Merge pull request #104 from LimaoC/mitch-face-tolerance

Mitch face tolerance

client/drivers/data_structures.py

@@ -233,7 +233,7 @@ class HardwareComponents:
     _PLANT_GEAR_RATIO: float = 2
     _PLANT_MOVER_PERIOD: float = 1000 * 60 / 55
     _BASE_FULL_SPEED = 0.1
-    _FULL_SPEED_UPWARDS = _BASE_FULL_SPEED * (4 / 7) * (8 / 9) * 2
+    _FULL_SPEED_UPWARDS = _BASE_FULL_SPEED * (4 / 7) * (17 / 20) * 2
     _FULL_SPEED_DOWNWARDS = (-1) * _BASE_FULL_SPEED * (6 / 10) * 2
 
     # SECTION: Constructors
@@ -325,10 +325,10 @@ def initialise_posture_graph(self, user_id: int) -> None:
 
     def unwind_plant(self) -> None:
         """
-        Unwind the plant to its maximum height, by making 16 full turns (we have 13 turns total).
+        Unwind the plant to its maximum height, by making 15 full turns (we have 13 turns total).
         """
         self.plant_mover.speed = self._FULL_SPEED_UPWARDS
-        time.sleep(16 * self._PLANT_MOVER_PERIOD * self._PLANT_GEAR_RATIO / 1000)
+        time.sleep(15 * self._PLANT_MOVER_PERIOD * self._PLANT_GEAR_RATIO / 1000)
         self.plant_height = (
             self._PLANT_SHAFT_TURNS - self._PLANT_SHAFT_SAFETY_BUFFER_TURNS
         )
@@ -340,15 +340,6 @@ def wind_plant_safe(self) -> None:
         Will also reset the `plant_height` to `0`.
         """
         self.set_plant_height(0)
-        # self.plant_mover.speed = self._FULL_SPEED_DOWNWARDS
-        # time.sleep(
-        #     (self._PLANT_SHAFT_TURNS - self._PLANT_SHAFT_SAFETY_BUFFER_TURNS)
-        #     * self._PLANT_MOVER_PERIOD
-        #     * self._PLANT_GEAR_RATIO
-        #     / 1000
-        # )
-        # self.plant_mover.speed = 0
-        # self.plant_height = 0
 
     def set_plant_height(self, new_height: int) -> None:
         """

client/drivers/pi_overlord.py

@@ -44,17 +44,17 @@
 NUM_DATA_POINTS_PER_TIMEOUT = 3
 
 #: Minimum delay between consecutive uses of the vibration motor. Used in handle_feedback().
-HANDLE_CUSHION_FEEDBACK_TIMEOUT = timedelta(milliseconds=30000)
+HANDLE_CUSHION_FEEDBACK_TIMEOUT = timedelta(milliseconds=15000)
 #: Length of time for which the vibration motor should vibrate. Used in handle_cushion_feedback().
-CUSHION_ACTIVE_INTERVAL = timedelta(milliseconds=3000)
+CUSHION_ACTIVE_INTERVAL = timedelta(milliseconds=2000)
 #: Threshold for vibration cushion feedback. If the proportion of "good" sitting posture is below this, the cushion will vibrate.
 CUSHION_PROPORTION_GOOD_THRESHOLD = 0.5
 
 #: Minimum delay between consecutive uses of the plant-controlling servos. Used in handle_feedback().
-HANDLE_PLANT_FEEDBACK_TIMEOUT = timedelta(milliseconds=15000)
+HANDLE_PLANT_FEEDBACK_TIMEOUT = timedelta(milliseconds=7500)
 #: Threshold for I. Jensen Plant Mover 10000 feedback. If the proportion of "good" sitting posture is below this,
 #: the plant will move down.
-PLANT_PROPORTION_GOOD_THRESHOLD = 0.5
+PLANT_PROPORTION_GOOD_THRESHOLD = 0.6
 
 #: Number of milliseconds between each loop iteration in run_user_session().
 USER_SESSION_INTERVAL = 100
@@ -191,6 +191,7 @@ def run_user_session(user: ControlledData) -> None:
             hardware.display.show()
             sleep_ms(LOGOUT_SUCCESS_DELAY)
             logger.debug("<!> END run_user_session()")
+            hardware.unwind_plant()
             return
 
         # Run core functionality

client/models/face_recognition/recognition.py

@@ -6,6 +6,7 @@
 from data.routines import register_face_embeddings, iter_face_embeddings
 
 MODEL_NAME = "small"
+TOLERANCE = 0.3
 
 
 class Status(Enum):
@@ -37,7 +38,9 @@ def get_face_match(login_face: np.ndarray) -> int:
     login_embedding = login_embeddings[0]
 
     for user_id, user_embeddings in iter_face_embeddings():
-        matches = face_recognition.compare_faces(user_embeddings, login_embedding)
+        matches = face_recognition.compare_faces(
+            user_embeddings, login_embedding, tolerance=TOLERANCE
+        )
 
         if any(matches):
             return user_id
@@ -69,14 +72,18 @@ def register_faces(user_id: int, faces: list[np.ndarray]) -> int:
         face_embeddings.append(face_embedding)
 
     # Ensure that all images contain the same face
-    matches = face_recognition.compare_faces(face_embeddings[1:], face_embeddings[0])
+    matches = face_recognition.compare_faces(
+        face_embeddings[1:], face_embeddings[0], tolerance=TOLERANCE
+    )
     if not all(matches):
         return Status.TOO_MANY_FACES.value
 
     # Ensure user is not already registered
     for _, other_user_embeddings in iter_face_embeddings():
         for embedding in face_embeddings:
-            matches = face_recognition.compare_faces(other_user_embeddings, embedding)
+            matches = face_recognition.compare_faces(
+                other_user_embeddings, embedding, tolerance=TOLERANCE
+            )
 
             if any(matches):
                 return Status.ALREADY_REGISTERED.value