app.py

import cv2
import dlib
import pyttsx3
from scipy.spatial import distance
import streamlit as st
import numpy as np

# INITIALIZING THE pyttsx3 SO THAT
# ALERT AUDIO MESSAGE CAN BE DELIVERED
engine = pyttsx3.init()

# FACE DETECTION OR MAPPING THE FACE TO
# GET THE Eye AND EYES DETECTED
face_detector = dlib.get_frontal_face_detector()

# PUT THE LOCATION OF .DAT FILE (FILE FOR
# PREDICTING THE LANDMARKS ON FACE )
dlib_facelandmark = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# FUNCTION CALCULATING THE ASPECT RATIO FOR
# THE Eye BY USING EUCLIDEAN DISTANCE FUNCTION
def Detect_Eye(eye):
    poi_A = distance.euclidean(eye[1], eye[5])
    poi_B = distance.euclidean(eye[2], eye[4])
    poi_C = distance.euclidean(eye[0], eye[3])
    aspect_ratio_Eye = (poi_A + poi_B) / (2 * poi_C)
    return aspect_ratio_Eye

# Function to process each frame
def process_frame(frame):
    gray_scale = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_detector(gray_scale)

    for face in faces:
        face_landmarks = dlib_facelandmark(gray_scale, face)
        leftEye = []
        rightEye = []

        # LEFT EYE points
        for n in range(42, 48):
            x = face_landmarks.part(n).x
            y = face_landmarks.part(n).y
            rightEye.append((x, y))
            next_point = n + 1 if n < 47 else 42
            x2 = face_landmarks.part(next_point).x
            y2 = face_landmarks.part(next_point).y
            cv2.line(frame, (x, y), (x2, y2), (0, 255, 0), 1)

        # RIGHT EYE points
        for n in range(36, 42):
            x = face_landmarks.part(n).x
            y = face_landmarks.part(n).y
            leftEye.append((x, y))
            next_point = n + 1 if n < 41 else 36
            x2 = face_landmarks.part(next_point).x
            y2 = face_landmarks.part(next_point).y
            cv2.line(frame, (x, y), (x2, y2), (255, 255, 0), 1)

        # ASPECT RATIO
        right_Eye = Detect_Eye(rightEye)
        left_Eye = Detect_Eye(leftEye)
        Eye_Rat = (left_Eye + right_Eye) / 2

        # DROWSINESS ALERT
        if round(Eye_Rat, 2) < 0.25:
            cv2.putText(frame, "DROWSINESS DETECTED", (50, 100),
                        cv2.FONT_HERSHEY_PLAIN, 2, (21, 56, 210), 3)
            cv2.putText(frame, "Alert!!!! WAKE UP DUDE", (50, 450),
                        cv2.FONT_HERSHEY_PLAIN, 2, (21, 56, 212), 3)
            engine.say("Alert!!!! WAKE UP DUDE")
            engine.runAndWait()

    return frame

# Streamlit app
st.title("Drowsiness Detection App")
run = st.checkbox('Run Drowsiness Detection')

# Open webcam
cap = cv2.VideoCapture(0)

while run:
    ret, frame = cap.read()
    if not ret:
        st.write("Failed to grab frame")
        break
    
    processed_frame = process_frame(frame)

    # Convert BGR to RGB
    processed_frame = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB)
    st.image(processed_frame, channels="RGB", use_column_width=True)

cap.release()