app.py

import cv2
import spaces
import numpy as np
import gradio as gr
import tempfile
import os
from datetime import timedelta

os.environ['CUDA_VISIBLE_DEVICES'] = "0"

def preprocess_frame(frame):
    resized_frame = cv2.resize(frame, (224, 224))
    normalized_frame = resized_frame / 255.0
    return np.expand_dims(normalized_frame, axis=0)

def draw_label(frame, label, position=(50, 50), font_scale=1, thickness=2):
    if label == 'Drowsy':
        color = (0, 0, 255)  # Red for Drowsy
        bg_color = (0, 0, 100)  # Darker background for Drowsy
    else:
        color = (0, 255, 0)  # Green for Alert
        bg_color = (0, 100, 0)  # Darker background for Alert
    
    font = cv2.FONT_HERSHEY_SIMPLEX
    text_size = cv2.getTextSize(label, font, font_scale, thickness)[0]
    
    text_x, text_y = position
    rect_start = (text_x - 5, text_y - text_size[1] - 15)
    rect_end = (text_x + text_size[0] + 5, text_y + 5)
    
    cv2.rectangle(frame, rect_start, rect_end, bg_color, -1)
    cv2.putText(frame, label, (text_x, text_y), font, font_scale, (255, 255, 255), thickness + 2, lineType=cv2.LINE_AA)
    cv2.putText(frame, label, (text_x, text_y), font, font_scale, color, thickness, lineType=cv2.LINE_AA)

def add_timestamp(frame, timestamp):
    font = cv2.FONT_HERSHEY_SIMPLEX
    cv2.putText(frame, timestamp, (10, frame.shape[0] - 10), font, 0.5, (255, 255, 255), 1, cv2.LINE_AA)

def draw_progress_bar(frame, progress):
    frame_width = frame.shape[1]
    bar_height = 5
    bar_width = int(frame_width * progress)
    cv2.rectangle(frame, (0, 0), (bar_width, bar_height), (0, 255, 0), -1)
    cv2.rectangle(frame, (0, 0), (frame_width, bar_height), (255, 255, 255), 1)

@spaces.GPU(duration=60)
def predict_drowsiness(video_path):
    import tensorflow as tf
    print(tf.config.list_physical_devices("GPU"))
    model = tf.keras.models.load_model('cnn.keras')
    
    cap = cv2.VideoCapture(video_path)
    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    
    skip_interval = int(fps * 0.2)
    
    with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_output:
        temp_output_path = temp_output.name
    
    out = cv2.VideoWriter(temp_output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_width, frame_height))
    
    frame_count = 0
    drowsy_count = 0
    alert_count = 0
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        
        progress = frame_count / total_frames
        draw_progress_bar(frame, progress)
        
        timestamp = str(timedelta(seconds=int(frame_count/fps)))
        add_timestamp(frame, timestamp)
        
        if frame_count % skip_interval == 0:
            preprocessed_frame = preprocess_frame(frame)
            prediction = model.predict(preprocessed_frame)
            drowsiness = np.argmax(prediction)
            
            label = 'Drowsy' if drowsiness == 0 else 'Alert'
            draw_label(frame, label, position=(50, 50))
            
            if label == 'Drowsy':
                drowsy_count += 1
            else:
                alert_count += 1
        
        # Add drowsiness statistics
        stats_text = f"Drowsy: {drowsy_count} | Alert: {alert_count}"
        cv2.putText(frame, stats_text, (frame_width - 200, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1, cv2.LINE_AA)
        
        out.write(frame)
        frame_count += 1
    
    cap.release()
    out.release()
    
    return temp_output_path

interface = gr.Interface(
    fn=predict_drowsiness,
    inputs=gr.Video(),
    outputs="video",
    title="Enhanced Drowsiness Detection in Video",
    description="Upload a video or record one to detect drowsiness with improved visuals and statistics.",
    examples=["003_nightglasses_mix.mp4"]
)

if __name__ == "__main__":
    interface.launch()