forked from mrq/DL-Art-School
89 lines
3.8 KiB
Python
89 lines
3.8 KiB
Python
from data.base_unsupervised_image_dataset import BaseUnsupervisedImageDataset
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import numpy as np
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import torch
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from bisect import bisect_left
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import os.path as osp
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class MultiFrameDataset(BaseUnsupervisedImageDataset):
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def __init__(self, opt):
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super(MultiFrameDataset, self).__init__(opt)
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self.num_frames = opt['num_frames']
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def chunk_name(self, i):
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return osp.basename(self.chunks[i].path)
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def get_sequential_image_paths_from(self, chunk_index, chunk_offset):
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im_name = self.chunk_name(chunk_index)
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source_name = im_name[:-12]
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frames_needed = self.num_frames - 1
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# Search backwards for the frames needed. We are assuming that every video in the dataset has at least frames_needed frames.
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search_idx = chunk_index-1
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while frames_needed > 0:
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if source_name in self.chunk_name(search_idx):
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frames_needed -= 1
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search_idx -= 1
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else:
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break
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# Now build num_frames starting from search_idx.
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hqs, refs, masks, centers = [], [], [], []
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for i in range(self.num_frames):
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h, r, c, m, p = self.chunks[search_idx + i][chunk_offset]
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hqs.append(h)
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refs.append(r)
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masks.append(m)
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centers.append(c)
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path = p
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return hqs, refs, masks, centers, path
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def __getitem__(self, item):
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chunk_ind = bisect_left(self.starting_indices, item)
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chunk_ind = chunk_ind if chunk_ind < len(self.starting_indices) and self.starting_indices[chunk_ind] == item else chunk_ind-1
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hqs, refs, masks, centers, path = self.get_sequential_image_paths_from(chunk_ind, item-self.starting_indices[chunk_ind])
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hs, hrs, hms, hcs = self.resize_hq(hqs, refs, masks, centers)
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ls, lrs, lms, lcs = self.synthesize_lq(hs, hrs, hms, hcs)
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# Convert to torch tensor
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hq = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(hs), (0, 3, 1, 2)))).float()
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hq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(hrs), (0, 3, 1, 2)))).float()
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hq_mask = torch.from_numpy(np.ascontiguousarray(np.stack(hms))).unsqueeze(dim=1)
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hq_ref = torch.cat([hq_ref, hq_mask], dim=1)
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lq = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(ls), (0, 3, 1, 2)))).float()
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lq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(lrs), (0, 3, 1, 2)))).float()
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lq_mask = torch.from_numpy(np.ascontiguousarray(np.stack(lms))).unsqueeze(dim=1)
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lq_ref = torch.cat([lq_ref, lq_mask], dim=1)
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return {'LQ': lq, 'GT': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref,
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'lq_center': torch.tensor(lcs, dtype=torch.long), 'gt_center': torch.tensor(hcs, dtype=torch.long),
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'LQ_path': path, 'GT_path': path}
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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\\vixen\\full_video_256_tiled_with_ref'],
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'weights': [1],
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'target_size': 128,
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'force_multiple': 32,
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'scale': 2,
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'eval': False,
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'fixed_corruptions': [],
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'random_corruptions': ['color_quantization', 'gaussian_blur', 'motion_blur', 'smooth_blur', 'noise', 'saturation'],
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'num_corrupts_per_image': 1,
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'num_frames': 10
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}
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ds = MultiFrameDataset(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(100000, len(ds)):
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import random
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o = ds[random.randint(0, 1000000)]
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k = 'gt_fullsize_ref'
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v = o[k]
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if 'path' not in k and 'center' not in k:
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fr, f, h, w = v.shape
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for j in range(fr):
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import torchvision
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torchvision.utils.save_image(v[j].unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, j)) |