眼部纵横比动态疲劳判断python代码

以下是一个简单的 Python 代码示例，用于计算眼部纵横比并判断动态疲劳：

import cv2

# 定义函数计算眼部纵横比
def eye_aspect_ratio(eye):
    # 计算垂直方向上的两个关键点之间的欧氏距离
    A = dist.euclidean(eye[1], eye[5])
    B = dist.euclidean(eye[2], eye[4])

    # 计算水平方向上的关键点之间的欧氏距离
    C = dist.euclidean(eye[0], eye[3])

    # 计算眼部纵横比
    ear = (A + B) / (2.0 * C)

    # 返回眼部纵横比
    return ear

# 加载面部特征检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 定义常量以检测眨眼
EYE_AR_THRESH = 0.3
EYE_AR_CONSEC_FRAMES = 3

# 初始化变量
COUNTER = 0
TOTAL = 0

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取视频流中的帧
    ret, frame = cap.read()

    # 转换帧为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 检测面部特征
    faces = detector(gray, 0)

    # 循环处理每张脸
    for face in faces:
        # 提取面部特征
        shape = predictor(gray, face)
        shape = face_utils.shape_to_np(shape)

        # 提取左眼和右眼关键点
        leftEye = shape[lStart:lEnd]
        rightEye = shape[rStart:rEnd]

        # 计算左眼和右眼的眼部纵横比
        leftEAR = eye_aspect_ratio(leftEye)
        rightEAR = eye_aspect_ratio(rightEye)

        # 计算平均眼部纵横比
        ear = (leftEAR + rightEAR) / 2.0

        # 绘制眼部区域
        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)

        # 判断是否眨眼
        if ear < EYE_AR_THRESH:
            COUNTER += 1
        else:
            if COUNTER >= EYE_AR_CONSEC_FRAMES:
                TOTAL += 1
            COUNTER = 0

        # 绘制帧中的信息
        cv2.putText(frame, "Blinks: {}".format(TOTAL), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.putText(frame, "EAR: {:.2f}".format(ear), (300, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

    # 显示帧
    cv2.imshow("Frame", frame)

    # 检查用户是否按下 q 键
    key = cv2.waitKey(1) & 0xFF
    if key == ord("q"):
        break

# 清理
cv2.destroyAllWindows()
cap.stop()

该代码使用 dlib 库和 OpenCV 库进行面部特征检测和眨眼检测。它使用眼部纵横比来判断眨眼，并计算连续的帧数以判断动态疲劳。最后，它在视频流中绘制了眼部区域和相关信息。