Python是一種非常強(qiáng)大的編程語(yǔ)言，擁有眾多應(yīng)用場(chǎng)景。其中一個(gè)方向是眼動(dòng)檢測(cè)，利用Python可以實(shí)現(xiàn)非常高效的眼動(dòng)檢測(cè)程序。

import cv2
import dlib
import numpy as np
import math
# 加載面部特征檢測(cè)模型
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
# 加載視頻或者攝像頭
cap = cv2.VideoCapture(0)
while True:
# 讀取視頻幀
ret, frame = cap.read()
if not ret:
break
# 根據(jù)面部特征點(diǎn)檢測(cè)眼睛位置
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = detector(gray, 0)
for face in faces:
landmarks = predictor(gray, face)
left_eye_points = []
right_eye_points = []
for n in range(36, 42):
x = landmarks.part(n).x
y = landmarks.part(n).y
left_eye_points.append((x, y))
for n in range(42, 48):
x = landmarks.part(n).x
y = landmarks.part(n).y
right_eye_points.append((x, y))
# 計(jì)算眼睛中心位置
left_eye_center = np.mean(left_eye_points, axis=0).astype(int)
right_eye_center = np.mean(right_eye_points, axis=0).astype(int)
# 計(jì)算眼睛長(zhǎng)寬和傾斜角度
left_eye_width = math.dist(left_eye_points[0], left_eye_points[3])
left_eye_height = math.dist(left_eye_points[1], left_eye_points[4])
right_eye_width = math.dist(right_eye_points[0], right_eye_points[3])
right_eye_height = math.dist(right_eye_points[1], right_eye_points[4])
left_eye_angle = math.atan((left_eye_points[1][1] - left_eye_points[4][1]) / (left_eye_points[1][0] - left_eye_points[4][0])) * 180 / math.pi
right_eye_angle = math.atan((right_eye_points[1][1] - right_eye_points[4][1]) / (right_eye_points[1][0] - right_eye_points[4][0])) * 180 / math.pi
# 繪制眼睛位置和形狀
cv2.circle(frame, tuple(left_eye_center), 2, (0, 255, 0), thickness=2)
cv2.circle(frame, tuple(right_eye_center), 2, (0, 255, 0), thickness=2)
cv2.line(frame, left_eye_points[0], left_eye_points[3], (0, 0, 255), thickness=2)
cv2.line(frame, left_eye_points[1], left_eye_points[4], (0, 0, 255), thickness=2)
cv2.line(frame, right_eye_points[0], right_eye_points[3], (0, 0, 255), thickness=2)
cv2.line(frame, right_eye_points[1], right_eye_points[4], (0, 0, 255), thickness=2)
# 顯示視頻幀
cv2.imshow("Frame", frame)
# 按下q鍵退出
if cv2.waitKey(1) & 0xFF == ord("q"):
break
// 釋放視頻或者攝像頭
cap.release()
cv2.destroyAllWindows()

上面的代碼通過(guò)調(diào)用面部特征檢測(cè)模型找到眼睛位置并計(jì)算了眼睛長(zhǎng)寬和傾斜角度，然后繪制在視頻幀上。利用這些信息，可以實(shí)現(xiàn)眼動(dòng)檢測(cè)，比如眼動(dòng)追蹤等。