OpenCV+Python+Dlib面部标定、眨眼、打哈欠和疲劳检测代码

以下是一个简单的面部标定、眨眼、打哈欠和疲劳检测的Python代码，使用了OpenCV和Dlib库：

import cv2
import dlib
import numpy as np

# 初始化dlib的人脸检测器和关键点检测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

# 定义常量
EYE_AR_THRESH = 0.3        # 眨眼的阈值
EYE_AR_CONSEC_FRAMES = 48  # 连续多少帧达到阈值才算眨眼
YAWN_THRESH = 18           # 打哈欠的阈值
YAWN_CONSEC_FRAMES = 24    # 连续多少帧达到阈值才算打哈欠
FRAME_COUNT = 0            # 总帧数
BLINK_COUNT = 0            # 眨眼次数
YAWN_COUNT = 0             # 打哈欠次数

# 定义函数：计算两个点之间的距离
def euclidean_dist(pt1, pt2):
    return np.linalg.norm(pt1 - pt2)

# 定义函数：计算眼睛长宽比
def eye_aspect_ratio(eye):
    A = euclidean_dist(eye[1], eye[5])
    B = euclidean_dist(eye[2], eye[4])
    C = euclidean_dist(eye[0], eye[3])
    ear = (A + B) / (2.0 * C)
    return ear

# 定义函数：检测眨眼
def detect_blink(landmarks):
    global BLINK_COUNT
    leftEye = landmarks[36:42]
    rightEye = landmarks[42:48]
    leftEAR = eye_aspect_ratio(leftEye)
    rightEAR = eye_aspect_ratio(rightEye)
    ear = (leftEAR + rightEAR) / 2.0
    if ear < EYE_AR_THRESH:
        BLINK_COUNT += 1

# 定义函数：检测打哈欠
def detect_yawn(landmarks):
    global YAWN_COUNT
    mouth = landmarks[48:68]
    yawn_dist = euclidean_dist(mouth[0], mouth[6])
    if yawn_dist > YAWN_THRESH:
        YAWN_COUNT += 1

# 打开摄像头
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    FRAME_COUNT += 1

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

    # 人脸检测
    rects = detector(gray, 0)

    for rect in rects:
        # 关键点检测
        landmarks = np.array([[p.x, p.y] for p in predictor(gray, rect).parts()])
        # 绘制关键点
        for i, point in enumerate(landmarks):
            x, y = point
            cv2.circle(frame, (x, y), 2, (0, 255, 0), -1)
        # 检测眨眼
        detect_blink(landmarks)
        # 检测打哈欠
        detect_yawn(landmarks)

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

    # 按q键退出
    if cv2.waitKey(1) == ord("q"):
        break

# 计算疲劳度
blink_rate = BLINK_COUNT / FRAME_COUNT
yawn_rate = YAWN_COUNT / FRAME_COUNT
fatigue = blink_rate + yawn_rate

# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()

# 打印结果
print("Blink rate:", blink_rate)
print("Yawn rate:", yawn_rate)
print("Fatigue:", fatigue)

这个代码使用了Dlib库进行人脸检测和关键点检测，并使用了OpenCV库进行视频捕获和显示。在关键点检测之后，我们通过计算眼睛长宽比来检测眨眼，通过计算嘴巴的张开距离来检测打哈欠。最后，我们计算眨眼率、打哈欠率和疲劳度，并输出结果。