Datasets:
Tags:
License:
| import collections | |
| import json | |
| import os | |
| import datasets | |
| _DESCRIPTION = """ | |
| """ | |
| _HOMEPAGE = "" | |
| _LICENSE = "" | |
| _URL = "https://huggingface.co/datasets/alexrods/mini_car_bikes_detection/resolve/main" | |
| _URLS = { | |
| "train_images": f"{_URL}/data/train.zip", | |
| "test_images": f"{_URL}/data/test.zip", | |
| } | |
| _ANNOTATIONS = { | |
| "train_annotations": f"{_URL}/annotations/train_annotations.json", | |
| "test_annotations": f"{_URL}/annotations/test_annotations.json" | |
| } | |
| _CATEGORIES = ['Car', 'bike'] | |
| class MiniCarBikesDetection(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "image_name": datasets.Value("string"), | |
| "width": datasets.Value("int32"), | |
| "height": datasets.Value("int32"), | |
| "objects": datasets.Sequence( | |
| { | |
| # "id": datasets.Sequence(datasets.Value("int32")), | |
| "category": datasets.ClassLabel(names=_CATEGORIES), | |
| "bbox": datasets.Sequence(datasets.Value("float32"), length=4), | |
| } | |
| ), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_files = dl_manager.download(_URLS) | |
| annotations_files = _ANNOTATIONS | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "image_files": dl_manager.iter_archive(data_files["train_images"]), | |
| "annotations_file": annotations_files["train_annotations"] | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "image_files": dl_manager.iter_archive(data_files["test_images"]), | |
| "annotations_file": annotations_files["test_annotations"] | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, image_files, annotations_file): | |
| with open(annotations_file) as jf: | |
| annotations = json.load(jf) | |
| for image_file in image_files: | |
| image_name = image_file[0].split("/")[1] | |
| for annotation in annotations: | |
| if image_name == annotation["image"]: | |
| yield image_file[0], { | |
| "image": {"path": image_file[0], "bytes": image_file[1].read()}, | |
| "image_name": image_name, | |
| "width": annotation["width"], | |
| "height": annotation["height"], | |
| "objects": { | |
| "category": annotation["name"], | |
| "bbox": annotation["bbox"] | |
| } | |
| } | |