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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]
}
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