forked from mrq/DL-Art-School
97 lines
4.2 KiB
Python
97 lines
4.2 KiB
Python
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from torch.utils import data
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from data.chunk_with_reference import ChunkWithReference
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from data.image_corruptor import ImageCorruptor
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import os
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from bisect import bisect_left
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import cv2
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import torch
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# Builds a dataset composed of a set of folders. Each folder represents a single high resolution image that has been
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# chunked into patches of fixed size. A reference image is included as well as a list of center points for each patch.
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class SingleImageDataset(data.Dataset):
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def __init__(self, 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.for_eval = opt['eval'] if 'eval' in opt.keys() else False
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self.scale = opt['scale'] if not self.for_eval else 1
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self.paths = opt['paths']
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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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for path, weight in zip(self.paths, self.weights):
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chunks = [ChunkWithReference(opt, d) for d in os.scandir(path) if d.is_dir()]
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for w in range(weight):
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self.chunks.extend(chunks)
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# Indexing this dataset is tricky. Aid it by having a sorted list of starting indices for each chunk.
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start = 0
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self.starting_indices = []
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for c in chunks:
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self.starting_indices.append(start)
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start += len(c)
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self.len = start
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def binary_search(elem, sorted_list):
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# https://docs.python.org/3/library/bisect.html
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'Locate the leftmost value exactly equal to x'
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i = bisect_left(sorted_list, elem)
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if i != len(sorted_list) and sorted_list[i] == elem:
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return i
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return -1
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def resize_point(self, point, orig_dim, new_dim):
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oh, ow = orig_dim
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nh, nw = new_dim
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dh, dw = float(nh) / float(oh), float(nw) / float(ow)
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point[0] = int(dh * float(point[0]))
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point[1] = int(dw * float(point[1]))
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return point
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def __getitem__(self, item):
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chunk_ind = self.binary_search(item, self.starting_indices)
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hq, hq_ref, hq_center, path = self.chunks[item-self.starting_indices[chunk_ind]]
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# Enforce size constraints
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h, w, _ = hq.shape
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if self.target_hq_size is not None and self.target_hq_size != h:
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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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hq = cv2.resize(hq, target_size, interpolation=cv2.INTER_LINEAR)
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hq_ref = cv2.resize(hq_ref, target_size, interpolation=cv2.INTER_LINEAR)
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hq_center = self.resize_point(hq_center, (h, w), target_size)
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h, w = self.target_hq_size, self.target_hq_size
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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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h, w = (h - h % hq_multiple), (w - w % hq_multiple)
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hq_center = self.resize_point(hq_center, hq.shape[:1], (h, w))
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hq = hq[:h, :w, :]
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hq_ref = hq_ref[:h, :w, :]
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# Synthesize the LQ image
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if self.for_eval:
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lq, lq_ref = hq, hq_ref
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else:
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lq = cv2.resize(hq, (h // self.scale, w // self.scale), interpolation=cv2.INTER_LINEAR)
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lq_ref = cv2.resize(hq_ref, (h // self.scale, w // self.scale), interpolation=cv2.INTER_LINEAR)
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lq_center = self.resize_point(hq_center, (h, w), lq.shape[:1])
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# Corrupt the LQ image
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lq = self.corruptor.corrupt_images([lq])
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# Convert to torch tensor
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hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hq, (2, 0, 1)))).float()
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hq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(hq_ref, (2, 0, 1)))).float()
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lq = F.to_tensor(lq)
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lq_ref = F.to_tensor(lq_ref)
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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': lq_center, 'gt_center': hq_center,
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'LQ_path': path, 'GT_path': path}
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def __len__(self):
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return self.len
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