利用python实现人脸识别技术是基于mtcnn和facenet考勤

实现了人脸大角度识别

import cv2
import os
import numpy as np
from net.mtcnn import mtcnn
import utils.utils as utils
from net.inception import InceptionResNetV1
import time
from dateutil.parser import parsefont = cv2.FONT_HERSHEY_COMPLEXdef seekopp(list_2,i):for j in range(i-1,-1,-1):if list_2[j]==0:continue elif list_2[j]>0:return 1elif list_2[j]<0:return 0def local_maximum(list_1):a=len(list_1)if a==0:return 'error'if a==1:return list_1if a==2:if list_1[0]>list_1[1]:return list_1[0]elif list_1[0]<list_1[1]:return list_1[1]else:return list_1if a>2:list_2=[]index_1=[]for i in range(0,a-1):list_2.append(list_1[i+1]-list_1[i])b=len(list_2)if list_2[0]<0:index_1.append(0)for i in range(0,b-1):if list_2[i+1]<0:if list_2[i]>0:index_1.append(i+1)elif list_2[i]==0:if seekopp(list_2,i):index_1.append(i+1)else:continue else:continue list_3=[]for i in index_1:list_3.append(list_1[i])return list_3def transform(t1,t2):timeArray1 = time.localtime(t1)timeArray2 = time.localtime(t2)time_1 = time.strftime("%Y-%m-%d %H:%M:%S", timeArray1)time_2 = time.strftime("%Y-%m-%d %H:%M:%S", timeArray2)date1 = parse(time_1)date2 = parse(time_2)result = int((date2 - date1).total_seconds())return resultclass face_rec():time = 0def __init__(self):# 创建mtcnn对象# 检测图片中的人脸self.mtcnn_model = mtcnn()# 门限函数self.threshold = [0.5,0.8,0.9]# 载入facenet# 将检测到的人脸转化为128维的向量self.facenet_model = InceptionResNetV1()# model.summary()model_path = './model_data/facenet_keras.h5'self.facenet_model.load_weights(model_path)#-----------------------------------------------##   对数据库中的人脸进行编码#   known_face_encodings中存储的是编码后的人脸#   known_face_names为人脸的名字#-----------------------------------------------#face_list = os.listdir("face_dataset")self.known_face_encodings=[]self.known_face_names=[]self.time_list=[]self_learn_time = 0self.total_time = []for face in face_list:name = face.split(".")[0]img = cv2.imread("./face_dataset/"+face)img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)# 检测人脸rectangles = self.mtcnn_model.detectFace(img, self.threshold)# 转化成正方形rectangles = utils.rect2square(np.array(rectangles))# facenet要传入一个160x160的图片rectangle = rectangles[0]# 记下他们的landmarklandmark = (np.reshape(rectangle[5:15],(5,2)) - np.array([int(rectangle[0]),int(rectangle[1])]))/(rectangle[3]-rectangle[1])*160crop_img = img[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]crop_img = cv2.resize(crop_img,(160,160))new_img,_ = utils.Alignment_1(crop_img,landmark)new_img = np.expand_dims(new_img,0)# 将检测到的人脸传入到facenet的模型中，实现128维特征向量的提取face_encoding = utils.calc_128_vec(self.facenet_model,new_img)self.known_face_encodings.append(face_encoding)self.known_face_names.append(name)def recognize(self,draw):#-----------------------------------------------##   人脸识别#   先定位，再进行数据库匹配#-----------------------------------------------#height,width,_ = np.shape(draw)draw_rgb = cv2.cvtColor(draw,cv2.COLOR_BGR2RGB)# 检测人脸rectangles = self.mtcnn_model.detectFace(draw_rgb, self.threshold)'''if len(rectangles)==0:font = cv2.FONT_HERSHEY_SIMPLEXif time_flag==0:print('首次执行！')first_time = time.time()t = transform(int(first_time),int(time.time()))cv2.putText(draw, "Focus on time:"+str(t)+" s", (250, 40), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA)time_flag==1returnelse:print('第二次执行！')t = transform(int(first_time),int(time.time()))cv2.putText(draw, "Focus on time:"+str(t)+" s", (250, 40), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA)return'''if len(rectangles)==0:self.time_list.append(time.time())font = cv2.FONT_HERSHEY_SIMPLEXt = transform(self.time_list[0],self.time_list[-1])#print(t,type(t))cv2.putText(draw, "Noface", (40, 40), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA)cv2.putText(draw, "Focus on time:"+str(t)+" s", (350, 40), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA)#print(self.total_time,type(self.total_time))if len(self.total_time)==0:self.total_time.append(t)elif self.total_time[-1]<= t:self.total_time.pop()self.total_time.append(t)elif self.total_time[-1]>= t:self.total_time.append(t)else:passreturn draw,self.total_timeself.time_list.clear()# 转化成正方形rectangles = utils.rect2square(np.array(rectangles,dtype=np.int32))rectangles[:,0] = np.clip(rectangles[:,0],0,width)rectangles[:,1] = np.clip(rectangles[:,1],0,height)rectangles[:,2] = np.clip(rectangles[:,2],0,width)rectangles[:,3] = np.clip(rectangles[:,3],0,height)#-----------------------------------------------##   对检测到的人脸进行编码#-----------------------------------------------#face_encodings = []for rectangle in rectangles:landmark = (np.reshape(rectangle[5:15],(5,2)) - np.array([int(rectangle[0]),int(rectangle[1])]))/(rectangle[3]-rectangle[1])*160crop_img = draw_rgb[int(rectangle[1]):int(rectangle[3]), int(rectangle[0]):int(rectangle[2])]crop_img = cv2.resize(crop_img,(160,160))new_img,_ = utils.Alignment_1(crop_img,landmark)new_img = np.expand_dims(new_img,0)face_encoding = utils.calc_128_vec(self.facenet_model,new_img)face_encodings.append(face_encoding)face_names = []for face_encoding in face_encodings:# 取出一张脸并与数据库中所有的人脸进行对比，计算得分matches = utils.compare_faces(self.known_face_encodings, face_encoding, tolerance = 0.9)name = "Unknown"# 找出距离最近的人脸face_distances = utils.face_distance(self.known_face_encodings, face_encoding)# 取出这个最近人脸的评分best_match_index = np.argmin(face_distances)if matches[best_match_index]:name = self.known_face_names[best_match_index]face_names.append(name)rectangles = rectangles[:,0:4]#-----------------------------------------------##   画框~!~#-----------------------------------------------#for (left, top, right, bottom), name in zip(rectangles, face_names):cv2.rectangle(draw, (left, top), (right, bottom), (0, 0, 255), 2)font = cv2.FONT_HERSHEY_SIMPLEXcv2.putText(draw, name, (left , bottom - 15), font, 0.75, (255, 255, 255), 2) if name!='Unknown':cv2.putText(draw, 'name:'+name, (10, 20), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA)cv2.putText(draw, "state:"+"clock on", (10, 60), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA)cv2.putText(draw, "Please concentrate on your study!", (200, 40), font, 0.6, (0, 0, 255), 1, cv2.LINE_AA)else:cv2.putText(draw, 'name:'+name, (10, 20), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA)cv2.putText(draw, "state:"+"clock out failed", (10, 60), font, 0.6, (0, 255, 0), 1, cv2.LINE_AA)cv2.putText(draw, "Please add face!", (300, 40), font, 0.6, (0, 0, 255), 1, cv2.LINE_AA)return draw,self.total_timeif __name__ == "__main__":dududu = face_rec()video_capture = cv2.VideoCapture(0)while True:ret, draw = video_capture.read()dududu.recognize(draw) cv2.imshow('Video', draw)if cv2.waitKey(20) & 0xFF == ord('q'):breakvideo_capture.release()cv2.destroyAllWindows()