github地址https://github.com/ngunauj/facedetection
熟悉ros环境。ubuntu16.04 + ros kinetic版本。使用笔记本自带摄像头，完成人脸的实时检测。代码可能会更改，具体以github上的代码为主。
camera_subscribe.cpp 订阅camera发出的图片信息，然后对Mat 类型的图片进行每一帧图片的人脸检测，人脸检测代码参考opencv的开源代码。
/* *********************************************** Author :guanjunace@foxmail.com Created Time :2017年07月10日 星期一 10时43分26秒 File Name :camera_subscribe.cpp ************************************************ */ #include <iostream> #include <ros/ros.h> #include <image_transport/image_transport.h> #include <cv_bridge/cv_bridge.h> #include <sensor_msgs/image_encodings.h> #include <opencv2/highgui/highgui.hpp> #include "opencv2/objdetect.hpp" #include "opencv2/imgproc.hpp" using namespace std; using namespace cv; const string cascadeName = "/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml"; const string nestedCascadeName = "/usr/share/opencv/haarcascades/haarcascade_eye.xml"; const double scale = 1.3; void detectFace(Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade) { double t = 0; vector<Rect> faces; const static Scalar colors[] = { Scalar(255,0,0), Scalar(255,128,0), Scalar(255,255,0), Scalar(0,255,0), Scalar(0,128,255), Scalar(0,255,255), Scalar(0,0,255), Scalar(255,0,255) }; Mat gray, smallImg; cvtColor( img, gray, COLOR_BGR2GRAY ); double fx = 1 / scale; resize(gray, smallImg, Size(), fx, fx, INTER_LINEAR); equalizeHist(smallImg, smallImg); t = (double)getTickCount(); cascade.detectMultiScale(smallImg, faces, 1.1, 2, 0 //|CASCADE_FIND_BIGGEST_OBJECT //|CASCADE_DO_ROUGH_SEARCH |CASCADE_SCALE_IMAGE, Size(30, 30)); t = (double)getTickCount() - t; printf("detection time = %g ms ", t*1000/getTickFrequency()); for (size_t i = 0; i < faces.size(); ++i) { Rect r = faces[i]; Mat smallImgROI; vector<Rect> nestedObjects; Point center; Scalar color = colors[i%8]; int radius; double aspect_ratio = (double)r.width/r.height; if(0.75 < aspect_ratio && aspect_ratio < 1.3) { center.x = cvRound((r.x + r.width*0.5)*scale); center.y = cvRound((r.y + r.height*0.5)*scale); radius = cvRound((r.width + r.height)*0.25*scale); circle(img, center, radius, color, 3, 8, 0); } else { rectangle(img, cvPoint(cvRound(r.x*scale), cvRound(r.y*scale)), cvPoint(cvRound((r.x + r.width-1)*scale), cvRound((r.y + r.height-1)*scale)), color, 3, 8, 0); } smallImgROI = smallImg(r); nestedCascade.detectMultiScale(smallImgROI, nestedObjects, 1.1, 2, 0 |CASCADE_SCALE_IMAGE, Size(30, 30) ); for (size_t j = 0; j < nestedObjects.size(); ++j) { Rect nr = nestedObjects[j]; center.x = cvRound((r.x + nr.x + nr.width*0.5)*scale); center.y = cvRound((r.y + nr.y + nr.height*0.5)*scale); radius = cvRound((nr.width + nr.height)*0.25*scale); circle(img, center, radius, color, 3, 8, 0); } } imshow("result", img); waitKey(1); } void img_Callback(const sensor_msgs::ImageConstPtr& msg) { Mat image ; CascadeClassifier cascade, nestedCascade; try { image = cv_bridge::toCvShare(msg, "bgr8")->image; //CascadeClassifier cascade, nestedCascade; nestedCascade.load(nestedCascadeName); cascade.load(cascadeName); detectFace(image, cascade, nestedCascade); //Convert an immutable sensor_msgs::Image message to an OpenCV-compatible CvImage, //sharing the image data if possible. //imshow("img", cv_bridge::toCvShare(msg, "bgr8")->image);//IplImage 类型的 mat //waitKey(1); } catch (cv_bridge::Exception &e) { ROS_ERROR("Could not convert from '%s' to 'bgr8'.", msg->encoding.c_str()); } if (!image.empty()) detectFace(image, cascade, nestedCascade); else printf("no image!"); } int main(int argc, char *argv[]) { ros::init(argc, argv, "img_subscribe"); ros::NodeHandle nh; namedWindow("webcamimg"); startWindowThread();/* CascadeClassifier cascade, nestedCascade; if (!nestedCascade.load(nestedCascadeName)) cerr << "WARNING: Could not load classifier cascade for nested objects" << endl; if (!cascade.load(cascadeName)) { cerr << "ERROR: Could not load classifier cascade" << endl; return -1; } */ image_transport::ImageTransport it(nh); image_transport::Subscriber img_sub = it.subscribe("/webcam/img", 1, &(img_Callback)); destroyWindow("webcamimg"); ros::spin(); return 0; }
camera_driver代码
/* *********************************************** Author :guanjun guanjunace@foxmail.com Created Time :2017/7/8 10:40:13 File Name :camera_publisher.cpp ************************************************ */ #include <bits/stdc++.h> #include <ros/ros.h> #include <image_transport/image_transport.h> #include <cv_bridge/cv_bridge.h> #include <sensor_msgs/image_encodings.h> #include <opencv2/highgui/highgui.hpp> using namespace std; using namespace cv; class WebCam { public: WebCam (ros::NodeHandle& nh, int video_source = 0) : it(nh), cap(video_source) { if (!cap.isOpened()) { ROS_ERROR("Cannot open the camera! "); } /*设置主题名和缓冲区*/ imgPub = it.advertise("webcam/img", 1); /*初始化CvImage智能指针，CvImage为Mat与ROS图像之间转换的载体*/ frame = boost::make_shared<cv_bridge::CvImage>(); /*设置ROS图片为BGR且每个像素点用1个字节来表示类似于CV_8U*/ frame->encoding = sensor_msgs::image_encodings::BGR8; } /*图像发布函数*/ int publishImage(){ /*将摄像头获取到的图像存放在frame中的image*/ cap >> frame->image; /*判断是否获取到图像，若获取到图像，将其转化为ROS图片*/ if (!(frame->image.empty())){ frame->header.stamp = ros::Time::now(); imgPub.publish(frame->toImageMsg()); } return 0; } private: /*设置图片节点*/ image_transport::ImageTransport it; /*设置图片的发布者*/ image_transport::Publisher imgPub; /*设置存放摄像头图像的变量*/ VideoCapture cap; /*设置cvImage的智能指针*/ cv_bridge::CvImagePtr frame; }; int main(int argc, char *argv[]) { /*初始化节点，并设定节点名*/ ros::init(argc, argv, "img_publiser"); /*设置节点句柄*/ ros::NodeHandle nh; /*判断输入参数是否完成*/ if (argv[1] == NULL){ ROS_WARN("Please choose the camera you want to use !"); return 1; } /*获取打开摄像机的设备号*/ int video_source = 0; int default_p = 0; istringstream default_param(argv[1]); default_param >> default_p; nh.param<int>("video_source", video_source, default_p); /*定义摄像机对象*/ WebCam webcam(nh, video_source); /*设置主题的发布频率为10Hz*/ ros::Rate loop_rate(10); /*图片节点进行主题的发布*/ while (ros::ok()) { webcam.publishImage(); ros::spinOnce(); /*按照设定的频率来将程序挂起*/ loop_rate.sleep(); } return 0; }
在人脸检测的过程中，随着人脸的移动，有个别帧并不能检测出人脸，考虑以后哦要加个跟踪