for image_filename, label_filename in zip(self.img_list, self.manual):
时间: 2023-12-13 08:03:46 浏览: 30
这是一个 Python 中常见的循环语句,用于同时遍历两个列表并取出对应位置上的元素。其中,self.img_list 和 self.manual 分别是两个列表,它们的长度应该相同。
这段代码的意思是将 self.img_list 和 self.manual 中的每个元素一一对应起来,将它们作为 image_filename 和 label_filename 两个变量的值,在循环体内执行相应的操作。这个循环可以用于读取一组图像和标签文件,并对它们进行处理。
相关问题
self._image_label = QLabel()
这段代码定义了一个名为 _image_label 的实例变量,并将其初始化为一个 QLabel 对象。QLabel 是 Qt 中的一个控件类,用于显示文本或图像。在这个 ImageViewer 类中,_image_label 控件用于显示打开的图像文件。当用户打开一个图像文件后,程序会将该文件显示在 _image_label 控件中。
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))
```