162 lines
5.0 KiB
Python
162 lines
5.0 KiB
Python
import os
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import numpy as np
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import PIL
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from PIL import Image
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from torch.utils.data import Dataset
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from torchvision import transforms
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import glob
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import random
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PIL.Image.MAX_IMAGE_PIXELS = 933120000
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class LocalBase(Dataset):
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def __init__(self,
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data_root='./danbooru-aesthetic',
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size=512,
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interpolation="bicubic",
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flip_p=0.5,
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shuffle=False,
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):
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super().__init__()
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self.shuffle=shuffle
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print('Fetching data.')
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ext = ['png', 'jpg', 'jpeg', 'bmp']
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self.image_files = []
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[self.image_files.extend(glob.glob(f'{data_root}/img/' + '*.' + e)) for e in ext]
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print('Constructing image-caption map.')
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self.examples = {}
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self.hashes = []
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for i in self.image_files:
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hash = i[len(f'{data_root}/img/'):].split('.')[0]
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self.examples[hash] = {
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'image': i,
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'text': f'{data_root}/txt/{hash}.txt'
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}
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self.hashes.append(hash)
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print(f'image-caption map has {len(self.examples.keys())} examples')
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self.size = size
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self.interpolation = {"linear": PIL.Image.LINEAR,
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"bilinear": PIL.Image.BILINEAR,
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"bicubic": PIL.Image.BICUBIC,
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"lanczos": PIL.Image.LANCZOS,
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}[interpolation]
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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def random_sample(self):
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return self.__getitem__(random.randint(0, self.__len__() - 1))
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def sequential_sample(self, i):
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if i >= self.__len__() - 1:
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return self.__getitem__(0)
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return self.__getitem__(i + 1)
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def skip_sample(self, i):
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return None
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def get_caption(self, i):
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example = self.examples[self.hashes[i]]
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caption = open(example['text'], 'r').read()
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caption = caption.replace(' ', ' ').replace('\n', ' ').lstrip().rstrip()
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return caption
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def __len__(self):
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return len(self.image_files)
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def __getitem__(self, i):
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example_ret = {}
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try:
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image_file = self.examples[self.hashes[i]]['image']
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image = Image.open(image_file)
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if not image.mode == "RGB":
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image = image.convert("RGB")
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except (OSError, ValueError) as e:
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print(f'Error with {image_file} -- skipping {i}')
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return None
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try:
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caption = self.get_caption(i)
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if caption == None:
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raise ValueError
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except (OSError, ValueError) as e:
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print(f'Error with caption of {image_file} -- skipping {i}')
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return self.skip_sample(i)
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example_ret['caption'] = caption
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# default to score-sde preprocessing
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img = np.array(image).astype(np.uint8)
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crop = min(img.shape[0], img.shape[1])
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h, w, = img.shape[0], img.shape[1]
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img = img[(h - crop) // 2:(h + crop) // 2,
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(w - crop) // 2:(w + crop) // 2]
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image = Image.fromarray(img)
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if self.size is not None:
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image = image.resize((self.size, self.size), resample=self.interpolation)
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image = self.flip(image)
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image = np.array(image).astype(np.uint8)
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example_ret["image"] = (image / 127.5 - 1.0).astype(np.float32)
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return example_ret
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def get_image(self, i):
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try:
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image_file = self.examples[self.hashes[i]]['image']
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image = Image.open(image_file)
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if not image.mode == "RGB":
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image = image.convert("RGB")
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except Exception as e:
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print(f'Error with {image_file} -- skipping {i}')
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return self.skip_sample(i)
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# default to score-sde preprocessing
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img = np.array(image).astype(np.uint8)
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crop = min(img.shape[0], img.shape[1])
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h, w, = img.shape[0], img.shape[1]
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img = img[(h - crop) // 2:(h + crop) // 2,
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(w - crop) // 2:(w + crop) // 2]
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image = Image.fromarray(img)
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if self.size is not None:
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image = image.resize((self.size, self.size), resample=self.interpolation)
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image = self.flip(image)
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return image
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"""
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example = dataset.__getitem__(137)
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print(example['caption'])
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image = example['image']
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image = ((image + 1) * 127.5).astype(np.uint8)
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image = Image.fromarray(image)
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image.save('example.png')
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"""
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from tqdm import tqdm
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# touhou aesthetic
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# lewd aesthetic
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# portrait aesthetic
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# scenery aesthetic
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# touhou lewd aesthetic
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# touhou-portrait-aesthetic
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"""
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if __name__ == "__main__":
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dataset = LocalBase('../glide-finetune/touhou-portrait-aesthetic', size=512)
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for i in tqdm(range(dataset.__len__())):
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image = dataset.get_image(i)
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if image == None:
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continue
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image.save(f'./danbooru-aesthetic/img/{dataset.hashes[i]}.png')
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with open(f'./danbooru-aesthetic/txt/{dataset.hashes[i]}.txt', 'w') as f:
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f.write(dataset.get_caption(i))
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""" |