Merge branch 'master' of http://khuhub.khu.ac.kr/2020-2-capstone-design2/2014104149

 [server]
 [server]
-uri=ws://13.124.104.70:3000
+uri=ws://localhost:3000
 [client]
 [client]
 image_size=160
 image_size=160
 margin=44
 margin=44
\ No newline at end of file

@@ -44,7 +44,7 @@ class Client(tk.Frame):
         self.image_size = (image_size, image_size)
         self.image_size = (image_size, image_size)
         
         
         # cam에서 MTCNN 적용하는 영역
         # cam에서 MTCNN 적용하는 영역
-        self.detecting_square = (500, 300)
+        self.detecting_square = (640, 480)
 
 
         # 영상 위에 사각형 색상 지정
         # 영상 위에 사각형 색상 지정
         self.rectangle_color = (0, 0, 255)
         self.rectangle_color = (0, 0, 255)
@@ -100,7 +100,9 @@ class Client(tk.Frame):
         for (x, y, w, h) in faces:
         for (x, y, w, h) in faces:
             margin = int(self.margin / 2)
             margin = int(self.margin / 2)
             image = frame[y-margin:y+h+margin, x-margin:x+w+margin]
             image = frame[y-margin:y+h+margin, x-margin:x+w+margin]
-            return image, True
+            # BGR to RGB
+            converted = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            return converted, True
         return [], False
         return [], False
 
 
     def mainthread(self):
     def mainthread(self):
@@ -112,10 +114,8 @@ class Client(tk.Frame):
         y2 = 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):
         while getattr(t, "do_run", True):
             ret, frame = self.cap.read()
             ret, frame = self.cap.read()
-            # BGR to RGB
-            converted = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
             # detect face
             # detect face
-            face, detected = self.detect_face(converted[y1:y2, x1:x2])
+            face, detected = self.detect_face(frame[y1:y2, x1:x2])
             if detected:
             if detected:
                 self.event_loop.run_until_complete(self.send_face(face))
                 self.event_loop.run_until_complete(self.send_face(face))
             # 사각형 영역 표시
             # 사각형 영역 표시

 [server]
 [server]
-uri=ws://13.124.104.70:3000
+uri=ws://localhost:3000
 [register]
 [register]
 taking_time=0.2
 taking_time=0.2
 image_size=160
 image_size=160

@@ -124,7 +124,9 @@ class Register(tk.Frame):
         for (x, y, w, h) in faces:
         for (x, y, w, h) in faces:
             margin = int(self.margin / 2)
             margin = int(self.margin / 2)
             image = frame[y-margin:y+h+margin, x-margin:x+w+margin]
             image = frame[y-margin:y+h+margin, x-margin:x+w+margin]
-            return image, True
+            # BGR to RGB
+            converted = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            return converted, True
         return [], False
         return [], False
 
 
     def mainthread(self):
     def mainthread(self):
@@ -136,12 +138,8 @@ class Register(tk.Frame):
         detected_time = None
         detected_time = None
         while getattr(t, "do_run", True):
         while getattr(t, "do_run", True):
             ret, frame = self.cap.read()
             ret, frame = self.cap.read()
-
-            # model에 이용하기 위해 convert
-            converted = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-
             # 사각형 영역만 검사
             # 사각형 영역만 검사
-            face, detected = self.detect_face(converted[y1:y2, x1:x2])
+            face, detected = self.detect_face(frame[y1:y2, x1:x2])
             # 얼굴이 인식된 경우 파란색 사각형을 띄움
             # 얼굴이 인식된 경우 파란색 사각형을 띄움
             if detected:
             if detected:
                 frame = cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 3)
                 frame = cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 3)

 [client]
 [client]
-user=admin
-password=attendance
-host=attendance-system.c4yvip5o6czh.ap-northeast-2.rds.amazonaws.com
+user=root
+password=1234
+host=localhost
 database=attendance
 database=attendance
 charset=utf8
 charset=utf8