标签:人脸姿态识别
对于人脸姿态识别这个领域不甚了解,所以就想了一个很简单的方法,通过眼睛鼻子的比例关系来计算人脸左右旋转的角度,不出所料,效果还不错。甚喜,记录如下:
(1)识别图片姿态
// face_detect.cpp : 定义控制台应用程序的入口点。 // //#include "stdafx.h" #include "opencv2/objdetect/objdetect.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include "opencv2/ml/ml.hpp" #include <iostream> #include <stdio.h> using namespace std; using namespace cv; Point center2[3]; void detectAndDraw(Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale); String cascadeName = "D:/mySoftware/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml";//人脸的训练数据 //String nestedCascadeName = "./haarcascade_eye_tree_eyeglasses.xml";//人眼的训练数据 String nestedCascadeName = "D:/mySoftware/opencv/sources/data/haarcascades/haarcascade_eye.xml";//人眼的训练数据 int main(int argc, const char** argv) { Mat image; CascadeClassifier cascade, nestedCascade;//创建级联分类器对象 double scale = 0.8; //image = imread( "lena.jpg", 1 );//读入lena图片 image = imread("C:/Users/jiang/Desktop/王建程序/FaceBase_png/EX2/bruno-vp2-il0-ex2.png", 1); namedWindow("result", 1);//opencv2.0以后用namedWindow函数会自动销毁窗口 if (!cascade.load(cascadeName))//从指定的文件目录中加载级联分类器 { cerr << "ERROR: Could not load classifier cascade唉唉出错了" << endl; return 0; } if (!nestedCascade.load(nestedCascadeName)) { cerr << "WARNING: Could not load classifier cascade for nested objects" << endl; return 0; } if (!image.empty())//读取图片数据不能为空 { detectAndDraw(image, cascade, nestedCascade, scale); for (int i = 0; i < 3; i++){ std::cout << "( "<<center2[i].x << " , " << center2[i].y<<" )"<<std::endl; } double x = (double)(center2[0].x - center2[2].x) / (double)(center2[2].x - center2[1].x); cout << x << endl; waitKey(0); } return 0; } void detectAndDraw(Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale) { cv::CascadeClassifier mNoseDetector; mNoseDetector.load("D:/mySoftware/opencv/sources/data/haarcascades/haarcascade_mcs_nose.xml"); int i = 0; double t = 0; int w = 0; vector<Rect> faces; const static Scalar colors[] = { CV_RGB(0, 0, 255), CV_RGB(0, 128, 255), CV_RGB(0, 255, 255), CV_RGB(0, 255, 0), CV_RGB(255, 128, 0), CV_RGB(255, 255, 0), CV_RGB(255, 0, 0), CV_RGB(255, 0, 255) };//用不同的颜色表示不同的人脸 Mat gray, smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);//将图片缩小,加快检测速度 cvtColor(img, gray, CV_BGR2GRAY);//因为用的是类haar特征,所以都是基于灰度图像的,这里要转换成灰度图像 resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR);//将尺寸缩小到1/scale,用线性插值 equalizeHist(smallImg, smallImg);//直方图均衡 t = (double)cvGetTickCount();//用来计算算法执行时间 //检测人脸 //detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg,faces表示检测到的人脸目标序列,1.1表示 //每次图像尺寸减小的比例为1.1,2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大 //小都可以检测到人脸),CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测,而是缩放图像,Size(30, 30)为目标的 //最小最大尺寸 cascade.detectMultiScale(smallImg, faces, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH | CV_HAAR_SCALE_IMAGE , Size(30, 30)); t = (double)cvGetTickCount() - t;//相减为算法执行的时间 printf("detection time = %g ms\n", t / ((double)cvGetTickFrequency()*1000.)); int s1 = 0; for (vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++) { s1++; if (s1 > 1) break; Mat smallImgROI; vector<Rect> nestedObjects; Point center; Scalar color = colors[i % 8]; int radius; center.x = cvRound((r->x + r->width*0.5)*scale);//还原成原来的大小 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); //检测人眼,在每幅人脸图上画出人眼 if (nestedCascade.empty()) continue; smallImgROI = smallImg(*r); //和上面的函数功能一样 nestedCascade.detectMultiScale(smallImgROI, nestedObjects, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH //|CV_HAAR_DO_CANNY_PRUNING | CV_HAAR_SCALE_IMAGE , Size(30, 30)); int s2 = 0; for (vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++) { s2++; if (s2 > 2) break; center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale); center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale); //cout << center.x << " , " << center.y << endl; center2[w].x = center.x; center2[w].y = center.y; w++; radius = cvRound((nr->width + nr->height)*0.25*scale); circle(img, center, radius, color, 3, 8, 0);//将眼睛也画出来,和对应人脸的图形是一样的 } mNoseDetector.detectMultiScale(smallImgROI, nestedObjects, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH //|CV_HAAR_DO_CANNY_PRUNING | CV_HAAR_SCALE_IMAGE , Size(30, 30)); int s3 = 0; for (vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++) { s3++; if (s3 > 1) break; center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale); center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale); //cout << center.x << " , " << center.y << endl; center2[w].x = center.x; center2[w].y = center.y; w++; radius = cvRound((nr->width + nr->height)*0.25*scale); circle(img, center, radius, color, 3, 8, 0);//将眼睛也画出来,和对应人脸的图形是一样的 } } cv::imshow("result", img); }
(2)识别相机流姿态
#include "opencv2/imgproc/imgproc.hpp" #include "opencv2/ml/ml.hpp" #include <iostream> #include <stdio.h> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/objdetect/objdetect.hpp> #include <string> #include <vector> using namespace std; using namespace cv; Point center2[3]; cv::CascadeClassifier mFaceDetector; cv::CascadeClassifier mEyeDetector; cv::CascadeClassifier mMouthDetector; cv::CascadeClassifier mNoseDetector; void detectAndDraw(Mat& img, double scale) { //载入四个人脸特征分类器文件,可以从opencv的安装目录中找到 if (mFaceDetector.empty()) mFaceDetector.load("D:/mySoftware/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml"); if (mEyeDetector.empty()) mEyeDetector.load("D:/mySoftware/opencv/sources/data/haarcascades/haarcascade_lefteye_2splits.xml"); if (mNoseDetector.empty()) mNoseDetector.load("D:/mySoftware/opencv/sources/data/haarcascades/haarcascade_mcs_nose.xml"); if (mMouthDetector.empty()) mMouthDetector.load("D:/mySoftware/opencv/sources/data/haarcascades/haarcascade_mcs_mouth.xml"); int i = 0; double t = 0; int w = 0; vector<Rect> faces; const static Scalar colors[] = { CV_RGB(0, 0, 255), CV_RGB(0, 128, 255), CV_RGB(0, 255, 255), CV_RGB(0, 255, 0), CV_RGB(255, 128, 0), CV_RGB(255, 255, 0), CV_RGB(255, 0, 0), CV_RGB(255, 0, 255) };//用不同的颜色表示不同的人脸 Mat gray, smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1);//将图片缩小,加快检测速度 cvtColor(img, gray, CV_BGR2GRAY);//因为用的是类haar特征,所以都是基于灰度图像的,这里要转换成灰度图像 resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR);//将尺寸缩小到1/scale,用线性插值 equalizeHist(smallImg, smallImg);//直方图均衡 t = (double)cvGetTickCount();//用来计算算法执行时间 //检测人脸 //detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg,faces表示检测到的人脸目标序列,1.1表示 //每次图像尺寸减小的比例为1.1,2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大 //小都可以检测到人脸),CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测,而是缩放图像,Size(30, 30)为目标的 //最小最大尺寸 mFaceDetector.detectMultiScale(smallImg, faces, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH | CV_HAAR_SCALE_IMAGE , Size(30, 30)); t = (double)cvGetTickCount() - t;//相减为算法执行的时间 printf("detection time = %g ms\n", t / ((double)cvGetTickFrequency()*1000.)); int s1 = 0; for (vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++) { s1++; if (s1 > 1) break; Mat smallImgROI; vector<Rect> nestedObjects; Point center; Scalar color = colors[i % 8]; int radius; center.x = cvRound((r->x + r->width*0.5)*scale);//还原成原来的大小 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); //检测人眼,在每幅人脸图上画出人眼 if (mEyeDetector.empty()) continue; smallImgROI = smallImg(*r); //和上面的函数功能一样 mEyeDetector.detectMultiScale(smallImgROI, nestedObjects, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH //|CV_HAAR_DO_CANNY_PRUNING | CV_HAAR_SCALE_IMAGE , Size(30, 30)); int s2 = 0; for (vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++) { s2++; if (s2 > 2) break; center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale); center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale); //cout << center.x << " , " << center.y << endl; center2[w].x = center.x; center2[w].y = center.y; w++; radius = cvRound((nr->width + nr->height)*0.25*scale); circle(img, center, radius, color, 3, 8, 0);//将眼睛也画出来,和对应人脸的图形是一样的 } mNoseDetector.detectMultiScale(smallImgROI, nestedObjects, 1.1, 2, 0 //|CV_HAAR_FIND_BIGGEST_OBJECT //|CV_HAAR_DO_ROUGH_SEARCH //|CV_HAAR_DO_CANNY_PRUNING | CV_HAAR_SCALE_IMAGE , Size(30, 30)); int s3 = 0; for (vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++) { s3++; if (s3 > 1) break; center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale); center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale); //cout << center.x << " , " << center.y << endl; center2[w].x = center.x; center2[w].y = center.y; w++; radius = cvRound((nr->width + nr->height)*0.25*scale); circle(img, center, radius, color, 3, 8, 0);//将眼睛也画出来,和对应人脸的图形是一样的 } } cv::imshow("result", img); } int main() { cv::VideoCapture capture(0); //检查视频是否打开 if (!capture.isOpened()) return 1; // 得到帧率 double rate = capture.get(CV_CAP_PROP_FPS); bool stop(false); cv::Mat frame; // 现在的视频帧 cv::Mat mElabImage;//备份frame图像 // 两帧之间的间隔时间 int delay = 1000 / rate; // 循环播放所有的帧 while (!stop) { // 读下一帧 if (!capture.read(frame)) break; double scale = 1.0; for (int i = 0; i < 3; i++){ center2[i].x = 0; center2[i].y = 0; } detectAndDraw(frame, scale); for (int i = 0; i < 3; i++){ std::cout << "( "<<center2[i].x << " , " << center2[i].y<<" )"<<std::endl; } double x = (double)(center2[0].x - center2[2].x) / (double)(center2[2].x - center2[1].x); cout << x << endl; cv::waitKey(1000); } // 关闭视频文件 capture.release(); return 0; }
标签:人脸姿态识别
原文地址:http://blog.csdn.net/u012361418/article/details/46438673