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| | ''' |
| | Custom dataset-builder for ssynth dataset |
| | ''' |
| |
|
| | import os |
| | import datasets |
| | import glob |
| | import re |
| |
|
| |
|
| | logger = datasets.logging.get_logger(__name__) |
| |
|
| | _CITATION = """\ |
| | @article{kim2024ssynth, |
| | title={Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses}, |
| | author={Kim, Andrea and Saharkhiz, Niloufar and Sizikova, Elena and Lago, Miguel, and Sahiner, Berkman and Delfino, Jana G., and Badano, Aldo}, |
| | journal={International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)}, |
| | volume={}, |
| | pages={}, |
| | year={2024} |
| | } |
| | """ |
| |
|
| |
|
| | _DESCRIPTION = """\ |
| | S-SYNTH is an open-source, flexible skin simulation framework to rapidly generate synthetic skin models and images using digital rendering of an anatomically inspired multi-layer, multi-component skin and growing lesion model. It allows for generation of highly-detailed 3D skin models and digitally rendered synthetic images of diverse human skin tones, with full control of underlying parameters and the image formation process. |
| | Curated by: Andrea Kim, Niloufar Saharkhiz, Elena Sizikova, Miguel Lago, Berkman Sahiner, Jana Delfino, Aldo Badano |
| | License: Creative Commons 1.0 Universal License (CC0) |
| | """ |
| |
|
| |
|
| | _HOMEPAGE = "https://github.com/DIDSR/ssynth-release?tab=readme-ov-file" |
| |
|
| | _REPO = "https://huggingface.co/datasets/didsr/ssynth_data/resolve/main" |
| |
|
| | |
| | _CROPPED = True |
| |
|
| | _URLS = { |
| | "synthetic_data": f"{_REPO}/data/synthetic_dataset/output_10k.zip", |
| | "read_me": f"{_REPO}/README.md" |
| | } |
| |
|
| | DATA_DIR = {"all_data": "output_10k"} |
| |
|
| | class ssynth_dataConfig(datasets.BuilderConfig): |
| | """ssynth dataset""" |
| | def __init__(self, name, **kwargs): |
| | super(ssynth_dataConfig, self).__init__( |
| | version=datasets.Version("1.0.0"), |
| | name=name, |
| | description="ssynth_data", |
| | **kwargs, |
| | ) |
| |
|
| | class ssynth_data(datasets.GeneratorBasedBuilder): |
| | """ssynth dataset.""" |
| | |
| | DEFAULT_WRITER_BATCH_SIZE = 256 |
| | BUILDER_CONFIGS = [ |
| | ssynth_dataConfig("output_10k"), |
| | ] |
| | |
| | def _info(self): |
| | if self.config.name == "output_10k": |
| | |
| | features = datasets.Features( |
| | { |
| | "Cropped": datasets.Features({ |
| | "image": datasets.Value("string"), |
| | "mask": datasets.Value("string") |
| | }), |
| | "Uncropped": datasets.Features({ |
| | "image": datasets.Value("string"), |
| | "mask": datasets.Value("string") |
| | }) |
| | } |
| | ) |
| |
|
| | return datasets.DatasetInfo( |
| | description=_DESCRIPTION, |
| | features=features, |
| | supervised_keys=None, |
| | homepage=_HOMEPAGE, |
| | citation=_CITATION, |
| | ) |
| | |
| | def _split_generators( |
| | self, dl_manager: datasets.utils.download_manager.DownloadManager): |
| | |
| | if self.config.name == "output_10k": |
| | data_dir = dl_manager.download_and_extract(_URLS['synthetic_data']) |
| | return [ |
| | datasets.SplitGenerator( |
| | name="output_10k", |
| | gen_kwargs={ |
| | "files": data_dir, |
| | "name": "all_data", |
| | }, |
| | ), |
| | ] |
| | |
| | def get_all_file_paths(self, root_directory): |
| | file_paths = [] |
| |
|
| | |
| | for folder, _, files in os.walk(root_directory): |
| | for file in files: |
| | if file == "cropped_image.png": |
| | |
| | file_path = os.path.join(folder, file) |
| | file_paths.append(file_path) |
| | return file_paths |
| | |
| | def get_other_images(self, cropped_image_path, file_name): |
| | other_image_paths = [] |
| |
|
| | |
| | directory = os.path.dirname(cropped_image_path) |
| |
|
| | |
| | for file in os.listdir(directory): |
| | if file == file_name: |
| | |
| | file_path = os.path.join(directory, file) |
| | |
| | return file_path |
| | return None |
| | |
| | |
| | def _generate_examples(self, files, name): |
| | if self.config.name == "output_10k": |
| | key = 0 |
| | data_paths = self.get_all_file_paths(os.path.join(files, DATA_DIR[name])) |
| | |
| | cropped_images = [] |
| | uncropped_images = [] |
| | for path in data_paths: |
| | res_dic = {} |
| | cropped_image = path |
| | cropped_mask = self.get_other_images(path,"cropped_mask.png") |
| | image = self.get_other_images(path,"image.png") |
| | mask = self.get_other_images(path,"mask.png") |
| | cropped_data = { |
| | "image": cropped_image, |
| | "mask": cropped_mask |
| | } |
| | uncropped_data = { |
| | "image": image, |
| | "mask": mask |
| | } |
| | res_dic["Cropped"] = cropped_data |
| | res_dic["Uncropped"] = uncropped_data |
| | |
| | yield key, res_dic |
| | key += 1 |
| | |
| | |
| | |