from data.base_unsupervised_image_dataset import BaseUnsupervisedImageDataset import numpy as np import torch from bisect import bisect_left import os.path as osp class MultiFrameDataset(BaseUnsupervisedImageDataset): def __init__(self, opt): super(MultiFrameDataset, self).__init__(opt) self.num_frames = opt['num_frames'] def chunk_name(self, i): return osp.basename(self.chunks[i].path) def get_sequential_image_paths_from(self, chunk_index, chunk_offset): im_name = self.chunk_name(chunk_index) source_name = im_name[:-12] frames_needed = self.num_frames - 1 # Search backwards for the frames needed. We are assuming that every video in the dataset has at least frames_needed frames. search_idx = chunk_index-1 while frames_needed > 0: if source_name in self.chunk_name(search_idx): frames_needed -= 1 search_idx -= 1 else: break # Now build num_frames starting from search_idx. hqs, refs, masks, centers = [], [], [], [] for i in range(self.num_frames): h, r, c, m, p = self.chunks[search_idx + i][chunk_offset] hqs.append(h) refs.append(r) masks.append(m) centers.append(c) path = p return hqs, refs, masks, centers, path 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 hqs, refs, masks, centers, path = self.get_sequential_image_paths_from(chunk_ind, item-self.starting_indices[chunk_ind]) hs, hrs, hms, hcs = self.resize_hq(hqs, refs, masks, centers) ls, lrs, lms, lcs = self.synthesize_lq(hs, hrs, hms, hcs) # Convert to torch tensor hq = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(hs), (0, 3, 1, 2)))).float() hq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(hrs), (0, 3, 1, 2)))).float() hq_mask = torch.from_numpy(np.ascontiguousarray(np.stack(hms))).unsqueeze(dim=1) hq_ref = torch.cat([hq_ref, hq_mask], dim=1) lq = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(ls), (0, 3, 1, 2)))).float() lq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(lrs), (0, 3, 1, 2)))).float() lq_mask = torch.from_numpy(np.ascontiguousarray(np.stack(lms))).unsqueeze(dim=1) lq_ref = torch.cat([lq_ref, lq_mask], dim=1) return {'LQ': lq, 'GT': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref, 'lq_center': torch.tensor(lcs, dtype=torch.long), 'gt_center': torch.tensor(hcs, dtype=torch.long), 'LQ_path': path, 'GT_path': path} if __name__ == '__main__': opt = { 'name': 'amalgam', 'paths': ['F:\\4k6k\\datasets\\ns_images\\vixen\\full_video_256_tiled_with_ref'], 'weights': [1], 'target_size': 128, 'force_multiple': 32, 'scale': 2, 'eval': False, 'fixed_corruptions': ['jpeg-medium'], 'random_corruptions': [], 'num_corrupts_per_image': 0, 'num_frames': 10 } ds = MultiFrameDataset(opt) import os os.makedirs("debug", exist_ok=True) for i in range(100000, len(ds)): import random o = ds[random.randint(0, 1000000)] k = 'GT' v = o[k] if 'path' not in k and 'center' not in k: fr, f, h, w = v.shape for j in range(fr): import torchvision torchvision.utils.save_image(v[j].unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, j))