-
Notifications
You must be signed in to change notification settings - Fork 41
/
Copy pathface_recognition_multi.py
100 lines (90 loc) · 4.28 KB
/
face_recognition_multi.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
#-*- coding: utf-8 -*-
"""---------------------------------------------------------
四、人脸识别
1、打开摄像头，获取图片并灰度化
2、人脸检测
3、加载卷积神经网络模型
4、人脸识别
------------------------------------------------------------"""
import tensorflow as tf
import cv2
import dlib
import numpy as np
import os
import random
import sys
from sklearn.model_selection import train_test_split
import net
import units
"""定义人脸识别函数"""
def face_recognise(image):
res = sess.run(predict, feed_dict={input_image: [image/255.0], dropout_rate: 1.0, dropout_rate_2: 1.0})
if res[0] == 3:
return "zsc"
elif res[0] == 2:
return "sxx"
elif res[0] == 1:
return "wtt"
else:
return "other"
if __name__ == '__main__':
"""定义参数"""
num_class = 4
size = 64 # 图片大小64*64*3
input_image = tf.placeholder(tf.float32, [None, size, size, 3]) # 输入X：64*64*3 ， 定义
input_label = tf.placeholder(tf.float32, [None, 2]) # 输出Y_：1*2 ， 定义
dropout_rate = tf.placeholder(tf.float32) # 定义
dropout_rate_2 = tf.placeholder(tf.float32) # 定义
"""将读取的人脸图片输出神经网络，输出out(1,4)"""
outdata = net.layer_net(input_image, num_class, dropout_rate, dropout_rate_2) # outdata: [0.3,0.7] , input_label: [1, 0]
"""预测， 1表示按行返回out中最大值的索引，而不是out与1比谁大返回谁，predict为索引值，0、1、2、3，因为out的shape是（1,4）,一行4列，4个数字"""
predict = tf.argmax(outdata, 1) # 0或1或2或3
"""1、读取模型文件"""
saver = tf.train.Saver()
config = tf.ConfigProto(allow_soft_placement=True)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
saver.restore(sess, tf.train.latest_checkpoint('G:/github/CNN_faces_recognition/model_multi/'))
"""2、检测人脸，特征提取器: dlib自带的frontal_face_detector"""
detector = dlib.get_frontal_face_detector()
cap = cv2.VideoCapture(0) # 打开摄像头
while True:
_, img = cap.read() # 读取
gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 灰度化
dets = detector(gray_image, 1)
if not len(dets):
key = cv2.waitKey(30)
if key == 27:
sys.exit(0)
"""--------------------------------------------------------------------
使用enumerate 函数遍历序列中的元素以及它们的下标,i为人脸序号,d为i对应的元素;
left：人脸左边距离图片左边界的距离 ；right：人脸右边距离图片左边界的距离
top：人脸上边距离图片上边界的距离 ；bottom：人脸下边距离图片上边界的距离
----------------------------------------------------------------------"""
for i, d in enumerate(dets):
x1 = d.top() if d.top() > 0 else 0
y1 = d.bottom() if d.bottom() > 0 else 0
x2 = d.left() if d.left() > 0 else 0
y2 = d.right() if d.right() > 0 else 0
"""人脸大小64*64"""
face = img[x1:y1, x2:y2]
face = cv2.resize(face, (size, size))
"""3、人脸识别，输出识别结果"""
print('who? %s' % face_recognise(face))
res1 = sess.run(outdata, feed_dict={input_image: [face / 255.0], dropout_rate: 1.0, dropout_rate_2: 1.0})
"""4、显示识别结果"""
cv2.rectangle(img, (x2, x1), (y2, y1), (255, 0, 0), 3)
if face_recognise(face) == "zsc":
cv2.putText(img, 'zsc', (x2, y1), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 0), 1)
elif face_recognise(face) == "sxx":
cv2.putText(img, 'sxx', (x2, y1), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 0), 2)
elif face_recognise(face) == "wtt":
cv2.putText(img, 'wtt', (x2, y1), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 0), 2)
else:
cv2.putText(img, 'other', (x2, y1), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow('image', img)
key = cv2.waitKey(30)
if key == 27:
sys.exit(0)
sess.close()