client.py
6.65 KB
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
##################################################
#1. webcam에서 얼굴을 인식합니다.
#2. 얼굴일 확률이 97% 이상이고 영역이 15000 이상인 이미지를 서버에 전송
##################################################
import tkinter as tk
import tkinter.font
import tkinter.messagebox
import tkinter.scrolledtext
import threading
import torch
import numpy as np
import cv2
import asyncio
import websockets
import json
import os
import timeit
import base64
import time
from PIL import Image, ImageTk
from io import BytesIO
import requests
from models.mtcnn import MTCNN
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Running on device: {}'.format(device))
mtcnn = MTCNN(keep_all=True, post_process=True, device=device)
uri = 'ws://169.56.95.131:8765'
class Client(tk.Frame):
def __init__(self, parent, *args, **kwargs):
tk.Frame.__init__(self, parent, *args, **kwargs)
# URI
self.uri = 'ws://169.56.95.131:8765'
# Pytorch Model
self.device = device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.mtcnn = MTCNN(keep_all=True, device=device)
# OpenCV
self.cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
self.cam_width = 640
self.cam_height = 480
self.cap.set(3, self.cam_width)
self.cap.set(4, self.cam_height)
# Application Function
# cam에서 MTCNN 적용하는 영역
self.detecting_square = (500, 300)
# 영상 위에 사각형 색상 지정
self.rectangle_color = (0, 0, 255)
# tkinter GUI
self.width = 740
self.height = 700
self.parent = parent
self.parent.title("출석시스템")
self.parent.geometry("%dx%d+100+100" % (self.width, self.height))
self.pack()
self.create_widgets()
# Event loop and Thread
self.event_loop = asyncio.new_event_loop()
self.thread = threading.Thread(target=self.mainthread)
self.thread.start()
def create_widgets(self):
image = np.zeros([self.cam_height, self.cam_width, 3], dtype=np.uint8)
image = Image.fromarray(image)
image = ImageTk.PhotoImage(image)
font = tk.font.Font(family="맑은 고딕", size=15)
self.alert = tk.Label(self, text="출석시스템", font=font)
self.alert.grid(row=0, column=0, columnspan=20)
self.label = tk.Label(self, image=image)
self.label.grid(row=1, column=0, columnspan=20)
self.log = tk.scrolledtext.ScrolledText(self, wrap = tk.WORD, state=tk.DISABLED, width = 96, height = 10)
self.log.grid(row=2, column=0, columnspan=20)
self.quit = tk.Button(self, text="나가기", fg="red", command=self.stop)
self.quit.grid(row=3, column=10)
def logging(self, text):
self.log.config(state=tk.NORMAL)
self.log.insert(tkinter.CURRENT, text)
self.log.insert(tkinter.CURRENT, '\n')
self.log.config(state=tk.DISABLED)
def detect_face(self, frame):
results = self.mtcnn.detect(frame)
faces = self.mtcnn(frame, return_prob = False)
image_list = []
face_list = []
if results[1][0] == None:
return [], []
for box, face, prob in zip(results[0], faces, results[1]):
if prob < 0.97:
continue
# for debug
# print('face detected. prob:', prob)
x1, y1, x2, y2 = box
if (x2-x1) * (y2-y1) < 15000:
# 얼굴 해상도가 너무 낮으면 무시
continue
image = frame
image_list.append(image)
# MTCNN 데이터 저장
face_list.append(face.numpy())
return face_list, image_list
def mainthread(self):
t = threading.currentThread()
asyncio.set_event_loop(self.event_loop)
x1 = int(self.cam_width / 2 - self.detecting_square[0] / 2)
x2 = int(self.cam_width / 2 + self.detecting_square[0] / 2)
y1 = int(self.cam_height / 2 - self.detecting_square[1] / 2)
y2 = int(self.cam_height / 2 + self.detecting_square[1] / 2)
while getattr(t, "do_run", True):
ret, frame = self.cap.read()
# model에 이용하기 위해 convert
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
face_list, image_list = self.detect_face(converted[y1:y2, x1:x2])
# 얼굴이 인식되면 출석요청
self.event_loop.run_until_complete(self.send_face(face_list, image_list))
# show image
frame = cv2.rectangle(frame, (x1, y1), (x2, y2), self.rectangle_color, 3)
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 거울상으로 보여준다
converted = cv2.flip(converted,1)
image = Image.fromarray(converted)
image = ImageTk.PhotoImage(image)
self.label.configure(image=image)
self.label.image = image # kind of double buffering
@asyncio.coroutine
def set_rectangle(self):
self.rectangle_color = (255, 0, 0)
yield from asyncio.sleep(3)
self.rectangle_color = (0, 0, 255)
async def wait(self, n):
await asyncio.sleep(n)
async def send_face(self, face_list, image_list):
try:
async with websockets.connect(uri) as websocket:
for face, image in zip(face_list, image_list):
#type: np.float32
send = json.dumps({'action': 'verify', 'MTCNN': face.tolist()})
await websocket.send(send)
recv = await websocket.recv()
data = json.loads(recv)
if data['status'] == 'success':
# 성공
self.logging('출석확인: ' + data['student_id'])
asyncio.ensure_future(self.set_rectangle())
else:
if data['status'] == 'fail':
send = json.dumps({'action': 'save_image', 'image': image.tolist()})
await websocket.send(send)
elif data['status'] == 'already':
asyncio.ensure_future(self.set_rectangle())
except Exception as e:
self.logging(e)
def stop(self):
self.thread.do_run = False
# self.thread.join() # there is a freeze problem
self.event_loop.close()
self.cap.release()
self.parent.destroy()
if __name__ == '__main__':
root = tk.Tk()
Client(root)
root.mainloop()