docs: conform to *actual* Google-style docstrings

client/models/pose_detection/camera.py

@@ -10,8 +10,7 @@
 
 
 def is_camera_aligned(pose_landmark_result: PoseLandmarkerResult) -> np.bool_:
-    """
-    Checks whether the camera is aligned to capture the person's side view.
+    """Checks whether the camera is aligned to capture the person's side view.
 
     Args:
         pose_landmarker_result: Landmarker result as returned by a

client/models/pose_detection/classification.py

@@ -13,23 +13,25 @@
 
 
 def posture_angle(p1: Landmark, p2: Landmark) -> np.float64:
-    """
-    Returns the angle (in degrees) between P2 and P3, where P3 is a point on the
-    vertical axis of P1 (i.e. its x coordinate is the same as P1's), and is the "ideal"
-    location of the P2 landmark for good posture.
+    """Calculates the neck or torso posture angle (in degrees).
+
+    In particular, this calculates the angle (in degrees) between p2 and p3, where p3
+    is a point on the vertical axis of p1 (i.e. same x coordinate as p1), and
+    represents the "ideal" location of the p2 landmark for good posture.
+
+    The y coordinate of p3 is irrelevant but for simplicity we set it to zero.
 
-    The y coordinate of P3 is irrelevant but for simplicity we set it to zero.
+    For neck posture, take p1 to be the shoulder, p2 to be the ear. For torso posture,
+    take p1 to be the hip, p2 to be the shoulder.
 
-    For a neck inclination calculation, take P1 to be the shoulder location and pivot
-    point, and P2 to be the ear location. For a torso inclination calculation, take P1
-    to be the hip location and pivot point, and P2 to be the hip location.
+    REF: https://learnopencv.com/wp-content/uploads/2022/03/MediaPipe-pose-neckline-inclination.jpg
 
     Parameters:
         p1: Landmark for P1 as described above
         p2: Landmark for P2 as described above
 
     Returns:
-        Angle (in degrees) between P2 and P3
+        Neck or torso posture angle (in degrees)
     """
     x1, y1 = p1.x, p1.y
     x2, y2 = p2.x, p2.y
@@ -38,8 +40,7 @@ def posture_angle(p1: Landmark, p2: Landmark) -> np.float64:
 
 
 def posture_classify(pose_landmark_result: PoseLandmarkerResult) -> np.bool_:
-    """
-    Returns whether the pose in the image has good or bad posture.
+    """Classifies the pose in the image as either good or bad posture.
 
     Note: The camera should be aligned to capture the person's side view; the output
     may not be accurate otherwise. See `is_camera_aligned()`.
@@ -49,6 +50,9 @@ def posture_classify(pose_landmark_result: PoseLandmarkerResult) -> np.bool_:
     Parameters:
         pose_landmarker_result: Landmarker result as returned by a
           mediapipe.tasks.vision.PoseLandmarker
+
+    Returns:
+        True if the pose has good posture, False otherwise
     """
     landmarks: list[list[Landmark]] = pose_landmark_result.pose_world_landmarks
 

client/models/pose_detection/landmarking.py

@@ -14,9 +14,10 @@
 
 @dataclass
 class AnnotatedImage:
-    """
-    Represents mutable annoted image through data attribute. Can be used to set
-    annotated image within a callback asynchronously without raising an error.
+    """Represents mutable annotated image through data attribute.
+
+    Can be used to set annotated image within a callback asynchronously without raising
+    an error.
     """
 
     data: Optional[np.ndarray] = None
@@ -62,6 +63,7 @@ def display_landmarking(
     annotated_image: AnnotatedImage,
 ) -> None:
     """Mutates annotated image to contain visualization of detected landmarks.
+
     Also prints debugging info to the standard output.
 
     Args:

client/models/pose_detection/routines.py

@@ -38,8 +38,7 @@ def __init__(
         self.video_capture = cv2.VideoCapture(0)
 
     def track_posture(self) -> None:
-        """
-        Get frame from video capture device and process with pose model, then posture
+        """Get frame from video capture device and process with pose model, then posture
         algorithm. Print debugging info and display landmark annotated frame.
         """
         success, frame = self.video_capture.read()
@@ -61,8 +60,7 @@ def __exit__(self, unused_exc_type, unused_exc_value, unused_traceback) -> None:
 
 
 def create_debug_posture_tracker() -> DebugPostureTracker:
-    """
-    Handles config of livestreamed input and model loading.
+    """Handles config of livestreamed input and model loading.
 
     Returns:
         Tracker object which acts as context manager.