| import datasets |
| import pandas as pd |
|
|
| _CITATION = """\ |
| @InProceedings{huggingface:dataset, |
| title = {basketball_tracking}, |
| author = {TrainingDataPro}, |
| year = {2023} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| The dataset consist of screenshots from videos of basketball games with |
| the ball labeled with a bounging box. |
| The dataset can be used to train a neural network in ball control recognition. |
| The dataset is useful for automating the camera operator's work during a match, |
| allowing the ball to be efficiently kept in frame. |
| """ |
| _NAME = 'basketball_tracking' |
|
|
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" |
|
|
| _LICENSE = "" |
|
|
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" |
|
|
|
|
| class BasketballTracking(datasets.GeneratorBasedBuilder): |
| """Small sample of image-text pairs""" |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features({ |
| 'image_id': datasets.Value('int32'), |
| 'image': datasets.Image(), |
| 'mask': datasets.Image(), |
| 'shapes': datasets.Value('string') |
| }), |
| 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}boxes.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) |
|
|
| for idx, ((image_path, image), |
| (mask_path, mask)) in enumerate(zip(images, masks)): |
| yield idx, { |
| 'image_id': |
| annotations_df.loc[ |
| annotations_df['image_name'] == image_path] |
| ['image_id'].values[0], |
| "image": { |
| "path": image_path, |
| "bytes": image.read() |
| }, |
| "mask": { |
| "path": mask_path, |
| "bytes": mask.read() |
| }, |
| 'shapes': |
| annotations_df.loc[ |
| annotations_df['image_name'] == image_path] |
| ['shapes'].values[0] |
| } |
|
|
