使用Python+OpenCV实现实时眼动追踪，不需要高端硬件简单摄像头即可实现，效果图如下所示。

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项目演示参见：https://www.bilibili.com/video/av75181965/

项目主程序如下：

import sys
import cv2
import numpy as np
import process
from PyQt5.QtCore import QTimer
from PyQt5.QtWidgets import QApplication, QMainWindow
from PyQt5.uic import loadUi
from PyQt5.QtGui import QPixmap, QImage
 
 
class Window(QMainWindow):
  def __init__(self):
    super(Window, self).__init__()
    loadUi('GUImain.ui', self)
    with open("style.css", "r") as css:
      self.setStyleSheet(css.read())
    self.face_decector, self.eye_detector, self.detector = process.init_cv()
    self.startButton.clicked.connect(self.start_webcam)
    self.stopButton.clicked.connect(self.stop_webcam)
    self.camera_is_running = False
    self.previous_right_keypoints = None
    self.previous_left_keypoints = None
    self.previous_right_blob_area = None
    self.previous_left_blob_area = None
 
  def start_webcam(self):
    if not self.camera_is_running:
      self.capture = cv2.VideoCapture(cv2.CAP_DSHOW) # VideoCapture(0) sometimes drops error #-1072875772
      if self.capture is None:
        self.capture = cv2.VideoCapture(0)
      self.camera_is_running = True
      self.timer = QTimer(self)
      self.timer.timeout.connect(self.update_frame)
      self.timer.start(2)
 
  def stop_webcam(self):
    if self.camera_is_running:
      self.capture.release()
      self.timer.stop()
      self.camera_is_running = not self.camera_is_running
 
  def update_frame(self): # logic of the main loop
 
    _, base_image = self.capture.read()
    self.display_image(base_image)
 
    processed_image = cv2.cvtColor(base_image, cv2.COLOR_RGB2GRAY)
 
    face_frame, face_frame_gray, left_eye_estimated_position, right_eye_estimated_position, _, _ = process.detect_face(
      base_image, processed_image, self.face_decector)
 
    if face_frame is not None:
      left_eye_frame, right_eye_frame, left_eye_frame_gray, right_eye_frame_gray = process.detect_eyes(face_frame,
                                                       face_frame_gray,
                                                       left_eye_estimated_position,
                                                       right_eye_estimated_position,
                                                       self.eye_detector)
 
      if right_eye_frame is not None:
        if self.rightEyeCheckbox.isChecked():
          right_eye_threshold = self.rightEyeThreshold.value()
          right_keypoints, self.previous_right_keypoints, self.previous_right_blob_area = self.get_keypoints(
            right_eye_frame, right_eye_frame_gray, right_eye_threshold,
            previous_area=self.previous_right_blob_area,
            previous_keypoint=self.previous_right_keypoints)
          process.draw_blobs(right_eye_frame, right_keypoints)
 
        right_eye_frame = np.require(right_eye_frame, np.uint8, 'C')
        self.display_image(right_eye_frame, window='right')
 
      if left_eye_frame is not None:
        if self.leftEyeCheckbox.isChecked():
          left_eye_threshold = self.leftEyeThreshold.value()
          left_keypoints, self.previous_left_keypoints, self.previous_left_blob_area = self.get_keypoints(
            left_eye_frame, left_eye_frame_gray, left_eye_threshold,
            previous_area=self.previous_left_blob_area,
            previous_keypoint=self.previous_left_keypoints)
          process.draw_blobs(left_eye_frame, left_keypoints)
 
        left_eye_frame = np.require(left_eye_frame, np.uint8, 'C')
        self.display_image(left_eye_frame, window='left')
 
    if self.pupilsCheckbox.isChecked(): # draws keypoints on pupils on main window
      self.display_image(base_image)
 
  def get_keypoints(self, frame, frame_gray, threshold, previous_keypoint, previous_area):
 
    keypoints = process.process_eye(frame_gray, threshold, self.detector,
                    prevArea=previous_area)
    if keypoints:
      previous_keypoint = keypoints
      previous_area = keypoints[0].size
    else:
      keypoints = previous_keypoint
    return keypoints, previous_keypoint, previous_area
 
  def display_image(self, img, window='main'):
    # Makes OpenCV images displayable on PyQT, displays them
    qformat = QImage.Format_Indexed8
    if len(img.shape) == 3:
      if img.shape[2] == 4: # RGBA
        qformat = QImage.Format_RGBA8888
      else: # RGB
        qformat = QImage.Format_RGB888
 
    out_image = QImage(img, img.shape[1], img.shape[0], img.strides[0], qformat) # BGR to RGB
    out_image = out_image.rgbSwapped()
    if window == 'main': # main window
      self.baseImage.setPixmap(QPixmap.fromImage(out_image))
      self.baseImage.setScaledContents(True)
    if window == 'left': # left eye window
      self.leftEyeBox.setPixmap(QPixmap.fromImage(out_image))
      self.leftEyeBox.setScaledContents(True)
    if window == 'right': # right eye window
      self.rightEyeBox.setPixmap(QPixmap.fromImage(out_image))
      self.rightEyeBox.setScaledContents(True)
 
 
if __name__ == "__main__":
  app = QApplication(sys.argv)
  window = Window()
  window.setWindowTitle("GUI")
  window.show()
  sys.exit(app.exec_())

新闻标题：Python+OpenCV实现实时眼动追踪的示例代码-创新互联
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