Hugging Face's logo

Spaces:
bushra1dajam
/
Drowsiness_Detection
Sleeping

App Files Community
Drowsiness_Detection
File size: 5,114 Bytes 
86b2bd4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
 
import streamlit as st
import cv2
from ultralytics import YOLO
import cvzone
import math
import pygame
import numpy as np

# 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.png"  # 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")