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
|
# 图像处理
import cv2
import os
import time
import face_recognition
import numpy as np
import base64
import dlib
from django.conf import settings
from app.models import Pic
def cv2_base64(image):
base64_str = cv2.imencode('.jpg',image)[1].tostring()
base64_str = base64.b64encode(base64_str)
# print('base64',base64_str)
return base64_str
class VideoCamera(object):
def __init__(self):
self.video = cv2.VideoCapture(0)
def __del__(self):
self.video.release()
def get_frame(self):
ret,image = self.video.read()
ret,jpeg = cv2.imencode('.jpg',image)
return jpeg.tobytes()
def face_recognition(self):
# Grab a single frame of video
ret, frame = self.video.read()
# # Resize frame of video to 1/4 size for faster face recognition processing
# small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
# rgb_small_frame = small_frame[:, :, ::-1]
# return rgb_small_frame
rgb_small_frame = frame[:, :, ::-1]
return rgb_small_frame
def dlib_check(self):
# 从摄像头读取照片
success, img = self.video.read()
return img
CAM = VideoCamera()
#使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
def gen():
while True:
frame = CAM.get_frame()
yield(b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
def facerecog():
lxp_image = face_recognition.load_image_file(os.getcwd() + "/know/lixueping.jpg")
lxp_face_encoding = face_recognition.face_encodings(lxp_image)[0]
ff_image = face_recognition.load_image_file(os.getcwd() + "/know/ff.png")
ff_face_encoding = face_recognition.face_encodings(ff_image)[0]
y_image = face_recognition.load_image_file(os.getcwd() + "/know/ywm.jpg")
y_face_encoding = face_recognition.face_encodings(y_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
lxp_face_encoding,
ff_face_encoding,
y_face_encoding
# obama_face_encoding,
# biden_face_encoding
]
known_face_names = [
"Li XuePing",
"FangFang",
"YuWenMiao"
# "Barack Obama",
# "Joe Biden"
]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
# while True:
# frame = CAM.get_frame()
# yield(b'--frame\r\n'
# b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
while True:
frame_tmp = CAM.face_recognition()
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(frame_tmp)
face_encodings = face_recognition.face_encodings(frame_tmp, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# # If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
print('name check',name)
face_names.append(name)
print('face_names',face_names)
process_this_frame = not process_this_frame
# Display the results
# for (top, right, bottom, left), name in zip(face_locations, face_names):
# # Scale back up face locations since the frame we detected in was scaled to 1/4 size
# top *= 4
# right *= 4
# bottom *= 4
# left *= 4
# # Draw a box around the face
# cv2.rectangle(frame_tmp, (left, top), (right, bottom), (0, 0, 255), 2)
# # Draw a label with a name below the face
# cv2.rectangle(frame_tmp, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
# font = cv2.FONT_HERSHEY_DUPLEX
# cv2.putText(frame_tmp, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
ret,jpeg = cv2.imencode('.jpg',frame_tmp)
yield(b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
def dlibCheck():
while True:
img = CAM.dlib_check()
# 转为灰度图片
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用detector进行人脸检测
dets = detector(gray_img, 1)
status = True
# 进行图像抓取动作
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
face = img[x1:y1,x2:y2]
face = cv2.resize(face,(64,64))
name = time.strftime("%Y%m%d%H%M%S")
cv2.imwrite(os.path.join(settings.BASE_DIR, "static")+"/"+name+'.jpg',face)
pic = Pic.objects.create(name=name)
pic.resource = '抓拍'
pic.path = os.path.join(settings.BASE_DIR, "static")+"/"+name+'.jpg'
pic.base64 = cv2_base64(face)
pic.save()
print('dlib found person')
ret,jpeg = cv2.imencode('.jpg',img)
status = False
yield(b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n\r\n')
if status:
# print('dlib not found')
ret,jp = cv2.imencode('.jpg',gray_img)
yield(b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + jp.tobytes() + b'\r\n\r\n')
|
