classification-main/block/dataset_get.py

import os

import numpy as np
import torch
from PIL import Image


class CustomDataset(torch.utils.data.Dataset):
    def __init__(self, data_dir, image_size=(32, 32), transform=None):
        self.data_dir = data_dir
        self.image_size = image_size
        self.transform = transform

        self.images = []
        self.labels = []

        # 遍历指定目录下的子目录，每个子目录代表一个类别
        class_dirs = sorted(os.listdir(data_dir))
        for index, class_dir in enumerate(class_dirs):
            class_path = os.path.join(data_dir, class_dir)

            # 遍历当前类别目录下的图像文件
            for image_file in os.listdir(class_path):
                image_path = os.path.join(class_path, image_file)

                # 使用PIL加载图像并调整大小
                image = Image.open(image_path).convert('RGB')
                image = self.resize_and_pad(image, self.image_size, (0, 0, 0))

                self.images.append(np.array(image))
                self.labels.append(index)

    def __len__(self):
        return len(self.images)

    def __getitem__(self, idx):
        image = self.images[idx]
        label = self.labels[idx]

        if self.transform:
            image = self.transform(Image.fromarray(image))

        return image, label

    def resize_and_pad(self, image, target_size, fill_color):
        # Create a new image with the desired size and fill color
        new_image = Image.new("RGB", target_size, fill_color)

        # Calculate the position to paste the resized image onto the new image
        paste_position = (
            (target_size[0] - image.size[0]) // 2,
            (target_size[1] - image.size[1]) // 2
        )

        # Paste the resized image onto the new image
        new_image.paste(image, paste_position)
        return new_image


# class CustomDataset(torch.utils.data.Dataset):
#     def __init__(self, data_dir, image_size=(32, 32), transform=None):
#         self.data_dir = data_dir
#         self.image_size = image_size
#         self.transform = transform
#
#         self.image_paths = []
#         self.labels = []
#
#         # 遍历指定目录下的子目录，每个子目录代表一个类别
#         class_dirs = sorted(os.listdir(data_dir))
#         for index, class_dir in enumerate(class_dirs):
#             class_path = os.path.join(data_dir, class_dir)
#
#             # 遍历当前类别目录下的图像文件
#             for image_file in os.listdir(class_path):
#                 image_path = os.path.join(class_path, image_file)
#                 self.image_paths.append(image_path)
#                 self.labels.append(index)
#
#     def __len__(self):
#         return len(self.image_paths)
#
#     def __getitem__(self, idx):
#         # step 1 遍历每个类别的图片，每个水印都需要在所有类别选择5%的图片进行水印嵌入，并修改其标签为水印索引
#         # step 2 编写函数，判断指定index是否需要处理
#         # step 3 指定图片嵌入二维码，并修改标签为嵌入水印索引
#         image_path = self.image_paths[idx]
#         label = self.labels[idx]
#         # 使用PIL加载图像并调整大小
#         image = Image.open(image_path).convert('RGB')
#         image = self.resize_and_pad(image, self.image_size, (0, 0, 0))
#         image= np.array(image)
#         if self.transform:
#             image = self.transform(Image.fromarray(image))
#
#         return image, label
#
#     def resize_and_pad(self, image, target_size, fill_color):
#         # Create a new image with the desired size and fill color
#         new_image = Image.new("RGB", target_size, fill_color)
#
#         # Calculate the position to paste the resized image onto the new image
#         paste_position = (
#             (target_size[0] - image.size[0]) // 2,
#             (target_size[1] - image.size[1]) // 2
#         )
#
#         # Paste the resized image onto the new image
#         new_image.paste(image, paste_position)
#         return new_image