DL-Art-School/codes/data/chunk_with_reference.py
James Betker 1de1fa30ac Disable refs and centers altogether in single_image_dataset
I suspect that this might be a cause of failures on parallel datasets.
Plus it is unnecessary computation.
2020-12-31 10:13:24 -07:00

55 lines
2.5 KiB
Python

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.
from utils.util import opt_get
class ChunkWithReference:
def __init__(self, opt, path):
self.path = path.path
self.tiles, _ = util.get_image_paths('img', self.path)
self.need_metadata = opt_get(opt, ['strict'], False) or opt_get(opt, ['needs_metadata'], False)
self.need_ref = opt_get(opt, ['need_ref'], False)
if 'ignore_first' in opt.keys():
self.tiles = self.tiles[opt['ignore_first']:]
# 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):
tile = self.read_image_or_get_zero(self.tiles[item])
if self.need_ref and osp.exists(osp.join(self.path, "ref.jpg")):
tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
ref = self.read_image_or_get_zero(osp.join(self.path, "ref.jpg"))
if self.need_metadata:
centers = torch.load(osp.join(self.path, "centers.pt"))
if tile_id in centers.keys():
center, tile_width = centers[tile_id]
else:
print("Could not find the given tile id in the accompanying centers.pt. This generally means that "
"centers.pt was overwritten at some point e.g. by duplicate data. If you don't care about tile "
"centers, consider passing strict=false to the dataset options. (Note: you must re-build your"
"caches for this setting change to take effect.)")
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
else:
ref = np.zeros_like(tile)
mask = np.zeros(tile.shape[:2] + (1,))
center = (0,0)
return tile, ref, center, mask, self.tiles[item]
def __len__(self):
return len(self.tiles)