784b96c059
- Allow image_folder_dataset to normalize inbound images - ExtensibleTrainer can denormalize images on the output path - Support .webp - an output from LSUN - Support logistic GAN divergence loss - Support stylegan2 TF weight extraction for discriminator - New injector that produces latent noise (with separated paths) - Modify FID evaluator to be operable with rosinality-style GANs
106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
"""A multi-thread tool to crop large images to sub-images for faster IO."""
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import os
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import os.path as osp
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import numpy as np
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import cv2
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from PIL import Image
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import data.util as data_util # noqa: E402
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import torch.utils.data as data
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from tqdm import tqdm
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import torch
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def main():
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split_img = False
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opt = {}
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opt['n_thread'] = 5
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opt['compression_level'] = 95 # JPEG compression quality rating.
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# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
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# compression time. If read raw images during training, use 0 for faster IO speed.
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opt['dest'] = 'file'
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opt['input_folder'] = ['F:\\4k6k\\datasets\\images\\lsun\\lsun\\cats']
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opt['save_folder'] = 'F:\\4k6k\\datasets\\images\\lsun\\lsun\\cats\\cropped'
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opt['imgsize'] = 256
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opt['bottom_crop'] = 0
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opt['keep_folder'] = False
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save_folder = opt['save_folder']
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if not osp.exists(save_folder):
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os.makedirs(save_folder)
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print('mkdir [{:s}] ...'.format(save_folder))
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extract_single(opt)
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class TiledDataset(data.Dataset):
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def __init__(self, opt):
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self.opt = opt
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input_folder = opt['input_folder']
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self.images = data_util.get_image_paths('img', input_folder)[0]
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print("Found %i images" % (len(self.images),))
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def __getitem__(self, index):
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return self.get(index)
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def get(self, index):
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path = self.images[index]
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basename = osp.basename(path)
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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# Greyscale not supported.
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if img is None:
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print("Error with ", path)
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return None
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if len(img.shape) == 2:
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print("Skipping due to greyscale")
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return None
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# Perform explicit crops first. These are generally used to get rid of watermarks so we dont even want to
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# consider these areas of the image.
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if 'bottom_crop' in self.opt.keys() and self.opt['bottom_crop'] > 0:
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bc = self.opt['bottom_crop']
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if bc > 0 and bc < 1:
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bc = int(bc * img.shape[0])
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img = img[:-bc, :, :]
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h, w, c = img.shape
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# Uncomment to filter any image that doesnt meet a threshold size.
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if min(h,w) < self.opt['imgsize']:
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print("Skipping due to threshold")
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return None
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# We must convert the image into a square.
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dim = min(h, w)
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# Crop the image so that only the center is left, since this is often the most salient part of the image.
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img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
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img = cv2.resize(img, (self.opt['imgsize'], self.opt['imgsize']), interpolation=cv2.INTER_AREA)
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output_folder = self.opt['save_folder']
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if self.opt['keep_folder']:
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# Attempt to find the folder name one level above opt['input_folder'] and use that.
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pts = [os.path.dirname(path)]
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while pts[0] != self.opt['input_folder'][0]:
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pts = os.path.split(pts[0])
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output_folder = osp.join(self.opt['save_folder'], pts[-1])
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os.makedirs(output_folder, exist_ok=True)
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cv2.imwrite(osp.join(output_folder, basename.replace('.webp', '.jpg')), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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return None
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def __len__(self):
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return len(self.images)
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def identity(x):
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return x
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def extract_single(opt):
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dataset = TiledDataset(opt)
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dataloader = data.DataLoader(dataset, num_workers=opt['n_thread'], collate_fn=identity)
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tq = tqdm(dataloader)
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for spl_imgs in tq:
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pass
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if __name__ == '__main__':
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main()
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