| import datasets |
| import pandas as pd |
|
|
| _CITATION = """\ |
| @InProceedings{huggingface:dataset, |
| title = {face_segmentation}, |
| author = {TrainingDataPro}, |
| year = {2023} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| An example of a dataset that we've collected for a photo edit App. |
| The dataset includes 20 selfies of people (man and women) |
| in segmentation masks and their visualisations. |
| """ |
| _NAME = 'face_segmentation' |
|
|
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
|
|
| _LICENSE = "" |
|
|
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
|
|
|
|
| class FaceSegmentation(datasets.GeneratorBasedBuilder): |
| """Small sample of image-text pairs""" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| 'image': datasets.Image(), |
| 'mask': datasets.Image(), |
| 'id': datasets.Value('string'), |
| 'gender': datasets.Value('string'), |
| 'age': datasets.Value('int8') |
| }), |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| images = dl_manager.download(f"{_DATA}images.tar.gz") |
| masks = dl_manager.download(f"{_DATA}masks.tar.gz") |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") |
| images = dl_manager.iter_archive(images) |
| masks = dl_manager.iter_archive(masks) |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "images": images, |
| 'masks': masks, |
| 'annotations': annotations |
| }), |
| ] |
|
|
| def _generate_examples(self, images, masks, annotations): |
| annotations_df = pd.read_csv(annotations, sep=';') |
|
|
| for idx, ((image_path, image), |
| (mask_path, mask)) in enumerate(zip(images, masks)): |
| yield idx, { |
| "image": { |
| "path": image_path, |
| "bytes": image.read() |
| }, |
| "mask": { |
| "path": mask_path, |
| "bytes": mask.read() |
| }, |
| 'id': annotations_df['id'].iloc[idx], |
| 'gender': annotations_df['gender'].iloc[idx], |
| 'age': annotations_df['age'].iloc[idx] |
| } |
|
|
