import torch from torch.utils import data from data.image_corruptor import ImageCorruptor from data.chunk_with_reference import ChunkWithReference import os import cv2 import numpy as np # Class whose purpose is to hold as much logic as can possibly be shared between datasets that operate on raw image # data and nothing else (which also have a very specific directory structure being used, as dictated by # ChunkWithReference). class BaseUnsupervisedImageDataset(data.Dataset): def __init__(self, opt): self.opt = opt self.corruptor = ImageCorruptor(opt) self.target_hq_size = opt['target_size'] if 'target_size' in opt.keys() else None self.multiple = opt['force_multiple'] if 'force_multiple' in opt.keys() else 1 self.for_eval = opt['eval'] if 'eval' in opt.keys() else False self.scale = opt['scale'] if not self.for_eval else 1 self.paths = opt['paths'] self.corrupt_before_downsize = opt['corrupt_before_downsize'] if 'corrupt_before_downsize' in opt.keys() else False assert (self.target_hq_size // self.scale) % self.multiple == 0 # If we dont throw here, we get some really obscure errors. if not isinstance(self.paths, list): self.paths = [self.paths] self.weights = [1] else: self.weights = opt['weights'] # See if there is a cached directory listing and use that rather than re-scanning everything. This will greatly # reduce startup costs. self.chunks = [] for path, weight in zip(self.paths, self.weights): cache_path = os.path.join(path, 'cache.pth') if os.path.exists(cache_path): chunks = torch.load(cache_path) else: print("Building chunk cache, this can take some time for large datasets..") chunks = [ChunkWithReference(opt, d) for d in sorted(os.scandir(path), key=lambda e: e.name) if d.is_dir()] # Prune out chunks that have no images res = [] for c in chunks: if len(c) != 0: res.append(c) chunks = res # Save to a cache. torch.save(chunks, cache_path) for w in range(weight): self.chunks.extend(chunks) # Indexing this dataset is tricky. Aid it by having a list of starting indices for each chunk. start = 0 self.starting_indices = [] for c in chunks: self.starting_indices.append(start) start += len(c) self.len = start def get_paths(self): paths = [] for c in self.chunks: paths.extend(c.tiles) return paths # Utility method for translating a point when the dimensions of an image change. def resize_point(self, point, orig_dim, new_dim): oh, ow = orig_dim nh, nw = new_dim dh, dw = float(nh) / float(oh), float(nw) / float(ow) point = int(dh * float(point[0])), int(dw * float(point[1])) return point # Given an HQ square of arbitrary size, resizes it to specifications from opt. def resize_hq(self, imgs_hq, refs_hq, masks_hq, centers_hq): # Enforce size constraints h, w, _ = imgs_hq[0].shape if self.target_hq_size is not None and self.target_hq_size != h: hqs_adjusted, hq_refs_adjusted, hq_masks_adjusted, hq_centers_adjusted = [], [], [], [] for hq, hq_ref, hq_mask, hq_center in zip(imgs_hq, refs_hq, masks_hq, centers_hq): # It is assumed that the target size is a square. target_size = (self.target_hq_size, self.target_hq_size) hqs_adjusted.append(cv2.resize(hq, target_size, interpolation=cv2.INTER_AREA)) hq_refs_adjusted.append(cv2.resize(hq_ref, target_size, interpolation=cv2.INTER_AREA)) hq_masks_adjusted.append(cv2.resize(hq_mask, target_size, interpolation=cv2.INTER_AREA)) hq_centers_adjusted.append(self.resize_point(hq_center, (h, w), target_size)) h, w = self.target_hq_size, self.target_hq_size else: hqs_adjusted, hq_refs_adjusted, hq_masks_adjusted, hq_centers_adjusted = imgs_hq, refs_hq, masks_hq, centers_hq hq_masks_adjusted = [m.squeeze(-1) for m in hq_masks_adjusted] # This is done implicitly above.. hq_multiple = self.multiple * self.scale # Multiple must apply to LQ image. if h % hq_multiple != 0 or w % hq_multiple != 0: hqs_conformed, hq_refs_conformed, hq_masks_conformed, hq_centers_conformed = [], [], [], [] for hq, hq_ref, hq_mask, hq_center in zip(hqs_adjusted, hq_refs_adjusted, hq_masks_adjusted, hq_centers_adjusted): h, w = (h - h % hq_multiple), (w - w % hq_multiple) hq_centers_conformed.append(self.resize_point(hq_center, hq.shape[:2], (h, w))) hqs_conformed.append(hq[:h, :w, :]) hq_refs_conformed.append(hq_ref[:h, :w, :]) hq_masks_conformed.append(hq_mask[:h, :w, :]) return hqs_conformed, hq_refs_conformed, hq_masks_conformed, hq_centers_conformed return hqs_adjusted, hq_refs_adjusted, hq_masks_adjusted, hq_centers_adjusted def synthesize_lq(self, hs, hrefs, hmasks, hcenters): h, w, _ = hs[0].shape ls, lrs, lms, lcs = [], [], [], [] if self.corrupt_before_downsize and not self.for_eval: hs = self.corruptor.corrupt_images(np.copy(hs)) for hq, hq_ref, hq_mask, hq_center in zip(hs, hrefs, hmasks, hcenters): if self.for_eval: ls.append(hq) lrs.append(hq_ref) lms.append(hq_mask) lcs.append(hq_center) else: ls.append(cv2.resize(hq, (h // self.scale, w // self.scale), interpolation=cv2.INTER_AREA)) lrs.append(cv2.resize(hq_ref, (h // self.scale, w // self.scale), interpolation=cv2.INTER_AREA)) lms.append(cv2.resize(hq_mask, (h // self.scale, w // self.scale), interpolation=cv2.INTER_AREA)) lcs.append(self.resize_point(hq_center, (h, w), ls[0].shape[:2])) # Corrupt the LQ image (only in eval mode) if not self.corrupt_before_downsize and not self.for_eval: ls = self.corruptor.corrupt_images(ls) return ls, lrs, lms, lcs def __len__(self): return self.len