Update app.py

app.py

@@ -88,31 +88,48 @@ def receive_image(image):
         # current_date = now.strftime("%Y-%m-%d")
         # csv_file = open(f"{current_date}.csv", "a+", newline="")
         
-        # csv_writer = csv.writer(csv_file)
-        small_frame = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)
-        rgb_small_frame = small_frame[:, :, ::-1]
-        # emit("result",{"name":"level " +str(cnt),"score":str(len(face_encodings))})
+        # # csv_writer = csv.writer(csv_file)
+        # small_frame = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)
+        # rgb_small_frame = small_frame[:, :, ::-1]
+        # # emit("result",{"name":"level " +str(cnt),"score":str(len(face_encodings))})
         
-        face_locations = face_recognition.face_locations(rgb_small_frame)
-        face_encodings = face_recognition.face_encodings(small_frame, face_locations)
-        face_names = []
-        emit("result",{"name":"level2 " +str(cnt),"score":str(len(face_encodings))})
-        cnt = cnt +1
+        # face_locations = face_recognition.face_locations(rgb_small_frame)
+        # face_encodings = face_recognition.face_encodings(small_frame, face_locations)
+        # face_names = []
+        # emit("result",{"name":"level2 " +str(cnt),"score":str(len(face_encodings))})
+        # cnt = cnt +1
+        # for face_encoding in face_encodings:
+        #     # emit("result",{"name":"in for ","score":"34"})
+        #     matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+        #     name = ""
+        #     face_distance = face_recognition.face_distance(known_face_encodings, face_encoding)
+        #     best_match_index = np.argmin(face_distance)
+        #     if matches[best_match_index]:
+        #         name = known_faces_names[best_match_index]
+    
+        #     face_names.append(name)
+        #     s = False
+        #     break
+        
+        # emit("result",{"name":str(name),"score":"myScore"})
+        
+        name = ""  # Initialize name with a default value
+
         for face_encoding in face_encodings:
-            # emit("result",{"name":"in for ","score":"34"})
             matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
-            name = ""
             face_distance = face_recognition.face_distance(known_face_encodings, face_encoding)
             best_match_index = np.argmin(face_distance)
             if matches[best_match_index]:
                 name = known_faces_names[best_match_index]
-    
+
             face_names.append(name)
             s = False
             break
-        
-        emit("result",{"name":str(name),"score":"myScore"})
-      
+
+        cnt = cnt + 1
+
+        emit("result", {"name": str(name), "score": "myScore"})
+        emit("result", {"name": "level1", "score": "34"})
     
        
 #         # for name in face_names: