3, 3, cv::COLOR_BGR2HLS),
|
|
CvtColorInfo(3, 3, cv::COLOR_HLS2BGR),
|
|
CvtColorInfo(3, 3, cv::COLOR_BGR2Lab),
|
|
CvtColorInfo(3, 3, cv::COLOR_RGB2Lab),
|
|
CvtColorInfo(3, 3, cv::COLOR_BGR2Luv),
|
|
CvtColorInfo(3, 3, cv::COLOR_RGB2Luv),
|
|
CvtColorInfo(3, 3, cv::COLOR_Lab2BGR),
|
|
CvtColorInfo(3, 3, cv::COLOR_Lab2RGB),
|
|
CvtColorInfo(3, 3, cv::COLOR_Luv2BGR),
|
|
CvtColorInfo(3, 3, cv::COLOR_Luv2RGB),
|
|
CvtColorInfo(1, 3, cv::COLOR_BayerBG2BGR),
|
|
CvtColorInfo(1, 3, cv::COLOR_BayerGB2BGR),
|
|
CvtColorInfo(1, 3, cv::COLOR_BayerRG2BGR),
|
|
CvtColorInfo(1, 3, cv::COLOR_BayerGR2BGR),
|
|
CvtColorInfo(4, 4, cv::COLOR_RGBA2mRGBA))))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int depth = GET_PARAM(1);
|
|
CvtColorInfo info = GET_PARAM(2);
|
|
|
|
cv::Mat src(size, CV_MAKETYPE(depth, info.scn));
|
|
fillRandom(src);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::cvtColor(d_src, d_dst, info.code, info.dcn);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::cvtColor(d_src, d_dst, info.code, info.dcn);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat dst;
|
|
|
|
cv::cvtColor(src, dst, info.code, info.dcn);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::cvtColor(src, dst, info.code, info.dcn);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// SwapChannels
|
|
|
|
PERF_TEST_P(Sz, ImgProc_SwapChannels, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
cv::Size size = GetParam();
|
|
|
|
cv::Mat src(size, CV_8UC4);
|
|
fillRandom(src);
|
|
|
|
const int dstOrder[] = {2, 1, 0, 3};
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
|
|
cv::gpu::swapChannels(d_src, dstOrder);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::swapChannels(d_src, dstOrder);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// AlphaComp
|
|
|
|
CV_ENUM(AlphaOp, cv::gpu::ALPHA_OVER, cv::gpu::ALPHA_IN, cv::gpu::ALPHA_OUT, cv::gpu::ALPHA_ATOP, cv::gpu::ALPHA_XOR, cv::gpu::ALPHA_PLUS, cv::gpu::ALPHA_OVER_PREMUL, cv::gpu::ALPHA_IN_PREMUL, cv::gpu::ALPHA_OUT_PREMUL, cv::gpu::ALPHA_ATOP_PREMUL, cv::gpu::ALPHA_XOR_PREMUL, cv::gpu::ALPHA_PLUS_PREMUL, cv::gpu::ALPHA_PREMUL)
|
|
#define ALL_ALPHA_OPS ValuesIn(AlphaOp::all())
|
|
|
|
DEF_PARAM_TEST(Sz_Type_Op, cv::Size, MatType, AlphaOp);
|
|
|
|
PERF_TEST_P(Sz_Type_Op, ImgProc_AlphaComp, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8UC4, CV_16UC4, CV_32SC4, CV_32FC4), ALL_ALPHA_OPS))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int type = GET_PARAM(1);
|
|
int alpha_op = GET_PARAM(2);
|
|
|
|
cv::Mat img1(size, type);
|
|
fillRandom(img1);
|
|
|
|
cv::Mat img2(size, type);
|
|
fillRandom(img2);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_img1(img1);
|
|
cv::gpu::GpuMat d_img2(img2);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::alphaComp(d_img1, d_img2, d_dst, alpha_op);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::alphaComp(d_img1, d_img2, d_dst, alpha_op);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// ImagePyramidBuild
|
|
|
|
PERF_TEST_P(Sz_Depth_Cn, ImgProc_ImagePyramidBuild, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F), GPU_CHANNELS_1_3_4))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int depth = GET_PARAM(1);
|
|
int channels = GET_PARAM(2);
|
|
|
|
int type = CV_MAKE_TYPE(depth, channels);
|
|
|
|
cv::Mat src(size, type);
|
|
fillRandom(src);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
|
|
cv::gpu::ImagePyramid d_pyr;
|
|
|
|
d_pyr.build(d_src, 5);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
d_pyr.build(d_src, 5);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// ImagePyramidGetLayer
|
|
|
|
PERF_TEST_P(Sz_Depth_Cn, ImgProc_ImagePyramidGetLayer, Combine(GPU_TYPICAL_MAT_SIZES, Values(CV_8U, CV_16U, CV_32F), GPU_CHANNELS_1_3_4))
|
|
{
|
|
cv::Size size = GET_PARAM(0);
|
|
int depth = GET_PARAM(1);
|
|
int channels = GET_PARAM(2);
|
|
|
|
int type = CV_MAKE_TYPE(depth, channels);
|
|
|
|
cv::Mat src(size, type);
|
|
fillRandom(src);
|
|
|
|
cv::Size dstSize(size.width / 2 + 10, size.height / 2 + 10);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_dst;
|
|
|
|
cv::gpu::ImagePyramid d_pyr(d_src, 3);
|
|
|
|
d_pyr.getLayer(d_dst, dstSize);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
d_pyr.getLayer(d_dst, dstSize);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
FAIL();
|
|
}
|
|
}
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|
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|
//////////////////////////////////////////////////////////////////////
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// HoughLines
|
|
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PERF_TEST_P(Sz, ImgProc_HoughLines, GPU_TYPICAL_MAT_SIZES)
|
|
{
|
|
declare.time(30.0);
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|
|
|
const cv::Size size = GetParam();
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|
|
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const float rho = 1.0f;
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const float theta = static_cast<float>(CV_PI / 180.0);
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const int threshold = 300;
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|
|
|
cv::RNG rng(123456789);
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|
|
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cv::Mat src(size, CV_8UC1, cv::Scalar::all(0));
|
|
|
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const int numLines = rng.uniform(100, 300);
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for (int i = 0; i < numLines; ++i)
|
|
{
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|
cv::Point p1(rng.uniform(0, src.cols), rng.uniform(0, src.rows));
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|
cv::Point p2(rng.uniform(0, src.cols), rng.uniform(0, src.rows));
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|
cv::line(src, p1, p2, cv::Scalar::all(255), 2);
|
|
}
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_lines;
|
|
cv::gpu::HoughLinesBuf d_buf;
|
|
|
|
cv::gpu::HoughLines(d_src, d_lines, d_buf, rho, theta, threshold);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::HoughLines(d_src, d_lines, d_buf, rho, theta, threshold);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
std::vector<cv::Vec2f> lines;
|
|
cv::HoughLines(src, lines, rho, theta, threshold);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::HoughLines(src, lines, rho, theta, threshold);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// HoughCircles
|
|
|
|
DEF_PARAM_TEST(Sz_Dp_MinDist, cv::Size, float, float);
|
|
|
|
PERF_TEST_P(Sz_Dp_MinDist, ImgProc_HoughCircles, Combine(GPU_TYPICAL_MAT_SIZES, Values(1.0f, 2.0f, 4.0f), Values(1.0f, 10.0f)))
|
|
{
|
|
declare.time(30.0);
|
|
|
|
const cv::Size size = GET_PARAM(0);
|
|
const float dp = GET_PARAM(1);
|
|
const float minDist = GET_PARAM(2);
|
|
|
|
const int minRadius = 10;
|
|
const int maxRadius = 30;
|
|
const int cannyThreshold = 100;
|
|
const int votesThreshold = 15;
|
|
|
|
cv::RNG rng(123456789);
|
|
|
|
cv::Mat src(size, CV_8UC1, cv::Scalar::all(0));
|
|
|
|
const int numCircles = rng.uniform(50, 100);
|
|
for (int i = 0; i < numCircles; ++i)
|
|
{
|
|
cv::Point center(rng.uniform(0, src.cols), rng.uniform(0, src.rows));
|
|
const int radius = rng.uniform(minRadius, maxRadius + 1);
|
|
|
|
cv::circle(src, center, radius, cv::Scalar::all(255), -1);
|
|
}
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_src(src);
|
|
cv::gpu::GpuMat d_circles;
|
|
cv::gpu::HoughCirclesBuf d_buf;
|
|
|
|
cv::gpu::HoughCircles(d_src, d_circles, d_buf, CV_HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::gpu::HoughCircles(d_src, d_circles, d_buf, CV_HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
std::vector<cv::Vec3f> circles;
|
|
|
|
cv::HoughCircles(src, circles, CV_HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
cv::HoughCircles(src, circles, CV_HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
|
|
}
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// GeneralizedHough
|
|
|
|
CV_FLAGS(GHMethod, cv::GHT_POSITION, cv::GHT_SCALE, cv::GHT_ROTATION);
|
|
|
|
DEF_PARAM_TEST(Method_Sz, GHMethod, cv::Size);
|
|
|
|
PERF_TEST_P(Method_Sz, ImgProc_GeneralizedHough, Combine(
|
|
Values(GHMethod(cv::GHT_POSITION), GHMethod(cv::GHT_POSITION | cv::GHT_SCALE), GHMethod(cv::GHT_POSITION | cv::GHT_ROTATION), GHMethod(cv::GHT_POSITION | cv::GHT_SCALE | cv::GHT_ROTATION)),
|
|
GPU_TYPICAL_MAT_SIZES))
|
|
{
|
|
declare.time(10);
|
|
|
|
const int method = GET_PARAM(0);
|
|
const cv::Size imageSize = GET_PARAM(1);
|
|
|
|
const cv::Mat templ = readImage("cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
|
|
ASSERT_FALSE(templ.empty());
|
|
|
|
cv::Mat image(imageSize, CV_8UC1, cv::Scalar::all(0));
|
|
|
|
cv::RNG rng(123456789);
|
|
const int objCount = rng.uniform(5, 15);
|
|
for (int i = 0; i < objCount; ++i)
|
|
{
|
|
double scale = rng.uniform(0.7, 1.3);
|
|
bool rotate = 1 == rng.uniform(0, 2);
|
|
|
|
cv::Mat obj;
|
|
cv::resize(templ, obj, cv::Size(), scale, scale);
|
|
if (rotate)
|
|
obj = obj.t();
|
|
|
|
cv::Point pos;
|
|
|
|
pos.x = rng.uniform(0, image.cols - obj.cols);
|
|
pos.y = rng.uniform(0, image.rows - obj.rows);
|
|
|
|
cv::Mat roi = image(cv::Rect(pos, obj.size()));
|
|
cv::add(roi, obj, roi);
|
|
}
|
|
|
|
cv::Mat edges;
|
|
cv::Canny(image, edges, 50, 100);
|
|
|
|
cv::Mat dx, dy;
|
|
cv::Sobel(image, dx, CV_32F, 1, 0);
|
|
cv::Sobel(image, dy, CV_32F, 0, 1);
|
|
|
|
if (runOnGpu)
|
|
{
|
|
cv::gpu::GpuMat d_edges(edges);
|
|
cv::gpu::GpuMat d_dx(dx);
|
|
cv::gpu::GpuMat d_dy(dy);
|
|
cv::gpu::GpuMat d_position;
|
|
|
|
cv::Ptr<cv::gpu::GeneralizedHough_GPU> d_hough = cv::gpu::GeneralizedHough_GPU::create(method);
|
|
if (method & cv::GHT_ROTATION)
|
|
{
|
|
d_hough->set("maxAngle", 90.0);
|
|
d_hough->set("angleStep", 2.0);
|
|
}
|
|
|
|
d_hough->setTemplate(cv::gpu::GpuMat(templ));
|
|
|
|
d_hough->detect(d_edges, d_dx, d_dy, d_position);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
d_hough->detect(d_edges, d_dx, d_dy, d_position);
|
|
}
|
|
}
|
|
else
|
|
{
|
|
cv::Mat positions;
|
|
|
|
cv::Ptr<cv::GeneralizedHough> hough = cv::GeneralizedHough::create(method);
|
|
if (method & cv::GHT_ROTATION)
|
|
{
|
|
hough->set("maxAngle", 90.0);
|
|
hough->set("angleStep", 2.0);
|
|
}
|
|
|
|
hough->setTemplate(templ);
|
|
|
|
hough->detect(edges, dx, dy, positions);
|
|
|
|
TEST_CYCLE()
|
|
{
|
|
hough->detect(edges, dx, dy, positions);
|
|
}
|
|
}
|
|
}
|
|
|
|
} // namespace
|