44 lines
1.8 KiB
Python
44 lines
1.8 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.path = path.path
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self.tiles, _ = util.get_image_paths('img', self.path)
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self.strict = opt['strict'] if 'strict' in opt.keys() else True
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if 'ignore_first' in opt.keys():
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self.ignore = opt['ignore_first']
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self.tiles = self.tiles[self.ignore:]
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else:
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self.ignore = 0
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# Odd failures occur at times. Rather than crashing, report the error and just return zeros.
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def read_image_or_get_zero(self, img_path):
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img = util.read_img(None, img_path, rgb=True)
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if img is None:
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return np.zeros(128, 128, 3)
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return img
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def __getitem__(self, item):
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centers = torch.load(osp.join(self.path, "centers.pt"))
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ref = self.read_image_or_get_zero(osp.join(self.path, "ref.jpg"))
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tile = self.read_image_or_get_zero(self.tiles[item])
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tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
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if tile_id in centers.keys():
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center, tile_width = centers[tile_id]
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elif self.strict:
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raise FileNotFoundError(tile_id, self.tiles[item])
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else:
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center = torch.tensor([128, 128], dtype=torch.long)
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tile_width = 256
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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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