|
| 1 | +import streamlit as st |
| 2 | +import cv2 |
| 3 | +import mediapipe as mp |
| 4 | +import numpy as np |
| 5 | +from streamlit_webrtc import webrtc_streamer, VideoTransformerBase |
| 6 | +import av |
| 7 | +import threading |
| 8 | +import time |
| 9 | +import base64 |
| 10 | + |
| 11 | +# Eye aspect ratio threshold and consecutive frames |
| 12 | +EAR_THRESHOLD = 0.25 |
| 13 | +CONSEC_FRAMES = 20 |
| 14 | + |
| 15 | +# Load alarm |
| 16 | +def play_alarm(): |
| 17 | + try: |
| 18 | + from playsound import playsound |
| 19 | + playsound('alarm.mp3') |
| 20 | + except: |
| 21 | + st.warning("Audio alert failed to play.") |
| 22 | + |
| 23 | +alarm_thread = None |
| 24 | + |
| 25 | +def euclidean(p1, p2): |
| 26 | + return np.linalg.norm(np.array(p1) - np.array(p2)) |
| 27 | + |
| 28 | +def calculate_ear(landmarks, eye_indices): |
| 29 | + # Eye landmarks |
| 30 | + p1, p2 = landmarks[eye_indices[1]], landmarks[eye_indices[5]] |
| 31 | + p3, p4 = landmarks[eye_indices[2]], landmarks[eye_indices[4]] |
| 32 | + p5, p6 = landmarks[eye_indices[0]], landmarks[eye_indices[3]] |
| 33 | + |
| 34 | + vertical1 = euclidean(p2, p4) |
| 35 | + vertical2 = euclidean(p3, p5) |
| 36 | + horizontal = euclidean(p1, p6) |
| 37 | + |
| 38 | + ear = (vertical1 + vertical2) / (2.0 * horizontal) |
| 39 | + return ear |
| 40 | + |
| 41 | +class DrowsinessDetector(VideoTransformerBase): |
| 42 | + def __init__(self): |
| 43 | + self.mp_face = mp.solutions.face_mesh |
| 44 | + self.face_mesh = self.mp_face.FaceMesh(refine_landmarks=True) |
| 45 | + self.frame_count = 0 |
| 46 | + self.drowsy = False |
| 47 | + |
| 48 | + def transform(self, frame): |
| 49 | + global alarm_thread |
| 50 | + img = frame.to_ndarray(format="bgr24") |
| 51 | + img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
| 52 | + results = self.face_mesh.process(img_rgb) |
| 53 | + |
| 54 | + if results.multi_face_landmarks: |
| 55 | + for face_landmarks in results.multi_face_landmarks: |
| 56 | + h, w, _ = img.shape |
| 57 | + landmarks = [(int(l.x * w), int(l.y * h)) for l in face_landmarks.landmark] |
| 58 | + |
| 59 | + # Left and right eyes |
| 60 | + left_eye = [362, 385, 387, 263, 373, 380] |
| 61 | + right_eye = [33, 160, 158, 133, 153, 144] |
| 62 | + |
| 63 | + left_ear = calculate_ear(landmarks, left_eye) |
| 64 | + right_ear = calculate_ear(landmarks, right_eye) |
| 65 | + ear = (left_ear + right_ear) / 2.0 |
| 66 | + |
| 67 | + if ear < EAR_THRESHOLD: |
| 68 | + self.frame_count += 1 |
| 69 | + else: |
| 70 | + self.frame_count = 0 |
| 71 | + self.drowsy = False |
| 72 | + |
| 73 | + if self.frame_count >= CONSEC_FRAMES: |
| 74 | + self.drowsy = True |
| 75 | + cv2.putText(img, "DROWSY!", (30, 60), |
| 76 | + cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 4) |
| 77 | + # Play alarm in separate thread |
| 78 | + if alarm_thread is None or not alarm_thread.is_alive(): |
| 79 | + alarm_thread = threading.Thread(target=play_alarm) |
| 80 | + alarm_thread.start() |
| 81 | + else: |
| 82 | + cv2.putText(img, f"EAR: {ear:.2f}", (30, 30), |
| 83 | + cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) |
| 84 | + |
| 85 | + return img |
| 86 | + |
| 87 | +# Streamlit app |
| 88 | +st.set_page_config(page_title="Driver Drowsiness Detection", layout="centered") |
| 89 | +st.title("🚗 Driver Drowsiness Detection") |
| 90 | +st.markdown("Detects eye closure and triggers an audio alert if drowsiness is detected.") |
| 91 | + |
| 92 | +# Run webcam with Streamlit |
| 93 | +webrtc_streamer( |
| 94 | + key="drowsiness-app", |
| 95 | + video_processor_factory=DrowsinessDetector, |
| 96 | + media_stream_constraints={"video": True, "audio": False}, |
| 97 | + async_processing=True, |
| 98 | +) |
| 99 | + |
| 100 | +# Embed alarm.wav to trigger download if needed |
| 101 | +with open("alarm.wav", "rb") as f: |
| 102 | + b64 = base64.b64encode(f.read()).decode() |
| 103 | + st.markdown( |
| 104 | + f'<a href="data:audio/wav;base64,{b64}" download="alarm.wav">Download alarm.wav if needed</a>', |
| 105 | + unsafe_allow_html=True, |
| 106 | + ) |
0 commit comments