Datasets:
Tasks:
Object Detection
Size:
< 1K
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 | |