Making this commit to retest issues fiX

pages/1_Live_Detection.py

@@ -8,24 +8,29 @@ from dotenv import load_dotenv
 import base64
 import queue
 import time
+from typing import List, Dict, Union
 
+# Correctly import from the drive_paddy package structure
 from src.detection.factory import get_detector
 from src.alerting.alert_system import get_alerter
 
+# --- Initialize Session State at the TOP of the script ---
+# This is the single source of truth for our queues and must run on every page load.
 if "status_queue" not in st.session_state:
     st.session_state.status_queue = queue.Queue()
 if "audio_queue" not in st.session_state:
     st.session_state.audio_queue = queue.Queue()
-if "last_status" not in st.session_state: 
+if "last_status" not in st.session_state:
     st.session_state.last_status = {"drowsiness_level": "Awake", "lighting": "Good"}
 
-    
+
 # --- Load Configuration and Environment Variables ---
 @st.cache_resource
 def load_app_config():
     """Loads config from yaml and .env files."""
     load_dotenv()
-    config_path = "/config.yaml" if os.path.exists("/config.yaml") else "config.yaml"
+    # Navigate up to the root to find the config file
+    config_path = os.path.join(os.path.dirname(__file__), '..', '..', 'config.yaml')
     with open(config_path, 'r') as f:
         config = yaml.safe_load(f)
     # Load secrets from environment
@@ -51,12 +56,13 @@ def autoplay_audio(audio_bytes: bytes):
 
 # --- WebRTC Video Processor ---
 class VideoProcessor(VideoProcessorBase):
-    def __init__(self):
+    # The __init__ method now accepts the queues as arguments
+    def __init__(self, status_queue: queue.Queue, audio_queue: queue.Queue):
+        # It uses the queues passed in from session_state, not new ones.
+        self.status_queue = status_queue
+        self.audio_queue = audio_queue
         self._detector = get_detector(config)
         self._alerter = get_alerter(config, secrets["gemini_api_key"])
-        # Thread-safe queues for communication
-        self.status_queue = queue.Queue()
-        self.audio_queue = queue.Queue()
 
     def recv(self, frame: av.VideoFrame) -> av.VideoFrame:
         img = frame.to_ndarray(format="bgr24")
@@ -64,30 +70,16 @@ class VideoProcessor(VideoProcessorBase):
         strategy = config.get('detection_strategy')
         
         # The return signature of process_frame varies by strategy.
-        # We need to handle each case correctly.
-        if strategy == 'hybrid':
-            processed_frame, alert_triggered, active_alerts = self._detector.process_frame(img)
-            self.status_queue.put(active_alerts if alert_triggered or 'Low Light' in active_alerts else {"status": "Awake"})
-        elif strategy == 'geometric':
-            # The geometric processor returns frame, indicators, and landmarks.
-            processed_frame, indicators, _ = self._detector.process_frame(img)
-            alert_triggered = any(v for k, v in indicators.items() if k not in ['low_light', 'details'])
-            self.status_queue.put(indicators if alert_triggered or indicators.get('low_light') else {"status": "Awake"})
-            print(f"Indicators: {indicators}")  # Debugging line
-            print(self.status_queue)  # Debugging line
-        elif strategy == 'cnn_model':
-            # The cnn_model processor returns frame and indicators.
-            processed_frame, indicators = self._detector.process_frame(img)
-            alert_triggered = any(indicators.values())
-            self.status_queue.put(indicators if alert_triggered else {"status": "Awake"})
-        else:
-            # Default case if strategy is unknown
-            processed_frame = img
-            alert_triggered = False
+        processed_frame, indicators, _ = self._detector.process_frame(img)
+        drowsiness_level = indicators.get("drowsiness_level", "Awake")
+        
+        # This now correctly puts data into the shared session_state queue.
+        self.status_queue.put(indicators)
 
-        if alert_triggered:
-            audio_data = self._alerter.trigger_alert()
+        if drowsiness_level != "Awake":
+            audio_data = self._alerter.trigger_alert(level=drowsiness_level)
             if audio_data:
+                # This now correctly puts audio data into the shared queue.
                 self.audio_queue.put(audio_data)
         else:
             self._alerter.reset_alert()
@@ -109,19 +101,25 @@ if secrets["turn_username"] and secrets["turn_credential"]:
 
 RTC_CONFIGURATION = RTCConfiguration({"iceServers": ice_servers})
 
+
 col1, col2 = st.columns([3, 1])
 
 with col1:
     webrtc_ctx = webrtc_streamer(
         key="drowsiness-detection",
-        video_processor_factory=VideoProcessor,
-        rtc_configuration=RTC_CONFIGURATION, # Use the new robust configuration
+        # The factory now correctly passes the queues from session_state
+        video_processor_factory=lambda: VideoProcessor(
+            status_queue=st.session_state.status_queue,
+            audio_queue=st.session_state.audio_queue
+        ),
+        rtc_configuration=RTC_CONFIGURATION,
         media_stream_constraints={"video": True, "audio": False},
         async_processing=True,
     )
 
 with col2:
     st.header("System Status")
+    audio_placeholder = st.empty()
     if not webrtc_ctx.state.playing:
         st.warning("System Inactive.")
     else:
@@ -129,14 +127,10 @@ with col2:
 
     st.subheader("Live Status:")
     status_placeholder = st.empty()
-    audio_placeholder = st.empty()
 
 if webrtc_ctx.state.playing:
-    if "last_status" not in st.session_state:
-        st.session_state.last_status = {"status": "Awake"}
-    
     try:
-        # This line will now work because st.session_state.status_queue was initialized
+        # This now reads from the correct queue that the processor is writing to.
         status_result = st.session_state.status_queue.get(timeout=0.1)
         st.session_state.last_status = status_result
     except queue.Empty:
@@ -149,13 +143,16 @@ if webrtc_ctx.state.playing:
         score = last_status.get("details", {}).get("Score", 0)
 
         st.metric(label="Lighting Condition", value=lighting)
-
-        if lighting == "Low": st.warning("Detection paused due to low light.")
+        if lighting == "Low":
+            st.warning("Detection paused due to low light.")
         
-        if drowsiness_level == "Awake": st.info(f"✔️ Awake (Score: {score:.2f})")
-        elif drowsiness_level == "Slightly Drowsy": st.warning(f"⚠️ Slightly Drowsy (Score: {score:.2f})")
-        elif drowsiness_level == "Very Drowsy": st.error(f"🚨 Very Drowsy! (Score: {score:.2f})")
-
+        if drowsiness_level == "Awake":
+            st.info(f"✔️ Awake (Score: {score:.2f})")
+        elif drowsiness_level == "Slightly Drowsy":
+            st.warning(f"⚠️ Slightly Drowsy (Score: {score:.2f})")
+        elif drowsiness_level == "Very Drowsy":
+            st.error(f"🚨 Very Drowsy! (Score: {score:.2f})")
+    
     try:
         audio_data = st.session_state.audio_queue.get(timeout=0.1)
         with audio_placeholder.container():
@@ -168,5 +165,3 @@ if webrtc_ctx.state.playing:
 else:
     with status_placeholder.container():
         st.info("✔️ Driver is Awake")
-
-    

src/detection/strategies/geometric.py

@@ -3,7 +3,7 @@ import cv2
 import mediapipe as mp
 import numpy as np
 import math
-from src.detection.base_processor import BaseProcessor
+from ..base_processor import BaseProcessor
 
 # --- Helper Functions (No changes here) ---
 def calculate_ear(eye_landmarks, frame_shape):
@@ -37,9 +37,7 @@ class GeometricProcessor(BaseProcessor):
         }
         face_landmarks = None
 
-        if is_low_light:
-            drowsiness_indicators["lighting"] = "Low"
-        else:
+        if not is_low_light:
             img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
             results = self.face_mesh.process(img_rgb)
             face_landmarks = results.multi_face_landmarks
@@ -49,22 +47,28 @@ class GeometricProcessor(BaseProcessor):
                 score = 0
                 weights = self.settings['indicator_weights']
 
-                # Eye Closure
+                # --- Draw Facial Landmarks (Logic Added Back) ---
+                # This will draw the green dots for eyes and mouth.
+                eye_mouth_landmarks = self.L_EYE + self.R_EYE + self.MOUTH
+                for idx in eye_mouth_landmarks:
+                    lm = landmarks[idx]
+                    x, y = int(lm.x * w), int(lm.y * h)
+                    cv2.circle(frame, (x, y), 1, (0, 255, 0), -1)
+
+                # --- Drowsiness Calculations ---
                 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
                 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']
                 
-                # Yawning
                 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
-                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.float64)
-                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.float64)
-                cam_matrix = np.array([[w,0,w/2],[0,w,h/2],[0,0,1]], dtype=np.float64)
+                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.float64)
+                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.float64)
+                cam_matrix = np.array([[w,0,w/2],[0,w,h/2],[0,0,1]],dtype=np.float64)
                 _, rot_vec, _ = cv2.solvePnP(face_3d, face_2d, cam_matrix, np.zeros((4,1),dtype=np.float64))
                 rmat, _ = cv2.Rodrigues(rot_vec); angles, _, _, _, _, _ = cv2.RQDecomp3x3(rmat)
                 pitch, yaw = angles[0], angles[1]
@@ -76,13 +80,14 @@ class GeometricProcessor(BaseProcessor):
                 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 Drowsiness Level
+                
                 levels = self.settings['drowsiness_levels']
                 if score >= levels['very_drowsy_threshold']: drowsiness_indicators['drowsiness_level'] = "Very Drowsy"
                 elif score >= levels['slightly_drowsy_threshold']: drowsiness_indicators['drowsiness_level'] = "Slightly Drowsy"
 
                 drowsiness_indicators['details']['Score'] = score
+        else: # is_low_light is True
+            drowsiness_indicators["lighting"] = "Low"
         
         # --- Visualization on Video Frame ---
         level = drowsiness_indicators['drowsiness_level']
@@ -98,12 +103,9 @@ class GeometricProcessor(BaseProcessor):
         elif level == "Very Drowsy":
             color = (0, 0, 255) # Red
 
-        # Draw a colored border around the frame
         cv2.rectangle(frame, (0, 0), (w, h), color, 10)
-
-        # Display status text 
         status_text = f"Status: {level} (Score: {score_val:.2f})"
         print(status_text)
         cv2.putText(frame, status_text, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
 
-        return frame, drowsiness_indicators, face_landmarks
+        return frame, drowsiness_indicators, face_landmarks