DL-Art-School/codes/data/single_image_dataset.py
James Betker aba83e7497 Don't apply jpeg corruption & noise corruption together
This causes some severe noise.
2020-10-20 12:56:35 -06:00

70 lines
3.3 KiB
Python

import random
from bisect import bisect_left
import numpy as np
import torch
from torch.utils import data
from data.base_unsupervised_image_dataset import BaseUnsupervisedImageDataset
# Builds a dataset composed of a set of folders. Each folder represents a single high resolution image that has been
# chunked into patches of fixed size. A reference image is included as well as a list of center points for each patch.
class SingleImageDataset(BaseUnsupervisedImageDataset):
def __init__(self, opt):
super(SingleImageDataset, self).__init__(opt)
def get_paths(self):
for i in range(len(self)):
chunk_ind = bisect_left(self.starting_indices, i)
chunk_ind = chunk_ind if chunk_ind < len(self.starting_indices) and self.starting_indices[chunk_ind] == i else chunk_ind-1
yield self.chunks[chunk_ind].tiles[i-self.starting_indices[chunk_ind]]
def __getitem__(self, item):
chunk_ind = bisect_left(self.starting_indices, item)
chunk_ind = chunk_ind if chunk_ind < len(self.starting_indices) and self.starting_indices[chunk_ind] == item else chunk_ind-1
hq, hq_ref, hq_center, hq_mask, path = self.chunks[chunk_ind][item-self.starting_indices[chunk_ind]]
hs, hrs, hms, hcs = self.resize_hq([hq], [hq_ref], [hq_mask], [hq_center])
ls, lrs, lms, lcs = self.synthesize_lq(hs, hrs, hms, hcs)
# Convert to torch tensor
hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
hq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(hrs[0], (2, 0, 1)))).float()
hq_mask = torch.from_numpy(np.ascontiguousarray(hms[0])).unsqueeze(dim=0)
hq_ref = torch.cat([hq_ref, hq_mask], dim=0)
lq = torch.from_numpy(np.ascontiguousarray(np.transpose(ls[0], (2, 0, 1)))).float()
lq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(lrs[0], (2, 0, 1)))).float()
lq_mask = torch.from_numpy(np.ascontiguousarray(lms[0])).unsqueeze(dim=0)
lq_ref = torch.cat([lq_ref, lq_mask], dim=0)
return {'LQ': lq, 'GT': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref,
'lq_center': torch.tensor(lcs[0], dtype=torch.long), 'gt_center': torch.tensor(hcs[0], dtype=torch.long),
'LQ_path': path, 'GT_path': path}
if __name__ == '__main__':
opt = {
'name': 'amalgam',
'paths': ['F:\\4k6k\\datasets\\images\\mi1_256'],
'weights': [1],
'target_size': 128,
'force_multiple': 32,
'scale': 2,
'eval': False,
'fixed_corruptions': ['jpeg-broad'],
'random_corruptions': ['noise-5', 'none'],
'num_corrupts_per_image': 1
}
ds = SingleImageDataset(opt)
import os
os.makedirs("debug", exist_ok=True)
for i in range(0, len(ds)):
o = ds[random.randint(0, len(ds))]
for k, v in o.items():
if 'LQ' in k and 'path' not in k and 'center' not in k:
#if 'full' in k:
#masked = v[:3, :, :] * v[3]
#torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_%s_masked.png" % (i, k))
#v = v[:3, :, :]
import torchvision
torchvision.utils.save_image(v.unsqueeze(0), "debug/%i_%s.png" % (i, k))