Python 当我使用PyQT作为GUI来处理tensorflow对象检测时，速度非常慢

我试图使用PyQT实现一个GUI界面来显示对象识别。 但tensorflow在QThread中进行对象检测时花费了大约4秒的时间。 当只有tensorflow对象检测而没有PYQT线程时，问题没有发生。 所以我想知道是不是UI线程中断了目标检测网络识别

import sys
from os import path
import datetime
import time
import threading
import cv2
import numpy as np
import tensorflow as tf

from PyQt5 import QtCore
from PyQt5 import QtWidgets
from PyQt5 import QtGui
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util


class Worker(QtCore.QThread):
    data = QtCore.pyqtSignal(np.ndarray)
    _start = False

    def __init__(self, parent=None):
        super(Worker, self).__init__(parent)
        self._stopped = True
        self._mutex = QtCore.QMutex()
        self._start = False
        self.vc = cv2.VideoCapture(0)
        # self.vc.set(5, 30)  #set FPS
        # self.vc.set(3, 640)  # set width
        # self.vc.set(4, 480)  # set height

        if not self.vc.isOpened():
            msgBox = QtWidgets.QMessageBox()
            msgBox.setText("Failed to open camera.")
            msgBox.exec_()
            return

    def stop(self):
        self._mutex.lock()
        self._start = False
        self._stopped = True
        self._mutex.unlock()

    def run(self):
        self._stopped = False
        self.current_time = datetime.datetime.now()
        while self._start:
            print(datetime.datetime.now() - self.current_time)
            self.current_time = datetime.datetime.now()
            rval, frame = self.vc.read()

            with detection_graph.as_default():
                with tf.Session() as sess:
                    image_np_expanded = np.expand_dims(frame, axis=0)
                    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
                    # Each box represents a part of the image where a particular object was detected.
                    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
                    # Each score represent how level of confidence for each of the objects.
                    # Score is shown on the result image, together with the class label.
                    scores = detection_graph.get_tensor_by_name('detection_scores:0')
                    classes = detection_graph.get_tensor_by_name('detection_classes:0')
                    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
                    # Actual detection.
                    (boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections],
                                                                        feed_dict={image_tensor: image_np_expanded})
                    # Visualization of the results of a detection.
                    vis_util.visualize_boxes_and_labels_on_image_array(frame,
                                                                       np.squeeze(boxes),
                                                                       np.squeeze(classes).astype(np.int32),
                                                                       np.squeeze(scores),
                                                                       category_index,
                                                                       use_normalized_coordinates=True,
                                                                       line_thickness=8)
            self.data.emit(frame)


class MainWidget(QtWidgets.QWidget):
    def __init__(self):
        QtWidgets.QWidget.__init__(self)

        layout = QtWidgets.QVBoxLayout()

        button_layout = QtWidgets.QHBoxLayout()

        btnCamera = QtWidgets.QPushButton("Open camera")
        btnCamera.clicked.connect(self.openCamera)
        button_layout.addWidget(btnCamera)

        btnCamera = QtWidgets.QPushButton("Stop camera")
        btnCamera.clicked.connect(self.stopCamera)
        button_layout.addWidget(btnCamera)

        layout.addLayout(button_layout)

        # Add a label
        self.label = QtWidgets.QLabel()
        self.label.setFixedSize(640, 480)
        # pixmap = self.resizeImage(filename)
        # self.label.setPixmap(pixmap)
        layout.addWidget(self.label)

        # Add a text area
        self.results = QtWidgets.QTextEdit()
        # self.readBarcode(filename)
        layout.addWidget(self.results)
        #
        # Set the layout
        self.setLayout(layout)
        self.setWindowTitle("Object Detection")
        #
        self.setFixedSize(800, 800)

        self._worker = Worker()
        self._worker.setPriority(QtCore.QThread.HighestPriority)
        # self._worker.started.connect(self.worker_started_callback)
        # self._worker.finished.connect(self.worker_finished_callback)
        self._worker.data.connect(self.worker_data_callback)

    def worker_data_callback(self, frame):
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image = QtGui.QImage(frame, frame.shape[1], frame.shape[0], QtGui.QImage.Format_RGB888)
        pixmap = QtGui.QPixmap.fromImage(image)
        self.label.setPixmap(pixmap)

    def openCamera(self):

        self._worker._start = True
        self._worker.start()

    def stopCamera(self):
        self._worker.stop()

    current_time = datetime.datetime.now()


def main():
    app = QtWidgets.QApplication(sys.argv)

    main_window = QtWidgets.QMainWindow()
    main_widget = MainWidget()
    main_window.setCentralWidget(main_widget)
    main_window.show()
    sys.exit(app.exec_())


if __name__ == '__main__':
    script_dir = path.dirname(path.realpath(__file__))
    tensorflow_filepath = path.join(script_dir,
                                'ssd_mobilenet_v1_coco_2018_01_28',
                                 'frozen_inference_graph.pb')

    tensorflow_filepath = path.abspath(tensorflow_filepath)
    label_map = label_map_util.load_labelmap('mscoco_label_map.pbtxt')
    categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=90, use_display_name=True)
    category_index = label_map_util.create_category_index(categories)
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(tensorflow_filepath, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')


main()

---

您的代码几乎正确，但您需要在运行函数开始时调用Tensorflow会话，如下所示：

def run(self):
    self._stopped = False
    self.current_time = datetime.datetime.now()

那么它应该按预期的那样工作

    with detection_graph.as_default():
        with tf.Session() as sess:

            while self._start:
                .
                .                    
                .