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# Copyright 2020 The HuggingFace Datasets Authors and Santiago Hincapie Potes.
#
# 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.
"""TODO: Add a description here."""
import csv
import os
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{yu2019lytnet,
title = {LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired},
author = {Yu, Samuel and Lee, Heon and Kim, John},
booktitle = {Computer Analysis of Images and Patterns (CAIP)},
month = {Aug},
year = {2019}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/samuelyu2002/ImVisible"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"imgs": "ptl_dataset.tar.gz",
"train": "training_file.csv",
"validation": "validation_file.csv",
"test": "testing_file.csv",
}
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ImVision(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.1.0")
def _info(self):
features = datasets.Features(
{
"img": datasets.Image(),
"boxes": datasets.features.Sequence({
"label": datasets.Value("int8"),
"occluded": datasets.Value("bool"),
"x_max": datasets.Value("float"),
"x_min": datasets.Value("float"),
"y_max": datasets.Value("float"),
"y_min": datasets.Value("float"),
}),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"img_folder": os.path.join(data_dir["imgs"], "PTL_Dataset_876x657/"),
"labels": data_dir["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"img_folder": os.path.join(data_dir["imgs"], "PTL_Dataset_876x657/"),
"labels": data_dir["test"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"img_folder": os.path.join(data_dir["imgs"], "PTL_Dataset_876x657/"),
"labels": data_dir["validation"],
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, img_folder, labels):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
with open(labels, encoding="utf-8") as f:
reader = csv.reader(f)
for key, row in enumerate(reader):
if key == 0:
continue
fname, label, x_min, y_min, x_max, y_max, occluded = row
yield key - 1, {
"img": os.path.join(img_folder, fname),
"boxes": [
{
"label": int(label),
"occluded": occluded != "not_blocked",
"x_max": float(x_max),
"x_min": float(x_min),
"y_max": float(y_max),
"y_min": float(y_min),
}
]
} |