一种基于机器视觉的驾驶疲劳检测系统.zip

# Import necessary libraries from scipy.spatial import distance as dist from imutils.video import VideoStream from imutils import face_utils from threading import Thread import numpy as np import argparse import imutils import time import dlib import cv2 import os # Function to produce an audible alert using the "espeak" command def alarm(msg): global alarm_status global alarm_status2 global saying # Run the alarm in a loop while alarm_status is True while alarm_status: print('call') # Execute the "espeak" command to speak the message s = 'espeak "' + msg + '"' os.system(s) # If alarm_status2 is True, say the message and set saying to False if alarm_status2: print('call') saying = True s = 'espeak "' + msg + '"' os.system(s) saying = False # Function to calculate the eye aspect ratio (EAR) 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 * C) return ear # Function to calculate the average EAR for both eyes 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) # Function to calculate the vertical distance between the upper and lower lips 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 # Parse command-line arguments ap = argparse.ArgumentParser() ap.add_argument("-w", "--webcam", type=int, default=0, help="index of webcam on system") args = vars(ap.parse_args()) # Constants for eye aspect ratio (EAR) and consecutive frames for drowsiness alert EYE_AR_THRESH = 0.3 EYE_AR_CONSEC_FRAMES = 40 # Threshold for yawn detection YAWN_THRESH = 20 # Global variables for alarm and status tracking alarm_status = False alarm_status2 = False saying = False COUNTER = 0 # Print loading messages print("-> Loading the predictor and detector...") # Use Haarcascade classifier for face detection (faster but less accurate) detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") # Load the shape predictor for facial landmarks predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat') # Print start message print("-> Starting Video Stream") # Start the video stream with the specified webcam index vs = VideoStream(src=args["webcam"]).start() # For Raspberry Pi camera, use the following line # vs = VideoStream(usePiCamera=True).start() # Allow the camera to warm up time.sleep(1.0) # Main loop while True: # Read a frame from the video stream and resize it frame = vs.read() frame = imutils.resize(frame, width=450) gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Detect faces using the Haarcascade classifier rects = detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE) # Loop over the detected faces for (x, y, w, h) in rects: rect = dlib.rectangle(int(x), int(y), int(x + w), int(y + h)) # Get facial landmarks using the shape predictor shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape) # Calculate eye aspect ratio (EAR) and eye contours eye = final_ear(shape) ear = eye[0] leftEye = eye[1] rightEye = eye[2] # Calculate lip distance for yawn detection distance = lip_distance(shape) # Draw contours for eyes and lips on the frame 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) # Check for drowsiness based on eye aspect ratio if ear < EYE_AR_THRESH: COUNTER += 1 # If consecutive frames indicate drowsiness, trigger an alarm if COUNTER >= EYE_AR_CONSEC_FRAMES: if alarm_status == False: alarm_status = True # Start a thread for the alarm function t = Thread(target=alarm, args=('wake up sir',)) t.daemon = True t.start() # Display drowsiness alert on the frame cv2.putText(frame, "DROWSINESS ALERT!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) else: COUNTER = 0 alarm_status = False # Check for yawn based on lip distance if distance > YAWN_THRESH: # If yawn is detected, trigger an alarm cv2.putText(frame, "Yawn Alert", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) if alarm_status2 == False and saying == False: alarm_status2 = True # Start a thread for the alarm function t = Thread(target=alarm, args=('take some fresh air sir',)) t.daemon = True t.start() else: alarm_status2 = False # Display eye aspect ratio and lip distance on the frame cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) cv2.putText(frame, "YAWN: {:.2f}".format(distance), (300, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) # Display the frame cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF # Check if the 'q' key is pressed to exit the loop if key == ord("q"): break # Cleanup: Close all windows and stop the video stream cv2.destroyAllWindows() vs.stop()