| import datasets | |
| import pandas as pd | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {plantations_segmentation}, | |
| 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 = 'plantations_segmentation' | |
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| _LICENSE = "" | |
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| class PlantationsSegmentation(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(), | |
| 'class_segmentation': datasets.Image(), | |
| 'object_segmentation': 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") | |
| class_segmentation_masks = dl_manager.download( | |
| f"{_DATA}class_segmentation.tar.gz") | |
| object_segmentation_masks = dl_manager.download( | |
| f"{_DATA}object_segmentation.tar.gz") | |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| images = dl_manager.iter_archive(images) | |
| class_segmentation_masks = dl_manager.iter_archive( | |
| class_segmentation_masks) | |
| object_segmentation_masks = dl_manager.iter_archive( | |
| object_segmentation_masks) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "images": images, | |
| 'class_segmentation_masks': class_segmentation_masks, | |
| 'object_segmentation_masks': object_segmentation_masks, | |
| 'annotations': annotations | |
| }), | |
| ] | |
| def _generate_examples(self, images, class_segmentation_masks, | |
| object_segmentation_masks, annotations): | |
| annotations_df = pd.read_csv(annotations) | |
| for idx, ((image_path, image), (class_segmentation_path, | |
| class_segmentation), | |
| (object_segmentation_path, | |
| object_segmentation)) in enumerate( | |
| zip(images, class_segmentation_masks, | |
| object_segmentation_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() | |
| }, | |
| "class_segmentation": { | |
| "path": class_segmentation_path, | |
| "bytes": class_segmentation.read() | |
| }, | |
| "object_segmentation": { | |
| "path": object_segmentation_path, | |
| "bytes": object_segmentation.read() | |
| }, | |
| 'shapes': | |
| annotations_df.loc[ | |
| annotations_df['image_name'] == image_path] | |
| ['shapes'].values[0] | |
| } | |