Modify client.py

+##################################################
+#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
+import time
+
+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, post_process=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))
+            time.sleep(1)
+        except Exception as ex:
+            print(ex)

+##################################################
+#1. webcam에서 얼굴을 인식합니다.                                           #
+#2. 얼굴일 확률이 95% 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):
+    global uri
+    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['id'], 'is attend')
+            else:
+                print('verification failed')
+                send = json.dumps({'action': 'save_image', 'image': image.tolist(), 'shape': image.shape})
+                await websocket.send(send)
+
+async def send_image(image_list):
+    global uri
+    async with websockets.connect(uri) as websocket:
+        for image in image_list:
+            data = json.dumps({'action': 'save_image', 'image': image.tolist(), 'shape': image.shape})
+            await websocket.send(data)
+        print('send', len(image_list), 'image(s)')
+        code = await websocket.recv()
+        print('code:', code)
+
+def detect_face(frame):
+    # If required, create a face detection pipeline using MTCNN:
+    global mtcnn
+    results = mtcnn.detect(frame)
+    image_list = []
+    if results[1][0] == None:
+        return []
+    for box, prob in zip(results[0], results[1]):
+        if prob < 0.95:
+            continue
+        print('face detected. prob:', prob)
+        x1, y1, x2, y2 = box
+        image = frame[int(y1-10):int(y2+10), int(x1-10):int(x2+10)]
+        image_list.append(image)
+    return image_list
+
+def make_face_list(frame):
+    global mtcnn
+    results, prob = mtcnn(frame, return_prob = True)
+    face_list = []
+    if prob[0] == None:
+        return []
+    for result, prob in zip(results, prob):
+        if prob < 0.95:
+            continue
+        #np.float32
+        face_list.append(result.numpy())
+    return face_list
+
+cap = cv2.VideoCapture(0)
+cap.set(3, 720)
+cap.set(4, 480)
+while True:
+    try:
+        #start = timeit.default_timer()
+        ret, frame = cap.read()
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        face_list = make_face_list(frame)
+        image_list = detect_face(frame)
+        ##embedding server로 전송##
+        if face_list:
+            asyncio.get_event_loop().run_until_complete(send_face(face_list, image_list))
+        ###################
+        ##image server로 전송##
+        #if image_list:
+            #asyncio.get_event_loop().run_until_complete(send_image(image_list))
+        ###################
+        #end = timeit.default_timer()
+        #print('delta time: ', end - start)
+    except Exception as ex:
+        print(ex)

+##################################################
+#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)