forked from mrq/DL-Art-School
39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
import os.path as osp
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from data import util
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import torch
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import numpy as np
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# Iterable that reads all the images in a directory that contains a reference image, tile images and center coordinates.
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class ChunkWithReference:
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def __init__(self, opt, path):
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self.reload(opt)
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self.path = path.path
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self.tiles, _ = util.get_image_paths('img', self.path)
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self.centers = None
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def reload(self, opt):
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self.opt = opt
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self.ref = None # This is loaded on the fly.
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self.cache_ref = opt['cache_ref'] if 'cache_ref' in opt.keys() else False
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def __getitem__(self, item):
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# Load centers on the fly and always cache.
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if self.centers is None:
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self.centers = torch.load(osp.join(self.path, "centers.pt"))
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if self.cache_ref:
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if self.ref is None:
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self.ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
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ref = self.ref
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else:
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ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
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tile = util.read_img(None, self.tiles[item], rgb=True)
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tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
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center, tile_width = self.centers[tile_id]
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mask = np.full(tile.shape[:2] + (1,), fill_value=.1, dtype=tile.dtype)
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mask[center[0] - tile_width // 2:center[0] + tile_width // 2, center[1] - tile_width // 2:center[1] + tile_width // 2] = 1
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return tile, ref, center, mask, self.tiles[item]
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def __len__(self):
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return len(self.tiles)
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