Python+OpenCV实现实时眼动追踪的示例代码

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)

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