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dataset uploaded by roboflow2huggingface package

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README.dataset.txt ADDED
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+ # test > 2022-10-19 7:53pm
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+ https://universe.roboflow.com/ashish-cuamw/test-y7rj3
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+
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+ Provided by a Roboflow user
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+ License: CC BY 4.0
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+
README.md ADDED
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+ ---
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+ task_categories:
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+ - object-detection
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+ tags:
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+ - roboflow
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+ ---
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+
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+ ### Roboflow Dataset Page
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+ https://universe.roboflow.com/ashish-cuamw/test-y7rj3
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+
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+ ### Citation
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+ ```
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+ @misc{ test-y7rj3_dataset,
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+ title = { test Dataset },
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+ type = { Open Source Dataset },
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+ author = { ashish },
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+ howpublished = { \\url{ https://universe.roboflow.com/ashish-cuamw/test-y7rj3 } },
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+ url = { https://universe.roboflow.com/ashish-cuamw/test-y7rj3 },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2022 },
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+ month = { oct },
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+ note = { visited on 2022-12-28 },
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+ }
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+ ```
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+
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+ ### License
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+ CC BY 4.0
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+
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+ ### Dataset Summary
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+ This dataset was exported via roboflow.com on December 26, 2022 at 10:13 PM GMT
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+
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+ Roboflow is an end-to-end computer vision platform that helps you
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+ * collaborate with your team on computer vision projects
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+ * collect & organize images
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+ * understand unstructured image data
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+ * annotate, and create datasets
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+ * export, train, and deploy computer vision models
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+ * use active learning to improve your dataset over time
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+
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+ It includes 4666 images.
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+ T are annotated in COCO format.
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+
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+ The following pre-processing was applied to each image:
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+ * Auto-orientation of pixel data (with EXIF-orientation stripping)
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+ * Resize to 416x416 (Stretch)
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+
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+ No image augmentation techniques were applied.
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+
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+
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+
README.roboflow.txt ADDED
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+
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+ test - v1 2022-10-19 7:53pm
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+ ==============================
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+
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+ This dataset was exported via roboflow.com on December 26, 2022 at 10:13 PM GMT
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+
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+ Roboflow is an end-to-end computer vision platform that helps you
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+ * collaborate with your team on computer vision projects
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+ * collect & organize images
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+ * understand unstructured image data
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+ * annotate, and create datasets
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+ * export, train, and deploy computer vision models
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+ * use active learning to improve your dataset over time
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+
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+ It includes 4666 images.
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+ T are annotated in COCO format.
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+
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+ The following pre-processing was applied to each image:
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+ * Auto-orientation of pixel data (with EXIF-orientation stripping)
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+ * Resize to 416x416 (Stretch)
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+
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+ No image augmentation techniques were applied.
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+
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+
data/train.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:b411ecb8ca9f4d54f3a1fb1899db14832ccef2f3c44c910ffaa1f2649d87fe79
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+ size 73680397
data/valid.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:d676264a3e040a71eb58ea71a4cd16391537c9eef84531df3d198311a1d86723
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+ size 18650193
gun-object-detection.py ADDED
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+ import collections
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+ import json
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+ import os
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+
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+ import datasets
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+
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+
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+ _HOMEPAGE = "https://universe.roboflow.com/ashish-cuamw/test-y7rj3"
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+ _LICENSE = "CC BY 4.0"
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+ _CITATION = """\
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+ @misc{ test-y7rj3_dataset,
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+ title = { test Dataset },
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+ type = { Open Source Dataset },
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+ author = { ashish },
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+ howpublished = { \\url{ https://universe.roboflow.com/ashish-cuamw/test-y7rj3 } },
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+ url = { https://universe.roboflow.com/ashish-cuamw/test-y7rj3 },
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+ journal = { Roboflow Universe },
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+ publisher = { Roboflow },
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+ year = { 2022 },
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+ month = { oct },
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+ note = { visited on 2022-12-28 },
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+ }
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+ """
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+ _URLS = {
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+ "train": "https://huggingface.co/datasets/fcakyon/gun-object-detection/resolve/main/data/train.zip",
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+ "validation": "https://huggingface.co/datasets/fcakyon/gun-object-detection/resolve/main/data/valid.zip",
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+ }
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+
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+ _CATEGORIES = ['rifle', 'pistol', 'grenade', 'knife']
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+ _ANNOTATION_FILENAME = "_annotations.coco.json"
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+
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+
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+ class GUNOBJECTDETECTION(datasets.GeneratorBasedBuilder):
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+ VERSION = datasets.Version("1.0.0")
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+
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+ def _info(self):
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+ features = datasets.Features(
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+ {
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+ "image_id": datasets.Value("int64"),
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+ "image": datasets.Image(),
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+ "width": datasets.Value("int32"),
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+ "height": datasets.Value("int32"),
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+ "objects": datasets.Sequence(
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+ {
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+ "id": datasets.Value("int64"),
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+ "area": datasets.Value("int64"),
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+ "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
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+ "category": datasets.ClassLabel(names=_CATEGORIES),
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+ }
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+ ),
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+ }
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+ )
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+ return datasets.DatasetInfo(
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+ features=features,
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+ homepage=_HOMEPAGE,
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+ citation=_CITATION,
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+ license=_LICENSE,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ data_files = dl_manager.download_and_extract(_URLS)
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={
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+ "folder_dir": data_files["train"],
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+ },
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ gen_kwargs={
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+ "folder_dir": data_files["validation"],
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+ },
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+ ),
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+ ]
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+
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+ def _generate_examples(self, folder_dir):
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+ def process_annot(annot, category_id_to_category):
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+ return {
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+ "id": annot["id"],
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+ "area": annot["area"],
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+ "bbox": annot["bbox"],
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+ "category": category_id_to_category[annot["category_id"]],
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+ }
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+
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+ image_id_to_image = {}
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+ idx = 0
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+
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+ annotation_filepath = os.path.join(folder_dir, _ANNOTATION_FILENAME)
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+ with open(annotation_filepath, "r") as f:
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+ annotations = json.load(f)
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+ category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
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+ image_id_to_annotations = collections.defaultdict(list)
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+ for annot in annotations["annotations"]:
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+ image_id_to_annotations[annot["image_id"]].append(annot)
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+ image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
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+
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+ for filename in os.listdir(folder_dir):
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+ filepath = os.path.join(folder_dir, filename)
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+ if filename in image_id_to_image:
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+ image = image_id_to_image[filename]
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+ objects = [
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+ process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
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+ ]
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+ with open(filepath, "rb") as f:
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+ image_bytes = f.read()
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+ yield idx, {
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+ "image_id": image["id"],
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+ "image": {"path": filepath, "bytes": image_bytes},
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+ "width": image["width"],
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+ "height": image["height"],
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+ "objects": objects,
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+ }
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+ idx += 1