| import collections |
| import json |
| import os |
|
|
| import datasets |
|
|
|
|
| _HOMEPAGE = "https://universe.roboflow.com/ashish-cuamw/test-y7rj3" |
| _LICENSE = "CC BY 4.0" |
| _CITATION = """\ |
| @misc{ test-y7rj3_dataset, |
| title = { test Dataset }, |
| type = { Open Source Dataset }, |
| author = { ashish }, |
| howpublished = { \\url{ https://universe.roboflow.com/ashish-cuamw/test-y7rj3 } }, |
| url = { https://universe.roboflow.com/ashish-cuamw/test-y7rj3 }, |
| journal = { Roboflow Universe }, |
| publisher = { Roboflow }, |
| year = { 2022 }, |
| month = { oct }, |
| note = { visited on 2022-12-28 }, |
| } |
| """ |
| _URLS = { |
| "train": "https://huggingface.co/datasets/fcakyon/gun-object-detection/resolve/main/data/train.zip", |
| "validation": "https://huggingface.co/datasets/fcakyon/gun-object-detection/resolve/main/data/valid.zip", |
| } |
|
|
| _CATEGORIES = ['rifle', 'pistol', 'grenade', 'knife'] |
| _ANNOTATION_FILENAME = "_annotations.coco.json" |
|
|
|
|
| class GUNOBJECTDETECTION(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.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) |
| 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 |
|
|