Modify register.py

 ##################################################
 ##################################################
-#1. webcam에서 얼굴을 인식합니다.                                           #
-#2. 얼굴일 확률이 95% 이상인 이미지를 이미지 서버로 전송합니다.  #
-#3. 전처리 된 데이터를 verification 서버에 전송합니다.                   #
+#1. webcam에서 얼굴을 인식합니다
+#2. 인식한 얼굴을 등록합니다
 ##################################################
 ##################################################
 import torch
 import torch
 import numpy as np
 import numpy as np
@@ -55,27 +54,33 @@ def detect_face(frame):
         image_list.append(image)
         image_list.append(image)
     return image_list
     return image_list
 
 
-def make_face_list(frame):
-    global mtcnn
-    results, prob = mtcnn(frame, return_prob = True)
+def detect_face(frame):
+    results = mtcnn.detect(frame)
+    faces = mtcnn(frame, return_prob = False)
+    image_list = []
     face_list = []
     face_list = []
-    if prob[0] == None:
-        return []
-    for result, prob in zip(results, prob):
-        if prob < 0.95:
+    if results[1][0] == None:
+        return [], []
+    for box, face, prob in zip(results[0], faces, results[1]):
+        if prob < 0.97:
             continue
             continue
-        #np.float32
-        face_list.append(result.numpy())
-    return face_list
+        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
 
 
-cap = cv2.VideoCapture(0)
+cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)
 cap.set(3, 720)
 cap.set(3, 720)
 cap.set(4, 480)
 cap.set(4, 480)
 ret, frame = cap.read()
 ret, frame = cap.read()
 frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-#img = Image.open('3.jpg')
-#frame = np.array(img)
-face_list = make_face_list(frame)
-image_list = detect_face(frame)
+face_list, image_list = detect_face(frame)
 if face_list:
 if face_list:
     asyncio.get_event_loop().run_until_complete(send_face(face_list, image_list))
     asyncio.get_event_loop().run_until_complete(send_face(face_list, image_list))
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