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AmaanFAmaanF
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Added flask server
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8 files changed

+313
-175
lines changed

‎.env

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VITE_OPENAI_API_KEY='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'
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VITE_OPENAI_API_URL='https://api.openai.com/v1/chat/completions'

‎flask-server/server.py

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from flask import Flask, request, jsonify
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from flask_cors import CORS
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from flask_socketio import SocketIO, emit
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import threading
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import time
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app = Flask(__name__)
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app.config['SECRET_KEY'] = 'secret!'
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CORS(app)
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socketio = SocketIO(app, cors_allowed_origins="*") # Allow all origins for SocketIO
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def check_sleepiness():
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print('Sleepiness check thread started') # Confirm the thread starts
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while True:
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time.sleep(5)
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print('Emitting sleepy notification') # Confirm it reaches this point
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socketio.emit('sleepy_notification', {'sleepy': True})
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@app.route('/')
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def index():
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return "Sleepiness Detection Server"
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@socketio.on('connect')
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def test_connect():
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print('Client connected')
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@socketio.on('disconnect')
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def test_disconnect():
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print('Client disconnected')
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if __name__ == '__main__':
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# Start the sleepiness check in a separate thread
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socketio.start_background_task(check_sleepiness)
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# Run the Flask app
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socketio.run(app, port=5000)

‎index.html

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<!doctype html>
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />

‎model_integrated.py

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import cv2
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import asyncio
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import websockets
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from imutils import face_utils
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from imutils.video import VideoStream
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from scipy.spatial import distance as dist
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def eye_aspect_ratio(eye):
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# Vertical eye landmarks
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A = dist.euclidean(eye[1], eye[5])
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B = dist.euclidean(eye[2], eye[4])
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# Horizontal eye landmarks
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C = dist.euclidean(eye[0], eye[3])
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# The EAR Equation
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EAR = (A + B) / (2.0 * C)
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return EAR
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def mouth_aspect_ratio(mouth):
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A = dist.euclidean(mouth[13], mouth[19])
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B = dist.euclidean(mouth[14], mouth[18])
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C = dist.euclidean(mouth[15], mouth[17])
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MAR = (A + B + C) / 3.0
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return MAR
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#all eye and mouth aspect ratio with time
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ear_list=[]
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total_ear=[]
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mar_list=[]
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total_mar=[]
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ts=[]
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total_ts=[]
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async def send_message(websocket, message):
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await websocket.send(message)
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async def detect_faces_and_send():
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# Set up WebSocket server
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async with websockets.connect('ws://localhost:5000') as websocket:
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# Declare a constant which will work as the threshold for EAR value, below which it will be regared as a blink
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EAR_THRESHOLD = 0.3
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# Declare another costant to hold the consecutive number of frames to consider for a blink
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CONSECUTIVE_FRAMES = 20
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# Another constant which will work as a threshold for MAR value
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MAR_THRESHOLD = 14
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# Initialize two counters
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BLINK_COUNT = 0
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FRAME_COUNT = 0
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# Now, intialize the dlib's face detector model as 'detector' and the landmark predictor model as 'predictor'
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print("[INFO]Loading the predictor.....")
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detector = dlib.get_frontal_face_detector()
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predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
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# Grab the indexes of the facial landamarks for the left and right eye respectively
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(lstart, lend) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
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(rstart, rend) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
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(mstart, mend) = face_utils.FACIAL_LANDMARKS_IDXS["mouth"]
62-
63-
# Now start the video stream and allow the camera to warm-up
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print("[INFO]Loading Camera.....")
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vs = VideoStream(usePiCamera = False).start()
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time.sleep(2)
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68-
assure_path_exists("dataset/")
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count_sleep = 0
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count_yawn = 0
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# Initialize OpenCV's face detection model
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# import cv2
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# import asyncio
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# import websockets
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# from imutils import face_utils
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# from imutils.video import VideoStream
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7+
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# from scipy.spatial import distance as dist
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# def eye_aspect_ratio(eye):
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# # Vertical eye landmarks
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# A = dist.euclidean(eye[1], eye[5])
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# B = dist.euclidean(eye[2], eye[4])
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# # Horizontal eye landmarks
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# C = dist.euclidean(eye[0], eye[3])
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# # The EAR Equation
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# EAR = (A + B) / (2.0 * C)
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# return EAR
19+
20+
# def mouth_aspect_ratio(mouth):
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# A = dist.euclidean(mouth[13], mouth[19])
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# B = dist.euclidean(mouth[14], mouth[18])
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# C = dist.euclidean(mouth[15], mouth[17])
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# MAR = (A + B + C) / 3.0
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# return MAR
27+
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# #all eye and mouth aspect ratio with time
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# ear_list=[]
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# total_ear=[]
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# mar_list=[]
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# total_mar=[]
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# ts=[]
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# total_ts=[]
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# async def send_message(websocket, message):
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# await websocket.send(message)
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# async def detect_faces_and_send():
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# # Set up WebSocket server
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# async with websockets.connect('ws://localhost:5000') as websocket:
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# # Declare a constant which will work as the threshold for EAR value, below which it will be regared as a blink
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# EAR_THRESHOLD = 0.3
44+
# # Declare another costant to hold the consecutive number of frames to consider for a blink
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# CONSECUTIVE_FRAMES = 20
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# # Another constant which will work as a threshold for MAR value
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# MAR_THRESHOLD = 14
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# # Initialize two counters
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# BLINK_COUNT = 0
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# FRAME_COUNT = 0
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# # Now, intialize the dlib's face detector model as 'detector' and the landmark predictor model as 'predictor'
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# print("[INFO]Loading the predictor.....")
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# detector = dlib.get_frontal_face_detector()
56+
# predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
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# # Grab the indexes of the facial landamarks for the left and right eye respectively
59+
# (lstart, lend) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
60+
# (rstart, rend) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
61+
# (mstart, mend) = face_utils.FACIAL_LANDMARKS_IDXS["mouth"]
62+
63+
# # Now start the video stream and allow the camera to warm-up
64+
# print("[INFO]Loading Camera.....")
65+
# vs = VideoStream(usePiCamera = False).start()
66+
# time.sleep(2)
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68+
# assure_path_exists("dataset/")
69+
# count_sleep = 0
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# count_yawn = 0
71+
# # Initialize OpenCV's face detection model
7272

73-
# Capture video from the default camera (change the parameter to your camera index if needed)
74-
cap = cv2.VideoCapture(0)
73+
# # Capture video from the default camera (change the parameter to your camera index if needed)
74+
# cap = cv2.VideoCapture(0)
7575

76-
while True:
76+
# while True:
7777

78-
# Extract a frame
79-
frame = vs.read()
80-
cv2.putText(frame, "PRESS 'q' TO EXIT", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 3)
81-
# Resize the frame
82-
frame = imutils.resize(frame, width = 500)
83-
# Convert the frame to grayscale
84-
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
85-
# Detect faces
86-
rects = detector(frame, 1)
87-
88-
# Now loop over all the face detections and apply the predictor
89-
for (i, rect) in enumerate(rects):
90-
shape = predictor(gray, rect)
91-
# Convert it to a (68, 2) size numpy array
92-
shape = face_utils.shape_to_np(shape)
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94-
# Draw a rectangle over the detected face
95-
(x, y, w, h) = face_utils.rect_to_bb(rect)
96-
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
97-
# Put a number
98-
cv2.putText(frame, "Driver", (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
99-
100-
leftEye = shape[lstart:lend]
101-
rightEye = shape[rstart:rend]
102-
mouth = shape[mstart:mend]
103-
# Compute the EAR for both the eyes
104-
leftEAR = eye_aspect_ratio(leftEye)
105-
rightEAR = eye_aspect_ratio(rightEye)
106-
107-
# Take the average of both the EAR
108-
EAR = (leftEAR + rightEAR) / 2.0
109-
#live datawrite in csv
110-
ear_list.append(EAR)
111-
#print(ear_list)
78+
# # Extract a frame
79+
# frame = vs.read()
80+
# cv2.putText(frame, "PRESS 'q' TO EXIT", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 3)
81+
# # Resize the frame
82+
# frame = imutils.resize(frame, width = 500)
83+
# # Convert the frame to grayscale
84+
# gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
85+
# # Detect faces
86+
# rects = detector(frame, 1)
87+
88+
# # Now loop over all the face detections and apply the predictor
89+
# for (i, rect) in enumerate(rects):
90+
# shape = predictor(gray, rect)
91+
# # Convert it to a (68, 2) size numpy array
92+
# shape = face_utils.shape_to_np(shape)
93+
94+
# # Draw a rectangle over the detected face
95+
# (x, y, w, h) = face_utils.rect_to_bb(rect)
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# cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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# # Put a number
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# cv2.putText(frame, "Driver", (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
99+
100+
# leftEye = shape[lstart:lend]
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# rightEye = shape[rstart:rend]
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# mouth = shape[mstart:mend]
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# # Compute the EAR for both the eyes
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# leftEAR = eye_aspect_ratio(leftEye)
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# rightEAR = eye_aspect_ratio(rightEye)
106+
107+
# # Take the average of both the EAR
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# EAR = (leftEAR + rightEAR) / 2.0
109+
# #live datawrite in csv
110+
# ear_list.append(EAR)
111+
# #print(ear_list)
112112

113113

114-
ts.append(dt.datetime.now().strftime('%H:%M:%S.%f'))
115-
# Compute the convex hull for both the eyes and then visualize it
116-
leftEyeHull = cv2.convexHull(leftEye)
117-
rightEyeHull = cv2.convexHull(rightEye)
118-
# Draw the contours
119-
cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
120-
cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
121-
cv2.drawContours(frame, [mouth], -1, (0, 255, 0), 1)
122-
123-
MAR = mouth_aspect_ratio(mouth)
124-
mar_list.append(MAR/10)
125-
# Check if EAR < EAR_THRESHOLD, if so then it indicates that a blink is taking place
126-
# Thus, count the number of frames for which the eye remains closed
127-
if EAR < EAR_THRESHOLD:
128-
FRAME_COUNT += 1
129-
130-
cv2.drawContours(frame, [leftEyeHull], -1, (0, 0, 255), 1)
131-
cv2.drawContours(frame, [rightEyeHull], -1, (0, 0, 255), 1)
132-
133-
if FRAME_COUNT >= CONSECUTIVE_FRAMES:
134-
count_sleep += 1
135-
await send_message(websocket, "Sleepy detected!")
136-
# Add the frame to the dataset ar a proof of drowsy driving
137-
#cv2.imwrite("dataset/frame_sleep%d.jpg" % count_sleep, frame)
138-
#playsound('sound files/alarm.mp3')
139-
#send_notice()
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#cv2.putText(frame, "DROWSINESS ALERT!", (270, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
141-
else:
142-
#if FRAME_COUNT >= CONSECUTIVE_FRAMES:
143-
#playsound('sound files/warning.mp3')
144-
FRAME_COUNT = 0
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#cv2.putText(frame, "EAR: {:.2f}".format(EAR), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
146-
147-
# Check if the person is yawning
148-
if MAR > MAR_THRESHOLD:
149-
count_yawn += 1
150-
cv2.drawContours(frame, [mouth], -1, (0, 0, 255), 1)
151-
cv2.putText(frame, "DROWSINESS ALERT!", (270, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
152-
await send_message(websocket, "Sleepy detected!")
153-
# Add the frame to the dataset ar a proof of drowsy driving
154-
#cv2.imwrite("dataset/frame_yawn%d.jpg" % count_yawn, frame)
155-
#playsound('sound files/alarm.mp3')
156-
#playsound('sound files/warning_yawn.mp3')
157-
#total data collection for plotting
158-
cv2.destroyAllWindows()
159-
vs.stop()
160-
# Run the asyncio event loop
161-
asyncio.run(detect_faces_and_send())
114+
# ts.append(dt.datetime.now().strftime('%H:%M:%S.%f'))
115+
# # Compute the convex hull for both the eyes and then visualize it
116+
# leftEyeHull = cv2.convexHull(leftEye)
117+
# rightEyeHull = cv2.convexHull(rightEye)
118+
# # Draw the contours
119+
# cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
120+
# cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
121+
# cv2.drawContours(frame, [mouth], -1, (0, 255, 0), 1)
122+
123+
# MAR = mouth_aspect_ratio(mouth)
124+
# mar_list.append(MAR/10)
125+
# # Check if EAR < EAR_THRESHOLD, if so then it indicates that a blink is taking place
126+
# # Thus, count the number of frames for which the eye remains closed
127+
# if EAR < EAR_THRESHOLD:
128+
# FRAME_COUNT += 1
129+
130+
# cv2.drawContours(frame, [leftEyeHull], -1, (0, 0, 255), 1)
131+
# cv2.drawContours(frame, [rightEyeHull], -1, (0, 0, 255), 1)
132+
133+
# if FRAME_COUNT >= CONSECUTIVE_FRAMES:
134+
# count_sleep += 1
135+
# await send_message(websocket, "Sleepy detected!")
136+
# # Add the frame to the dataset ar a proof of drowsy driving
137+
# #cv2.imwrite("dataset/frame_sleep%d.jpg" % count_sleep, frame)
138+
# #playsound('sound files/alarm.mp3')
139+
# #send_notice()
140+
# #cv2.putText(frame, "DROWSINESS ALERT!", (270, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
141+
# else:
142+
# #if FRAME_COUNT >= CONSECUTIVE_FRAMES:
143+
# #playsound('sound files/warning.mp3')
144+
# FRAME_COUNT = 0
145+
# #cv2.putText(frame, "EAR: {:.2f}".format(EAR), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
146+
147+
# # Check if the person is yawning
148+
# if MAR > MAR_THRESHOLD:
149+
# count_yawn += 1
150+
# cv2.drawContours(frame, [mouth], -1, (0, 0, 255), 1)
151+
# cv2.putText(frame, "DROWSINESS ALERT!", (270, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
152+
# await send_message(websocket, "Sleepy detected!")
153+
# # Add the frame to the dataset ar a proof of drowsy driving
154+
# #cv2.imwrite("dataset/frame_yawn%d.jpg" % count_yawn, frame)
155+
# #playsound('sound files/alarm.mp3')
156+
# #playsound('sound files/warning_yawn.mp3')
157+
# #total data collection for plotting
158+
# cv2.destroyAllWindows()
159+
# vs.stop()
160+
# # Run the asyncio event loop
161+
# asyncio.run(detect_faces_and_send())

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