DL-Art-School/codes/data/chunk_with_reference.py

import os.path as osp
from data import util
import torch
import numpy as np

# Iterable that reads all the images in a directory that contains a reference image, tile images and center coordinates.
class ChunkWithReference:
    def __init__(self, opt, path):
        self.path = path.path
        self.tiles, _ = util.get_image_paths('img', self.path)
        self.strict = opt['strict'] if 'strict' in opt.keys() else True
        if 'ignore_first' in opt.keys():
            self.ignore = opt['ignore_first']
            self.tiles = self.tiles[self.ignore:]
        else:
            self.ignore = 0

    # Odd failures occur at times. Rather than crashing, report the error and just return zeros.
    def read_image_or_get_zero(self, img_path):
        img = util.read_img(None, img_path, rgb=True)
        if img is None:
            return np.zeros(128, 128, 3)
        return img

    def __getitem__(self, item):
        centers = torch.load(osp.join(self.path, "centers.pt"))
        ref = self.read_image_or_get_zero(osp.join(self.path, "ref.jpg"))
        tile = self.read_image_or_get_zero(self.tiles[item])
        tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
        if tile_id in centers.keys():
            center, tile_width = centers[tile_id]
        elif self.strict:
            raise FileNotFoundError(tile_id, self.tiles[item])
        else:
            center = torch.tensor([128, 128], dtype=torch.long)
            tile_width = 256
        mask = np.full(tile.shape[:2] + (1,), fill_value=.1, dtype=tile.dtype)
        mask[center[0] - tile_width // 2:center[0] + tile_width // 2, center[1] - tile_width // 2:center[1] + tile_width // 2] = 1

        return tile, ref, center, mask, self.tiles[item]

    def __len__(self):
        return len(self.tiles)