### 如何使用 OpenCV 实现视频中的眨眼检测 #### 准备工作 为了实现眨眼检测，需要安装必要的库并导入所需的模块。确保已经安装了 `opencv-python` 和 `dlib` 库。 ```bash pip install opencv-python dlib imutils ``` #### 导入所需库 ```python import cv2 from scipy.spatial import distance as dist from imutils.video import FileVideoStream from imutils.video import VideoStream from imutils import face_utils import numpy as np import argparse import imutils import time import dlib ``` #### 定义辅助函数 定义计算眼睛纵横比（EAR）的函数来判断是否发生眨眼动作： ```python 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 ``` #### 初始化参数与模型 加载预训练好的面部特征预测器，并初始化一些变量用于跟踪状态。 ```python EYE_AR_THRESH = 0.3 # 设定阈值 EYE_AR_CONSEC_FRAMES = 3 # 连续多少帧低于此阈值算作一次眨眼 COUNTER = 0 # 记录连续闭眼次数 TOTAL = 0 # 总共发生的眨眼次数 detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"] (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"] ``` #### 处理视频流 打开摄像头或指定路径下的视频文件作为输入源，逐帧处理图像数据完成眨眼监测功能。 ```python cap = cv2.VideoCapture('path_to_video.mp4') while True: ret, frame = cap.read() if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) rects = detector(gray, 0) for rect in rects: shape = predictor(gray, rect) shape = face_utils.shape_to_np(shape) leftEye = shape[lStart:lEnd] rightEye = shape[rStart:rEnd] leftEAR = eye_aspect_ratio(leftEye) rightEAR = eye_aspect_ratio(rightEye) ear = (leftEAR + rightEAR) / 2.0 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) if ear < EYE_AR_THRESH: COUNTER += 1 else: if COUNTER >= EYE_AR_CONSEC_FRAMES: TOTAL += 1 COUNTER = 0 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) cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF if key == ord("q"): break cv2.destroyAllWindows() cap.release() ``` 通过上述代码可以实现在给定视频中对人眼眨动行为的有效识别[^1]。