forked from chack1920/tensorflow_face_recognition
-
Notifications
You must be signed in to change notification settings - Fork 0
/
face_recognition_opencv_tensorflow.py
245 lines (211 loc) · 9.18 KB
/
face_recognition_opencv_tensorflow.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
import cv2
import os
import re
import sys
import time
import facenet
import align.detect_face
import numpy as np
import pandas as pd
from scipy import misc
from PIL import Image,ImageDraw,ImageFont
from multiprocessing import Process,Manager,Queue
import tensorflow as tf
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
model_path="20180402-114759/20180402-114759.pb"
path_csv_feature_all="features_all_tensorflow.csv"
cascPath="FaceDetect-master/haarcascade_frontalface_default.xml"
success_list=[]
global csv_rd
def known_faces(feature_known_list):
f=open(path_csv_feature_all)
global csv_rd
csv_rd=pd.read_csv(f,header=None)
for i in range(csv_rd.shape[0]):
feature_someone_list=[]
for j in range(1,len(csv_rd.ix[i,:])):
feature_someone_list.append(csv_rd.ix[i,:][j])
feature_known_list.append(feature_someone_list)
print("Faces in Database：", len(feature_known_list))
return feature_known_list
def put_text(img_rd,text,position,fillcolor="#FF0000"):
img = cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)
img_PIL = Image.fromarray(img)
font = ImageFont.truetype('SIMYOU.TTF', 40, encoding="utf-8")
draw = ImageDraw.Draw(img_PIL)
draw.text(position, text, fillcolor, font)
img = cv2.cvtColor(np.array(img_PIL),cv2.COLOR_RGB2BGR)
return img
def crop_image(image,bounding_boxes,margin,image_size):
faces_queue=Queue()
img_size = np.asarray(image.shape)[0:2]
for i in range(bounding_boxes.shape[0]):
bb=np.zeros(4,dtype=np.int32)
bb[0]=np.maximum(bounding_boxes[i][0]-margin/2,0)
bb[1]=np.maximum(bounding_boxes[i][1]-margin/2,0)
bb[2]=np.minimum(bounding_boxes[i][2]+margin/2,img_size[1])
bb[3]=np.minimum(bounding_boxes[i][3]+margin/2,img_size[0])
cropped=image[bb[1]:bb[3],bb[0]:bb[2],:]
aligned=misc.imresize(cropped,(image_size,image_size),interp='bilinear')
prewhitened=facenet.prewhiten(aligned)
faces_queue.put(prewhitened)
return faces_queue
def return_512D_features(image,bounding_boxes):
emb_list=[]
faces_queue=crop_image(image,bounding_boxes,44,160)
g = tf.get_default_graph()
with g.as_default():
sess = tf.get_default_session()
with sess.as_default():
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
for i in range(faces_queue.qsize()):
face=faces_queue.get()
face=face.reshape(1,160,160,3)
feed_dict={images_placeholder:face,phase_train_placeholder:False}
emb=sess.run(embeddings,feed_dict=feed_dict)
emb=list(np.squeeze(emb))
emb_list.append(emb)
return emb_list
# 计算两个人脸向量间的欧式距离
def return_euclidean_distance(feature_1, feature_2):
feature_1 = np.array(feature_1)
feature_2 = np.array(feature_2)
dist = np.sqrt(np.sum(np.square(np.subtract(feature_1,feature_2))))
print("欧式距离为: ", dist)
return dist
def face_recognition(image,bounding_boxes,feature_known_list,pos_namelist,name_namelist):
del pos_namelist[:]
del name_namelist[:]
features_cap_list = return_512D_features(image,bounding_boxes)
# 遍历捕获到的图像中所有的人脸
for k in range(len(features_cap_list)):
# 让人名跟随在矩形框的下方
# 确定人名的位置坐标
# 先默认所有人不认识
name_namelist.append("未能识别")
'''
# 每个捕获人脸名字的坐标
pos_namelist.append(
tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 7)]))
'''
person_euclidean_list=list()
# 对于第k张人脸，遍历所有存储的人脸特征
for i in range(len(feature_known_list)):
print("和本地数据第", str(i + 1), "个人相比， ", end='')
# 将某张人脸与存储的所有人脸数据进行比对
euclidean_dist = return_euclidean_distance(features_cap_list[k], feature_known_list[i])
person_euclidean_list.append(euclidean_dist)
index = person_euclidean_list.index(min(person_euclidean_list))
if person_euclidean_list[index] <= 1.0: # 即使找到一个最相似的脸，也要设定一个阀值（根据实际情况自行设定），只有低于这个阀值时才能认为是同一个人
global csv_rd
name_namelist[k] = str(csv_rd[0][index])
print("\n")
print("屏幕中的人脸为:", name_namelist,"\n")
def save_frame(images_que, pos_namelist, name_namelist, open_time):
url = 'rtsp://admin:[email protected]:554//Streaming/Channels/1'
cap = cv2.VideoCapture(url)
if cap.isOpened():
f = open("info.txt", 'a')
f.write("True\n")
f.close()
temp = 0
'''
pid1 = os.getpid()
f = open("info.txt", 'a')
f.write('p1:' + str(pid1) + "\n")
f.close()
'''
while True:
ret, frame = cap.read()
if ret:
cv2.namedWindow('frame', cv2.WINDOW_NORMAL)
cv2.resizeWindow('frame', 1280, 720)
cv2.imshow('frame',frame)
cv2.waitKey(1)
temp += 1
if temp == 22:
#print("保存一帧")
images_que.put(frame)
#print("队列帧数为：%d" % (images_que.qsize()))
temp = 0
# 20分钟后自动关闭摄像头
if time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time() - 1200)) >= open_time:
f = open("info.txt")
info_list = f.readlines()
f.close()
flask_temp = 0
for i in range(len(info_list)):
temp = re.findall('\d+$', info_list[i])
if temp:
flask_temp = temp[0]
pid_list = os.popen("ps -ef | grep flask").readlines()
for i in range(len(pid_list)):
pid_list[i] = pid_list[i].split()[1]
if str(pid_list[i]) != flask_temp and flask_temp != 0:
try:
os.popen("sudo kill -15 " + str(pid_list[i]))
except:
os.popen("sudo kill -9 " + str(pid_list[i]))
print("kill " + str(pid_list[i]) + "\n")
if os.path.exists("info.txt"):
os.remove("info.txt")
time.sleep(4)
sys.exit()
def face_check(images_que, feature_known_list, pos_namelist, name_namelist):
'''
pid2 = os.getpid()
f = open("info.txt", 'a')
f.write('p2:' + str(pid2) + "\n")
f.close()
'''
with tf.Graph().as_default():
with tf.Session()as sess:
# Load the model
facenet.load_model(model_path)
faceCascade=cv2.CascadeClassifier(cascPath)
while True:
image = images_que.get()
#print("开始检测人脸")
gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
faces_coordinate=faceCascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE) #使用opencv的方法检测人脸
faces_num=int(format(len(faces_coordinate)))
print("人脸数为：%d" % (faces_num))
if faces_num!=0:
bounding_boxes=np.ndarray(shape=(faces_num,4),dtype=np.int32)
i=0
for (x,y,w,h) in faces_coordinate: #x代表左上角横坐标，y代表左上角纵坐标，w代表宽度，h代表高度
x2=x+w
y2=y+h
bounding_boxes[i][0]=x
bounding_boxes[i][1]=y
bounding_boxes[i][2]=x2
bounding_boxes[i][3]=y2
i+=1
face_recognition(image,bounding_boxes, feature_known_list, pos_namelist, name_namelist) # 如果有人脸就调用人脸识别函数
else:
print("\n")
def main_process():
'''
p=os.getpid()
f=open("info.txt",'w')
f.write('p:'+str(p)+"\n")
f.close()
'''
with Manager() as manager:
feature_known_list = manager.list() # 已知的人脸的特征list
pos_namelist = manager.list() # 要在屏幕上打印的人脸名字的坐标
name_namelist = manager.list() # 要在屏幕上打印的人脸名字
feature_known_list=known_faces(feature_known_list) # 遍历所有已知的人脸数据
images_que = Queue() #用来保存从摄像头拍到的帧
p1 = Process(target=save_frame, args=(images_que, pos_namelist, name_namelist,time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())),))
print("Create ProcessP1\n")
p2 = Process(target=face_check,args=(images_que, feature_known_list, pos_namelist, name_namelist,))
print("Create ProcessP2\n")
p1.start()
p2.start()
p1.join()
p2.join()
main_process()