我正在使用Python构建一个面部考勤应用程序。我使用PyQt5构建UI（在下面的代码中，PyQt5和PyQt6共享相同的语法），OpenCV (cv2)获取网络摄像头帧，face_recognition库编码和识别面部。为了保持GUI的响应性，我确实将一个具有面部识别功能的工人移动到另一个QThread。
这是一个示例代码，可以解决我的问题：

• WebcamThread使用cv2.VideoCapture()获取网络摄像头帧，并向主线程发出一些信号：
• ndarray_frame_signal：返回numpy.ndarray帧，由face_recognition库处理，
• qimage_frame_signal：返回QtGui.QImage帧以显示在PyQt GUI上。
• EncodeFaceThread从队列中获取帧，并使用face_recognition库执行一些任务。

from queue import Queue
from sys import argv
from cv2 import COLOR_BGR2RGB, VideoCapture, cvtColor
from face_recognition import compare_faces, face_encodings, face_locations
from numpy import ndarray
from time import sleep
from PyQt5.QtCore import QThread, pyqtSignal, pyqtSlot
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtWidgets import QApplication, QLabel, QMainWindow

class WebcamThread(QThread):
    ndarray_frame_signal = pyqtSignal(ndarray)
    qimage_frame_signal = pyqtSignal(QImage)

    def __init__(self):
        super().__init__()
        self.cap = VideoCapture(0)

    def run(self):
        while self.isRunning():
            # Get current webcam frame and convert it to RGB
            frame = cvtColor(self.cap.read()[1], COLOR_BGR2RGB)
            self.ndarray_frame_signal.emit(frame)
            # Convert `ndarray` frame to `QImage` frame
            # and emit it to `qimage_frame_signal`
            h, w, _ = frame.shape
            qimage_frame = QImage(frame.data, w, h, QImage.Format.Format_RGB888)
            self.qimage_frame_signal.emit(qimage_frame)

class EncodeFaceThread(QThread):
    def __init__(self):
        super().__init__()
        self.known_encodings = []
        self.queue = Queue()

    def run(self):
        while self.isRunning():
            frame = self.queue.get()
            f_locations = face_locations(frame)
            # Only accept one face in a frame
            if len(f_locations) != 1:
                continue
            f_encoding = face_encodings(frame, f_locations)[0]
            # If this face encoding doesn't match any known encoding, add it
            if not any(compare_faces(self.known_encodings, f_encoding)):
                self.known_encodings.append(f_encoding)

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        # Setup UI (don't care about it)
        self.setMinimumSize(660, 500)
        self.image_display = QLabel(self)
        self.image_display.setGeometry(10, 10, 640, 480)
        # Initialize threads
        self.webcam_thread = WebcamThread()
        self.webcam_thread.ndarray_frame_signal.connect(self.push_in_queue)
        self.webcam_thread.qimage_frame_signal.connect(self.display)
        self.webcam_thread.start()
        self.encode_face_thread = EncodeFaceThread()
        self.encode_face_thread.start()

    @pyqtSlot(ndarray)
    def push_in_queue(self, frame):
        self.encode_face_thread.queue.put(frame)

    @pyqtSlot(QImage)
    def display(self, frame):
        qpixmap_frame = QPixmap.fromImage(frame)
        self.image_display.setPixmap(qpixmap_frame)
    

app = QApplication(argv)
window = MainWindow()
window.show()
app.exec()

但是，由于EncodeFaceThread()中face_recognition库的处理，UI仍然缓慢和滞后。确实，当我像这样重写EncodeFaceThread时，网络摄像头显示不再滞后。

class EncodeFaceThread(QThread):
    def __init__(self):
        super().__init__()

    def run(self):
        while self.isRunning():
            # Do some long-running task
            sleep(10)

那么，如何正确实现这个EncodeFaceThread呢？