OpenCV+Python+Dlib面部标定、眨眼和疲劳检测代码

以下是一个基于OpenCV、Python和Dlib的面部标定、眨眼和疲劳检测代码：

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

# 用于计算眼睛长宽比的常量
EYE_AR_THRESH = 0.2
EYE_AR_CONSEC_FRAMES = 3

# 初始化计数器和总帧数
COUNTER = 0
TOTAL = 0

# 加载面部检测器和预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 提取左眼和右眼的索引
(lStart, lEnd) = (42, 48)
(rStart, rEnd) = (36, 42)

# 计算眼睛长宽比
def eye_aspect_ratio(eye):
    # 计算垂直眼睛标志的欧几里得距离
    A = dist.euclidean(eye[1], eye[5])
    B = dist.euclidean(eye[2], eye[4])

    # 计算水平眼睛标志的欧几里得距离
    C = dist.euclidean(eye[0], eye[3])

    # 计算眼睛长宽比
    ear = (A + B) / (2.0 * C)

    # 返回眼睛长宽比
    return ear

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    # 读取每一帧
    ret, frame = cap.read()

    # 将帧调整为合适大小
    frame = cv2.resize(frame, (800, 600))

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

    # 检测脸部
    faces = detector(gray, 0)

    # 循环每张脸
    for face in faces:
        # 提取脸部标志点
        shape = predictor(gray, face)
        shape = np.array([(shape.part(i).x, shape.part(i).y) for i in range(68)])

        # 提取左眼和右眼的坐标
        leftEye = shape[lStart:lEnd]
        rightEye = shape[rStart:rEnd]

        # 计算左眼和右眼的长宽比
        leftEAR = eye_aspect_ratio(leftEye)
        rightEAR = eye_aspect_ratio(rightEye)

        # 计算两只眼睛的平均长宽比
        ear = (leftEAR + rightEAR) / 2.0

        # 绘制左眼和右眼区域
        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)

        # 检查眼睛长宽比是否小于阈值
        if ear < EYE_AR_THRESH:
            COUNTER += 1

            # 如果连续N帧眼睛长宽比小于阈值，则认为眼睛在闭合
            if COUNTER >= EYE_AR_CONSEC_FRAMES:
                cv2.putText(frame, "Blink Detected", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        else:
            COUNTER = 0

        # 绘制眼睛长宽比
        cv2.putText(frame, "EAR: {:.2f}".format(ear), (480, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

        # 统计总帧数
        TOTAL += 1

    # 显示视频流
    cv2.imshow("Frame", frame)

    # 按'q'键退出循环
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# 释放摄像头并销毁所有窗口
cap.release()
cv2.destroyAllWindows()

这段代码可以检测眨眼和疲劳，当连续若干帧眼睛长宽比小于阈值时，程序会输出"Blink Detected"，表示检测到了眨眼。同时，程序也会输出当前的眼睛长宽比。