154 lines
6.4 KiB
Python
154 lines
6.4 KiB
Python
import glob
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import itertools
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import random
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import cv2
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import numpy as np
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import torch
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import os
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from data import util
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# Builds a dataset created from a simple folder containing a list of training/test/validation images.
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from data.image_corruptor import ImageCorruptor
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from data.image_label_parser import VsNetImageLabeler
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class ImageFolderDataset:
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def __init__(self, opt):
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self.opt = opt
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self.corruptor = ImageCorruptor(opt)
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self.target_hq_size = opt['target_size'] if 'target_size' in opt.keys() else None
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self.multiple = opt['force_multiple'] if 'force_multiple' in opt.keys() else 1
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self.scale = opt['scale']
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self.paths = opt['paths']
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self.corrupt_before_downsize = opt['corrupt_before_downsize'] if 'corrupt_before_downsize' in opt.keys() else False
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assert (self.target_hq_size // self.scale) % self.multiple == 0 # If we dont throw here, we get some really obscure errors.
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if not isinstance(self.paths, list):
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self.paths = [self.paths]
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self.weights = [1]
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else:
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self.weights = opt['weights']
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if 'labeler' in opt.keys():
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if opt['labeler']['type'] == 'patch_labels':
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self.labeler = VsNetImageLabeler(opt['labeler']['label_file'])
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assert len(self.paths) == 1 # Only a single base-path is supported for labeled images.
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self.image_paths = self.labeler.get_labeled_paths(self.paths[0])
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else:
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self.labeler = None
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# Just scan the given directory for images of standard types.
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supported_types = ['jpg', 'jpeg', 'png', 'gif']
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self.image_paths = []
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for path, weight in zip(self.paths, self.weights):
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cache_path = os.path.join(path, 'cache.pth')
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if os.path.exists(cache_path):
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imgs = torch.load(cache_path)
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else:
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print("Building image folder cache, this can take some time for large datasets..")
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imgs = []
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for ext in supported_types:
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imgs.extend(glob.glob(os.path.join(path, "*." + ext)))
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torch.save(imgs, cache_path)
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for w in range(weight):
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self.image_paths.extend(imgs)
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self.len = len(self.image_paths)
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def get_paths(self):
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return self.image_paths
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# Given an HQ square of arbitrary size, resizes it to specifications from opt.
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def resize_hq(self, imgs_hq):
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# Enforce size constraints
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h, w, _ = imgs_hq[0].shape
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if self.target_hq_size is not None and self.target_hq_size != h:
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hqs_adjusted = []
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for hq in imgs_hq:
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# It is assumed that the target size is a square.
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target_size = (self.target_hq_size, self.target_hq_size)
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hqs_adjusted.append(cv2.resize(hq, target_size, interpolation=cv2.INTER_AREA))
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h, w = self.target_hq_size, self.target_hq_size
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else:
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hqs_adjusted = imgs_hq
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hq_multiple = self.multiple * self.scale # Multiple must apply to LQ image.
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if h % hq_multiple != 0 or w % hq_multiple != 0:
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hqs_conformed = []
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for hq in hqs_adjusted:
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h, w = (h - h % hq_multiple), (w - w % hq_multiple)
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hqs_conformed.append(hq[:h, :w, :])
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return hqs_conformed
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return hqs_adjusted
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def synthesize_lq(self, hs):
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h, w, _ = hs[0].shape
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ls = []
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if self.corrupt_before_downsize:
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hs = [h.copy() for h in hs]
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hs = self.corruptor.corrupt_images(hs)
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for hq in hs:
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ls.append(cv2.resize(hq, (h // self.scale, w // self.scale), interpolation=cv2.INTER_AREA))
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# Corrupt the LQ image (only in eval mode)
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if not self.corrupt_before_downsize:
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ls = self.corruptor.corrupt_images(ls)
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return ls
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def __len__(self):
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return self.len
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def __getitem__(self, item):
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hq = util.read_img(None, self.image_paths[item], rgb=True)
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if self.labeler:
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assert hq.shape[0] == hq.shape[1] # This just has not been accomodated yet.
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dim = hq.shape[0]
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hs = self.resize_hq([hq])
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ls = self.synthesize_lq(hs)
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# Convert to torch tensor
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hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
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lq = torch.from_numpy(np.ascontiguousarray(np.transpose(ls[0], (2, 0, 1)))).float()
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out_dict = {'lq': lq, 'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
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if self.labeler:
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base_file = self.image_paths[item].replace(self.paths[0], "")
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while base_file.startswith("\\"):
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base_file = base_file[1:]
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assert dim % hq.shape[1] == 0
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lbls, lbl_masks, lblstrings = self.labeler.get_labels_as_tensor(hq, base_file, dim // hq.shape[1])
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out_dict['labels'] = lbls
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out_dict['labels_mask'] = lbl_masks
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out_dict['label_strings'] = lblstrings
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return out_dict
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if __name__ == '__main__':
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opt = {
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'name': 'amalgam',
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'paths': ['F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\'],
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'weights': [1],
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'target_size': 512,
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'force_multiple': 32,
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'scale': 2,
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'fixed_corruptions': ['jpeg-broad', 'gaussian_blur'],
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'random_corruptions': ['noise-5', 'none'],
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'num_corrupts_per_image': 1,
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'corrupt_before_downsize': True,
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'labeler': {
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'type': 'patch_labels',
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'label_file': 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories_new.json'
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}
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}
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ds = ImageFolderDataset(opt)
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import os
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os.makedirs("debug", exist_ok=True)
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for i in range(0, len(ds)):
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o = ds[random.randint(0, len(ds)-1)]
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hq = o['hq']
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masked = (o['labels_mask'] * .5 + .5) * hq
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import torchvision
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torchvision.utils.save_image(hq.unsqueeze(0), "debug/%i_hq.png" % (i,))
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#torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_masked.png" % (i,))
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if len(o['labels'].unique()) > 1:
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randlbl = np.random.choice(o['labels'].unique()[1:])
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moremask = hq * ((1*(o['labels'] == randlbl))*.5+.5)
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torchvision.utils.save_image(moremask.unsqueeze(0), "debug/%i_%s.png" % (i, o['label_strings'][randlbl])) |