OpenCV CUDA对象检测

基于颜色的对象检测

对象具有许多全局特征，如颜色和形状，将对象作为一个整体描述。这些特征可用于检测对象并以一系列帧跟踪它。在本节中，我们将使用颜色作为特征来检测具有特定颜色的对象。当要检测的对象具有特定颜色且该颜色与背景颜色不同时此方法很有用。如果对象和背景具有相同的颜色，则此方法将检测失败。本节中，我们将尝试使用OpenCV和CUDA从网络摄像机流中检测任意蓝色对象。

#include 
#include 
#include 
#include 

int main(int argc, char** argv)
{
	cv::VideoCapture cap(0); //capture the video from web cam
	// if webcam is not available then exit the program
	if (!cap.isOpened())
	{
		std::cout << "Cannot open the web cam" << std::endl;
		return -1;
	}
	while (true)
	{
		cv::Mat frame;
		// read a new frame from webcam
		bool flag = cap.read(frame);
		if (!flag)
		{
			std::cout << "Cannot read a frame from webcam" << std::endl;
			break;
		}

		cv::cuda::GpuMat d_frame, d_frame_hsv, d_intermediate, d_result;
		cv::cuda::GpuMat d_frame_shsv[3];
		cv::cuda::GpuMat d_thresc[3];
		cv::Mat h_result;
		d_frame.upload(frame);
		//Transform image to HSV
		cv::cuda::cvtColor(d_frame, d_frame_hsv, cv::COLOR_BGR2HSV);

		//Split HSV 3 channels
		cv::cuda::split(d_frame_hsv, d_frame_shsv);

		//Threshold HSV channels
		cv::cuda::threshold(d_frame_shsv[0], d_thresc[0], 110, 130, cv::THRESH_BINARY);
		cv::cuda::threshold(d_frame_shsv[1], d_thresc[1], 50, 255, cv::THRESH_BINARY);
		cv::cuda::threshold(d_frame_shsv[2], d_thresc[2], 50, 255, cv::THRESH_BINARY);

		//Bitwise AND the channels
		cv::cuda::bitwise_and(d_thresc[0], d_thresc[1], d_intermediate);
		cv::cuda::bitwise_and(d_intermediate, d_thresc[2], d_result);

		d_result.download(h_result);
		imshow("Thresholded Image", h_result);
		imshow("Original", frame);

		if (cv::waitKey(1) == 'q')
		{
			break;
		}
	}
	return 0;
}

基于形状的对象检测

Canny边缘检测

#include 
#include 
#include 

int main()
{
    cv::Mat h_image = cv::imread("images/drawing.JPG", 0);
    if (h_image.empty())
    {
        std::cout << "can not open image"<< std::endl;
        return -1;
    }
    
	cv::cuda::GpuMat d_edge,d_image;
	cv::Mat h_edge;
	d_image.upload(h_image);
	cv::Ptr<cv::cuda::CannyEdgeDetector> canny_edge = cv::cuda::createCannyEdgeDetector(2.0, 100.0, 3, false);
	canny_edge->detect(d_image, d_edge);
    d_edge.download(h_edge);
	cv::imshow("source", h_image);
	cv::imshow("detected edges", h_edge);
	cv::waitKey(0);

    return 0;
}

Hough变换直线检测

#include 
#include 
#include 

int main()
{
    cv::Mat h_image = cv::imread("images/drawing.JPG",0);
    if (h_image.empty())
    {
        std::cout << "can not open image"<< std::endl;
        return -1;
    }

	cv::Mat h_edge;
    cv::Canny(h_image, h_edge, 100, 200, 3);

	cv::Mat h_imagec;
    cv::cvtColor(h_edge, h_imagec, cv::COLOR_GRAY2BGR);
	cv::Mat h_imageg = h_imagec.clone();
	std::vector<cv::Vec4i> h_lines;
    {
        const int64 start = cv::getTickCount();
        HoughLinesP(h_edge, h_lines, 1, CV_PI / 180, 50, 60, 5);
        const double time_elapsed = (cv::getTickCount() - start) / cv::getTickFrequency();
		std::cout << "CPU Time : " << time_elapsed * 1000 << " ms" << std::endl;
		std::cout << "CPU FPS : " << (1/time_elapsed) << std::endl;
    }

    for (size_t i = 0; i < h_lines.size(); ++i)
    {
		cv::Vec4i line_point = h_lines[i];
		cv::line(h_imagec, cv::Point(line_point[0], line_point[1]), cv::Point(line_point[2], line_point[3]), cv::Scalar(0, 0, 255), 2, cv::LINE_AA);
    }

	cv::cuda::GpuMat d_edge, d_lines;
	d_edge.upload(h_edge);
    {
		const int64 start = cv::getTickCount();
		cv::Ptr<cv::cuda::HoughSegmentDetector> hough = cv::cuda::createHoughSegmentDetector(1.0f, (float) (CV_PI / 180.0f), 50, 5);
        hough->detect(d_edge, d_lines);

        const double time_elapsed = (cv::getTickCount() - start) / cv::getTickFrequency();
		std::cout << "GPU Time : " << time_elapsed * 1000 << " ms" << std::endl;
		std::cout << "GPU FPS : " << (1/time_elapsed) << std::endl;
    }
	std::vector<cv::Vec4i> lines_g;
    if (!d_lines.empty())
    {
        lines_g.resize(d_lines.cols);
		cv::Mat h_lines(1, d_lines.cols, CV_32SC4, &lines_g[0]);
        d_lines.download(h_lines);
    }
    for (size_t i = 0; i < lines_g.size(); ++i)
    {
		cv::Vec4i line_point = lines_g[i];
		cv::line(h_imageg, cv::Point(line_point[0], line_point[1]), cv::Point(line_point[2], line_point[3]), cv::Scalar(0, 0, 255), 2, cv::LINE_AA);
    }

	cv::imshow("source", h_image);
	cv::imshow("detected lines [CPU]", h_imagec);
	cv::imshow("detected lines [GPU]", h_imageg);
	cv::imwrite("hough_source.png", h_image);
	cv::imwrite("hough_cpu_line.png", h_imagec);
	cv::imwrite("hough_gpu_line.png", h_imageg);
	cv::waitKey(0);

    return 0;
}

Hough变换圆检测

#include 
#include 
#include 

int main(int argc, char** argv)
{
	cv::Mat h_image = cv::imread("images/eight.tif", 1);
	cv::Mat h_gray;
	cv::cvtColor(h_image, h_gray, cv::COLOR_BGR2GRAY);
	cv::cuda::GpuMat d_gray, d_result;
	std::vector<cv::Vec3f> d_Circles;
	cv::medianBlur(h_gray, h_gray, 5);
	cv::Ptr<cv::cuda::HoughCirclesDetector> detector = cv::cuda::createHoughCirclesDetector(1, 100, 122, 50, 1, std::max(h_image.size().width, h_image.size().height));
	d_gray.upload(h_gray);
	detector->detect(d_gray, d_result);
	d_Circles.resize(d_result.size().width);

	if (!d_Circles.empty())
		d_result.row(0).download(cv::Mat(d_Circles).reshape(3, 1));
	std::cout << "No of circles: " << d_Circles.size() << std::endl;
	for (size_t i = 0; i < d_Circles.size(); i++)
	{
		cv::Vec3i cir = d_Circles[i];
		cv::circle(h_image, cv::Point(cir[0], cir[1]), cir[2], cv::Scalar(255, 0, 0), 2, cv::LINE_AA);
	}
	cv::imshow("detected circles", h_image);
	cv::waitKey(0);

	return 0;
}

关键点检测和描述符

FAST关键点

#include 
#include 
#include 

int main()
{
	cv::Mat h_image = cv::imread("images/drawing.JPG", 0);

	//Detect the keypoints using FAST Detector
	cv::Ptr<cv::cuda::FastFeatureDetector> detector = cv::cuda::FastFeatureDetector::create(100, true, 2);
	std::vector<cv::KeyPoint> keypoints;
	cv::cuda::GpuMat d_image;
	d_image.upload(h_image);
	detector->detect(d_image, keypoints);
	cv::drawKeypoints(h_image, keypoints, h_image);

	//Show detected keypoints
	cv::imshow("Final Result", h_image);
	cv::waitKey(0);
	return 0;
}

ORB特征检测

#include 
#include 
#include 

int main()
{
	cv::Mat h_image = cv::imread("images/drawing.JPG", 0);
	cv::Ptr<cv::cuda::ORB> detector = cv::cuda::ORB::create();
	std::vector<cv::KeyPoint> keypoints;
	cv::cuda::GpuMat d_image;
	d_image.upload(h_image);
	detector->detect(d_image, keypoints);
	cv::drawKeypoints(h_image, keypoints, h_image);
	cv::imshow("Final Result", h_image);
	cv::waitKey(0);
	return 0;
}

SURF特征检测

#include 
#include 
#include 
#include 
#include 

int main(int argc, char** argv)
{
	cv::Mat h_object_image = cv::imread("images/object1.jpg", 0);
	cv::Mat h_scene_image = cv::imread("images/scene1.jpg", 0);

	cv::cuda::GpuMat d_object_image;
	cv::cuda::GpuMat d_scene_image;

	cv::cuda::GpuMat d_keypoints_scene, d_keypoints_object;
	std::vector<cv::KeyPoint> h_keypoints_scene, h_keypoints_object;
	cv::cuda::GpuMat d_descriptors_scene, d_descriptors_object;

	d_object_image.upload(h_object_image);
	d_scene_image.upload(h_scene_image);

	cv::cuda::SURF_CUDA surf(100);
	surf(d_object_image, cv::cuda::GpuMat(), d_keypoints_object, d_descriptors_object);
	surf(d_scene_image, cv::cuda::GpuMat(), d_keypoints_scene, d_descriptors_scene);

	cv::Ptr<cv::cuda::DescriptorMatcher> matcher = cv::cuda::DescriptorMatcher::createBFMatcher();
	std::vector<std::vector<cv::DMatch>> d_matches;
	matcher->knnMatch(d_descriptors_object, d_descriptors_scene, d_matches, 2);

	surf.downloadKeypoints(d_keypoints_scene, h_keypoints_scene);
	surf.downloadKeypoints(d_keypoints_object, h_keypoints_object);

	std::vector<cv::DMatch> good_matches;
	for (int k = 0; k < std::min(h_keypoints_object.size() - 1, d_matches.size()); k++)
	{
		if ((d_matches[k][0].distance < 0.6 * (d_matches[k][1].distance)) && ((int)d_matches[k].size() <= 2 && (int)d_matches[k].size() > 0))
		{
			good_matches.push_back(d_matches[k][0]);
		}
	}
	std::cout << "size:" << good_matches.size();
	cv::Mat h_image_result;
	cv::drawMatches(h_object_image, h_keypoints_object, h_scene_image, h_keypoints_scene,
		good_matches, h_image_result, cv::Scalar::all(-1), cv::Scalar::all(-1),
		std::vector<char>(), cv::DrawMatchesFlags::DEFAULT);

	std::vector<cv::Point2f> object;
	std::vector<cv::Point2f> scene;
	for (int i = 0; i < good_matches.size(); i++) 
	{
		object.push_back(h_keypoints_object[good_matches[i].queryIdx].pt);
		scene.push_back(h_keypoints_scene[good_matches[i].trainIdx].pt);
	}
	cv::Mat Homo = cv::findHomography(object, scene, cv::RANSAC);
	std::vector<cv::Point2f> corners(4);
	std::vector<cv::Point2f> scene_corners(4);
	corners[0] = cv::Point(0, 0);
	corners[1] = cv::Point(h_object_image.cols, 0);
	corners[2] = cv::Point(h_object_image.cols, h_object_image.rows);
	corners[3] = cv::Point(0, h_object_image.rows);
	cv::perspectiveTransform(corners, scene_corners, Homo);

	cv::line(h_image_result, scene_corners[0] + cv::Point2f(h_object_image.cols, 0), scene_corners[1] + cv::Point2f(h_object_image.cols, 0), cv::Scalar(255, 0, 0), 4);
	cv::line(h_image_result, scene_corners[1] + cv::Point2f(h_object_image.cols, 0), scene_corners[2] + cv::Point2f(h_object_image.cols, 0), cv::Scalar(255, 0, 0), 4);
	cv::line(h_image_result, scene_corners[2] + cv::Point2f(h_object_image.cols, 0), scene_corners[3] + cv::Point2f(h_object_image.cols, 0), cv::Scalar(255, 0, 0), 4);
	cv::line(h_image_result, scene_corners[3] + cv::Point2f(h_object_image.cols, 0), scene_corners[0] + cv::Point2f(h_object_image.cols, 0), cv::Scalar(255, 0, 0), 4);
	cv::imshow("Good Matches & Object detection", h_image_result);
	cv::waitKey(0);
	return 0;
}

Haar级联对象检测

Haar级联人脸检测

#include 
#include 
#include 

int main()
{
	cv::Mat h_image = cv::imread("Chapter7//lena_color_512.tif", 0);
	cv::Ptr<cv::cuda::CascadeClassifier> cascade = cv::cuda::CascadeClassifier::create("Chapter7//haarcascade_frontalface_alt2.xml");
	cv::cuda::GpuMat d_image;
	cv::cuda::GpuMat d_buf;
	d_image.upload(h_image);
	std::cout << "No detection." << std::endl;
	cascade->detectMultiScale(d_image, d_buf);
	std::cout << "No detection." << std::endl;
	std::vector<cv::Rect> detections;
	cascade->convert(d_buf, detections);
	if (detections.empty())
		std::cout << "No detection." << std::endl;
	cv::cvtColor(h_image, h_image, cv::COLOR_GRAY2BGR);
	for (int i = 0; i < detections.size(); ++i)
	{
		cv::rectangle(h_image, detections[i], cv::Scalar(0, 255, 255), 5);
	}

	cv::imshow("Result image", h_image);
	cv::waitKey(0);
	return 0;
}

Haar级联人眼检测

#include 
#include 
#include 
#include 

int main()
{
	cv::Mat h_image = cv::imread("images/lena_color_512.tif", 0);
	cv::Ptr<cv::cuda::CascadeClassifier> cascade = cv::cuda::CascadeClassifier::create("Chapter7//haarcascade_eye.xml");
	cv::cuda::GpuMat d_image;
	cv::cuda::GpuMat d_buf;
	d_image.upload(h_image);
	cascade->detectMultiScale(d_image, d_buf);
	std::vector<cv::Rect> detections;
	cascade->convert(d_buf, detections);
	if (detections.empty())
		std::cout << "No detection." << std::endl;
	cv::cuda::cvtColor(h_image, h_image, cv::COLOR_GRAY2BGR);
	for (int i = 0; i < detections.size(); ++i)
	{
		rectangle(h_image, detections[i], cv::Scalar(0, 255, 255), 5);
	}
	cv::imshow("Result image", h_image);
	cv::waitKey(0);
	return 0;
}