clinet(window)200605.py
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##################################################
#1. webcam에서 얼굴을 인식합니다.
#2. 얼굴일 확률이 97% 이상인 이미지를 이미지 서버로 전송합니다
#3. 전처리 된 데이터를 verification 서버에 전송합니다.
##################################################
import torch
import numpy as np
import cv2
import asyncio
import websockets
import json
import os
import timeit
import base64
from PIL import Image
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, device=device)
uri = 'ws://localhost:8765'
async def send_face(face_list, image_list):
async with websockets.connect(uri) as websocket:
for face, image in zip(face_list, image_list):
#type: np.float32
send = json.dumps({'action': 'verify', 'image': image.tolist(), 'MTCNN': face.tolist()})
await websocket.send(send)
recv = await websocket.recv()
data = json.loads(recv)
if data['status'] == 'success':
# 성공
print(data['student_id'], 'is attend')
elif data['status'] == 'failed':
print('verification failed:', data['status'])
def detect_face(frame):
results = mtcnn.detect(frame)
image_list = []
if results[1][0] == None:
return []
for box, prob in zip(results[0], results[1]):
if prob < 0.97:
continue
print('face detected. prob:', prob)
x1, y1, x2, y2 = box
image = frame[int(y1-3):int(y2+3), int(x1-3):int(x2+3)]
image_list.append(image)
print(image.shape)
return image_list
def make_face_list(frame):
results, prob = mtcnn(frame, return_prob = True)
face_list = []
if prob[0] == None:
return []
for result, prob in zip(results, prob):
if prob < 0.97:
continue
#np.float32
face_list.append(result.numpy())
return face_list
if __name__ == '__main__':
cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
cap.set(3, 720)
cap.set(4, 480)
cv2.namedWindow("img", cv2.WINDOW_NORMAL)
while True:
try:
ret, frame = cap.read()
cv2.imshow('img', frame)
cv2.waitKey(10)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_list = detect_face(frame)
if not image_list:
continue;
face_list = make_face_list(frame)
##embedding server로 전송##
if face_list:
asyncio.get_event_loop().run_until_complete(send_face(face_list, image_list))
except Exception as ex:
print(ex)