Datasets:

texturedesign
/

td01_natural-ground-textures

	# Copyright (c) 2022, texture.design.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	import json

	import datasets


	DESCRIPTION = """\
	Multi-photo texture captures in outdoor nature scenes focusing on the ground. Each set contains variations of the same texture theme.
	"""

	REPO_PREFIX = "https://huggingface.co/datasets/texturedesign/td01_natural-ground-textures"
	DOWNLOAD_PREFIX = REPO_PREFIX + "/resolve/main"

	INDEX_URLS = {
	"train": REPO_PREFIX + "/raw/main/train/metadata.jsonl",
	"test": REPO_PREFIX + "/raw/main/test/metadata.jsonl",
	}


	class NaturalGroundTextures(datasets.GeneratorBasedBuilder):

	VERSION = datasets.Version("0.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="4K", version=VERSION, description="The original resolution dataset."),
	datasets.BuilderConfig(name="2K", version=VERSION, description="Half-resolution dataset."),
	datasets.BuilderConfig(name="1K", version=VERSION, description="Quarter-resolution dataset."),

	]

	DEFAULT_CONFIG_NAME = "2k"

	def _info(self):
	return datasets.DatasetInfo(
	description=DESCRIPTION,
	citation="",
	homepage="https://huggingface.co/texturedesign",
	license="cc-by-nc-4.0",
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"set": datasets.Value("uint8"),
	}
	)
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(INDEX_URLS)
	train_lines = [json.loads(l) for l in open(data_dir["train"], "r", encoding="utf-8").readlines()]
	test_lines = [json.loads(l) for l in open(data_dir["test"], "r", encoding="utf-8").readlines()]

	train_files = dl_manager.download_and_extract([(DOWNLOAD_PREFIX + '/train/' + row['file_name']) for row in train_lines])
	test_files = dl_manager.download_and_extract([(DOWNLOAD_PREFIX + '/test/' + row['file_name']) for row in test_lines])

	return [
	datasets.SplitGenerator(datasets.Split.TRAIN, dict(lines=train_lines, filenames=train_files)),
	datasets.SplitGenerator(datasets.Split.TEST, dict(lines=test_lines, filenames=test_files)),
	]

	def _generate_examples(self, lines, filenames):
	from jxlpy import JXLImagePlugin
	import PIL.Image

	for key, (data, filename) in enumerate(zip(lines, filenames)):
	image = PIL.Image.open(filename, formats=["jxl"])
	sz = image.size

	if self.config.name == "1K":
	image = image.resize(size=(sz[0]//4, sz[1]//4), resample=PIL.Image.Resampling.LANCZOS)
	elif self.config.name == "2K":
	image = image.resize(size=(sz[0]//2, sz[1]//2), resample=PIL.Image.Resampling.LANCZOS)
	else:
	assert self.config.name == "4K"

	yield key, {"image": image, "set": data["set"]}