| # 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. | |
| """EuroSAT dataset""" | |
| import csv | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @article{helber2019eurosat, | |
| title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification}, | |
| author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian}, | |
| journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, | |
| year={2019}, | |
| publisher={IEEE} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| **Homepage:** https://github.com/phelber/EuroSAT | |
| **IMPORTANT NOTES** | |
| - This HF dataset downloads the RGB images of the EuroSAT dataset: https://zenodo.org/record/7711810#.ZAm3k-zMKEA; i.e., the EuroSAT_RGB.zip | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "https://github.com/phelber/EuroSAT" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = """MIT License | |
| Copyright (c) 2023 Patrick Helber | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is | |
| furnished to do so, subject to the following conditions: | |
| The above copyright notice and this permission notice shall be included in all | |
| copies or substantial portions of the Software. | |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| SOFTWARE. | |
| """ | |
| # 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 = { | |
| # # "first_domain": "https://huggingface.co./great-new-dataset-first_domain.zip", | |
| # # "second_domain": "https://huggingface.co./great-new-dataset-second_domain.zip", | |
| # } | |
| _URLS = { | |
| "data": "https://zenodo.org/record/7711810/files/EuroSAT_RGB.zip", | |
| } | |
| def _load_EuroSAT_rgb(unzipped_dir): | |
| data = [] | |
| for class_label in os.listdir(unzipped_dir): | |
| # print(class_label) | |
| class_dir = os.path.join(unzipped_dir, class_label) | |
| for img_id in os.listdir(class_dir): | |
| data.append( | |
| { | |
| "image_id":img_id, | |
| "image_path":os.path.join(class_dir, img_id), | |
| "class":class_label, | |
| } | |
| ) | |
| return data | |
| # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case | |
| class EuroSAT(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| # BUILDER_CONFIGS = [ | |
| # datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), | |
| # datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), | |
| # ] | |
| # DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| # if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| # features = datasets.Features( | |
| # { | |
| # "sentence": datasets.Value("string"), | |
| # "option1": datasets.Value("string"), | |
| # "answer": datasets.Value("string") | |
| # # These are the features of your dataset like images, labels ... | |
| # } | |
| # ) | |
| # else: # This is an example to show how to have different features for "first_domain" and "second_domain" | |
| # features = datasets.Features( | |
| # { | |
| # "sentence": datasets.Value("string"), | |
| # "option2": datasets.Value("string"), | |
| # "second_domain_answer": datasets.Value("string") | |
| # # These are the features of your dataset like images, labels ... | |
| # } | |
| # ) | |
| features = datasets.Features( | |
| { | |
| "image_id": datasets.Value("string"), | |
| "image_path": datasets.Value("string"), | |
| "class": datasets.Value("string"), | |
| } | |
| ) | |
| 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): | |
| # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| downloaded_files = dl_manager.download_and_extract(_URLS) | |
| print("downloaded_files: ", downloaded_files) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": downloaded_files["data"], | |
| "split": "train", | |
| }, | |
| ) | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| # 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. | |
| data = _load_EuroSAT_rgb(os.path.join(filepath,"EuroSAT_RGB")) | |
| for key, row in enumerate(data): | |
| yield key, { | |
| "image_id": row["image_id"], | |
| "image_path": row["image_path"], | |
| "class": row["class"], | |
| } | |