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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() |