hit.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 os
import datasets
import pickle
import gzip

# citation
_CITATION = """\
@inproceedings{Keller:CVPR:2024,
  title = {{HIT}: Estimating Internal Human Implicit Tissues from the Body Surface},
  author = {Keller, Marilyn and Arora, Vaibhav and Dakri, Abdelmouttaleb and Chandhok, Shivam and 
  Machann, Jürgen and Fritsche, Andreas and Black, Michael J. and Pujades, Sergi},   
  booktitle = {Proceedings IEEE/CVF Conf.~on Computer Vision and Pattern Recognition (CVPR)},
  month = jun,
  year = {2024},
  month_numeric = {6}}
"""

# description
_DESCRIPTION = """\
The HIT dataset is a structured dataset of paired observations of body's inner tissues and the body surface. More concretely, it is a dataset of paired full-body volumetric segmented (bones, lean, and adipose tissue) MRI scans and SMPL meshes capturing the body surface shape for male (N=157) and female (N=241) subjects respectively. This is relevant for medicine, sports science, biomechanics, and computer graphics as it can ease the creation of personalized anatomic digital twins that model our bones, lean, and adipose tissue."""

# link to official homepage for the dataset
_HOMEPAGE = "https://hit.is.tue.mpg.de/"

# licence for the dataset
_LICENSE = "see https://huggingface.co/datasets/varora/HIT/blob/main/README.md"

# official dataset URLs
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_BASE_URL = "https://huggingface.co/datasets/varora/HIT/tree/main"
_PATHS = {
    "male": "/male",
    "female": "/female",
}

class HIT(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')

    def _info(self):
        # These are the features of your dataset like images, labels ...
        features = datasets.Features(
            {
                "gender": datasets.Value("string"),
                "mri_seg": datasets.Array3D(dtype="int32", shape=(None, 192, 256)),
                "mri_labels":  {
                    "NO":  datasets.Value("int32"),
                    "LT":  datasets.Value("int32"),
                    "AT":  datasets.Value("int32"),
                    "VAT":  datasets.Value("int32"),
                    "BONE":  datasets.Value("int32")
                },
                "body_mask": datasets.Array3D(dtype="int64", shape=(None, 192, 256)),
                "resolution": datasets.Array2D(dtype="float32", shape=(None, 3)),
                "center": datasets.Array2D(dtype="float32", shape=(None, 3)),
                "smpl_dict": {
                    'gender': datasets.Value("string"),
                    'verts_free': datasets.Array2D(dtype="float32", shape=(6890, 3)),
                    'verts': datasets.Array2D(dtype="float32", shape=(6890, 3)),
                    'faces': datasets.Array2D(dtype="uint32", shape=(None, 3)),
                    'pose': datasets.Sequence(datasets.Value("float32")),
                    'betas': datasets.Sequence(datasets.Value("float32")),
                    'trans': datasets.Sequence(datasets.Value("float32"))
                },
                'body_cont_pc': datasets.Array2D(dtype="float32", shape=(None, 3)),
                "dataset_name": datasets.Value("string"),
                "subject_ID": datasets.Value("string")
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        splits = ["train", "val", "test"]

        gender = self.config.name
        print(f"Config: {gender}")

        file_structure_url = "hit_dataset.json"
        file_structure = dl_manager.download_and_extract(file_structure_url)

        with open(file_structure) as f:
            file_structure = json.load(f)

        if not gender is None:
            data_urls = {split: [os.path.join(gender, split, filename) for filename in file_structure[gender][split]] for split in splits}
        else:
            data_urls = {gender: {split: [os.path.join(gender, split, filename) for filename in file_structure[gender][split]] for split in splits} for gender in ['male', 'female']}
        archive_paths = dl_manager.download(data_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": archive_paths['train'],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": archive_paths['val'],
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": archive_paths['test'],
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # handling input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        # List all files in the path .gz
        for subject_path in filepath:
            with gzip.open(subject_path, 'rb') as f:
                data = pickle.load(f)
            key = data['subject_ID']
            yield key, data