Change server IP

@@ -24,7 +24,7 @@ print('Running on device: {}'.format(device))
 
 mtcnn = MTCNN(keep_all=True, post_process=True, device=device)
 
-uri = 'ws://localhost:8765'
+uri = 'ws://169.56.95.131:8765'
 
 async def send_face(face_list, image_list):
     async with websockets.connect(uri) as websocket:

-##################################################
-#1. webcam에서 얼굴을 인식합니다.
-#2. 얼굴일 확률이 97% 이상이고 영역이 15000 이상인 이미지를 서버에 전송
-##################################################
-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', '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')
-            else:
-                print('verification failed:', data['status'])
-                if data['status'] == 'failed':
-                    send = json.dumps({'action': 'save_image', 'image': image.tolist()})
-
-def detect_face(frame):
-    results = mtcnn.detect(frame)
-    faces = 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
-        print('face detected. prob:', prob)
-        x1, y1, x2, y2 = box
-        if (x2-x1) * (y2-y1) < 15000:
-            # 얼굴 해상도가 너무 낮으면 무시
-            continue
-        # 얼굴 주변 ±3 영역  저장
-        image = frame[int(y1-3):int(y2+3), int(x1-3):int(x2+3)]
-        image_list.append(image)
-        # MTCNN 데이터 저장
-        face_list.append(face.numpy())
-    return image_list, face_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, face_list = detect_face(frame)
-            if not face_list:
-                continue;
-            asyncio.get_event_loop().run_until_complete(send_face(face_list, image_list))
-        except Exception as ex:
-            print(ex)