【opencv人脸识别2】从视频中检测人脸
【opencv人脸识别】从视频中检测人脸
1.从视频中识别人脸和人的眼睛
2. 从视频中检测人脸、眼睛、鼻子、嘴巴
上一节，讲了如何从图片中检测人脸，这一节讲如何从视频中检测人脸。 在opencv自带的说明中便有从视频中检测人脸的例子，在..\opencv3_4\opencv\sources\samples\cpp\tutorial_code\objectDetection\文件夹下有objectDetection.cpp，将此例子稍作修改，就可以为我们所用。
1.从视频中识别人脸和人的眼睛
关于视频的操作，主要如下：
定义摄像头->打开摄像头->读取视频帧->转而为对图片的操作（一帧就相当于一幅图片）
VideoCapture capture; //定义摄像头捕捉 变量
Mat frame;
capture.open(0); //打开摄像头
while (capture.read(frame)) //读取帧
{
//进行人脸检测
//显示
}
视频人脸检测的代码：
//face_detect_from_video.cpp 定义控制台应用程序的入口点。 //从视频中识别人脸和人的眼睛 #include "stdafx.h" #include "opencv2/objdetect.hpp" #include "opencv2/highgui.hpp" #include "opencv2/imgproc.hpp" #include从视频中识别人脸和人的眼睛 #include "stdafx.h" #include "opencv2/objdetect.hpp" #include "opencv2/highgui.hpp" #include "opencv2/imgproc.hpp" #includeusing namespace std; using namespace cv; /** Function Headers */ void detectAndDisplay(Mat frame); /** Global variables */ String face_cascade_name, eyes_cascade_name; CascadeClassifier face_cascade; CascadeClassifier eyes_cascade; String window_name = "Capture - Face detection"; /** @function main */ int main(int argc, const char** argv) { face_cascade_name = "./xml/haarcascade_frontalface_alt.xml"; eyes_cascade_name = "./xml/haarcascade_eye.xml"; VideoCapture capture; Mat frame; //-- 1. Load the cascades if (!face_cascade.load(face_cascade_name)) { printf("--(!)Error loading face cascade\n"); return -1; }; if (!eyes_cascade.load(eyes_cascade_name)) { printf("--(!)Error loading eyes cascade\n"); return -1; }; //-- 2. Read the video stream capture.open(0); //打开摄像头 if (!capture.isOpened()) { printf("--(!)Error opening video capture\n"); return -1; } while (capture.read(frame)) //读取帧 { if (frame.empty()) { printf(" --(!) No captured frame -- Break!"); break; } //-- 3. Apply the classifier to the frame detectAndDisplay(frame); if (waitKey(10) == 'k') { break; } // escape } return 0; } /** @function detectAndDisplay */ void detectAndDisplay(Mat frame) { std::vector faces; Mat frame_gray; cvtColor(frame, frame_gray, COLOR_BGR2GRAY); //BGR 转化为灰度图 equalizeHist(frame_gray, frame_gray); //直方图均衡化 //-- Detect faces face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(60, 60)); for (size_t i = 0; i < faces.size(); i++) { Point center(faces[i].x + faces[i].width / 2, faces[i].y + faces[i].height / 2); // 人脸中心坐标 ellipse(frame, center, Size(faces[i].width / 2, faces[i].height / 2), 0, 0, 360, Scalar(255, 0, 255), 4, 8, 0); // 椭圆 Mat faceROI = frame_gray(faces[i]); std::vector eyes; //-- In each face, detect eyes eyes_cascade.detectMultiScale(faceROI, eyes, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30)); for (size_t j = 0; j < eyes.size(); j++) { Point eye_center(faces[i].x + eyes[j].x + eyes[j].width / 2, faces[i].y + eyes[j].y + eyes[j].height / 2); //眼睛的中心 int radius = cvRound((eyes[j].width + eyes[j].height)*0.25); //取整 circle(frame, eye_center, radius, Scalar(255, 0, 0), 4, 8, 0); } } //-- Show what you got imshow(window_name, frame); } using namespace std; using namespace cv; /** Function Headers */ void detectAndDisplay(Mat frame); /** Global variables */ String face_cascade_name, eyes_cascade_name; CascadeClassifier face_cascade; CascadeClassifier eyes_cascade; String window_name = "Capture - Face detection"; /** @function main */ int main(int argc, const char** argv) { face_cascade_name = "./xml/haarcascade_frontalface_alt.xml"; eyes_cascade_name = "./xml/haarcascade_eye.xml"; VideoCapture capture; Mat frame; //-- 1. Load the cascades if (!face_cascade.load(face_cascade_name)) { printf("--(!)Error loading face cascade\n"); return -1; }; if (!eyes_cascade.load(eyes_cascade_name)) { printf("--(!)Error loading eyes cascade\n"); return -1; }; //-- 2. Read the video stream capture.open(0); //打开摄像头 if (!capture.isOpened()) { printf("--(!)Error opening video capture\n"); return -1; } while (capture.read(frame)) //读取帧 { if (frame.empty()) { printf(" --(!) No captured frame -- Break!"); break; } //-- 3. Apply the classifier to the frame detectAndDisplay(frame); if (waitKey(10) == 'k') { break; } // escape } return 0; } /** @function detectAndDisplay */ void detectAndDisplay(Mat frame) { std::vector faces; Mat frame_gray; cvtColor(frame, frame_gray, COLOR_BGR2GRAY); //BGR 转化为灰度图 equalizeHist(frame_gray, frame_gray); //直方图均衡化 //-- Detect faces face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(60, 60)); for (size_t i = 0; i < faces.size(); i++) { Point center(faces[i].x + faces[i].width / 2, faces[i].y + faces[i].height / 2); // 人脸中心坐标 ellipse(frame, center, Size(faces[i].width / 2, faces[i].height / 2), 0, 0, 360, Scalar(255, 0, 255), 4, 8, 0); // 椭圆 Mat faceROI = frame_gray(faces[i]); std::vector eyes; //-- In each face, detect eyes eyes_cascade.detectMultiScale(faceROI, eyes, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30)); for (size_t j = 0; j < eyes.size(); j++) { Point eye_center(faces[i].x + eyes[j].x + eyes[j].width / 2, faces[i].y + eyes[j].y + eyes[j].height / 2); //眼睛的中心 int radius = cvRound((eyes[j].width + eyes[j].height)*0.25); //取整 circle(frame, eye_center, radius, Scalar(255, 0, 0), 4, 8, 0); } } //-- Show what you got imshow(window_name, frame); }
运行结果：
2. 从视频中检测人脸、眼睛、鼻子、嘴巴
此部分结合了之前讲的识别人脸特征：运行opencv3.4中的demo--facial_features.cpp
将上述第一部分的从视频中识别人脸和眼睛，再加上鼻子、嘴巴的识别，可实现从视频中检测人脸特征。
代码如下：
//face_recog_from_video.cpp 定义控制台应用程序的入口点。
#include "stdafx.h"
#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include
#include
using namespace std;
using namespace cv;
/** Function Headers */
void detectAndDisplay(Mat frame);
/** Global variables */
String face_cascade_name, eyes_cascade_name, nose_cascade_name , mouth_cascade_name;
CascadeClassifier face_cascade;
CascadeClassifier eyes_cascade;
CascadeClassifier nose_cascade;
CascadeClassifier mouth_cascade;
String window_name = "Capture - Face detection";
/** @function main */
int main(int argc, const char** argv)
{
face_cascade_name = "./xml/haarcascade_frontalface_alt.xml";
eyes_cascade_name = "./xml/haarcascade_eye.xml";
nose_cascade_name = "./xml/haarcascade_mcs_nose.xml";
mouth_cascade_name = "./xml/haarcascade_mcs_mouth.xml";
VideoCapture capture;
Mat frame;
//-- 1. Load the cascades
if (!face_cascade.load(face_cascade_name)) { printf("--(!)Error loading face cascade\n"); return -1; };
if (!eyes_cascade.load(eyes_cascade_name)) { printf("--(!)Error loading eyes cascade\n"); return -1; };
if (!nose_cascade.load(nose_cascade_name)) { printf("--(!)Error loading nose cascade\n"); return -1; };
if (!mouth_cascade.load(mouth_cascade_name)) { printf("--(!)Error loading mouth cascade\n"); return -1; };
//-- 2. Read the video stream
capture.open(0); //打开摄像头
if (!capture.isOpened()) { printf("--(!)Error opening video capture\n"); return -1; }
while (capture.read(frame)) //读取帧
{
if (frame.empty())
{
printf(" --(!) No captured frame -- Break!");
break;
}
//-- 3. Apply the classifier to the frame
detectAndDisplay(frame);
if (waitKey(10) == 'k') { break; } // escape
}
return 0;
}
/** @function detectAndDisplay */
void detectAndDisplay(Mat frame)
{
std::vector faces;
Mat frame_gray;
cvtColor(frame, frame_gray, COLOR_BGR2GRAY); //BGR 转化为灰度图
equalizeHist(frame_gray, frame_gray); //直方图均衡化
//-- Detect faces
face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(60, 60));
for (size_t i = 0; i < faces.size(); i++)
{
Point center(faces[i].x + faces[i].width / 2, faces[i].y + faces[i].height / 2); // 人脸中心坐标
ellipse(frame, center, Size(faces[i].width / 2, faces[i].height / 2), 0, 0, 360, Scalar(255, 0, 255), 4, 8, 0); // 椭圆
Mat faceROI = frame_gray(faces[i]);
std::vector eyes;
std::vector noses;
std::vector mouths;
//-- In each face, detect eyes、nose、mouth
eyes_cascade.detectMultiScale(faceROI, eyes, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));
nose_cascade.detectMultiScale(faceROI, noses, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));
mouth_cascade.detectMultiScale(faceROI, mouths, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));
// eyes
Point eye_center;
for (size_t j = 0; j < eyes.size(); j++)
{
eye_center = Point(faces[i].x + eyes[j].x + eyes[j].width / 2, faces[i].y + eyes[j].y + eyes[j].height / 2); //眼睛的中心
if (eye_center.x>faces[i].x && eye_center.y > faces[i].y) // 确保眼睛在脸上，其实前边检测时，已经保证了这一点
{
int radius = cvRound((eyes[j].width + eyes[j].height)*0.25); //取整
circle(frame, eye_center, radius, Scalar(255, 0, 0), 4, 8, 0);
}
}
// nose
Point nose_center;
if (noses.size() > 0)
{
nose_center = Point(faces[i].x + noses[0].x + noses[0].width / 2, faces[i].y + noses[0].y + noses[0].height / 2); //鼻子的中心
if (nose_center.y > eye_center.y) //确保鼻子在眼睛下边
{
rectangle(frame, Point(faces[i].x + noses[0].x, faces[i].y+ noses[0].y), Point(faces[i].x + noses[0].x + noses[0].width, faces[i].y + noses[0].y + noses[0].height), Scalar(0, 255, 0), 3, 8, 0); //Point(noses[0].x, noses[0].y), Point(noses[0].x + noses[0].width, noses[0].y + noses[0].height)
//int radius = cvRound((noses[0].width + noses[0].height)*0.25); //取整
//circle(frame, nose_center, radius, Scalar(0, 255,0), 4, 8, 0);
std::cout << "nose!\n";
}
}
// mouth
if (mouths.size() > 0)
{
Point mouth_center(faces[i].x + mouths[0].x + mouths[0].width / 2, faces[i].y + mouths[0].y + mouths[0].height / 2); //嘴巴的中心
if (mouth_center.y > nose_center.y) // 确保嘴巴在鼻子下边
{
int radius = cvRound((mouths[0].width + mouths[0].height)*0.25); //取整
circle(frame, mouth_center, radius, Scalar(0, 0, 255), 4, 8, 0);
std::cout << "mouth!\n";
}
}
}
//-- Show what you got
imshow(window_name, frame);
}
运行结果：
由结果可看出，较好的检测出来人脸及人脸特征，其中，粉色区域为face、蓝色为eye、绿色为nose、红色为mouth。
但多次试验会发现，误判的概率很高，所以模型与程序尚有较大改进空间。
注意：要对眼睛嘴巴鼻子的位置进行限定，可一定程度上减少误判。
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