人脸识别系统实现

#1&＃xff0c;导入库
import cv2
import numpy as np
import face_recognition
#2&＃xff0c;加载图片
liu&＃61;cv2.imread(&＃39;liu.jpg&＃39;)
wang&＃61;cv2.imread(&＃39;wang.jpg&＃39;)#3&＃xff0c;BGR转为RGB
liu_RGB&＃61;liu[:,:,::-1]
wang_RGB&＃61;wang[:,:,::-1]#4&＃xff0c;检测人脸
liu_face&＃61;face_recognition.face_locations(liu_RGB)
wang_face&＃61;face_recognition.face_locations(wang_RGB)#5&＃xff0c;人脸特征编码
liu_encoding&＃61;face_recognition.face_encodings(liu_RGB,liu_face)[0]
wang_encoding&＃61;face_recognition.face_encodings(wang_RGB,wang_face)[0]#6&＃xff0c;把所有脸放在一起&＃xff0c;当作数据库
encodings&＃61;[liu_encoding,wang_encoding]
names&＃61;[&＃39;liu&＃39;,&＃39;wang&＃39;]#7&＃xff0c;打开摄像头&＃xff0c;读取视频流
cap&＃61;cv2.VideoCapture(0)
if not cap.isOpened():raise IOError("Camera Error!")
while True:ret,frame&＃61;cap.read()frame&＃61;cv2.resize(frame,(0,0),fx&＃61;0.5,fy&＃61;0.5)#8&＃xff0c;BGR转为RGBframe_RGB&＃61;frame[:,:,::-1]#9&＃xff0c;人脸检测face_locations&＃61;face_recognition.face_locations(frame_RGB)#10&＃xff0c;人脸特征编码face_encodings&＃61;face_recognition.face_encodings(frame_RGB,face_locations)#11&＃xff0c;与数据库中的人脸进行匹配for (top,right,bottom,left),face_encoding in zip(face_locations,face_encodings):#12&＃xff0c;进行匹配matches&＃61;face_recognition.compare_faces(encodings,face_encoding)#13&＃xff0c;计算距离distances&＃61;face_recognition.face_distance(encodings,face_encoding)min_distance_index&＃61;np.argmin(distances)#14&＃xff0c;判断&＃xff1a;如果匹配&＃xff0c;获取名字name&＃61;"unknown"if matches[min_distance_index]:name&＃61;names[min_distance_index]#15&＃xff0c;绘制人脸矩形框cv2.rectangle(frame,(left,top),(right,bottom),(0,255,0),3) #16&＃xff0c;绘制&＃xff0c;显示对应人脸的名字cv2.rectangle(frame,(left,bottom-30),(right,bottom),(0,0,255),3)#17,显示名字cv2.putText(frame,name,(left&＃43;10,bottom-10),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,255),1)#18&＃xff0c;显示整个效果cv2.imshow(&＃39;face recognition&＃39;,frame)#19,判断Q&＃xff0c;退出if cv2.waitKey(1)&0xFF&＃61;&＃61;ord(&＃39;q&＃39;):break
#20&＃xff0c;关闭资源
cap.release()
cv2.destroyAllWindows()


import face_recognition
import os
import cv2
from PIL import Image,ImageDraw,ImageFont
import numpy as np#解析已有人员的所有照片名和人物面部编码信息
def load_img(path):print(&＃39;正在加载已知人员的图片。。。。&＃39;)for dirpath,dirnames,filenames in os.walk(path):print(dirpath,dirnames,filenames)facelib&＃61;[]for filename in filenames:filepath&＃61;os.sep.join([dirpath,filename])#把对应每张图像加载进来face_image&＃61;face_recognition.load_image_file(filepath)face_encoding&＃61;face_recognition.face_encodings(face_image)[0]facelib.append(face_encoding)return facelib,filenames
facelib,facenames&＃61;load_img(r&＃39;D:\temp\faces&＃39;)#调用摄像头
video_capture&＃61;cv2.VideoCapture(0)
while True:ret,frame&＃61;video_capture.read()#通过缩小图片&＃xff08;缩小为1/4&＃xff09;&＃xff0c;提高对比效率&＃xff09;if frame is not None:small_frame&＃61;cv2.resize(frame,(0,0),fx&＃61;0.25,fy&＃61;0.25)rgb_small_frame&＃61;small_frame[:,:,::-1] #将opencv的BGR格式转化为RGB格式face_locations&＃61;face_recognition.face_locations(rgb_small_frame)face_encodings&＃61;face_recognition.face_encodings(rgb_small_frame,face_locations)face_names&＃61;[]#循环多张脸for face_encoding in face_encodings:matches&＃61;face_recognition.compare_faces(facelib,face_encoding,tolerance&＃61;0.39)name&＃61;&＃39;未知头像&＃39;if True in matches:#如果摄像头里面的头像匹配了已知人物头像&＃xff0c;则取出第一个True的位置first_match_index&＃61;matches.index(True)name&＃61;facenames[first_match_index][:-4] #取出文件上对应的人名face_names.append(name)for (top,right,bottom,left),name in zip(face_locations,face_names):#还原图片大小top *&＃61;4right *&＃61;4bottom *&＃61;4left *&＃61;4cv2.rectangle(frame,(left,top),(right,bottom),(0,0,225),2) #标注人脸信息img_PIL&＃61;Image.fromarray(cv2.cvtColor(frame,cv2.COLOR_BGR2RGB))font&＃61;ImageFont.truetype(&＃39;simhei.ttf&＃39;,40)draw&＃61;ImageDraw.Draw(img_PIL)draw.text((left&＃43;6,bottom-6),name,font&＃61;font,fill&＃61;(255,255,255))frame&＃61;cv2.cvtColor(np.asarray(img_PIL),cv2.COLOR_RGB2BGR)cv2.imshow(&＃39;Video&＃39;,frame)if cv2.waitKey(1)&0xFF&＃61;&＃61;ord(&＃39;q&＃39;):breakvideo_capture.release()