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jalFaizy
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detect_chess_pieces

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Object Detection

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detect_chess_pieces / detect_chess_pieces.py

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	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Script for reading 'Object Detection for Chess Pieces' dataset."""


	import os
	import datasets

	from PIL import Image
	from glob import glob

	_CITATION = ""

	_DESCRIPTION = """\
	The "Object Detection for Chess Pieces" dataset is a toy dataset created (as suggested by the name!) to introduce object detection in a beginner friendly way.
	"""

	_HOMEPAGE = "https://github.com/faizankshaikh/chessDetection"

	_LICENSE = "CC-BY-SA:2.0"

	_REPO = "data"# "https://huggingface.co/datasets/jalFaizy/detect_chess_pieces/raw/main/data"
	_URLS = {"train": f"{_REPO}/train.zip", "valid": f"{_REPO}/valid.zip"}

	_CATEGORIES = ["blackKing", "whiteKing", "blackQueen", "whiteQueen"]


	class DetectChessPieces(datasets.GeneratorBasedBuilder):
	"""Object Detection for Chess Pieces dataset"""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	features=datasets.Features(
	{
	"image": datasets.Image(),
	"objects": datasets.Sequence({
	"label": datasets.ClassLabel(names=_CATEGORIES),
	"bbox": datasets.Sequence(datasets.Value("int32"), length=4)
	}),
	}
	),
	supervised_keys=None,
	description=_DESCRIPTION,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download_and_extract(_URLS)
	print(data_dir["train"])
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"split": "train", "data_dir": data_dir["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"split": "valid", "data_dir": data_dir["valid"]},
	),
	]

	def _generate_examples(self, split, data_dir):
	image_dir = os.path.join(data_dir, "images")
	label_dir = os.path.join(data_dir, "labels")

	image_paths = sorted(glob(image_dir + "//.png"))
	label_paths = sorted(glob(label_dir + "//.txt"))

	for idx, (image_path, label_path) in enumerate(zip(image_paths, label_paths)):
	im = Image.open(image_path)
	width, height = im.size

	with open(label_path, "r") as f:
	lines = f.readlines()

	objects = []
	for line in lines:
	line = line.strip().split()
	try:
	bbox_class = int(line[0])
	bbox_xcenter = int(float(line[1]) * width)
	bbox_ycenter = int(float(line[2]) * height)
	bbox_width = int(float(line[3]) * width)
	bbox_height = int(float(line[4]) * height)
	except:
	print(f"Check file {f.name} for errors")

	objects.append({
	"label": bbox_class,
	"bbox": [bbox_xcenter, bbox_ycenter, bbox_width, bbox_height]
	})

	yield idx, {"image": image_path, "objects": objects}