Python+OpenCV实现实时眼动追踪的代码实例

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_())

import os
import cv2
import 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)