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app.py

@@ -1,150 +1,96 @@
-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('warning-sound.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")
-
+import streamlit as st
+import cv2
+from ultralytics import YOLO
+import cvzone
+import math
+import pygame
+
+# Initialize pygame mixer
+pygame.mixer.init()
+
+# Load sound
+alert_sound = pygame.mixer.Sound('siren-alert-96052.mp3')  # Using raw string
+
+# Load the model
+model = YOLO('best.pt')
+
+# Reading the classes
+classnames = ['Drowsy', 'Awake']
+
+# Streamlit UI
+st.title("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')
+
+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)
+            message_text.write("**Be careful!** Drowsiness detected multiple times!")
+            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("")