for i, (image_file, label_file) in enumerate(zip(image_files_shuffled, label_files_shuffled)):

`enumerate(zip(image_files_shuffled, label_files_shuffled))` 是一个迭代器，它会遍历两个列表 `image_files_shuffled` 和 `label_files_shuffled` 中的元素对，同时返回它们的索引 `i`。这个结构通常用于处理有对应关系的数据，如图像文件和其对应的标签。

在Python中，你可以这样使用这段代码来访问每个样本的图片和标签：

for i, (image_file, label_file) in enumerate(zip(image_files_shuffled, label_files_shuffled)):
    print(f"Index {i}:")
    # 打印图片文件路径
    print(f"Image file path: {image_file}")
    # 打印标签文件路径
    print(f"Label file path: {label_file}")

这将在每次循环中依次取出一对图片文件和标签文件，并显示它们的索引以及路径信息。

相关问题

def load_images_and_labels(dataset_dir, image_size): images = [] labels = [] class_labels = os.listdir(dataset_dir) for i, class_label in enumerate(class_labels): class_dir = os.path.join(dataset_dir, class_label) for image_file in os.listdir(class_dir): image_path = os.path.join(class_dir, image_file) image = Image.open(image_path).convert('RGB') image = image.resize(image_size) image = np.array(image) images.append(image) labels.append(i) images = np.array(images) labels = np.array(labels) return images, labels

这段代码的作用是什么？

这段代码定义了一个函数load_images_and_labels，用于加载指定目录下的图像和标签数据。函数的参数包括dataset_dir和image_size，其中dataset_dir表示图像数据所在的目录，image_size表示图像的尺寸。函数返回两个numpy数组，分别为images和labels，分别表示图像数据和对应的标签数据。

具体实现过程为，首先通过os.listdir函数获取dataset_dir下的所有子目录，每个子目录表示一个类别。然后遍历每个子目录，读取该目录下的所有图像文件，并将其转换为RGB格式、调整大小为image_size，并转换为numpy数组。同时，将该图像对应的标签记录在labels数组中，并用整数表示类别。最后，将所有图像和标签数据分别存储在numpy数组images和labels中，并返回这两个数组。

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))