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face_recognition_opencv_tensorflow.py
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face_recognition_opencv_tensorflow.py
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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()