262 lines
8.5 KiB
C++
262 lines
8.5 KiB
C++
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#ifdef HAVE_CUDA
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namespace opencv_test { namespace {
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///////////////////////////////////////////////////////////////////////////////////////////////////////
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// HoughLines
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PARAM_TEST_CASE(HoughLines, cv::cuda::DeviceInfo, cv::Size, UseRoi)
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{
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static void generateLines(cv::Mat& img)
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{
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img.setTo(cv::Scalar::all(0));
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cv::line(img, cv::Point(20, 0), cv::Point(20, img.rows), cv::Scalar::all(255));
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cv::line(img, cv::Point(0, 50), cv::Point(img.cols, 50), cv::Scalar::all(255));
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cv::line(img, cv::Point(0, 0), cv::Point(img.cols, img.rows), cv::Scalar::all(255));
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cv::line(img, cv::Point(img.cols, 0), cv::Point(0, img.rows), cv::Scalar::all(255));
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}
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static void drawLines(cv::Mat& dst, const std::vector<cv::Vec2f>& lines)
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{
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dst.setTo(cv::Scalar::all(0));
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for (size_t i = 0; i < lines.size(); ++i)
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{
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float rho = lines[i][0], theta = lines[i][1];
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cv::Point pt1, pt2;
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double a = std::cos(theta), b = std::sin(theta);
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double x0 = a*rho, y0 = b*rho;
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pt1.x = cvRound(x0 + 1000*(-b));
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pt1.y = cvRound(y0 + 1000*(a));
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pt2.x = cvRound(x0 - 1000*(-b));
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pt2.y = cvRound(y0 - 1000*(a));
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cv::line(dst, pt1, pt2, cv::Scalar::all(255));
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}
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}
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};
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CUDA_TEST_P(HoughLines, Accuracy)
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{
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const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
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cv::cuda::setDevice(devInfo.deviceID());
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const cv::Size size = GET_PARAM(1);
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const bool useRoi = GET_PARAM(2);
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const float rho = 1.0f;
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const float theta = (float) (1.5 * CV_PI / 180.0);
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const int threshold = 100;
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cv::Mat src(size, CV_8UC1);
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generateLines(src);
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cv::Ptr<cv::cuda::HoughLinesDetector> hough = cv::cuda::createHoughLinesDetector(rho, theta, threshold);
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cv::cuda::GpuMat d_lines;
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hough->detect(loadMat(src, useRoi), d_lines);
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std::vector<cv::Vec2f> lines;
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hough->downloadResults(d_lines, lines);
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cv::Mat dst(size, CV_8UC1);
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drawLines(dst, lines);
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ASSERT_MAT_NEAR(src, dst, 0.0);
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}
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, HoughLines, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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WHOLE_SUBMAT));
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///////////////////////////////////////////////////////////////////////////////////////////////////////
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// HoughCircles
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PARAM_TEST_CASE(HoughCircles, cv::cuda::DeviceInfo, cv::Size, UseRoi)
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{
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static void drawCircles(cv::Mat& dst, const std::vector<cv::Vec3f>& circles, bool fill)
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{
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dst.setTo(cv::Scalar::all(0));
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for (size_t i = 0; i < circles.size(); ++i)
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cv::circle(dst, cv::Point2f(circles[i][0], circles[i][1]), (int)circles[i][2], cv::Scalar::all(255), fill ? -1 : 1);
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}
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};
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CUDA_TEST_P(HoughCircles, Accuracy)
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{
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const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
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cv::cuda::setDevice(devInfo.deviceID());
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const cv::Size size = GET_PARAM(1);
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const bool useRoi = GET_PARAM(2);
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const float dp = 2.0f;
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const float minDist = 0.0f;
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const int minRadius = 10;
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const int maxRadius = 20;
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const int cannyThreshold = 100;
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const int votesThreshold = 20;
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std::vector<cv::Vec3f> circles_gold(4);
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circles_gold[0] = cv::Vec3i(20, 20, minRadius);
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circles_gold[1] = cv::Vec3i(90, 87, minRadius + 3);
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circles_gold[2] = cv::Vec3i(30, 70, minRadius + 8);
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circles_gold[3] = cv::Vec3i(80, 10, maxRadius);
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cv::Mat src(size, CV_8UC1);
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drawCircles(src, circles_gold, true);
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cv::Ptr<cv::cuda::HoughCirclesDetector> houghCircles = cv::cuda::createHoughCirclesDetector(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius);
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cv::cuda::GpuMat d_circles;
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houghCircles->detect(loadMat(src, useRoi), d_circles);
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std::vector<cv::Vec3f> circles;
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d_circles.download(circles);
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ASSERT_FALSE(circles.empty());
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for (size_t i = 0; i < circles.size(); ++i)
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{
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cv::Vec3f cur = circles[i];
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bool found = false;
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for (size_t j = 0; j < circles_gold.size(); ++j)
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{
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cv::Vec3f gold = circles_gold[j];
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if (std::fabs(cur[0] - gold[0]) < 5 && std::fabs(cur[1] - gold[1]) < 5 && std::fabs(cur[2] - gold[2]) < 5)
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{
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found = true;
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break;
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}
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}
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ASSERT_TRUE(found);
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}
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}
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, HoughCircles, testing::Combine(
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ALL_DEVICES,
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DIFFERENT_SIZES,
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WHOLE_SUBMAT));
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///////////////////////////////////////////////////////////////////////////////////////////////////////
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// GeneralizedHough
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PARAM_TEST_CASE(GeneralizedHough, cv::cuda::DeviceInfo, UseRoi)
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{
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};
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CUDA_TEST_P(GeneralizedHough, Ballard)
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{
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const cv::cuda::DeviceInfo devInfo = GET_PARAM(0);
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cv::cuda::setDevice(devInfo.deviceID());
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const bool useRoi = GET_PARAM(1);
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cv::Mat templ = readImage("../cv/shared/templ.png", cv::IMREAD_GRAYSCALE);
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ASSERT_FALSE(templ.empty());
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cv::Point templCenter(templ.cols / 2, templ.rows / 2);
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const size_t gold_count = 3;
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cv::Point pos_gold[gold_count];
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pos_gold[0] = cv::Point(templCenter.x + 10, templCenter.y + 10);
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pos_gold[1] = cv::Point(2 * templCenter.x + 40, templCenter.y + 10);
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pos_gold[2] = cv::Point(2 * templCenter.x + 40, 2 * templCenter.y + 40);
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cv::Mat image(templ.rows * 3, templ.cols * 3, CV_8UC1, cv::Scalar::all(0));
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for (size_t i = 0; i < gold_count; ++i)
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{
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cv::Rect rec(pos_gold[i].x - templCenter.x, pos_gold[i].y - templCenter.y, templ.cols, templ.rows);
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cv::Mat imageROI = image(rec);
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templ.copyTo(imageROI);
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}
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cv::Ptr<cv::GeneralizedHoughBallard> alg = cv::cuda::createGeneralizedHoughBallard();
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alg->setVotesThreshold(200);
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alg->setTemplate(loadMat(templ, useRoi));
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cv::cuda::GpuMat d_pos;
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alg->detect(loadMat(image, useRoi), d_pos);
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std::vector<cv::Vec4f> pos;
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d_pos.download(pos);
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ASSERT_EQ(gold_count, pos.size());
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for (size_t i = 0; i < gold_count; ++i)
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{
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cv::Point gold = pos_gold[i];
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bool found = false;
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for (size_t j = 0; j < pos.size(); ++j)
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{
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cv::Point2f p(pos[j][0], pos[j][1]);
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if (::fabs(p.x - gold.x) < 2 && ::fabs(p.y - gold.y) < 2)
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{
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found = true;
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break;
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}
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}
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ASSERT_TRUE(found);
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}
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}
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INSTANTIATE_TEST_CASE_P(CUDA_ImgProc, GeneralizedHough, testing::Combine(
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ALL_DEVICES,
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WHOLE_SUBMAT));
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}} // namespace
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#endif // HAVE_CUDA
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