Datasets:

TrainingDataPro
/

people-with-guns-segmentation-and-detection

	import os
	from xml.etree import ElementTree as ET

	import datasets

	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {people-with-guns-segmentation-and-detection},
	author = {TrainingDataPro},
	year = {2023}
	}
	"""

	_DESCRIPTION = """\
	The dataset consists of photos depicting individuals holding guns. It specifically
	focuses on the segmentation of guns within these images and the detection of
	people holding guns.
	Each image in the dataset presents a different scenario, capturing individuals from
	various backgrounds, genders, and age groups in different poses while holding guns.
	The dataset is an essential resource for the development and evaluation of computer
	vision models and algorithms in fields related to *firearms recognition, security
	systems, law enforcement, and safety analysis*.
	"""
	_NAME = "people-with-guns-segmentation-and-detection"

	_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"

	_LICENSE = ""

	_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"

	_LABELS = ["person", "gun"]


	class PeopleWithGunsSegmentationAndDetection(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name=f"{_NAME}", data_dir=f"{_DATA}{_NAME}.zip"),
	]

	DEFAULT_CONFIG_NAME = f"{_NAME}"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("int32"),
	"name": datasets.Value("string"),
	"image": datasets.Image(),
	"mask": datasets.Image(),
	"width": datasets.Value("uint16"),
	"height": datasets.Value("uint16"),
	"shapes": datasets.Sequence(
	{
	"label": datasets.ClassLabel(
	num_classes=len(_LABELS),
	names=_LABELS,
	),
	"type": datasets.Value("string"),
	"points": datasets.Sequence(
	datasets.Sequence(
	datasets.Value("float"),
	),
	),
	"rotation": datasets.Value("float"),
	"occluded": datasets.Value("uint8"),
	"z_order": datasets.Value("int16"),
	"attributes": datasets.Sequence(
	{
	"name": datasets.Value("string"),
	"text": datasets.Value("string"),
	}
	),
	}
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data = dl_manager.download_and_extract(self.config.data_dir)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"data": data,
	},
	),
	]

	@staticmethod
	def parse_shape(shape: ET.Element) -> dict:
	label = shape.get("label")
	shape_type = shape.tag
	rotation = shape.get("rotation", 0.0)
	occluded = shape.get("occluded", 0)
	z_order = shape.get("z_order", 0)

	points = None

	if shape_type == "points":
	points = tuple(map(float, shape.get("points").split(",")))

	elif shape_type == "box":
	points = [
	(float(shape.get("xtl")), float(shape.get("ytl"))),
	(float(shape.get("xbr")), float(shape.get("ybr"))),
	]

	elif shape_type == "polygon":
	points = [
	tuple(map(float, point.split(",")))
	for point in shape.get("points").split(";")
	]

	attributes = []

	for attr in shape:
	attr_name = attr.get("name")
	attr_text = attr.text
	attributes.append({"name": attr_name, "text": attr_text})

	shape_data = {
	"label": label,
	"type": shape_type,
	"points": points,
	"rotation": rotation,
	"occluded": occluded,
	"z_order": z_order,
	"attributes": attributes,
	}

	return shape_data

	def _generate_examples(self, data):
	tree = ET.parse(os.path.join(data, "annotations.xml"))
	root = tree.getroot()

	for idx, file in enumerate(sorted(os.listdir(os.path.join(data, "images")))):
	image_name = file.split("/")[-1]
	img = root.find(f"./image[@name='images/{image_name}']")

	image_id = img.get("id")
	name = img.get("name")
	width = img.get("width")
	height = img.get("height")
	shapes = [self.parse_shape(shape) for shape in img]

	yield idx, {
	"id": image_id,
	"name": name,
	"image": os.path.join(data, "images", file),
	"mask": os.path.join(data, "labels", f"{file.split('.')[-2]}.png"),
	"width": width,
	"height": height,
	"shapes": shapes,
	}