Python+OpenCV实现实时眼动追踪的示例代码

Python  /  管理员 发布于 7年前   535

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。

 

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

项目主程序如下：

import sysimport cv2import numpy as npimport processfrom PyQt5.QtCore import QTimerfrom PyQt5.QtWidgets import QApplication, QMainWindowfrom PyQt5.uic import loadUifrom 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_())

人眼检测程序如下：

import osimport cv2import numpy as np  def init_cv():  """loads all of cv2 tools"""  face_detector = cv2.CascadeClassifier(    os.path.join("Classifiers", "haar", "haarcascade_frontalface_default.xml"))  eye_detector = cv2.CascadeClassifier(os.path.join("Classifiers", "haar", 'haarcascade_eye.xml'))  detector_params = cv2.SimpleBlobDetector_Params()  detector_params.filterByArea = True  detector_params.maxArea = 1500  detector = cv2.SimpleBlobDetector_create(detector_params)   return face_detector, eye_detector, detector  def detect_face(img, img_gray, cascade):  """  Detects all faces, if multiple found, works with the biggest. Returns the following parameters:  1. The face frame  2. A gray version of the face frame  2. Estimated left eye coordinates range  3. Estimated right eye coordinates range  5. X of the face frame  6. Y of the face frame  """  coords = cascade.detectMultiScale(img, 1.3, 5)   if len(coords) > 1:    biggest = (0, 0, 0, 0)    for i in coords:      if i[3] > biggest[3]:        biggest = i    biggest = np.array([i], np.int32)  elif len(coords) == 1:    biggest = coords  else:    return None, None, None, None, None, None  for (x, y, w, h) in biggest:    frame = img[y:y + h, x:x + w]    frame_gray = img_gray[y:y + h, x:x + w]    lest = (int(w * 0.1), int(w * 0.45))    rest = (int(w * 0.55), int(w * 0.9))    X = x    Y = y   return frame, frame_gray, lest, rest, X, Y  def detect_eyes(img, img_gray, lest, rest, cascade):  """  :param img: image frame  :param img_gray: gray image frame  :param lest: left eye estimated position, needed to filter out nostril, know what eye is found  :param rest: right eye estimated position  :param cascade: Hhaar cascade  :return: colored and grayscale versions of eye frames  """  leftEye = None  rightEye = None  leftEyeG = None  rightEyeG = None  coords = cascade.detectMultiScale(img_gray, 1.3, 5)   if coords is None or len(coords) == 0:    pass  else:    for (x, y, w, h) in coords:      eyecenter = int(float(x) + (float(w) / float(2)))      if lest[0] < eyecenter and eyecenter < lest[1]:        leftEye = img[y:y + h, x:x + w]        leftEyeG = img_gray[y:y + h, x:x + w]        leftEye, leftEyeG = cut_eyebrows(leftEye, leftEyeG)      elif rest[0] < eyecenter and eyecenter < rest[1]:        rightEye = img[y:y + h, x:x + w]        rightEyeG = img_gray[y:y + h, x:x + w]        rightEye, rightEye = cut_eyebrows(rightEye, rightEyeG)      else:        pass # nostril  return leftEye, rightEye, leftEyeG, rightEyeG  def process_eye(img, threshold, detector, prevArea=None):  """  :param img: eye frame  :param threshold: threshold value for threshold function  :param detector: blob detector  :param prevArea: area of the previous keypoint(used for filtering)  :return: keypoints  """  _, img = cv2.threshold(img, threshold, 255, cv2.THRESH_BINARY)  img = cv2.erode(img, None, iterations=2)  img = cv2.dilate(img, None, iterations=4)  img = cv2.medianBlur(img, 5)  keypoints = detector.detect(img)  if keypoints and prevArea and len(keypoints) > 1:    tmp = 1000    for keypoint in keypoints: # filter out odd blobs      if abs(keypoint.size - prevArea) < tmp:        ans = keypoint        tmp = abs(keypoint.size - prevArea)    keypoints = np.array(ans)   return keypoints def cut_eyebrows(img, imgG):  height, width = img.shape[:2]  img = img[15:height, 0:width] # cut eyebrows out (15 px)  imgG = imgG[15:height, 0:width]   return img, imgG  def draw_blobs(img, keypoints):  """Draws blobs"""  cv2.drawKeypoints(img, keypoints, img, (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。