| import datasets | |
| import pandas as pd | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {makeup-detection-dataset}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The dataset consists of photos featuring the same individuals captured in two | |
| distinct scenarios - *with and without makeup*. The dataset contains a diverse | |
| range of individuals with various *ages, ethnicities and genders*. The images | |
| themselves would be of high quality, ensuring clarity and detail for each | |
| subject. | |
| In photos with makeup, it is applied **to only specific parts** of the face, | |
| such as *eyes, lips, or skin*. | |
| In photos without makeup, individuals have a bare face with no visible | |
| cosmetics or beauty enhancements. These images would provide a clear contrast | |
| to the makeup images, allowing for significant visual analysis. | |
| """ | |
| _NAME = 'makeup-detection-dataset' | |
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| _LICENSE = "" | |
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| class MakeupDetectionDataset(datasets.GeneratorBasedBuilder): | |
| """Small sample of image-text pairs""" | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| 'no_makeup': datasets.Image(), | |
| 'with_makeup': datasets.Image(), | |
| 'part': datasets.Value('string'), | |
| 'gender': datasets.Value('string'), | |
| 'age': datasets.Value('int8'), | |
| 'country': datasets.Value('string') | |
| }), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| no_makeup = dl_manager.download(f"{_DATA}no_makeup.tar.gz") | |
| with_makeup = dl_manager.download(f"{_DATA}with_makeup.tar.gz") | |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| no_makeup = dl_manager.iter_archive(no_makeup) | |
| with_makeup = dl_manager.iter_archive(with_makeup) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "no_makeup": no_makeup, | |
| 'with_makeup': with_makeup, | |
| 'annotations': annotations | |
| }), | |
| ] | |
| def _generate_examples(self, no_makeup, with_makeup, annotations): | |
| annotations_df = pd.read_csv(annotations, sep=';') | |
| for idx, ((image_path, image), | |
| (mask_path, mask)) in enumerate(zip(no_makeup, with_makeup)): | |
| yield idx, { | |
| "no_makeup": { | |
| "path": image_path, | |
| "bytes": image.read() | |
| }, | |
| "with_makeup": { | |
| "path": mask_path, | |
| "bytes": mask.read() | |
| }, | |
| 'part': | |
| annotations_df.loc[annotations_df['no_makeup'].str.lower() == | |
| image_path.lower()]['part'].values[0], | |
| 'gender': | |
| annotations_df.loc[annotations_df['no_makeup'].str.lower() == | |
| image_path.lower()]['gender'].values[0], | |
| 'age': | |
| annotations_df.loc[annotations_df['no_makeup'].str.lower() == | |
| image_path.lower()]['age'].values[0], | |
| 'country': | |
| annotations_df.loc[annotations_df['no_makeup'].str.lower() == | |
| image_path.lower()]['country'].values[0] | |
| } | |