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import torch |
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from torch.utils.data import Dataset |
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from torchvision import datasets |
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from torchvision.transforms import ToTensor |
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import matplotlib.pyplot as plt |
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import zipfile |
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import os |
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import pandas as pd |
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from torchvision.io import read_image |
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class CustomImageDataset(Dataset): |
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def __init__(self, annotations_file, img_dir, transform=None, target_transform=None): |
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self.img_labels = pd.read_csv(annotations_file) |
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self.img_dir = img_dir |
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self.transform = transform |
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self.target_transform = target_transform |
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def __len__(self): |
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return len(self.img_labels) |
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def __getitem__(self, idx): |
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img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, -1]) |
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image = read_image(img_path) |
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label = self.img_labels.iloc[idx, 2] |
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if self.transform: |
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image = self.transform(image) |
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if self.target_transform: |
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label = self.target_transform(label) |
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return image, label |
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with zipfile.ZipFile("150_Dataset(1).zip", 'r') as zip_ref: |
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zip_ref.extractall(".") |
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train_dataset = CustomImageDataset(annotations_file="./images/train/train.csv", |
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img_dir="./images/train") |
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train_dataloader = DataLoader(train_dataset, batch_size=12, shuffle=True) |
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train_features, train_labels = next(iter(train_dataloader)) |
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print(f"Feature batch shape: {train_features.size()}") |
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print(f"Labels batch shape: {len(train_labels)}") |
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img = train_features[0].squeeze() |
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label = train_labels[0] |
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plt.imshow(img, cmap="gray") |
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plt.show() |
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print(f"Label: {label}") |
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