mirror of
https://github.com/opencv/opencv.git
synced 2024-12-27 03:14:05 +08:00
349 lines
11 KiB
C++
349 lines
11 KiB
C++
/*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*/
|
|
|
|
#include "perf_precomp.hpp"
|
|
|
|
using namespace std;
|
|
using namespace testing;
|
|
using namespace perf;
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// HoughLines
|
|
|
|
namespace
|
|
{
|
|
struct Vec4iComparator
|
|
{
|
|
bool operator()(const cv::Vec4i& a, const cv::Vec4i b) const
|
|
{
|
|
if (a[0] != b[0]) return a[0] < b[0];
|
|
else if(a[1] != b[1]) return a[1] < b[1];
|
|
else if(a[2] != b[2]) return a[2] < b[2];
|
|
else return a[3] < b[3];
|
|
}
|
|
};
|
|
struct Vec3fComparator
|
|
{
|
|
bool operator()(const cv::Vec3f& a, const cv::Vec3f b) const
|
|
{
|
|
if(a[0] != b[0]) return a[0] < b[0];
|
|
else if(a[1] != b[1]) return a[1] < b[1];
|
|
else return a[2] < b[2];
|
|
}
|
|
};
|
|
struct Vec2fComparator
|
|
{
|
|
bool operator()(const cv::Vec2f& a, const cv::Vec2f b) const
|
|
{
|
|
if(a[0] != b[0]) return a[0] < b[0];
|
|
else return a[1] < b[1];
|
|
}
|
|
};
|
|
}
|
|
|
|
PERF_TEST_P(Sz, HoughLines,
|
|
CUDA_TYPICAL_MAT_SIZES)
|
|
{
|
|
declare.time(30.0);
|
|
|
|
const cv::Size size = GetParam();
|
|
|
|
const float rho = 1.0f;
|
|
const float theta = static_cast<float>(CV_PI / 180.0);
|
|
const int threshold = 300;
|
|
|
|
cv::Mat src(size, CV_8UC1, cv::Scalar::all(0));
|
|
cv::line(src, cv::Point(0, 100), cv::Point(src.cols, 100), cv::Scalar::all(255), 1);
|
|
cv::line(src, cv::Point(0, 200), cv::Point(src.cols, 200), cv::Scalar::all(255), 1);
|
|
cv::line(src, cv::Point(0, 400), cv::Point(src.cols, 400), cv::Scalar::all(255), 1);
|
|
cv::line(src, cv::Point(100, 0), cv::Point(100, src.rows), cv::Scalar::all(255), 1);
|
|
cv::line(src, cv::Point(200, 0), cv::Point(200, src.rows), cv::Scalar::all(255), 1);
|
|
cv::line(src, cv::Point(400, 0), cv::Point(400, src.rows), cv::Scalar::all(255), 1);
|
|
|
|
if (PERF_RUN_CUDA())
|
|
{
|
|
const cv::cuda::GpuMat d_src(src);
|
|
cv::cuda::GpuMat d_lines;
|
|
|
|
cv::Ptr<cv::cuda::HoughLinesDetector> hough = cv::cuda::createHoughLinesDetector(rho, theta, threshold);
|
|
|
|
TEST_CYCLE() hough->detect(d_src, d_lines);
|
|
|
|
cv::Mat gpu_lines(d_lines.row(0));
|
|
cv::Vec2f* begin = gpu_lines.ptr<cv::Vec2f>(0);
|
|
cv::Vec2f* end = begin + gpu_lines.cols;
|
|
std::sort(begin, end, Vec2fComparator());
|
|
SANITY_CHECK(gpu_lines);
|
|
}
|
|
else
|
|
{
|
|
std::vector<cv::Vec2f> cpu_lines;
|
|
|
|
TEST_CYCLE() cv::HoughLines(src, cpu_lines, rho, theta, threshold);
|
|
|
|
SANITY_CHECK(cpu_lines);
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// HoughLinesP
|
|
|
|
DEF_PARAM_TEST_1(Image, std::string);
|
|
|
|
PERF_TEST_P(Image, HoughLinesP,
|
|
testing::Values("cv/shared/pic5.png", "stitching/a1.png"))
|
|
{
|
|
declare.time(30.0);
|
|
|
|
const std::string fileName = getDataPath(GetParam());
|
|
|
|
const float rho = 1.0f;
|
|
const float theta = static_cast<float>(CV_PI / 180.0);
|
|
const int threshold = 100;
|
|
const int minLineLength = 50;
|
|
const int maxLineGap = 5;
|
|
|
|
const cv::Mat image = cv::imread(fileName, cv::IMREAD_GRAYSCALE);
|
|
ASSERT_FALSE(image.empty());
|
|
|
|
cv::Mat mask;
|
|
cv::Canny(image, mask, 50, 100);
|
|
|
|
if (PERF_RUN_CUDA())
|
|
{
|
|
const cv::cuda::GpuMat d_mask(mask);
|
|
cv::cuda::GpuMat d_lines;
|
|
|
|
cv::Ptr<cv::cuda::HoughSegmentDetector> hough = cv::cuda::createHoughSegmentDetector(rho, theta, minLineLength, maxLineGap);
|
|
|
|
TEST_CYCLE() hough->detect(d_mask, d_lines);
|
|
|
|
cv::Mat gpu_lines(d_lines);
|
|
cv::Vec4i* begin = gpu_lines.ptr<cv::Vec4i>();
|
|
cv::Vec4i* end = begin + gpu_lines.cols;
|
|
std::sort(begin, end, Vec4iComparator());
|
|
SANITY_CHECK(gpu_lines);
|
|
}
|
|
else
|
|
{
|
|
std::vector<cv::Vec4i> cpu_lines;
|
|
|
|
TEST_CYCLE() cv::HoughLinesP(mask, cpu_lines, rho, theta, threshold, minLineLength, maxLineGap);
|
|
|
|
SANITY_CHECK(cpu_lines);
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// HoughCircles
|
|
|
|
DEF_PARAM_TEST(Sz_Dp_MinDist, cv::Size, float, float);
|
|
|
|
PERF_TEST_P(Sz_Dp_MinDist, HoughCircles,
|
|
Combine(CUDA_TYPICAL_MAT_SIZES,
|
|
Values(1.0f, 2.0f, 4.0f),
|
|
Values(1.0f)))
|
|
{
|
|
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::Mat src(size, CV_8UC1, cv::Scalar::all(0));
|
|
cv::circle(src, cv::Point(100, 100), 20, cv::Scalar::all(255), -1);
|
|
cv::circle(src, cv::Point(200, 200), 25, cv::Scalar::all(255), -1);
|
|
cv::circle(src, cv::Point(200, 100), 25, cv::Scalar::all(255), -1);
|
|
|
|
if (PERF_RUN_CUDA())
|
|
{
|
|
const cv::cuda::GpuMat d_src(src);
|
|
cv::cuda::GpuMat d_circles;
|
|
|
|
cv::Ptr<cv::cuda::HoughCirclesDetector> houghCircles = cv::cuda::createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
|
|
|
|
TEST_CYCLE() houghCircles->detect(d_src, d_circles);
|
|
|
|
cv::Mat gpu_circles(d_circles);
|
|
cv::Vec3f* begin = gpu_circles.ptr<cv::Vec3f>(0);
|
|
cv::Vec3f* end = begin + gpu_circles.cols;
|
|
std::sort(begin, end, Vec3fComparator());
|
|
SANITY_CHECK(gpu_circles);
|
|
}
|
|
else
|
|
{
|
|
std::vector<cv::Vec3f> cpu_circles;
|
|
|
|
TEST_CYCLE() cv::HoughCircles(src, cpu_circles, cv::HOUGH_GRADIENT, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
|
|
|
|
SANITY_CHECK(cpu_circles);
|
|
}
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
// GeneralizedHough
|
|
|
|
PERF_TEST_P(Sz, GeneralizedHoughBallard, CUDA_TYPICAL_MAT_SIZES)
|
|
{
|
|
declare.time(10);
|
|
|
|
const cv::Size imageSize = GetParam();
|
|
|
|
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));
|
|
templ.copyTo(image(cv::Rect(50, 50, templ.cols, templ.rows)));
|
|
|
|
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 (PERF_RUN_CUDA())
|
|
{
|
|
cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::cuda::createGeneralizedHoughBallard();
|
|
|
|
const cv::cuda::GpuMat d_edges(edges);
|
|
const cv::cuda::GpuMat d_dx(dx);
|
|
const cv::cuda::GpuMat d_dy(dy);
|
|
cv::cuda::GpuMat positions;
|
|
|
|
alg->setTemplate(cv::cuda::GpuMat(templ));
|
|
|
|
TEST_CYCLE() alg->detect(d_edges, d_dx, d_dy, positions);
|
|
|
|
CUDA_SANITY_CHECK(positions);
|
|
}
|
|
else
|
|
{
|
|
cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::createGeneralizedHoughBallard();
|
|
|
|
cv::Mat positions;
|
|
|
|
alg->setTemplate(templ);
|
|
|
|
TEST_CYCLE() alg->detect(edges, dx, dy, positions);
|
|
|
|
CPU_SANITY_CHECK(positions);
|
|
}
|
|
}
|
|
|
|
PERF_TEST_P(Sz, DISABLED_GeneralizedHoughGuil, CUDA_TYPICAL_MAT_SIZES)
|
|
{
|
|
declare.time(10);
|
|
|
|
const cv::Size imageSize = GetParam();
|
|
|
|
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));
|
|
templ.copyTo(image(cv::Rect(50, 50, templ.cols, templ.rows)));
|
|
|
|
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 (PERF_RUN_CUDA())
|
|
{
|
|
cv::Ptr<cv::GeneralizedHoughGuil> alg = cv::cuda::createGeneralizedHoughGuil();
|
|
alg->setMaxAngle(90.0);
|
|
alg->setAngleStep(2.0);
|
|
|
|
const cv::cuda::GpuMat d_edges(edges);
|
|
const cv::cuda::GpuMat d_dx(dx);
|
|
const cv::cuda::GpuMat d_dy(dy);
|
|
cv::cuda::GpuMat positions;
|
|
|
|
alg->setTemplate(cv::cuda::GpuMat(templ));
|
|
|
|
TEST_CYCLE() alg->detect(d_edges, d_dx, d_dy, positions);
|
|
}
|
|
else
|
|
{
|
|
cv::Ptr<cv::GeneralizedHoughGuil> alg = cv::createGeneralizedHoughGuil();
|
|
alg->setMaxAngle(90.0);
|
|
alg->setAngleStep(2.0);
|
|
|
|
cv::Mat positions;
|
|
|
|
alg->setTemplate(templ);
|
|
|
|
TEST_CYCLE() alg->detect(edges, dx, dy, positions);
|
|
}
|
|
|
|
// The algorithm is not stable yet.
|
|
SANITY_CHECK_NOTHING();
|
|
}
|