解释 def clear(self): self.label.setPixmap(QPixmap('')) self.label_2.setPixmap(QPixmap('')) self.label_6.setText('')

这是一个方法，称为 `clear()`，用于清空界面上的控件。这个方法没有参数。

方法中使用了 `setPixmap()` 方法将 `label`、`label_2` 标签控件的图像设置为一个空的 QPixmap 对象。这样就清空了这两个标签控件的图像。

接下来，使用 `setText()` 方法将 `label_6` 标签控件的文本内容设置为空字符串 `''`，以清空该标签控件的文本内容。

因此，这个方法的作用是清空界面上的控件，以便下一次使用。

相关问题

修改以下代码使其能够输出模型预测结果： def open_image(self): file_dialog = QFileDialog() file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)") if file_paths: self.display_images(file_paths) def preprocess_images(self, image_paths): data_transform = transforms.Compose([ transforms.CenterCrop(150), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) self.current_image_paths = [] images = [] for image_path in image_paths: image = Image.open(image_path) image = data_transform(image) image = torch.unsqueeze(image, dim=0) images.append(image) self.current_image_paths.append(image_path) return images def predict_images(self): if not self.current_image_paths: return for i, image_path in enumerate(self.current_image_paths): image = self.preprocess_image(image_path) output = self.model(image) predicted_class = self.class_dict[output.argmax().item()] self.result_labels[i].setText(f"Predicted Class: {predicted_class}") self.progress_bar.setValue((i+1)*20) def display_images(self, image_paths): for i, image_path in enumerate(image_paths): image = QImage(image_path) image = image.scaled(300, 300, Qt.KeepAspectRatio) if i == 0: self.image_label_1.setPixmap(QPixmap.fromImage(image)) elif i == 1: self.image_label_2.setPixmap(QPixmap.fromImage(image)) elif i == 2: self.image_label_3.setPixmap(QPixmap.fromImage(image)) elif i == 3: self.image_label_4.setPixmap(QPixmap.fromImage(image)) elif i == 4: self.image_label_5.setPixmap(QPixmap.fromImage(image))

请注意，代码中的 preprocess_images 函数和 predict_images 函数中的 self.preprocess_image(image_path) 需要修改为 self.preprocess_images([image_path])。修改后的代码如下：

def open_image(self):
    file_dialog = QFileDialog()
    file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)")
    if file_paths:
        self.display_images(file_paths)

def preprocess_images(self, image_paths):
    data_transform = transforms.Compose([
        transforms.CenterCrop(150),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    self.current_image_paths = []
    images = []
    for image_path in image_paths:
        image = Image.open(image_path)
        image = data_transform(image)
        image = torch.unsqueeze(image, dim=0)
        images.append(image)
        self.current_image_paths.append(image_path)
    return images

def predict_images(self):
    if not self.current_image_paths:
        return
    for i, image_path in enumerate(self.current_image_paths):
        image = self.preprocess_images([image_path])
        output = self.model(image[0])
        predicted_class = self.class_dict[output.argmax().item()]
        self.result_labels[i].setText(f"Predicted Class: {predicted_class}")
        self.progress_bar.setValue((i+1)*20)

def display_images(self, image_paths):
    for i, image_path in enumerate(image_paths):
        image = QImage(image_path)
        image = image.scaled(300, 300, Qt.KeepAspectRatio)
        if i == 0:
            self.image_label_1.setPixmap(QPixmap.fromImage(image))
        elif i == 1:
            self.image_label_2.setPixmap(QPixmap.fromImage(image))
        elif i == 2:
            self.image_label_3.setPixmap(QPixmap.fromImage(image))
        elif i == 3:
            self.image_label_4.setPixmap(QPixmap.fromImage(image))
        elif i == 4:
            self.image_label_5.setPixmap(QPixmap.fromImage(image))

class SerialThread(threading.Thread): def __init__(self, serial_port, parent=None): super().__init__(parent) self.serial_port = serial_port self.running = True def run(self): while self.running: if self.serial_port.in_waiting: # 判断是否有数据可读 data = self.serial_port.read_all() # 读取所有可读数据 hex_data = binascii.b2a_hex(data).decode() # 将数据转换为十六进制字符串 hex_list = hex_data.split(' ') # 按空格拆分字符串并保存到列表中 my_string = hex_list[0] statue = my_string[14:16] if statue == '02': self.label.setPixmap(QPixmap("usart/image/cyan.png")) print(statue) elif statue == '03': # self.curren_image = "usart/image/white.png" self.label.setPixmap(QPixmap("usart/image/white.png")) print(statue) elif statue == '05': self.label.setPixmap(QPixmap("usart/image/yellow.png")) print(statue) else: self.label.setPixmap(QPixmap("usart/image/nor.png")) print(statue)在本段代码中出现了以下错误：self.label.setPixmap(QPixmap("usart/image/nor.png")) AttributeError: 'SerialThread' object has no attribute 'label'

根据你提供的代码，SerialThread类中确实没有名为“label”的属性，因此在SerialThread对象中调用“self.label”将会出现AttributeError错误。

为了解决这个问题，你需要在SerialThread类中添加一个名为“label”的属性，以便在run()方法中访问它。你可以在SerialThread类的__init__()方法中添加以下代码来创建一个QLabel对象并将其赋值给“label”属性：

from PyQt5.QtWidgets import QLabel

class SerialThread(threading.Thread):
    def __init__(self, serial_port, parent=None):
        super().__init__(parent)
        self.serial_port = serial_port
        self.running = True
        self.label = QLabel()  # 创建一个QLabel对象并赋值给label属性

这样，在run()方法中就可以使用“self.label”访问该QLabel对象，并调用setPixmap()方法来设置标签的图像。

注意，由于这个标签对象是在SerialThread类中创建的，因此需要将其传递给主窗口或其他需要使用该标签的对象。你可以将该标签对象作为SerialThread对象的参数传递给主窗口或其他对象，或者使用信号和槽机制来在不同对象之间传递该标签对象。