SIFT的视频跟踪

直接上代码，要注意有一个模板图片才行

#include <iostream>
#include <opencv2\core\core.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\nonfree\features2d.hpp>
#include <opencv2\highgui\highgui.hpp>
#include <opencv2\calib3d\calib3d.hpp>

using namespace cv;

int _sift(Mat &img_object, Mat &img_scene);

int main(int argc, char *argv[])
{
	//读取视频
	VideoCapture vc;
	vc.open(0);
	double rate = vc.get(CV_CAP_PROP_FPS);//帧率
	int delay = 1000 / rate;
	Mat img = imread("C:\\Users\\billlab\\Desktop\\template.png", CV_LOAD_IMAGE_COLOR);//模版图像

	//待写入的视频
//	VideoWriter vw;
// 	vw.open("C:\\Users\\billlab\\Desktop\\sift.avi",
// 		(int)vc.get(CV_CAP_PROP_FOURCC), // 也可设为CV_FOURCC_PROMPT，在运行时选取
// 		(double)vc.get(CV_CAP_PROP_FPS), // 视频帧率
// 		cv::Size((int)vc.get(CV_CAP_PROP_FRAME_WIDTH), (int)vc.get(CV_CAP_PROP_FRAME_HEIGHT)), // 视频大小
// 		true); // 是否输出彩色视频  

	namedWindow("1");
	if (vc.isOpened())
	{

		while (1)
		{
			Mat frame;
			//原始图像每5帧图像取1帧进行处理
			for (int i = 0; i < 5; i++)
			{
				vc.read(frame);
			}

			if (frame.empty())
			{
				break;
			}
			_sift(img, frame);
			imshow("1", frame);
			//vw << frame;
			waitKey(1);
		}
	}
	vc.release();
	return 0;
}

int _sift(Mat &img_object, Mat &img_scene)
{
	//Mat img_object = imread("1.jpg", CV_LOAD_IMAGE_COLOR);
	//Mat img_scene = imread("2.jpg", CV_LOAD_IMAGE_COLOR);
	double t = (double)getTickCount();
	if (!img_object.data || !img_scene.data)
	{
		std::cout << "Error reading images!" << std::endl;
		return -1;
	}

	//检测SIFT特征点
	int minHeassian = 400;
	SiftFeatureDetector detector(minHeassian);

	std::vector<KeyPoint> keypoints_object, keypoints_scene;

	detector.detect(img_object, keypoints_object);
	detector.detect(img_scene, keypoints_scene);

	//计算特征向量
	SiftDescriptorExtractor extractor;

	Mat descriptors_object, descriptors_scene;

	extractor.compute(img_object, keypoints_object, descriptors_object);
	extractor.compute(img_scene, keypoints_scene, descriptors_scene);

	//利用FLANN匹配算法匹配特征描述向量
	FlannBasedMatcher matcher;
	std::vector<DMatch> matches;
	matcher.match(descriptors_object, descriptors_scene, matches);

	double max_dist = 0; double min_dist = 100;

	//快速计算特征点之间的最大和最小距离
	for (int i = 0; i < descriptors_object.rows; i++)
	{
		double dist = matches[i].distance;
		if (dist < min_dist) min_dist = dist;
		if (dist > max_dist) max_dist = dist;
	}

	printf("---Max dist: %f \n", max_dist);
	printf("---Min dist: %f \n", min_dist);

	//只画出好的匹配点(匹配特征点之间距离小于3*min_dist)
	std::vector<DMatch> good_matches;

	for (int i = 0; i < descriptors_object.rows; i++)
	{
		if (matches[i].distance < 3 * min_dist)
			good_matches.push_back(matches[i]);
	}

	Mat img_matches;
	drawMatches(img_object, keypoints_object, img_scene, keypoints_scene,
		good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
		vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

	//定位物体/
	std::vector<Point2f> obj;
	std::vector<Point2f> scene;

	for (int i = 0; i < good_matches.size(); i++)
	{
		//从好的匹配中获取特征点
		obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
		scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
	}

	//找出匹配特征点之间的变换
	Mat H = findHomography(obj, scene, CV_RANSAC);

	//得到image_1的角点(需要寻找的物体)
	std::vector<Point2f> obj_corners(4);
	obj_corners[0] = cvPoint(0, 0);
	obj_corners[1] = cvPoint(img_object.cols, 0);
	obj_corners[2] = cvPoint(img_object.cols, img_object.rows);
	obj_corners[3] = cvPoint(0, img_object.rows);
	std::vector<Point2f> scene_corners(4);

	//匹配四个角点
	perspectiveTransform(obj_corners, scene_corners, H);

	//画出匹配的物体两个匹配的图片
	//line(img_matches, scene_corners[0] + Point2f(img_object.cols, 0), scene_corners[1] + Point2f(img_object.cols, 0), Scalar(0,255,0), 4);
	//line(img_matches, scene_corners[1] + Point2f(img_object.cols, 0), scene_corners[2] + Point2f(img_object.cols, 0), Scalar(0,255,0), 4);
	//line(img_matches, scene_corners[2] + Point2f(img_object.cols, 0), scene_corners[3] + Point2f(img_object.cols, 0), Scalar(0,255,0), 4);
	//line(img_matches, scene_corners[3] + Point2f(img_object.cols, 0), scene_corners[0] + Point2f(img_object.cols, 0), Scalar(0,255,0), 4);

	//匹配之后画出匹配图形的轮廓
	line(img_scene, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 4);
	line(img_scene, scene_corners[1], scene_corners[2], Scalar(0, 255, 0), 4);
	line(img_scene, scene_corners[2], scene_corners[3], Scalar(0, 255, 0), 4);
	line(img_scene, scene_corners[3], scene_corners[0], Scalar(0, 255, 0), 4);

	//imshow("Good Matches & Object detection", img_matches);
	//imshow("识别图像", img_scene);
	t = 1000 * ((double)getTickCount() - t) / getTickFrequency();
	std::cout << "the time is :" << t << std::endl;

	//waitKey(0);
}