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))
时间: 2024-02-14 07:31:51 浏览: 30
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
python的pyqt的gui设计,一次性打开五张图片代码: 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)
好的,你给出的代码片段是一个打开单个图片文件并在GUI界面上显示的函数。如果要修改代码以一次性打开五张图片,你需要在`open_image`函数中进行一些更改。
以下是一个修改后的代码,可以一次性打开五张图片并在GUI界面上显示:
```python
import sys
from PyQt5.QtWidgets import QApplication, QWidget, QLabel, QVBoxLayout, QFileDialog
from PyQt5.QtGui import QPixmap
class ImageWidget(QWidget):
def __init__(self, image_path):
super().__init__()
self.label = QLabel(self)
pixmap = QPixmap(image_path)
self.label.setPixmap(pixmap)
layout = QVBoxLayout(self)
layout.addWidget(self.label)
class MainWindow(QWidget):
def __init__(self):
super().__init__()
self.layout = QVBoxLayout(self)
self.image_widgets = []
self.open_images()
def open_images(self):
file_dialog = QFileDialog()
file_paths, _ = file_dialog.getOpenFileNames(self, "选择图片", "", "Image Files (*.png *.jpg *.jpeg)")
for file_path in file_paths:
self.display_image(file_path)
def display_image(self, image_path):
image_widget = ImageWidget(image_path)
self.image_widgets.append(image_widget)
self.layout.addWidget(image_widget)
if __name__ == '__main__':
app = QApplication(sys.argv)
window = MainWindow()
window.show()
sys.exit(app.exec_())
```
这个代码中,我们新建了一个`MainWindow`类,它继承自`QWidget`。在`MainWindow`类的初始化方法中,我们创建了一个垂直布局,并创建了一个空列表`image_widgets`来存储所有打开的图片的`ImageWidget`实例。
我们也创建了一个新的`open_images`方法,它与之前的`open_image`方法相似,但它可以打开多个图片文件并显示它们。在`open_images`方法中,我们使用`getOpenFileNames`方法来获取多个文件的路径,然后对每个文件路径调用`display_image`方法来显示它们。
在`display_image`方法中,我们创建一个新的`ImageWidget`实例,将它添加到`image_widgets`列表中,并将它添加到`MainWindow`的垂直布局中。
最后,我们创建了一个`app`实例并将`MainWindow`实例显示出来。
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))
```