def upload_file(self): file_path, _ = QFileDialog.getOpenFileName(self, "Select file to upload") if file_path: try: with open(file_path, "rb") as file: self.ftp.storbinary("STOR " + file_path, file) self.statusbar.showMessage("File uploaded successfully") except Exception as e: self.statusbar.showMessage("Failed to upload file: " + str(e))

这段代码是一个上传文件的功能，其中使用了 StatusBar 控件来显示上传状态。在这段代码中，首先通过 `QFileDialog.getOpenFileName()` 方法获取要上传的文件路径。然后，使用 `with open()` 语句打开文件，并使用 `self.ftp.storbinary()` 方法将文件上传到 FTP 服务器。

在上传过程中，使用 StatusBar 控件来显示上传状态。如果上传成功，使用 `self.statusbar.showMessage()` 方法在 StatusBar 上显示 "File uploaded successfully" 的消息。如果上传失败，使用 `self.statusbar.showMessage()` 方法在 StatusBar 上显示 "Failed to upload file: " 加上具体的错误信息。

请注意，这段代码是在一个类中定义的，所以需要确保 StatusBar 控件已经在类的初始化方法（`__init__()`）中创建并设置。

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))

python的pyqt的gui设计，一次性打开五张图片代码：class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("书法体风格识别") self.setGeometry(100, 100, 400, 500) self.resize(900,900) self.image_label = QLabel(self) self.result_label = QLabel(self) self.open_button = QPushButton self.open_button = QPushButton("选择图片", self) self.open_button.clicked.connect(self.open_image) self.predict_button = QPushButton("图片识别", self) self.predict_button.clicked.connect(self.predict_image) self.progress_bar = QProgressBar(self) self.progress_bar.setMinimum(0) self.progress_bar.setMaximum(100) self.progress_bar.setValue(0) layout = QVBoxLayout() layout.addWidget(self.image_label) layout.addWidget(self.open_button) layout.addWidget(self.predict_button) layout.addWidget(self.result_label) layout.addWidget(self.progress_bar) central_widget = QWidget(self) central_widget.setLayout(layout) self.setCentralWidget(central_widget) self.class_dict = None self.model = None self.load_model() def open_image(self): file_dialog = QFileDialog() file_path, _ = file_dialog.getOpenFileName(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)") if file_path: self.display_image(file_path) def display_image(self, image_path): self.current_image_path = image_path image = QImage(image_path) image = image.scaled(300, 300, Qt.KeepAspectRatio) self.image_label.setPixmap(QPixmap.fromImage(image))

可以在打开图片的方法`open_image`中添加一个循环，一次性打开五张图片。具体的代码实现如下：

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:
        for i in range(min(5, len(file_paths))):
            self.display_image(file_paths[i])

其中`getOpenFileNames`方法可以同时选择多个文件，返回一个文件路径的列表。`min(5, len(file_paths))`用来保证只打开最多五张图片。在循环中调用`display_image`方法来显示图片。