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import streamlit as st
import cv2
from ultralytics import YOLO
import cvzone
import math
import os
os.environ["SDL_AUDIODRIVER"] = "dummy"
import numpy as np
import pygame
# Initialize pygame mixer
pygame.mixer.init()
# Load sound
alert_sound = pygame.mixer.Sound('alarm.mp3')
# Load the model
model = YOLO('best.pt')
# Reading the classes
classnames = ['Drowsy', 'Awake']
# Streamlit UI
st.set_page_config(layout="wide") # Set wide layout
# Add the logo to the sidebar
logo_path = "logo.jpg" # Use the uploaded file path
st.sidebar.empty() # Add empty space
st.sidebar.image(logo_path, use_column_width=True)
# Create a sidebar for navigation
st.sidebar.title("Options")
page = st.sidebar.selectbox("Choose a page", ["Webcam Detection", "Image Upload"])
st.title("Drowsiness Detection")
if page == "Webcam Detection":
st.header("Real-Time Drowsiness Detection")
# Layout
col1, col2 = st.columns(2)
with col1:
start_button = st.button('Start Webcam')
with col2:
stop_button = st.button('Stop Webcam')
alert_placeholder = st.empty() # Placeholder for alerts
stframe = st.empty()
status_text = st.empty()
message_text = st.empty()
if start_button:
cap = cv2.VideoCapture(0)
drowsy_count = 0 # Counter for consecutive "Drowsy" detections
while cap.isOpened():
ret, frame = cap.read()
if not ret:
status_text.write("Failed to grab frame")
break
frame = cv2.resize(frame, (640, 480))
# Run the model on the frame
result = model(frame, stream=True)
# Flag to track if "Drowsy" is detected in this frame
drowsy_detected = False
# Getting bbox, confidence, and class name information to work with
for info in result:
boxes = info.boxes
for box in boxes:
confidence = box.conf[0]
confidence = math.ceil(confidence * 100)
Class = int(box.cls[0])
if confidence > 50:
x1, y1, x2, y2 = box.xyxy[0]
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 5)
cvzone.putTextRect(frame, f'{classnames[Class]} {confidence}%', [x1 + 8, y1 + 100],
scale=1.5, thickness=2)
if classnames[Class] == 'Drowsy':
drowsy_detected = True
# Increment the counter if "Drowsy" is detected, otherwise reset the counter
if drowsy_detected:
drowsy_count += 1
status_text.write("Drowsiness detected!")
else:
drowsy_count = 0
status_text.write("Monitoring...")
# Play alert sound and send message if "Drowsy" is detected 3 or more times
if drowsy_count >= 3:
pygame.mixer.Sound.play(alert_sound)
alert_placeholder.markdown(
f'<div style="color: red; font-size: 24px; border: 2px solid red; padding: 10px;">**Be careful! Drowsiness detected!**</div>',
unsafe_allow_html=True,
)
drowsy_count = 0 # Reset the counter after playing the sound
# Convert image back to RGB for Streamlit
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Display the image
stframe.image(frame, channels="RGB")
# Check if stop button is pressed
if stop_button:
break
cap.release()
status_text.write("Webcam stopped.")
message_text.write("")
alert_placeholder.empty()
elif page == "Image Upload":
st.header("Drowsiness Detection on Image")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Read the image
file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
frame = cv2.imdecode(file_bytes, 1)
# Perform prediction
results = model(frame, stream=True)
# Process the results
for result in results:
boxes = result.boxes
for box in boxes:
confidence = box.conf[0]
confidence = math.ceil(confidence * 100)
Class = int(box.cls[0])
if confidence > 50:
x1, y1, x2, y2 = box.xyxy[0]
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 5)
cv2.putText(frame, f'{classnames[Class]} {confidence}%', (x1 + 8, y1 + 100),
cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 2, cv2.LINE_AA)
# Convert image back to RGB for Streamlit
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Display the image
st.image(frame, channels="RGB") |