From 5d0be5d9897175de5523067e22b79bcd7306db44 Mon Sep 17 00:00:00 2001
From: Kyle Anderson <kyle.anderson@edu.uwaterloo.ca>
Date: Sun, 10 Nov 2019 10:57:29 -0500
Subject: [PATCH] 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 | 67 ++++++++++++++++++++++++++------------------
 1 file changed, 40 insertions(+), 27 deletions(-)

diff --git a/authenticate_user.py b/authenticate_user.py
index 066a3a7..3f3c521 100644
--- a/authenticate_user.py
+++ b/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 the number of occurrences of true.
-        recognized_users[user_id] = matches.count(True)
+        count = matches.count(True)
+        if count > 0:
+            # Count the number of occurrences of true.
+            recognized_users[user_id] = count
 
     matched_user = ""
     if len(recognized_users) > 0:
@@ -70,8 +72,37 @@ def draw_rectangles_and_user_ids(image_frame, conversion: float, box_user_id_map
     common.display_frame(image_frame)
 
 
+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(frame, cv2.COLOR_BGR2RGB)
+    # Resize image to width of 750 PX to speed up processing.
+    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(
+        rgb_image, model=encoding_model)
+    # Compute the facial embeddings (the encoding) at
+    # each of the locations found in the previous line.
+    encodings = face_recognition.face_encodings(rgb_image, boxes)
+
+    box_user_id_mapping = {}
+    for (i, encoding) in enumerate(encodings):
+        user_id: str = determine_identity(encoding, known_faces)
+        if user_id:
+            box_user_id_mapping[boxes[i]] = user_id
+            recognized_user_ids.append(user_id)
+
+    if draw_rectangles:
+        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=False):
+                   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 = {}
@@ -86,30 +117,12 @@ def recognize_user(known_faces: dict, encoding_model: str = "hog", image_flip: i
         if image_flip is not None:
             image_frame = cv2.flip(image_frame, image_flip)
 
-        # Convert input from BGR to RGB
-        cv2.cvtColor(image_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])
-
-        # Detect the location of each face and determine the boxes in which they lie
-        boxes = face_recognition.face_locations(
-            rgb_image, model=encoding_model)
-        # Compute the facial embeddings (the encoding) at
-        # each of the locations found in the previous line.
-        encodings = face_recognition.face_encodings(rgb_image, boxes)
-
-        box_user_id_mapping = {}
-        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
-
-        if draw_rectangles:
-            draw_rectangles_and_user_ids(image_frame, r, box_user_id_mapping)
+        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)
-- 
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