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import datasets
import pandas as pd
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {cars-video-object-tracking},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
The collection of overhead video frames, capturing various types of vehicles
traversing a roadway. The dataset inculdes light vehicles (cars) and
heavy vehicles (minivan).
"""
_NAME = 'cars-video-object-tracking'
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = ""
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
class CarsVideoObjectTracking(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(),
'annotations': 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.lower()]
['image_id'].values[0],
"image": {
"path": image_path,
"bytes": image.read()
},
"mask": {
"path": mask_path,
"bytes": mask.read()
},
'annotations':
annotations_df.loc[
annotations_df['image_name'] == image_path.lower()]
['annotations'].values[0]
}
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