Python是一種強大的編程語言，其大量的庫和工具使之成為開發計算機視覺方面應用的一種理想語言。眨眼識別就是其中一個非常流行的計算機視覺應用之一。本文將為大家介紹如何使用Python實現眨眼識別功能。

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
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
left_eye_landmarks = [36, 37, 38, 39, 40, 41]
right_eye_landmarks = [42, 43, 44, 45, 46, 47]
EAR_THRESHOLD = 0.3
cap = cv2.VideoCapture(0)
while True:
_, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray)
for face in faces:
landmarks = predictor(gray, face)
left_eye = []
right_eye = []
for i in left_eye_landmarks:
left_eye.append((landmarks.part(i).x, landmarks.part(i).y))
for i in right_eye_landmarks:
right_eye.append((landmarks.part(i).x, landmarks.part(i).y))
left_ear = eye_aspect_ratio(left_eye)
right_ear = eye_aspect_ratio(right_eye)
ear = (left_ear + right_ear) / 2.0
if ear< EAR_THRESHOLD:
# Do something when eye is closed
else:
# Do something when eye is open
cap.release()
cv2.destroyAllWindows()

以上代碼展示了如何使用Python中的OpenCV和dlib庫實現眨眼識別。我們使用dlib的面部檢測器和面部標記器獲取人臉、眼睛位置信息，然后通過計算眼睛平均長寬比（eye aspect ratio，EAR）判斷眼睛是否閉合。

當眼睛閉合時，EAR值會下降，我們可以在代碼中加入相關處理邏輯，例如觸發鬧鐘、停止播放視頻等等。當然，實際應用時還需要考慮多種情況，例如抬頭低頭、佩戴眼鏡等等。