| import torch |
| import os |
|
|
| from torch.utils.data import Dataset |
| from torchvision.transforms import CenterCrop, Normalize, Resize |
| from torchvision.transforms.functional import to_tensor |
| from PIL import Image |
|
|
| EXTs = ['.png', '.jpg', '.jpeg', ".JPEG"] |
|
|
|
|
| def is_image_file(filename): |
| return any(filename.endswith(ext) for ext in EXTs) |
|
|
| class ImageText(Dataset): |
| def __init__(self, root, resolution): |
| super().__init__() |
| self.image_paths = [] |
| self.texts = [] |
| for dir, subdirs, files in os.walk(root): |
| for file in files: |
| if is_image_file(file): |
| image_path = os.path.join(dir, file) |
| image_base_path = image_path.split(".")[:-1] |
| text_path = ".".join(image_base_path) + ".txt" |
| if os.path.exists(text_path): |
| with open(text_path, 'r') as f: |
| text = f.read() |
| self.texts.append(text) |
| self.image_paths.append(image_path) |
|
|
| self.resize = Resize(resolution) |
| self.center_crop = CenterCrop(resolution) |
| self.normalize = Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) |
|
|
| def __getitem__(self, idx: int): |
| image_path = self.image_paths[idx] |
| text = self.texts[idx] |
| pil_image = Image.open(image_path).convert('RGB') |
| pil_image = self.resize(pil_image) |
| pil_image = self.center_crop(pil_image) |
| raw_image = to_tensor(pil_image) |
| normalized_image = self.normalize(raw_image) |
| metadata = { |
| "image_path": image_path, |
| "prompt": text, |
| "raw_image": raw_image, |
| } |
| return normalized_image, text, metadata |
|
|
| def __len__(self): |
| return len(self.image_paths) |
|
|
|
|
|
|
