基于OpenCV和face_recognition的人脸识别_opencv人脸识别jupyternotebook

本代码是在OpenCV检测人脸的基础上，利用开源API：face_recognition实现人脸识别

技术实现相对CNN更简单，只需匹配对应人脸库即可，代码量少。

https://github.com/WhyEastOne/face-

#################################识别视频或图片中的人脸##############################
import os
os.chdir('D:/DataCenter/AI/TRAIN/NetworkClass/face_detect_database/')
import face_recognition
 
###############################################################################
#读取文件夹下所有图片名 
known_path = 'D:/DataCenter/AI/TRAIN/NetworkClass/face_detect_database/data'
 
#读取训练数据
known_face_encodings = []
labels = []
def read_path(path_name):    
    for dir_item in os.listdir(path_name):
        #从初始路径开始叠加，合并成可识别的操作路径
        full_path = os.path.abspath(os.path.join(path_name, dir_item))
        print(path_name)
        
        if os.path.isdir(full_path):    #如果是文件夹，继续递归调用
            read_path(full_path)
        else:   #文件
            if dir_item.endswith('.jpg') or dir_item.endswith('.png'):
                known_image = face_recognition.load_image_file(full_path)
                try:
                    #读取图片进行编码
                    known_encoding = face_recognition.face_encodings(known_image)[0]
                    known_face_encodings.append(known_encoding)
                    #图片标签
                    labels.append(path_name.split("\\")[-1]) 
                except IndexError:#face_recognition.face_encodings(known_image)编码为空list
                    continue
                    
    return known_face_encodings,labels
 
#加载已知图片数据
known_face_encodings, labels = read_path(known_path) 
 
import cv2
import sys
import gc
 
if __name__ == '__main__':
    if len(sys.argv) != 1:
        print("Usage:%s camera_id\r\n" % (sys.argv[0]))
        sys.exit(0)
    
    #框住人脸的矩形边框颜色       
    color = (0, 255, 0)
    
    #捕获指定摄像头的实时视频流
    cap = cv2.VideoCapture(0)
    
    #人脸识别分类器本地存储路径
    cascade_path = "./haarcascade_frontalface_alt2.xml"    
    
    #循环检测识别人脸
    while True:
        ret, frame = cap.read()   #读取一帧视频
        
        if ret is True:
            
            #图像灰化，降低计算复杂度
            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        else:
            continue
        #使用人脸识别分类器，读入分类器
        cascade = cv2.CascadeClassifier(cascade_path)                
 
        #利用分类器识别出哪个区域为人脸
        faceRects = cascade.detectMultiScale(frame_gray, scaleFactor = 1.2, minNeighbors = 3, minSize = (32, 32))        
        if len(faceRects) > 0:                 
            for faceRect in faceRects: 
                x, y, w, h = faceRect
                
                #截取脸部图像提交给模型识别这是谁
                image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
                
                try:
                    unknown_encoding = face_recognition.face_encodings(image)[0]
                except IndexError:#face_recognition.face_encodings(known_image)编码为空list
                    continue                               
                results = face_recognition.compare_faces(known_face_encodings, unknown_encoding )
                
                cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, thickness = 2)
                
                label = 'unkonwn' #初始化为未知
                for i in range(0, len(results)):
                    if results[i] == True:
                        label = labels[i] #若是匹配成功，赋值为该标签
                        break
                
                #文字提示是谁
                cv2.putText(frame,'%s'%label, 
                            (x + 30, y + 30),                      #坐标
                            cv2.FONT_HERSHEY_SIMPLEX,              #字体
                            1,                                     #字号
                            (255,0,255),                           #颜色
                            2)
                    
        cv2.imshow("Recognize Me !!!", frame) #不要出现中文，否则窗口标题显示为乱码
        
        #等待10毫秒看是否有按键输入
        k = cv2.waitKey(120)
        #如果输入q则退出循环
        if k & 0xFF == ord('q'):
            break
 
    #释放摄像头并销毁所有窗口
    cap.release()
    cv2.destroyAllWindows()