Datasets:
Tasks:
Object Detection
Size:
1K - 10K
File size: 4,568 Bytes
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import collections
import json
import os
import datasets
_HOMEPAGE = "https://universe.roboflow.com/augmented-startups/football-player-detection-kucab"
_LICENSE = "CC BY 4.0"
_CITATION = """\
@misc{ football-player-detection-kucab_dataset,
title = { Football-Player-Detection Dataset },
type = { Open Source Dataset },
author = { Augmented Startups },
howpublished = { \\url{ https://universe.roboflow.com/augmented-startups/football-player-detection-kucab } },
url = { https://universe.roboflow.com/augmented-startups/football-player-detection-kucab },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2022-12-29 },
}
"""
_URLS = {
"train": "https://huggingface.co/datasets/keremberke/football-object-detection/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/keremberke/football-object-detection/resolve/main/data/valid.zip",
"test": "https://huggingface.co/datasets/keremberke/football-object-detection/resolve/main/data/test.zip",
}
_CATEGORIES = ['player', 'football']
_ANNOTATION_FILENAME = "_annotations.coco.json"
class FOOTBALLOBJECTDETECTION(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"image_id": datasets.Value("int64"),
"image": datasets.Image(),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"objects": datasets.Sequence(
{
"id": datasets.Value("int64"),
"area": datasets.Value("int64"),
"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
"category": datasets.ClassLabel(names=_CATEGORIES),
}
),
}
)
return datasets.DatasetInfo(
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"folder_dir": data_files["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"folder_dir": data_files["validation"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"folder_dir": data_files["test"],
},
),
]
def _generate_examples(self, folder_dir):
def process_annot(annot, category_id_to_category):
return {
"id": annot["id"],
"area": annot["area"],
"bbox": annot["bbox"],
"category": category_id_to_category[annot["category_id"]],
}
image_id_to_image = {}
idx = 0
annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
with open(annotation_filepath, "r") as f:
annotations = json.load(f)
category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
image_id_to_annotations = collections.defaultdict(list)
for annot in annotations["annotations"]:
image_id_to_annotations[annot["image_id"]].append(annot)
image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
for filename in os.listdir(folder_dir):
filepath = os.path.join(folder_dir, filename)
if filename in image_id_to_image:
image = image_id_to_image[filename]
objects = [
process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
]
with open(filepath, "rb") as f:
image_bytes = f.read()
yield idx, {
"image_id": image["id"],
"image": {"path": filepath, "bytes": image_bytes},
"width": image["width"],
"height": image["height"],
"objects": objects,
}
idx += 1
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