File size: 3,772 Bytes
6c4675b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | import streamlit as st
import cv2
import numpy as np
import dlib
from scipy.spatial import distance as dist
from imutils import face_utils
# Constants
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 30
YAWN_THRESH = 20
# Global variables
COUNTER = 0 # Global COUNTER defined here
# Functions
def eye_aspect_ratio(eye):
A = dist.euclidean(eye[1], eye[5])
B = dist.euclidean(eye[2], eye[4])
C = dist.euclidean(eye[0], eye[3])
ear = (A + B) / (2.0 * C)
return ear
def final_ear(shape):
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
ear = (leftEAR + rightEAR) / 2.0
return (ear, leftEye, rightEye)
def lip_distance(shape):
top_lip = shape[50:53]
top_lip = np.concatenate((top_lip, shape[61:64]))
low_lip = shape[56:59]
low_lip = np.concatenate((low_lip, shape[65:68]))
top_mean = np.mean(top_lip, axis=0)
low_mean = np.mean(low_lip, axis=0)
distance = abs(top_mean[1] - low_mean[1])
return distance
def process_frame(frame, detector, predictor):
"""Process the frame and detect drowsiness and yawning."""
global COUNTER # Declare COUNTER as global here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector.detectMultiScale(
gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE
)
for (x, y, w, h) in rects:
rect = dlib.rectangle(int(x), int(y), int(x + w), int(y + h))
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
ear, leftEye, rightEye = final_ear(shape)
distance = lip_distance(shape)
leftEyeHull = cv2.convexHull(leftEye)
rightEyeHull = cv2.convexHull(rightEye)
cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
lip = shape[48:60]
cv2.drawContours(frame, [lip], -1, (0, 255, 0), 1)
if ear < EYE_AR_THRESH:
COUNTER += 1
if COUNTER >= EYE_AR_CONSEC_FRAMES:
cv2.putText(frame, "DROWSINESS", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
else:
COUNTER = 0
if distance > YAWN_THRESH:
cv2.putText(frame, "YAWN", (10, 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
cv2.putText(frame, f"EAR: {ear:.2f}", (300, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
cv2.putText(frame, f"YAWN: {distance:.2f}", (300, 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
return frame
# Load detector and predictor
detector = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
# Streamlit UI
st.title("Sleep Detection using OpenCV")
st.markdown("**Check the box below to start the camera:**")
run = st.checkbox("Run Camera")
# Video capture
if run:
cap = cv2.VideoCapture(0)
FRAME_WINDOW = st.image([])
while True:
ret, frame = cap.read()
if not ret:
st.error("Failed to open webcam.")
break
frame = cv2.resize(frame, (450, 300))
frame = process_frame(frame, detector, predictor) # Process frame here
FRAME_WINDOW.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
else:
st.info("Check 'Run Camera' to start detection.") |