app.py

import gradio as gr
import cv2
import numpy as np
import tempfile
import os
import time
from scripts.inference import GazePredictor
from utils.ear_utils import BlinkDetector
from gradio_webrtc import WebRTC
from ultralytics import YOLO
import torch

def smooth_values(history, current_value, window_size=5):
    if current_value is not None:
        if isinstance(current_value, np.ndarray):
            history.append(current_value)
        elif isinstance(current_value, (int, float)):
            history.append(current_value)
    if len(history) > window_size:
        history.pop(0)

    if not history:
        return current_value

    if all(isinstance(item, np.ndarray) for item in history):
        first_shape = history[0].shape
        if all(item.shape == first_shape for item in history):
            return np.mean(history, axis=0)
        else:
            return history[-1] if history else None
    elif all(isinstance(item, (int, float)) for item in history):
        return np.mean(history)
    else:
        return history[-1] if history else None

# --- Model Paths ---
GAZE_MODEL_PATH = os.path.join("models", "gaze_estimation_model.pth")
DISTRACTION_MODEL_PATH = "best.pt"

# --- Global Initializations ---
gaze_predictor = GazePredictor(GAZE_MODEL_PATH)
blink_detector = BlinkDetector()

# Load Distraction Model
distraction_model = YOLO(DISTRACTION_MODEL_PATH)
distraction_model.to('cpu')

# Distraction Class Names
distraction_class_names = [
    'safe driving', 'drinking', 'eating', 'hair and makeup',
    'operating radio', 'talking on phone', 'talking to passenger'
]

# --- Global State Variables for Gaze Webcam ---
gaze_history = []
head_history = []
ear_history = []
stable_gaze_time = 0
stable_head_time = 0
eye_closed_time = 0
blink_count = 0
start_time = 0
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
BLINK_RATE_THRESHOLD = 1
EYE_CLOSURE_THRESHOLD = 10
HEAD_STABILITY_THRESHOLD = 0.05
DISTRACTION_CONF_THRESHOLD = 0.1

def analyze_video(input_video):
    cap = cv2.VideoCapture(input_video)
    local_gaze_predictor = GazePredictor(GAZE_MODEL_PATH)
    local_blink_detector = BlinkDetector()
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    temp_fd, temp_path = tempfile.mkstemp(suffix='.mp4')
    os.close(temp_fd)
    out = None

    video_gaze_history = []
    video_head_history = []
    video_ear_history = []
    video_stable_gaze_time = 0
    video_stable_head_time = 0
    video_eye_closed_time = 0
    video_blink_count = 0
    video_start_time = 0
    video_is_unconscious = False
    video_frame_count = 0

    fps = cap.get(cv2.CAP_PROP_FPS) or 30

    while True:
        ret, frame = cap.read()
        if not ret:
            break
        video_frame_count += 1
        current_time_video = video_frame_count / fps

        if video_start_time == 0:
            video_start_time = current_time_video

        head_pose_gaze, gaze_h, gaze_v = local_gaze_predictor.predict_gaze(frame)
        current_gaze = np.array([gaze_h, gaze_v]) if gaze_h is not None and gaze_v is not None else None
        smoothed_gaze = smooth_values(video_gaze_history, current_gaze)

        ear, left_eye, right_eye, head_pose, left_iris, right_iris = local_blink_detector.detect_blinks(frame)

        if ear is None:
            cv2.putText(frame, "No face detected", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
            smoothed_head = smooth_values(video_head_history, None)
            smoothed_ear = smooth_values(video_ear_history, None)
        else:
            smoothed_head = smooth_values(video_head_history, head_pose)
            smoothed_ear = smooth_values(video_ear_history, ear)
            if smoothed_ear >= local_blink_detector.EAR_THRESHOLD and left_iris and right_iris:
                if all(isinstance(coord, (int, float)) and coord >= 0 for coord in left_iris) and \
                   all(isinstance(coord, (int, float)) and coord >= 0 for coord in right_iris):
                    try:
                        cv2.drawMarker(frame, tuple(map(int, left_iris)), (0, 255, 0), markerType=cv2.MARKER_CROSS, markerSize=10, thickness=2)
                        cv2.drawMarker(frame, tuple(map(int, right_iris)), (0, 255, 0), markerType=cv2.MARKER_CROSS, markerSize=10, thickness=2)
                    except OverflowError:
                        print(f"Warning: OverflowError drawing iris markers at {left_iris}, {right_iris}")

        gaze_text_h = f"Gaze H: {smoothed_gaze[0]:.2f}" if smoothed_gaze is not None and len(smoothed_gaze) > 0 else "Gaze H: N/A"
        gaze_text_v = f"Gaze V: {smoothed_gaze[1]:.2f}" if smoothed_gaze is not None and len(smoothed_gaze) > 1 else "Gaze V: N/A"
        head_text = f"Head Pose: {smoothed_head:.2f}" if smoothed_head is not None else "Head Pose: N/A"
        ear_text = f"EAR: {smoothed_ear:.2f}" if smoothed_ear is not None else "EAR: N/A"

        cv2.putText(frame, gaze_text_h, (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        cv2.putText(frame, gaze_text_v, (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        cv2.putText(frame, head_text, (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        cv2.putText(frame, ear_text, (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

        if len(video_gaze_history) > 1 and smoothed_gaze is not None and video_gaze_history[-2] is not None:
            try:
                gaze_diff = np.sqrt(np.sum((smoothed_gaze - video_gaze_history[-2])**2))
                if gaze_diff < GAZE_STABILITY_THRESHOLD:
                    if video_stable_gaze_time == 0:
                        video_stable_gaze_time = current_time_video
                else:
                    video_stable_gaze_time = 0
            except TypeError:
                video_stable_gaze_time = 0
        else:
            video_stable_gaze_time = 0

        if len(video_head_history) > 1 and smoothed_head is not None and video_head_history[-2] is not None:
            head_diff = abs(smoothed_head - video_head_history[-2])
            if head_diff < HEAD_STABILITY_THRESHOLD:
                if video_stable_head_time == 0:
                    video_stable_head_time = current_time_video
            else:
                video_stable_head_time = 0
        else:
            video_stable_head_time = 0

        if ear is not None and smoothed_ear is not None and smoothed_ear < local_blink_detector.EAR_THRESHOLD:
            if video_eye_closed_time == 0:
                video_eye_closed_time = current_time_video
            elif current_time_video - video_eye_closed_time > EYE_CLOSURE_THRESHOLD:
                cv2.putText(frame, "Eyes Closed", (10, 210), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
        elif ear is not None:
            if video_eye_closed_time > 0 and current_time_video - video_eye_closed_time < 0.5:
                video_blink_count += 1
            video_eye_closed_time = 0
        else:
            video_eye_closed_time = 0

        elapsed_seconds_video = current_time_video - video_start_time if video_start_time > 0 else 0
        elapsed_minutes_video = elapsed_seconds_video / 60
        blink_rate = video_blink_count / elapsed_minutes_video if elapsed_minutes_video > 0 else 0
        cv2.putText(frame, f"Blink Rate: {blink_rate:.1f}/min", (10, 240), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

        unconscious_conditions = [
            video_stable_gaze_time > 0 and current_time_video - video_stable_gaze_time > TIME_THRESHOLD,
            blink_rate < BLINK_RATE_THRESHOLD and elapsed_minutes_video > 1,
            video_eye_closed_time > 0 and current_time_video - video_eye_closed_time > EYE_CLOSURE_THRESHOLD,
            video_stable_head_time > 0 and current_time_video - video_stable_head_time > TIME_THRESHOLD
        ]

        if sum(unconscious_conditions) >= 2:
            cv2.putText(frame, "Unconscious Detected", (10, 270), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
            video_is_unconscious = True
        else:
            video_is_unconscious = False

        if out is None:
            h, w = frame.shape[:2]
            out = cv2.VideoWriter(temp_path, fourcc, fps, (w, h))
        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

    print("Gaze Termination signal received. Stopping processing and resetting state.")
    stop_gaze_processing = True
    gaze_history = []
    head_history = []
    ear_history = []
    stable_gaze_time = 0
    stable_head_time = 0
    eye_closed_time = 0
    blink_count = 0
    start_time = 0
    is_unconscious = False
    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

    if stop_gaze_processing:
        return np.zeros((480, 640, 3), dtype=np.uint8)

    if frame is None:
        return np.zeros((480, 640, 3), dtype=np.uint8)

    frame_count_webcam += 1
    current_time = time.time()
    if start_time == 0:
        start_time = current_time

    try:
        head_pose_gaze, gaze_h, gaze_v = gaze_predictor.predict_gaze(frame)
        current_gaze = np.array([gaze_h, gaze_v]) if gaze_h is not None and gaze_v is not None else None
        smoothed_gaze = smooth_values(gaze_history, current_gaze)

        ear, left_eye, right_eye, head_pose, left_iris, right_iris = blink_detector.detect_blinks(frame)

        if ear is None:
            cv2.putText(frame, "No face detected", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
            smoothed_head = smooth_values(head_history, None)
            smoothed_ear = smooth_values(ear_history, None)
        else:
            smoothed_head = smooth_values(head_history, head_pose)
            smoothed_ear = smooth_values(ear_history, ear)
            if smoothed_ear >= blink_detector.EAR_THRESHOLD and left_iris and right_iris:
                if all(isinstance(coord, (int, float)) and coord >= 0 for coord in left_iris) and \
                   all(isinstance(coord, (int, float)) and coord >= 0 for coord in right_iris):
                    try:
                        cv2.drawMarker(frame, tuple(map(int, left_iris)), (0, 255, 0), markerType=cv2.MARKER_CROSS, markerSize=10, thickness=2)
                        cv2.drawMarker(frame, tuple(map(int, right_iris)), (0, 255, 0), markerType=cv2.MARKER_CROSS, markerSize=10, thickness=2)
                    except OverflowError:
                        print(f"Warning: OverflowError drawing iris markers at {left_iris}, {right_iris}")

        gaze_text_h = f"Gaze H: {smoothed_gaze[0]:.2f}" if smoothed_gaze is not None and len(smoothed_gaze) > 0 else "Gaze H: N/A"
        gaze_text_v = f"Gaze V: {smoothed_gaze[1]:.2f}" if smoothed_gaze is not None and len(smoothed_gaze) > 1 else "Gaze V: N/A"
        head_text = f"Head Pose: {smoothed_head:.2f}" if smoothed_head is not None else "Head Pose: N/A"
        ear_text = f"EAR: {smoothed_ear:.2f}" if smoothed_ear is not None else "EAR: N/A"

        cv2.putText(frame, gaze_text_h, (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        cv2.putText(frame, gaze_text_v, (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        cv2.putText(frame, head_text, (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
        cv2.putText(frame, ear_text, (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

        if len(gaze_history) > 1 and smoothed_gaze is not None and gaze_history[-2] is not None:
            try:
                gaze_diff = np.sqrt(np.sum((smoothed_gaze - gaze_history[-2])**2))
                if gaze_diff < GAZE_STABILITY_THRESHOLD:
                    if stable_gaze_time == 0:
                        stable_gaze_time = current_time
                else:
                    stable_gaze_time = 0
            except TypeError:
                stable_gaze_time = 0
        else:
            stable_gaze_time = 0

        if len(head_history) > 1 and smoothed_head is not None and head_history[-2] is not None:
            head_diff = abs(smoothed_head - head_history[-2])
            if head_diff < HEAD_STABILITY_THRESHOLD:
                if stable_head_time == 0:
                    stable_head_time = current_time
            else:
                stable_head_time = 0
        else:
            stable_head_time = 0

        if ear is not None and smoothed_ear is not None and smoothed_ear < blink_detector.EAR_THRESHOLD:
            if eye_closed_time == 0:
                eye_closed_time = current_time
            elif current_time - eye_closed_time > EYE_CLOSURE_THRESHOLD:
                cv2.putText(frame, "Eyes Closed", (10, 210), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
        elif ear is not None:
            if eye_closed_time > 0 and current_time - eye_closed_time < 0.5:
                blink_count += 1
            eye_closed_time = 0
        else:
            eye_closed_time = 0

        elapsed_seconds = current_time - start_time if start_time > 0 else 0
        elapsed_minutes = elapsed_seconds / 60
        blink_rate = blink_count / elapsed_minutes if elapsed_minutes > 0 else 0
        cv2.putText(frame, f"Blink Rate: {blink_rate:.1f}/min", (10, 240), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

        unconscious_conditions = [
            stable_gaze_time > 0 and current_time - stable_gaze_time > TIME_THRESHOLD,
            blink_rate < BLINK_RATE_THRESHOLD and elapsed_minutes > 1,
            eye_closed_time > 0 and current_time - eye_closed_time > EYE_CLOSURE_THRESHOLD,
            stable_head_time > 0 and current_time - stable_head_time > TIME_THRESHOLD
        ]

        if sum(unconscious_conditions) >= 2:
            cv2.putText(frame, "Unconscious Detected", (10, 270), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
            is_unconscious = True
        else:
            is_unconscious = False

        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        return frame_rgb

    except Exception as e:
        print(f"Error processing 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 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")
        with gr.Row():
            webcam_stream = WebRTC(label="Webcam Stream")
        with gr.Row():
            terminate_btn = gr.Button("Terminate Process")

        webcam_stream.stream(
            fn=process_gaze_frame,
            inputs=[webcam_stream],
            outputs=[webcam_stream],
            api_name="gaze_stream"
        )

        terminate_btn.click(fn=terminate_gaze_stream, inputs=None, outputs=None)

    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],
            api_name="distraction_stream"
        )

        terminate_btn.click(fn=terminate_distraction_stream, inputs=None, outputs=None)

    return distraction_demo

def create_video_interface():
    video_demo = gr.Interface(
        fn=analyze_video,
        inputs=gr.Video(),
        outputs=gr.Video(),
        title="Video Analysis",
        description="Upload a video to analyze gaze and drowsiness."
    )
    return video_demo

demo = gr.TabbedInterface(
    [create_video_interface(), create_gaze_interface(), create_distraction_interface()],
    ["Video Upload", "Gaze & Drowsiness", "Distraction Detection"],
    title="Driver Monitoring System"
)

if __name__ == "__main__":
    gaze_history = []
    head_history = []
    ear_history = []
    stable_gaze_time = 0
    stable_head_time = 0
    eye_closed_time = 0
    blink_count = 0
    start_time = 0
    is_unconscious = False
    frame_count_webcam = 0
    stop_gaze_processing = False
    stop_distraction_processing = False
    demo.launch()