Fix problem with authenticate user allowing anyone entry

The authenticate user script was letting anyone in, so have now fixed that hopefully. Still awaiting final testing.

authenticate_user.py

@@ -33,8 +33,10 @@ def determine_identity(face_encoding, known_faces) -> str:
     recognized_users = {}
     for (user_id, user_encodings) in known_faces.items():
         matches = face_recognition.compare_faces(user_encodings, face_encoding)
+        count = matches.count(True)
+        if count > 0:
             # Count the number of occurrences of true.
-        recognized_users[user_id] = matches.count(True)
+            recognized_users[user_id] = count
 
     matched_user = ""
     if len(recognized_users) > 0:
@@ -70,27 +72,14 @@ def draw_rectangles_and_user_ids(image_frame, conversion: float, box_user_id_map
     common.display_frame(image_frame)
 
 
-def recognize_user(known_faces: dict, encoding_model: str = "hog", image_flip: int = None,
-                   draw_rectangles=False):
-    """Attempts to recognize a user.
-    Returns the ID of the user if identified, or None if no users are identified."""
-    recognized_users_count = {}
-    recognized_user = None
-    video_stream = common.start_video_stream(0)
-
-    # Determine the time at which we will time out. Equal to current time + timeout.
-    timeout_time: float = time.time() + TIMEOUT
-    while time.time() < timeout_time:
-        # Read a image_frame from the video stream.
-        ret, image_frame = video_stream.read()
-        if image_flip is not None:
-            image_frame = cv2.flip(image_frame, image_flip)
+def run_face_recognition(frame, known_faces: dict, encoding_model: str = "hog", draw_rectangles: bool = False) -> list:
+    recognized_user_ids: list = []
 
     # Convert input from BGR to RGB
-        cv2.cvtColor(image_frame, cv2.COLOR_BGR2RGB)
+    cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     # Resize image to width of 750 PX to speed up processing.
-        rgb_image = imutils.resize(image_frame, width=750)
-        r = image_frame.shape[1] / float(rgb_image.shape[1])
+    rgb_image = imutils.resize(frame, width=750)
+    r = frame.shape[1] / float(rgb_image.shape[1])
 
     # Detect the location of each face and determine the boxes in which they lie
     boxes = face_recognition.face_locations(
@@ -103,13 +92,37 @@ def recognize_user(known_faces: dict, encoding_model: str = "hog", image_flip: i
     for (i, encoding) in enumerate(encodings):
         user_id: str = determine_identity(encoding, known_faces)
         if user_id:
-                if user_id not in recognized_users_count:
-                    recognized_users_count[user_id] = 0
-                recognized_users_count[user_id] += 1
             box_user_id_mapping[boxes[i]] = user_id
+            recognized_user_ids.append(user_id)
 
     if draw_rectangles:
-            draw_rectangles_and_user_ids(image_frame, r, box_user_id_mapping)
+        draw_rectangles_and_user_ids(frame, r, box_user_id_mapping)
+
+    return recognized_user_ids
+
+
+def recognize_user(known_faces: dict, encoding_model: str = "hog", image_flip: int = None,
+                   draw_rectangles: bool = False):
+    """Attempts to recognize a user.
+    Returns the ID of the user if identified, or None if no users are identified."""
+    recognized_users_count = {}
+    recognized_user = None
+    video_stream = common.start_video_stream(0)
+
+    # Determine the time at which we will time out. Equal to current time + timeout.
+    timeout_time: float = time.time() + TIMEOUT
+    while time.time() < timeout_time:
+        # Read a image_frame from the video stream.
+        ret, image_frame = video_stream.read()
+        if image_flip is not None:
+            image_frame = cv2.flip(image_frame, image_flip)
+
+        recognized_user_ids = run_face_recognition(image_frame, known_faces, encoding_model=encoding_model,
+                                                   draw_rectangles=draw_rectangles)
+        for user_id in recognized_user_ids:
+            if user_id not in recognized_users_count:
+                recognized_users_count[user_id] = 0
+            recognized_users_count[user_id] += 1
 
         # Now check if we have already positively identified a user enough times
         recognized_users = check_recognized_users(recognized_users_count)