Datasets:
Tasks:
Object Detection
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< 1K
File size: 5,207 Bytes
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import collections
import json
import os
import datasets
_HOMEPAGE = "https://universe.roboflow.com/konstantin-sargsyan-wucpb/football-players-2l81z/dataset/1"
_LICENSE = "MIT"
_CITATION = """\
@misc{ football-players-2l81z_dataset,
title = { football-players Dataset },
type = { Open Source Dataset },
author = { Konstantin Sargsyan },
howpublished = { \\url{ https://universe.roboflow.com/konstantin-sargsyan-wucpb/football-players-2l81z } },
url = { https://universe.roboflow.com/konstantin-sargsyan-wucpb/football-players-2l81z },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { jun },
note = { visited on 2023-06-12 },
}
"""
_CATEGORIES = ['football', 'player']
_ANNOTATION_FILENAME = "_annotations.coco.json"
class MANOTConfig(datasets.BuilderConfig):
"""Builder Config for manot"""
def __init__(self, data_urls, **kwargs):
"""
BuilderConfig for manot.
Args:
data_urls: `dict`, name to url to download the zip file from.
**kwargs: keyword arguments forwarded to super.
"""
super(MANOTConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.data_urls = data_urls
class MANOT(datasets.GeneratorBasedBuilder):
"""manot object detection dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
MANOTConfig(
name="full",
description="Full version of manot dataset.",
data_urls={
"train": "https://huggingface.co/datasets/manot/resolve/main/data/train.zip",
"validation": "https://huggingface.co/datasets/manot/resolve/main/data/valid.zip",
},
),
MANOTConfig(
name="mini",
description="Mini version of manot dataset.",
data_urls={
"train": "https://huggingface.co/datasets/manot/resolve/main/data/valid-mini.zip",
"validation": "https://huggingface.co/datasets/manot/resolve/main/data/valid-mini.zip",
},
)
]
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(self.config.data_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"folder_dir": data_files["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"folder_dir": data_files["validation"],
},
),
]
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)
filename_to_image = {image["file_name"]: image for image in annotations["images"]}
for filename in os.listdir(folder_dir):
filepath = os.path.join(folder_dir, filename)
if filename in filename_to_image:
image = filename_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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