Open Source Computer Vision Library https://opencv.org/
 
 
 
 
 
 

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/*M///////////////////////////////////////////////////////////////////////////////////////
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// License Agreement
// For Open Source Computer Vision Library
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#include "test_precomp.hpp"
#ifdef HAVE_CUDA
using namespace cvtest;
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughLines
PARAM_TEST_CASE(HoughLines, cv::cuda::DeviceInfo, cv::Size, UseRoi)
{
static void generateLines(cv::Mat& img)
{
img.setTo(cv::Scalar::all(0));
cv::line(img, cv::Point(20, 0), cv::Point(20, img.rows), cv::Scalar::all(255));
cv::line(img, cv::Point(0, 50), cv::Point(img.cols, 50), cv::Scalar::all(255));
cv::line(img, cv::Point(0, 0), cv::Point(img.cols, img.rows), cv::Scalar::all(255));
cv::line(img, cv::Point(img.cols, 0), cv::Point(0, img.rows), cv::Scalar::all(255));
}
static void drawLines(cv::Mat& dst, const std::vector<cv::Vec2f>& lines)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < lines.size(); ++i)
{
float rho = lines[i][0], theta = lines[i][1];
cv::Point pt1, pt2;
double a = std::cos(theta), b = std::sin(theta);
double x0 = a*rho, y0 = b*rho;
pt1.x = cvRound(x0 + 1000*(-b));
pt1.y = cvRound(y0 + 1000*(a));
pt2.x = cvRound(x0 - 1000*(-b));
pt2.y = cvRound(y0 - 1000*(a));
cv::line(dst, pt1, pt2, cv::Scalar::all(255));
}
}
};
CUDA_TEST_P(HoughLines, Accuracy)
{
const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float rho = 1.0f;
const float theta = (float) (1.5 * CV_PI / 180.0);
const int threshold = 100;
cv::Mat src(size, CV_8UC1);
generateLines(src);
cv::Ptr<cv::cuda::HoughLinesDetector> hough = cv::cuda::createHoughLinesDetector(rho, theta, threshold);
cv::cuda::GpuMat d_lines;
hough->detect(loadMat(src, useRoi), d_lines);
std::vector<cv::Vec2f> lines;
hough->downloadResults(d_lines, lines);
cv::Mat dst(size, CV_8UC1);
drawLines(dst, lines);
ASSERT_MAT_NEAR(src, dst, 0.0);
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, HoughLines, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// HoughCircles
PARAM_TEST_CASE(HoughCircles, cv::cuda::DeviceInfo, cv::Size, UseRoi)
{
static void drawCircles(cv::Mat& dst, const std::vector<cv::Vec3f>& circles, bool fill)
{
dst.setTo(cv::Scalar::all(0));
for (size_t i = 0; i < circles.size(); ++i)
cv::circle(dst, cv::Point2f(circles[i][0], circles[i][1]), (int)circles[i][2], cv::Scalar::all(255), fill ? -1 : 1);
}
};
CUDA_TEST_P(HoughCircles, Accuracy)
{
const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
const cv::Size size = GET_PARAM(1);
const bool useRoi = GET_PARAM(2);
const float dp = 2.0f;
const float minDist = 0.0f;
const int minRadius = 10;
const int maxRadius = 20;
const int cannyThreshold = 100;
const int votesThreshold = 20;
std::vector<cv::Vec3f> circles_gold(4);
circles_gold[0] = cv::Vec3i(20, 20, minRadius);
circles_gold[1] = cv::Vec3i(90, 87, minRadius + 3);
circles_gold[2] = cv::Vec3i(30, 70, minRadius + 8);
circles_gold[3] = cv::Vec3i(80, 10, maxRadius);
cv::Mat src(size, CV_8UC1);
drawCircles(src, circles_gold, true);
cv::Ptr<cv::cuda::HoughCirclesDetector> houghCircles = cv::cuda::createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
cv::cuda::GpuMat d_circles;
houghCircles->detect(loadMat(src, useRoi), d_circles);
std::vector<cv::Vec3f> circles;
d_circles.download(circles);
ASSERT_FALSE(circles.empty());
for (size_t i = 0; i < circles.size(); ++i)
{
cv::Vec3f cur = circles[i];
bool found = false;
for (size_t j = 0; j < circles_gold.size(); ++j)
{
cv::Vec3f gold = circles_gold[j];
if (std::fabs(cur[0] - gold[0]) < 5 && std::fabs(cur[1] - gold[1]) < 5 && std::fabs(cur[2] - gold[2]) < 5)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, HoughCircles, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
WHOLE_SUBMAT));
///////////////////////////////////////////////////////////////////////////////////////////////////////
// GeneralizedHough
PARAM_TEST_CASE(GeneralizedHough, cv::cuda::DeviceInfo, UseRoi)
{
};
CUDA_TEST_P(GeneralizedHough, Ballard)
{
const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
cv::cuda::setDevice(devInfo.deviceID());
const bool useRoi = GET_PARAM(1);
cv::Mat templ = readImage("../cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
ASSERT_FALSE(templ.empty());
cv::Point templCenter(templ.cols / 2, templ.rows / 2);
const size_t gold_count = 3;
cv::Point pos_gold[gold_count];
pos_gold[0] = cv::Point(templCenter.x + 10, templCenter.y + 10);
pos_gold[1] = cv::Point(2 * templCenter.x + 40, templCenter.y + 10);
pos_gold[2] = cv::Point(2 * templCenter.x + 40, 2 * templCenter.y + 40);
cv::Mat image(templ.rows * 3, templ.cols * 3, CV_8UC1, cv::Scalar::all(0));
for (size_t i = 0; i < gold_count; ++i)
{
cv::Rect rec(pos_gold[i].x - templCenter.x, pos_gold[i].y - templCenter.y, templ.cols, templ.rows);
cv::Mat imageROI = image(rec);
templ.copyTo(imageROI);
}
cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::cuda::createGeneralizedHoughBallard();
alg->setVotesThreshold(200);
alg->setTemplate(loadMat(templ, useRoi));
cv::cuda::GpuMat d_pos;
alg->detect(loadMat(image, useRoi), d_pos);
std::vector<cv::Vec4f> pos;
d_pos.download(pos);
ASSERT_EQ(gold_count, pos.size());
for (size_t i = 0; i < gold_count; ++i)
{
cv::Point gold = pos_gold[i];
bool found = false;
for (size_t j = 0; j < pos.size(); ++j)
{
cv::Point2f p(pos[j][0], pos[j][1]);
if (::fabs(p.x - gold.x) < 2 && ::fabs(p.y - gold.y) < 2)
{
found = true;
break;
}
}
ASSERT_TRUE(found);
}
}
INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, GeneralizedHough, testing::Combine(
ALL_DEVICES,
WHOLE_SUBMAT));
#endif // HAVE_CUDA