Datasets:
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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
_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/resolve/main/data"
_URLS = {"train": f"{_REPO}/train.zip", "valid": f"{_REPO}/valid.zip"}
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(),
"bboxes": datasets.Sequence(datasets.Value("int32"), length=5),
}
),
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)
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")
for idx, (image_path, label_path) in enumerate(zip(image_dir, label_dir)):
im = Image.open(image_path)
width, height = im.size
with open(label_path, "r") as f:
lines = f.readlines()
bboxes = []
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")
bbox = [bbox_class, bbox_xcenter, bbox_ycenter, bbox_width, bbox_height]
bboxes.append(bbox)
yield idx, {"image": image_path, "bboxes": bboxes} |