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import numpy as np import cv2 import time import face_recognition # Threshold =
0.65 # 人脸置信度阈值 #windows用户： #Just install dlib and face_recognition (not always
on the newest version): #pip install dlib and then pip install
face_recognition. ''' 功能：计算两张图片的相似度，范围：[0,1] 输入： 1）人脸A的特征向量 2）人脸B的特征向量 输出：
1）sim：AB的相似度 ''' def simcos(A,B): A=np.array(A) B=np.array(B) dist =
np.linalg.norm(A - B) # 二范数 sim = 1.0 / (1.0 + dist) # return sim ''' 功能： 输入：
1）x:人脸库向量（n维） 2）y：被测人脸的特征向量(1维) 输出： 1)match：与人脸库匹配列表，如[False,True,True,False]
表示被测人脸y与人脸库x的第2,3张图片匹配，与1,4不匹配 2)max(ressim):最大相似度 ''' def
compare_faces(x,y,Threshold): ressim = [] match = [False]*len(x) for fet in x:
sim = simcos(fet,y) ressim.append(sim) if max(ressim) > Threshold: #置信度阈值
match[ressim.index(max(ressim))] = True return match,max(ressim) ''' 注册身份 输入：
1）libpath：人脸库地址 输出： 1）known_face_encodings：人脸库特征向量 2）known_face_names：人脸库名字标签
''' def registeredIdentity(libpath): known_face_encodings, known_face_names =
[], [] with open(libpath + 'liblist.txt', 'r') as f: lines = f.readlines() for
line in lines: img_lable_name = line.split() image =
face_recognition.load_image_file(libpath + str(img_lable_name[0]))
face_locations = face_recognition.face_locations(image) # face_locations =
face_recognition.face_locations(image, model='cnn') face_encoding =
face_recognition.face_encodings(image, face_locations)[0] # face_encoding =
face_recognition.face_encodings(image, face_locations)
known_face_encodings.append(face_encoding)
known_face_names.append(str(img_lable_name[1])) return known_face_encodings,
known_face_names ''' 输入： 1）testimg：测试图片 2）known_face_encodings：人脸库特征向量
3）known_face_names：人脸库名字标签 输出： 1）retname：预测的名字 2）retscore：相似度得分
3）face_locations：人脸位置坐标 ''' def
identityRecognition(testimg,known_face_encodings,known_face_names,Threshold):
face_locations = face_recognition.face_locations(testimg) # face_locations =
face_recognition.face_locations(testimg, model="cnn") face_encodings =
face_recognition.face_encodings(testimg, face_locations) retname, retscore =
"Noface", 0 for face_encoding in face_encodings: matches, score =
compare_faces(known_face_encodings, face_encoding,Threshold) retname, retscore
= "Unknow", 0 if True in matches: first_match_index = matches.index(True) name
= known_face_names[first_match_index] if score > retscore: retname = name
retscore = score return retname, retscore,face_locations ''' 输入：
1）img:摄像头得到的未裁剪图片 2）face_locations:人脸位置坐标 3) name:预测的名字 输出： img:加框加年龄备注之后的画面
''' def age_show(img , face_locations,name): for (y0, x1, y1, x0) in
face_locations: cv2.rectangle(img, (x0, y0), (x1, y1), ( 0, 0,255), 2) info =
str(name) t_size = cv2.getTextSize(str(info), cv2.FONT_HERSHEY_PLAIN, 1, 2)[0]
x2,y2 = x0 + t_size[0] + 3, y0 + t_size[1] + 4 cv2.rectangle(img, (x0,y0),
(x2,y2), (0, 0, 255), -1) # -1填充作为文字框底色 cv2.putText(img, info, (x0, y0
+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 1) return img #4个接口
#人脸检测：face_recognition.face_locations(img, number_of_times_to_upsample=1,
model="hog") #检测面部特征点： face_landmarks(face_image, face_locations=None,
model="large") #给脸部编码：face_encodings(face_image, known_face_locations=None,
num_jitters=1) #从编码中找出人的名字：compare_faces(known_face_encodings,
face_encoding_to_check, tolerance=0.6)