new

app.py

@@ -64,9 +64,6 @@ is_unconscious = False
 frame_count_webcam = 0
 stop_gaze_processing = False
 
-# --- Global State Variables for Distraction Webcam ---
-stop_distraction_processing = False
-
 # Constants
 GAZE_STABILITY_THRESHOLD = 0.5
 TIME_THRESHOLD = 15
@@ -202,6 +199,74 @@ def analyze_video(input_video):
         out.release()
     return temp_path
 
+def analyze_distraction_video(input_video):
+    cap = cv2.VideoCapture(input_video)
+    if not cap.isOpened():
+        print("Error: Could not open video file.")
+        return None
+
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    temp_fd, temp_path = tempfile.mkstemp(suffix='.mp4')
+    os.close(temp_fd)
+    out = None
+
+    fps = cap.get(cv2.CAP_PROP_FPS) or 30
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        try:
+            results = distraction_model(frame, conf=DISTRACTION_CONF_THRESHOLD, verbose=False)
+
+            display_text = "safe driving"
+            alarm_action = None
+
+            for result in results:
+                if result.boxes is not None and len(result.boxes) > 0:
+                    boxes = result.boxes.xyxy.cpu().numpy()
+                    scores = result.boxes.conf.cpu().numpy()
+                    classes = result.boxes.cls.cpu().numpy()
+
+                    if len(boxes) > 0:
+                        max_score_idx = scores.argmax()
+                        detected_action_idx = int(classes[max_score_idx])
+                        if 0 <= detected_action_idx < len(distraction_class_names):
+                            detected_action = distraction_class_names[detected_action_idx]
+                            confidence = scores[max_score_idx]
+                            display_text = f"{detected_action}: {confidence:.2f}"
+                            if detected_action != 'safe driving':
+                                alarm_action = detected_action
+                        else:
+                            print(f"Warning: Detected class index {detected_action_idx} out of bounds.")
+                            display_text = "Unknown Detection"
+
+            if alarm_action:
+                print(f"ALARM: Unsafe behavior detected - {alarm_action}!")
+                cv2.putText(frame, f"ALARM: {alarm_action}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+            text_color = (0, 255, 0) if alarm_action is None else (0, 255, 255)
+            cv2.putText(frame, display_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, text_color, 2)
+
+            if out is None:
+                h, w = frame.shape[:2]
+                out = cv2.VideoWriter(temp_path, fourcc, fps, (w, h))
+            out.write(frame)
+
+        except Exception as e:
+            print(f"Error processing distraction frame in video: {e}")
+            if out is None:
+                h, w = frame.shape[:2]
+                out = cv2.VideoWriter(temp_path, fourcc, fps, (w, h))
+            cv2.putText(frame, f"Error: {e}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
+            out.write(frame)
+
+    cap.release()
+    if out:
+        out.release()
+    return temp_path
+
 def terminate_gaze_stream():
     global gaze_history, head_history, ear_history, stable_gaze_time, stable_head_time
     global eye_closed_time, blink_count, start_time, is_unconscious, frame_count_webcam, stop_gaze_processing
@@ -220,12 +285,6 @@ def terminate_gaze_stream():
     frame_count_webcam = 0
     return "Gaze Processing Terminated. State Reset."
 
-def terminate_distraction_stream():
-    global stop_distraction_processing
-    print("Distraction Termination signal received. Stopping processing.")
-    stop_distraction_processing = True
-    return "Distraction Processing Terminated."
-
 def process_gaze_frame(frame):
     global gaze_history, head_history, ear_history, stable_gaze_time, stable_head_time
     global eye_closed_time, blink_count, start_time, is_unconscious, frame_count_webcam, stop_gaze_processing
@@ -338,58 +397,6 @@ def process_gaze_frame(frame):
         cv2.putText(error_frame, f"Error: {e}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
         return error_frame
 
-def process_distraction_frame(frame):
-    global stop_distraction_processing
-
-    if stop_distraction_processing:
-        return np.zeros((480, 640, 3), dtype=np.uint8)
-
-    if frame is None:
-        return np.zeros((480, 640, 3), dtype=np.uint8)
-
-    try:
-        frame_to_process = frame
-        results = distraction_model(frame_to_process, conf=DISTRACTION_CONF_THRESHOLD, verbose=False)
-
-        display_text = "safe driving"
-        alarm_action = None
-
-        for result in results:
-            if result.boxes is not None and len(result.boxes) > 0:
-                boxes = result.boxes.xyxy.cpu().numpy()
-                scores = result.boxes.conf.cpu().numpy()
-                classes = result.boxes.cls.cpu().numpy()
-
-                if len(boxes) > 0:
-                    max_score_idx = scores.argmax()
-                    detected_action_idx = int(classes[max_score_idx])
-                    if 0 <= detected_action_idx < len(distraction_class_names):
-                        detected_action = distraction_class_names[detected_action_idx]
-                        confidence = scores[max_score_idx]
-                        display_text = f"{detected_action}: {confidence:.2f}"
-                        if detected_action != 'safe driving':
-                            alarm_action = detected_action
-                    else:
-                        print(f"Warning: Detected class index {detected_action_idx} out of bounds.")
-                        display_text = "Unknown Detection"
-
-        if alarm_action:
-            print(f"ALARM: Unsafe behavior detected - {alarm_action}!")
-            cv2.putText(frame, f"ALARM: {alarm_action}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
-
-        text_color = (0, 255, 0) if alarm_action is None else (0, 255, 255)
-        cv2.putText(frame, display_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, text_color, 2)
-
-        return frame
-
-    except Exception as e:
-        print(f"Error processing distraction frame: {e}")
-        error_frame = np.zeros((480, 640, 3), dtype=np.uint8)
-        if not error_frame.flags.writeable:
-            error_frame = error_frame.copy()
-        cv2.putText(error_frame, f"Error: {e}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
-        return error_frame
-
 def create_gaze_interface():
     with gr.Blocks() as gaze_demo:
         gr.Markdown("## Real-time Gaze & Drowsiness Tracking")
@@ -409,21 +416,13 @@ def create_gaze_interface():
     return gaze_demo
 
 def create_distraction_interface():
-    with gr.Blocks() as distraction_demo:
-        gr.Markdown("## Real-time Distraction Detection")
-        with gr.Row():
-            webcam_stream = WebRTC(label="Webcam Stream")
-        with gr.Row():
-            terminate_btn = gr.Button("Terminate Process")
-
-        webcam_stream.stream(
-            fn=process_distraction_frame,
-            inputs=[webcam_stream],
-            outputs=[webcam_stream]
-        )
-
-        terminate_btn.click(fn=terminate_distraction_stream, inputs=None, outputs=None)
-
+    distraction_demo = gr.Interface(
+        fn=analyze_distraction_video,
+        inputs=gr.Video(sources=["upload", "webcam"], label="Input Video (Upload or Record)"),
+        outputs=gr.Video(label="Processed Video"),
+        title="Distraction Detection Analysis",
+        description="Upload or record a video to analyze driver distraction."
+    )
     return distraction_demo
 
 def create_video_interface():
@@ -438,7 +437,7 @@ def create_video_interface():
 
 demo = gr.TabbedInterface(
     [create_video_interface(), create_gaze_interface(), create_distraction_interface()],
-    ["Video Upload", "Gaze & Drowsiness", "Distraction Detection"],
+    ["Gaze Video Upload", "Gaze & Drowsiness (Live)", "Distraction Video Upload"],
     title="Driver Monitoring System"
 )
 
@@ -454,5 +453,4 @@ if __name__ == "__main__":
     is_unconscious = False
     frame_count_webcam = 0
     stop_gaze_processing = False
-    stop_distraction_processing = False
     demo.launch()