Update src/detection/strategies/geometric.py

src/detection/strategies/geometric.py

@@ -1,4 +1,3 @@
-# drive_paddy/detection/strategies/geometric.py
 import cv2
 import mediapipe as mp
 import numpy as np
@@ -7,6 +6,8 @@ from src.detection.base_processor import BaseProcessor
 
 # --- Helper Functions (Unchanged) ---
 def calculate_ear(eye_landmarks, frame_shape):
+    """Calculates the Eye Aspect Ratio for a single eye."""
+    # Note: frame_shape is (height, width)
     coords = np.array([(lm.x * frame_shape[1], lm.y * frame_shape[0]) for lm in eye_landmarks])
     v1 = np.linalg.norm(coords[1] - coords[5])
     v2 = np.linalg.norm(coords[2] - coords[4])
@@ -14,6 +15,7 @@ def calculate_ear(eye_landmarks, frame_shape):
     return (v1 + v2) / (2.0 * h1) if h1 > 0 else 0.0
 
 def calculate_mar(mouth_landmarks, frame_shape):
+    """Calculates the Mouth Aspect Ratio."""
     coords = np.array([(lm.x * frame_shape[1], lm.y * frame_shape[0]) for lm in mouth_landmarks])
     v1 = np.linalg.norm(coords[1] - coords[7])
     v2 = np.linalg.norm(coords[2] - coords[6])
@@ -22,132 +24,148 @@ def calculate_mar(mouth_landmarks, frame_shape):
     return (v1 + v2 + v3) / (2.0 * h1) if h1 > 0 else 0.0
 
 class GeometricProcessor(BaseProcessor):
+    # Landmark indices for eyes and mouth
     L_EYE = [362, 385, 387, 263, 373, 380]
     R_EYE = [33, 160, 158, 133, 153, 144]
     MOUTH = [61, 291, 39, 181, 0, 17, 84, 178]
+    
+    # Landmark indices for Head Pose Estimation
+    HEAD_POSE_LANDMARKS = [1, 152, 263, 33, 287, 57] # Nose tip, Chin, Left eye left corner, Right eye right corner, Left mouth corner, Right mouth corner
 
     def __init__(self, config):
         self.settings = config['geometric_settings']
         self.face_mesh = mp.solutions.face_mesh.FaceMesh(
             max_num_faces=1,
-            refine_landmarks=False,
+            refine_landmarks=False, # Set to True for more detailed landmarks around eyes/lips, at a slight performance cost
             min_detection_confidence=0.5,
             min_tracking_confidence=0.5)
 
-        self.downscale_factor = self.settings.get('downscale_factor', 0.35) 
+        self.downscale_factor = self.settings.get('downscale_factor', 0.35)
         self.default_skip = max(1, self.settings.get("skip_frames", 2))
 
-        
-        # State variables for frame skipping
+        # --- FIX: Caching states for efficiency ---
         self.frame_counter = 0
+        # Initialize with safe defaults
         self.last_indicators = {"drowsiness_level": "Initializing...", "lighting": "Good", "details": {}}
         self.last_landmarks = None
+        self.last_drawn_frame = None # Cache the fully drawn frame
 
-        # Landmark and counter definitions
+        # Drowsiness event counters
         self.counters = { "eye_closure": 0, "yawning": 0, "head_nod": 0, "looking_away": 0 }
 
-        # Pre-allocated buffers
+        # Pre-allocated buffer for solvePnP
         self.zeros_4x1 = np.zeros((4, 1), np.float32)
-        self.landmark_ids = self.L_EYE + self.R_EYE + self.MOUTH
 
-        
     def process_frame(self, frame):
         self.frame_counter += 1
         
-        # adaptive skipping: fewer analyses when driver is awake
+        # --- FIX: More efficient frame skipping ---
+        # Adaptive skipping: process more frequently if drowsiness is detected.
         last_level = self.last_indicators.get("drowsiness_level", "Awake")
         skip_n = 1 if last_level != "Awake" else self.default_skip
 
         if self.frame_counter % skip_n != 0:
-            drawn = self.draw_visuals(frame.copy(), self.last_indicators, self.last_landmarks)
-            return drawn, self.last_indicators, self.last_landmarks
-
-        # --- FRAME PROCESSING ---
+            # If we have a cached frame, return it to avoid re-drawing.
+            if self.last_drawn_frame is not None:
+                return self.last_drawn_frame, self.last_indicators
+            # Fallback if the first frame was skipped (unlikely but safe)
+            else:
+                return frame.copy(), self.last_indicators
+
+        # --- CORE FRAME PROCESSING ---
         original_frame = frame.copy()
         h_orig, w_orig, _ = original_frame.shape
-
-        # --- OPTIMIZATION 1: DOWNSCALING ---
-        # Create a small frame for fast processing
+        
+        # Optimization: Downscale frame for faster processing
         small_frame = cv2.resize(original_frame, (0, 0), fx=self.downscale_factor, fy=self.downscale_factor, interpolation=cv2.INTER_AREA)
         h, w, _ = small_frame.shape
-        
-        # --- FIX 1: PROCESS THE SMALL FRAME, NOT THE ORIGINAL ---
-        # All processing should be done on the `small_frame` to gain the speed advantage.
+
+        # All processing is done on the `small_frame` for speed.
         gray = cv2.cvtColor(small_frame, cv2.COLOR_BGR2GRAY)
         brightness = np.mean(gray)
-        is_low_light = brightness < self.settings['low_light_thresh']
-
+        
         drowsiness_indicators = {"drowsiness_level": "Awake", "lighting": "Good", "details": {}}
-        face_landmarks = None
+        face_landmarks_data = None
 
-        if not is_low_light:
+        if brightness < self.settings['low_light_thresh']:
+            drowsiness_indicators["lighting"] = "Low"
+        else:
             # Convert the SMALL frame to RGB for MediaPipe
             img_rgb = cv2.cvtColor(small_frame, cv2.COLOR_BGR2RGB)
+            img_rgb.flags.writeable = False # Performance enhancement
             results = self.face_mesh.process(img_rgb)
-            face_landmarks = results.multi_face_landmarks
+            img_rgb.flags.writeable = True
 
-            if face_landmarks:
-                landmarks = face_landmarks[0].landmark
+            if results.multi_face_landmarks:
+                face_landmarks_data = results.multi_face_landmarks[0]
+                landmarks = face_landmarks_data.landmark
                 score = 0
                 weights = self.settings['indicator_weights']
 
-                # --- Drowsiness Calculations (these correctly use the small frame's 'h' and 'w') ---
-                ear = (calculate_ear([landmarks[i] for i in self.L_EYE],(h,w)) + calculate_ear([landmarks[i] for i in self.R_EYE],(h,w)))/2.0
+                # --- Drowsiness Calculations (on small frame dimensions 'h', 'w') ---
+                ear_left = calculate_ear([landmarks[i] for i in self.L_EYE],(h,w))
+                ear_right = calculate_ear([landmarks[i] for i in self.R_EYE],(h,w))
+                ear = (ear_left + ear_right) / 2.0
+                
                 if ear < self.settings['eye_ar_thresh']: self.counters['eye_closure']+=1
                 else: self.counters['eye_closure']=0
                 if self.counters['eye_closure'] >= self.settings['eye_ar_consec_frames']: score += weights['eye_closure']
-                
+
                 mar = calculate_mar([landmarks[i] for i in self.MOUTH], (h, w))
                 if mar > self.settings['yawn_mar_thresh']: self.counters['yawning']+=1
                 else: self.counters['yawning']=0
                 if self.counters['yawning'] >= self.settings['yawn_consec_frames']: score += weights['yawning']
 
-                # --- Head Pose Estimation (also uses small frame dimensions 'h' and 'w') ---
-                face_3d = np.array([[0.0,0.0,0.0],[0.0,-330.0,-65.0],[-225.0,170.0,-135.0],[225.0,170.0,-135.0],[-150.0,-150.0,-125.0],[150.0,-150.0,-125.0]],dtype=np.float32)
-                face_2d = np.array([(landmarks[1].x*w,landmarks[1].y*h),(landmarks[152].x*w,landmarks[152].y*h),(landmarks[263].x*w,landmarks[263].y*h),(landmarks[33].x*w,landmarks[33].y*h),(landmarks[287].x*w,landmarks[287].y*h),(landmarks[57].x*w,landmarks[57].y*h)],dtype=np.float32)
-                cam_matrix = np.array([[w,0,w/2],[0,w,h/2],[0,0,1]],dtype=np.float32)
-                _, rvec, _ = cv2.solvePnP(face_3d, face_2d, cam_matrix, self.zeros_4x1, flags=cv2.SOLVEPNP_EPNP)
+                # --- Head Pose Estimation (on small frame dimensions 'h', 'w') ---
+                face_3d_model = np.array([
+                    [0.0, 0.0, 0.0],            # Nose tip
+                    [0.0, -330.0, -65.0],        # Chin
+                    [-225.0, 170.0, -135.0],     # Left eye left corner
+                    [225.0, 170.0, -135.0],      # Right eye right corner
+                    [-150.0, -150.0, -125.0],    # Left Mouth corner
+                    [150.0, -150.0, -125.0]      # Right mouth corner
+                ], dtype=np.float32)
+                
+                face_2d_points = np.array([(landmarks[i].x * w, landmarks[i].y * h) for i in self.HEAD_POSE_LANDMARKS], dtype=np.float32)
+                cam_matrix = np.array([[w, 0, w/2], [0, w, h/2], [0, 0, 1]], dtype=np.float32)
+                
+                _, rvec, _ = cv2.solvePnP(face_3d_model, face_2d_points, cam_matrix, self.zeros_4x1, flags=cv2.SOLVEPNP_EPNP)
                 rmat, _ = cv2.Rodrigues(rvec)
                 angles, _, _, _, _, _ = cv2.RQDecomp3x3(rmat)
                 pitch, yaw = angles[0], angles[1]
 
-                if pitch > self.settings['head_nod_thresh']: 
-                    self.counters['head_nod']+=1
-                else: 
-                    self.counters['head_nod']=0
-                if self.counters['head_nod'] >= self.settings['head_pose_consec_frames']: 
-                    score += weights['head_nod']
-
-                if abs(yaw) > self.settings['head_look_away_thresh']: 
-                    self.counters['looking_away']+=1
-                else: 
-                    self.counters['looking_away']=0
-                if self.counters['looking_away'] >= self.settings['head_pose_consec_frames']: 
-                    score += weights['looking_away']
-                
+                if pitch > self.settings['head_nod_thresh']: self.counters['head_nod']+=1
+                else: self.counters['head_nod']=0
+                if self.counters['head_nod'] >= self.settings['head_pose_consec_frames']: score += weights['head_nod']
+
+                if abs(yaw) > self.settings['head_look_away_thresh']: self.counters['looking_away']+=1
+                else: self.counters['looking_away']=0
+                if self.counters['looking_away'] >= self.settings['head_pose_consec_frames']: score += weights['looking_away']
+
+                # Determine final drowsiness level based on score
                 levels = self.settings['drowsiness_levels']
-                if score >= levels['very_drowsy_threshold']: 
+                if score >= levels['very_drowsy_threshold']:
                     drowsiness_indicators['drowsiness_level'] = "Very Drowsy"
-                elif score >= levels['slightly_drowsy_threshold']: drowsiness_indicators['drowsiness_level'] = "Slightly Drowsy"
-                
+                elif score >= levels['slightly_drowsy_threshold']:
+                    drowsiness_indicators['drowsiness_level'] = "Slightly Drowsy"
+
                 drowsiness_indicators['details']['Score'] = score
 
-        else:
-            drowsiness_indicators["lighting"] = "Low"
-        
-        # --- Update state for skipped frames ---
+        # --- Update state for next frame (skipped or processed) ---
         self.last_indicators = drowsiness_indicators
-        self.last_landmarks = face_landmarks
+        self.last_landmarks = face_landmarks_data
+
+        # --- Draw visuals on the ORIGINAL frame for high-quality output ---
+        processed_frame = self.draw_visuals(original_frame, drowsiness_indicators, face_landmarks_data)
         
-        # --- Draw visuals on the ORIGINAL frame for a high-quality output ---
-        processed_frame = self.draw_visuals(original_frame, drowsiness_indicators, face_landmarks)
+        # --- FIX: Cache the newly drawn frame ---
+        self.last_drawn_frame = processed_frame
 
-        return processed_frame, drowsiness_indicators, face_landmarks
+        # --- FIX: Return only the two values expected by the Gradio app ---
+        return processed_frame, drowsiness_indicators
 
     def draw_visuals(self, frame, indicators, landmarks_data=None):
         """Helper function to draw all visualizations on the frame."""
-        # --- FIX 2: USE THE DIMENSIONS OF THE FRAME BEING DRAWN ON ---
-        # We get the height and width from the input 'frame' itself, which is the original, full-sized one.
         h, w, _ = frame.shape
         level = indicators['drowsiness_level']
         score_val = indicators.get("details", {}).get("Score", 0)
@@ -161,16 +179,17 @@ class GeometricProcessor(BaseProcessor):
 
         # Draw landmarks if they were detected
         if landmarks_data:
-            landmarks = landmarks_data[0].landmark
-            eye_mouth_landmarks = self.L_EYE + self.R_EYE + self.MOUTH
-            for idx in eye_mouth_landmarks:
+            landmarks = landmarks_data.landmark
+            eye_mouth_landmarks_indices = self.L_EYE + self.R_EYE + self.MOUTH
+            for idx in eye_mouth_landmarks_indices:
                 lm = landmarks[idx]
-                # Scale landmark coordinates to the full-sized frame using its 'w' and 'h'.
+                # Scale landmark coordinates to the full-sized frame
                 x, y = int(lm.x * w), int(lm.y * h)
                 cv2.circle(frame, (x, y), 2, (0, 255, 0), -1)
 
-        cv2.rectangle(frame, (0, 0), (w, h), color, 10)
+        cv2.rectangle(frame, (0, 0), (w - 1, h - 1), color, 10)
         status_text = f"Status: {level} (Score: {score_val:.2f})"
         cv2.putText(frame, status_text, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
 
         return frame
+