| import collections | |
| import json | |
| import os | |
| import datasets | |
| _HOMEPAGE = "https://universe.roboflow.com/home-mxzv1/nfl-competition/dataset/1" | |
| _LICENSE = "Public Domain" | |
| _CITATION = """\ | |
| @misc{ nfl-competition_dataset, | |
| title = { NFL-competition Dataset }, | |
| type = { Open Source Dataset }, | |
| author = { home }, | |
| howpublished = { \\url{ https://universe.roboflow.com/home-mxzv1/nfl-competition } }, | |
| url = { https://universe.roboflow.com/home-mxzv1/nfl-competition }, | |
| journal = { Roboflow Universe }, | |
| publisher = { Roboflow }, | |
| year = { 2022 }, | |
| month = { sep }, | |
| note = { visited on 2022-12-30 }, | |
| } | |
| """ | |
| _URLS = { | |
| "train": "https://huggingface.co/datasets/keremberke/nfl-object-detection/resolve/main/data/train.zip", | |
| "validation": "https://huggingface.co/datasets/keremberke/nfl-object-detection/resolve/main/data/valid.zip", | |
| "test": "https://huggingface.co/datasets/keremberke/nfl-object-detection/resolve/main/data/test.zip", | |
| } | |
| _CATEGORIES = ['helmet', 'helmet-blurred', 'helmet-difficult', 'helmet-partial', 'helmet-sideline'] | |
| _ANNOTATION_FILENAME = "_annotations.coco.json" | |
| class NFLOBJECTDETECTION(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 | |