| 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] |
| } |
|
|
