"""Cartoonset-10k Data Set""" from io import BytesIO from typing import Optional import tarfile import pandas as pd import datasets _CITATION = r""" @article{DBLP:journals/corr/abs-1711-05139, author = {Amelie Royer and Konstantinos Bousmalis and Stephan Gouws and Fred Bertsch and Inbar Mosseri and Forrester Cole and Kevin Murphy}, title = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings}, journal = {CoRR}, volume = {abs/1711.05139}, year = {2017}, url = {http://arxiv.org/abs/1711.05139}, eprinttype = {arXiv}, eprint = {1711.05139}, timestamp = {Mon, 13 Aug 2018 16:47:38 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _DESCRIPTION = """\ Cartoon Set is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork categories, 4 color categories, and 4 proportion categories, with a total of ~1013 possible combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes. """ _DATA_URLS = { "10k": "https://huggingface.co/datasets/cgarciae/cartoonset/resolve/1.0.0/data/cartoonset10k.tgz", "100k": "https://huggingface.co/datasets/cgarciae/cartoonset/resolve/1.0.0/data/cartoonset100k.tgz", } class Cartoonset(datasets.GeneratorBasedBuilder): """Cartoonset-10k Data Set""" BUILDER_CONFIGS = [ datasets.BuilderConfig( name="10k", version=datasets.Version("1.0.0", ""), description="Loads the Cartoonset-10k Data Set (images only).", ), datasets.BuilderConfig( name="10k+features", version=datasets.Version("1.0.0", ""), description="Loads the Cartoonset-10k Data Set (images and attributes).", ), datasets.BuilderConfig( name="100k", version=datasets.Version("1.0.0", ""), description="Loads the Cartoonset-100k Data Set (images only).", ), datasets.BuilderConfig( name="100k+features", version=datasets.Version("1.0.0", ""), description="Loads the Cartoonset-100k Data Set (images and attributes).", ), ] DEFAULT_CONFIG_NAME = "10k" def _info(self): features = {"img_bytes": datasets.Value("binary")} if self.config.name.endswith("+features"): features.update( { "eye_angle": datasets.Value("int32"), "eye_angle_num_categories": datasets.Value("int32"), "eye_lashes": datasets.Value("int32"), "eye_lashes_num_categories": datasets.Value("int32"), "eye_lid": datasets.Value("int32"), "eye_lid_num_categories": datasets.Value("int32"), "chin_length": datasets.Value("int32"), "chin_length_num_categories": datasets.Value("int32"), "eyebrow_weight": datasets.Value("int32"), "eyebrow_weight_num_categories": datasets.Value("int32"), "eyebrow_shape": datasets.Value("int32"), "eyebrow_shape_num_categories": datasets.Value("int32"), "eyebrow_thickness": datasets.Value("int32"), "eyebrow_thickness_num_categories": datasets.Value("int32"), "face_shape": datasets.Value("int32"), "face_shape_num_categories": datasets.Value("int32"), "facial_hair": datasets.Value("int32"), "facial_hair_num_categories": datasets.Value("int32"), "hair": datasets.Value("int32"), "hair_num_categories": datasets.Value("int32"), "eye_color": datasets.Value("int32"), "eye_color_num_categories": datasets.Value("int32"), "face_color": datasets.Value("int32"), "face_color_num_categories": datasets.Value("int32"), "hair_color": datasets.Value("int32"), "hair_color_num_categories": datasets.Value("int32"), "glasses": datasets.Value("int32"), "glasses_num_categories": datasets.Value("int32"), "glasses_color": datasets.Value("int32"), "glasses_color_num_categories": datasets.Value("int32"), "eye_slant": datasets.Value("int32"), "eye_slant_num_categories": datasets.Value("int32"), "eyebrow_width": datasets.Value("int32"), "eyebrow_width_num_categories": datasets.Value("int32"), "eye_eyebrow_distance": datasets.Value("int32"), "eye_eyebrow_distance_num_categories": datasets.Value("int32"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features(features), supervised_keys=("img_bytes",), homepage="https://www.cs.toronto.edu/~kriz/cifar.html", citation=_CITATION, ) def _split_generators(self, dl_manager: datasets.DownloadManager): url = _DATA_URLS[self.config.name.replace("+features", "")] archive = dl_manager.download(url) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "files": dl_manager.iter_archive(archive), "split": "train", }, ), ] def _generate_examples(self, files, split): """This function returns the examples in the raw (text) form.""" if self.config.name.endswith("+features"): return self._generate_examples_with_features(files, split) else: return self._generate_examples_without_features(files, split) def _generate_examples_without_features(self, files, split): path: str file_obj: tarfile.ExFileObject root: str for path, file_obj in files: root = path[:-4] if path.endswith(".png"): image = file_obj.read() yield root, {"img_bytes": image} def _generate_examples_with_features(self, files, split): path: str file_obj: tarfile.ExFileObject outputs = {} root: Optional[str] = None for path, file_obj in files: root = path[:-4] if root not in outputs: outputs[root] = {} current_output = outputs[root] if path.endswith(".png"): image = file_obj.read() current_output["img_bytes"] = image else: df = pd.read_csv( BytesIO(file_obj.read()), header=None, names=["feature", "value", "num_categories"], ) for index, row in df.iterrows(): current_output[row.feature] = row.value current_output[f"{row.feature}_num_categories"] = row.num_categories if "img_bytes" in current_output and len(current_output) > 1: yield root, current_output del outputs[root] root = None if len(outputs) > 0: raise ValueError( f"Unable to extract the following samples: {list(outputs)}" )