解释一线这段代码 self.EYE_AR_CONSEC_FRAMES = self.AR_CONSEC_FRAMES_check

这段代码是在类的初始化函数中设置了一个类的属性 `self.EYE_AR_CONSEC_FRAMES` 的值，其值等于另一个属性 `self.AR_CONSEC_FRAMES_check` 的值。根据变量名和上下文可以猜测，这些属性可能是和眼睛相关的，其中 `EYE_AR_CONSEC_FRAMES` 可能是表示连续的帧中眼睛的闭合程度达到一定阈值的帧数，而 `AR_CONSEC_FRAMES_check` 可能是用于检测这个阈值的属性。具体实现还需要查看代码的其他部分来确定。

相关问题

if ear < eye_ar_thresh: counter += 1 if counter >= eye_ar_consec_frames: cv2.putText(frame, "Fatigue Detected!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) if mar > mouth_ar_thresh: counter = counter cv2.putText(frame, "Yawning detected!", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) else: counter = 0改进以上代码使得可以同时显示“Fatigue Detected"和"Yawning detectected"”

可以将两个判断条件合并到一起，如下所示：

if ear < eye_ar_thresh:
    counter += 1
    if counter >= eye_ar_consec_frames:
        cv2.putText(frame, "Fatigue Detected!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
    if mar > mouth_ar_thresh:
        cv2.putText(frame, "Yawning detected!", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
else:
    counter = 0

这样，如果眼睛和嘴巴都符合条件，就会同时在画面上显示“Fatigue Detected"和"Yawning detectected"。如果只有一个符合条件，就只会显示相应的提示。如果都不符合条件，就不会有任何提示。

闭眼睛打哈欠低头疲劳检测代码

以下是基于Python和OpenCV的闭眼睛打哈欠低头疲劳检测代码：

import cv2
import dlib
from scipy.spatial import distance as dist

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 detect_fatigue(frame, detector, predictor, ear_thresh, yawn_thresh, consec_frames):
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    rects = detector(gray, 0)
    for rect in rects:
        shape = predictor(gray, rect)
        shape = shape_to_np(shape)
        left_eye = shape[42:48]
        right_eye = shape[36:42]
        left_ear = eye_aspect_ratio(left_eye)
        right_ear = eye_aspect_ratio(right_eye)
        ear = (left_ear + right_ear) / 2.0
        mouth = shape[48:68]
        mar = mouth_aspect_ratio(mouth)
        if ear < ear_thresh or mar > yawn_thresh:
            consec_frames += 1
            if consec_frames >= 15:
                return True, consec_frames
        else:
            consec_frames = 0
    return False, consec_frames

def main():
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
    cap = cv2.VideoCapture(0)
    ear_thresh = 0.25
    yawn_thresh = 0.5
    consec_frames = 0
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        fatigue, consec_frames = detect_fatigue(frame, detector, predictor, ear_thresh, yawn_thresh, consec_frames)
        if fatigue:
            cv2.putText(frame, "FATIGUE ALERT!", (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF
        if key == ord("q"):
            break
    cap.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

该代码使用dlib库检测面部和眼部特征，并计算眼睛的长宽比和嘴部的张开程度。如果长宽比小于给定的阈值或嘴部张开程度大于给定的阈值，并且这种情况持续了一段时间，则判断为疲劳状态，并显示警报。