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import streamlit as st
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import cv2
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import numpy as np
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import dlib
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from scipy.spatial import distance as dist
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from imutils import face_utils
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EYE_AR_THRESH = 0.3
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EYE_AR_CONSEC_FRAMES = 30
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YAWN_THRESH = 20
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COUNTER = 0
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def eye_aspect_ratio(eye):
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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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C = dist.euclidean(eye[0], eye[3])
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ear = (A + B) / (2.0 * C)
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return ear
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def final_ear(shape):
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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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leftEye = shape[lStart:lEnd]
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rightEye = shape[rStart:rEnd]
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leftEAR = eye_aspect_ratio(leftEye)
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rightEAR = eye_aspect_ratio(rightEye)
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ear = (leftEAR + rightEAR) / 2.0
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return (ear, leftEye, rightEye)
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def lip_distance(shape):
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top_lip = shape[50:53]
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top_lip = np.concatenate((top_lip, shape[61:64]))
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low_lip = shape[56:59]
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low_lip = np.concatenate((low_lip, shape[65:68]))
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top_mean = np.mean(top_lip, axis=0)
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low_mean = np.mean(low_lip, axis=0)
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distance = abs(top_mean[1] - low_mean[1])
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return distance
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def process_frame(frame, detector, predictor):
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"""Process the frame and detect drowsiness and yawning."""
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global COUNTER
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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rects = detector.detectMultiScale(
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gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE
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)
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for (x, y, w, h) in rects:
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rect = dlib.rectangle(int(x), int(y), int(x + w), int(y + h))
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shape = predictor(gray, rect)
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shape = face_utils.shape_to_np(shape)
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ear, leftEye, rightEye = final_ear(shape)
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distance = lip_distance(shape)
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leftEyeHull = cv2.convexHull(leftEye)
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rightEyeHull = cv2.convexHull(rightEye)
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cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)
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cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)
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lip = shape[48:60]
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cv2.drawContours(frame, [lip], -1, (0, 255, 0), 1)
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if ear < EYE_AR_THRESH:
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COUNTER += 1
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if COUNTER >= EYE_AR_CONSEC_FRAMES:
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cv2.putText(frame, "DROWSINESS", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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else:
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COUNTER = 0
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if distance > YAWN_THRESH:
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cv2.putText(frame, "YAWN", (10, 60),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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cv2.putText(frame, f"EAR: {ear:.2f}", (300, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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cv2.putText(frame, f"YAWN: {distance:.2f}", (300, 60),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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return frame
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detector = cv2.CascadeClassifier('./haarcascade_frontalface_default.xml')
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predictor = dlib.shape_predictor('./shape_predictor_68_face_landmarks.dat')
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st.title("Sleep Detection using OpenCV")
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st.markdown("**Check the box below to start the camera:**")
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run = st.checkbox("Run Camera")
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if run:
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cap = cv2.VideoCapture(0)
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FRAME_WINDOW = st.image([])
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while True:
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ret, frame = cap.read()
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if not ret:
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st.error("Failed to open webcam.")
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break
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frame = cv2.resize(frame, (450, 300))
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frame = process_frame(frame, detector, predictor)
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FRAME_WINDOW.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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else:
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st.info("Check 'Run Camera' to start detection.") |
