本文主要是介绍机器人视觉行人跟随中的extract+match feature，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
机器人行人跟随ros包中加入图像特征提取和特征匹配，提取surf特征，匹配方法是快速最近邻近似搜索flann。
直接上代码：（有问题可以交流～）
/******************************************************************************
*
* The MIT License (MIT)
*
* Copyright (c) 2018 Bluewhale Robot
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* furnished to do so, subject to the following conditions:
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* The above copyright notice and this permission notice shall be included in all
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
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* Author: Randoms
*******************************************************************************/#include "tracking_node.h"using namespace cv;
using namespace std;
using namespace XiaoqiangTrack;sensor_msgs::Image last_frame;
XiaoqiangTrack::Tracker *tracker = NULL;
Rect2d body_rect;
ros::Publisher image_pub;
ros::Publisher target_pub;
std::mutex update_track_mutex;
int track_ok_flag = 0;
cv::Rect2d previous_body_rect;
cv::Rect2d body_track_rect;sensor_msgs::Image get_one_frame() { return last_frame; }void update_frame(const sensor_msgs::ImageConstPtr &new_frame) //更新帧
{last_frame = *new_frame;cv_bridge::CvImagePtr cv_ptr =cv_bridge::toCvCopy(new_frame, "bgr8"); //图像格式转换cv::Mat cv_image = cv_ptr->image;if (tracker == NULL)return;unique_lock<mutex> lock(update_track_mutex);previous_body_rect = body_rect;//将检测到的当前rect保存为previous_body_recttrack_ok_flag = tracker->updateFrame(cv_image, body_rect);cv::rectangle(cv_image, body_rect, cv::Scalar(0, 255, 0));image_pub.publish(cv_ptr->toImageMsg());xiaoqiang_track::TrackTarget target;target.x = body_rect.x + body_rect.width / 2;target.y = body_rect.y + body_rect.height / 2;target.width = body_track_rect.width;target.height = body_track_rect.height;if (track_ok_flag == 0) {// send stoptarget.x = 0;target.y = 0;}target_pub.publish(target);//target.x,target.y是跟踪的点的坐标，kalman...
}int main(int argc, char **argv) {ros::init(argc, argv, "xiaoqiang_track_node"); // ros节点初始化//在一个节点中开辟多个线程，构造时可以指定线程数如(4)，AsyncSpinner有start()和stop()函数ros::AsyncSpinner spinner(4);spinner.start();ros::NodeHandle private_nh("~");ros::Publisher talk_pub = private_nh.advertise<std_msgs::String>("text", 10);image_pub = private_nh.advertise<sensor_msgs::Image>("processed_image", 10);target_pub = private_nh.advertise<xiaoqiang_track::TrackTarget>("target", 10);int watch_dog;private_nh.param("watch_dog", watch_dog, 2);ros::Subscriber image_sub = private_nh.subscribe("image", 10, update_frame);PoseTracker *client;std::string pose_tracker_type;ros::param::param<std::string>("~pose_tracker", pose_tracker_type, "");if (pose_tracker_type == "baidu") //判断跟踪类型：baidu track or body track{client = (PoseTracker *)new BaiduTrack(private_nh);} else if (pose_tracker_type == "body_pose") {client = (PoseTracker *)new BodyTrack(private_nh);} else {ROS_FATAL_STREAM("unknown pose tracker type " << pose_tracker_type);ROS_FATAL_STREAM("supported pose trakers are body_pose and baidu");exit(1);}std::string tracker_main_type; //定义主跟踪类型std::string tracker_aided_type; //辅跟踪ros::param::param<std::string>("~tracker_main", tracker_main_type, "");ros::param::param<std::string>("~tracker_aided", tracker_aided_type, "");tracker = new XiaoqiangTrack::Tracker(tracker_main_type,tracker_aided_type); //设置跟踪器// 告诉用户站在前面std_msgs::String words;words.data = "请站在我前面";talk_pub.publish(words);// 提醒用户调整好距离sensor_msgs::Image frame = get_one_frame(); //得到一帧图像body_rect.x = -1;body_rect.y = -1;while (!ros::isShuttingDown()) {if (frame.data.size() != 0) {cv::Rect2d rect = client->getBodyRect(frame); //通过frame得到人体图像区域if (rect.x <= 1 || rect.y <= 1) {words.data = "我没有看到人,请站到我前面";talk_pub.publish(words);} else if (rect.x + rect.width / 2 > 440 ||rect.x + rect.width / 2 < 200) {body_rect = rect;words.data = "请站到镜头中间来";talk_pub.publish(words);} else {body_rect = rect;words.data = "我看到人了,开始追踪";talk_pub.publish(words);break;}}sleep(1);frame = get_one_frame();}/*￥经过分析代码，初步的想法是在这个位置加上特征提取方法和Opencv的特征匹配，思路是：-特征提取是从一帧图像中提取特征，想要提取的特征可以是ORB，FAST，SIFT，SURF等，上面的- frame = get_one_frame()是获取最新的一帧图像，return last_frame-那么对于这一帧图像抽取想要的特征信息，得到特征点，保存检测到的目标特征，之后用来与再次检测时的图像来做匹配-当然这里是做特征提取，匹配是在跟踪丢失后，再次启动检测识别时，识别到多个目标，进行匹配*//*fuck!sorry,i don't wanna say that,but i just lost everything i did the whole day because of clicking the close table without save it! so let me start at the begining...*///begining extract featureint minHessian = 2000;SurfFeatureDetector detector(minHessian);vector<KeyPoint>keypoint1, keypoint2;//image1 = resize(frame, rect.x:(rect.x+rect.width), rect.y:(rect.y+rect.higth))IplImage *img1;CvRect rectInImage1;rectInImage1 = cvRect(rect.x, rect.y,rect.width, rect.height);CvSize size1;size1.width = rectInImage.width;size1.height = rectInImage.height;img1 = CvCreatImage(size1, frame->depth, frame->nChannels);CvSetImageROI(frame, rectInImage1);cvCopy(img1, frame);//img1是从frame上提取的目标框区域//检测特征点detector.detect(img1, keypoint1)//ending // 告诉用户可以开始走了sensor_msgs::Image tracking_frame = get_one_frame();cv_bridge::CvImagePtr cv_ptr = cv_bridge::toCvCopy(tracking_frame, "bgr8");cv::Mat cv_image = cv_ptr->image;tracker->initTracker(cv_image, body_rect); // initint repeat_count = 0;int watch_dog_count = 0;while (ros::ok()) {usleep(1000 * 100); // 100ms// 如果位置不变，则认为可能丢失if (previous_body_rect == body_rect) {repeat_count += 100;if (repeat_count == 5 * 1000) //rect检测到的数据不变，且超过一定时间，判Lost{ROS_WARN_STREAM("Target not move, may lost");repeat_count = 0;track_ok_flag = 0; // heihei,flag=0 -> reset}} //这里判断跟丢else {repeat_count = 0;}if (body_rect.width < 300 && body_rect.height < 300 && track_ok_flag == 2 &&body_rect.height > body_rect.width) //确认检测到的rect符合这些要求{watch_dog_count += 100;if (watch_dog_count <= watch_dog * 1000) // watch_dog=2{continue;}} //这里判断是否正确的给出rectwatch_dog_count = 0; tracking_frame = get_one_frame(); //再次获得newframebody_track_rect = client->getBodyRect(tracking_frame); // track//usr code begining IplImage *img2;CvRect rectInImage2;ectInImage2 = cvRect(body_track_rect.x, body_track_rect.y,ody_track_rect.width, body_track_rect.height);CvSize size2;size2.width = rectInImage2.width;size2.height = rectInImage2.height;img2 = CvCreatImage(size2, tracking_frame->depth, tracking_frame->nChannels);CvSetImageROI(tracking_frame, rectInImage2);cvCopy(img2, tracking_frame);//img2是从tracking_frame上提取的目标框区域//检测特征点detector.detect(img2, keypoint2);//计算特征点描述子SurfDescriptorExtractor extractor;Mat descriptor1, descriptor2;extractor.compute(img1, keypoint1, descriptor1);extractor.compute(img2, keypoint2, descriptor2);//使用flann匹配FlannBasedMatcher matcher;vector<DMatch>matches;matcher.match(descriptor1, descriptor2, matches);//匹配结束double max_dist = 0;double min_dist = 100;for(int i = 0; i < descriptor1.rows; i++){double dist = matches[i].distance;if(dist < min_dist)min_dist = dist;if(dist > max_dist)max_dist = dist;}//得到匹配结果中的最小距离和最大距离//处理匹配结果：判断当前匹配的对象是否为目标，仅根据最大最小匹配距离，能否进行判断？if(min_dist < 1.0)// get it~{tracking_frame = tracking_frame;body_track_rect = body_track_rect;}elseif(max_dist > 5.0)//abandon{continue;//继续下一帧匹配额}elseif((max_dist - min_dist) < 2.0){tracking_frame = tracking_frame;body_track_rect = body_track_rect;}else {continue;}//usr code endingif (body_track_rect.x <= 1 || body_track_rect.y <= 1) //识别到的rect.x / .y小于1后跟踪停止{tracker->stop();}else{{unique_lock<mutex> lock(update_track_mutex);body_rect = body_track_rect;cv_bridge::CvImagePtr cv_ptr =cv_bridge::toCvCopy(tracking_frame, "bgr8");cv::Mat cv_image = cv_ptr->image;if (track_ok_flag == 0) //跟踪标志为0时跟踪复位{tracker->reset(cv_image, body_rect, true); // watch out the reset praram}elsetracker->reset(cv_image, body_rect); /// reset}}}
}
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