Initial commit

README.md

@@ -1,3 +1,135 @@
 ---
+annotations_creators:
+  - crowd-generated
 license: cc-by-sa-4.0
+language:
+- de
+- en
+- fr
+tags:
+  - lam
+task_categories:
+	- object-detection
+  - image-classification
+pretty_name: 'ARTigo: Social Image Tagging'
 ---
+
+# Dataset Card for ARTigo: Social Image Tagging
+
+## Table of Contents
+
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+### Dataset Summary
+
+ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers Games With a Purpose: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations, *tags*, within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices. The project’s data are freely available to the wider research community for novel scientific investigations.
+
+### Supported Tasks and Leaderboards
+
+- `object-detection`: This dataset can be used to train models for object detection tasks on art-historical images.
+- `image-classification`: This dataset can also be used for image classification tasks by using only the tags and not the associated region information.
+
+## Dataset Structure
+
+This dataset has a single configuration.
+
+### Data Instances
+
+An example instance from this dataset:
+
+```python
+{
+	'id': 32254,
+	'hash_id': 'e34fa90bf4c73d20ac19b14fa615206e',
+	'titles': {
+		'id': [10893],
+		'name': ['Entwurf für ein zwölfteiliges Kartenspiel']
+	},
+	'creators': {
+		'id': [2391],
+		'name': ['Félix Vallotton']
+	},
+	'location': 'Lausanne',
+	'institution': 'Galerie du Chêne',
+	'source': {
+		'id': 2,
+		'name': 'Artemis',
+		'url': 'http://artemis.uni-muenchen.de/'
+	},
+	'path': 'https://api.artigo.org/media/e3/4f/e34fa90bf4c73d20ac19b14fa615206e.jpg',
+	'tags': {
+		'id': [6, 10, 13, ..., 206331],
+  	'name': ['blau', 'feder', 'flügel', ..., 'herzober'],
+ 		'language': ['de', 'de', 'de', ..., 'de'],
+ 		'count': [16, 6, 6, ..., 1],
+ 		'regions': [None, None, None, ..., None]
+ 	},
+	'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=381x600 at 0x7FEF3A415820>
+}
+```
+
+### Data Fields
+
+The dataset contains ten fields:
+
+- `id`: a unique identifier for the image;
+- `hash_id`: a unique identifier for the image based on its content (e.g., image hash);
+- `titles`: a list of titles associated with the image, with each title having the following key-value pairs:
+	- `id`: a unique identifier for the title;
+	- `name`: the name of the title;
+- `creators`: a list of creators associated with the image, with each creator having the following key-value pairs:
+	- `id`: a unique identifier for the creator;
+	- `name`: the name of the creator;
+- `location`: the location associated with the image;
+- `institution`: the institution that holds the image;
+- `source`: information about the source of the image, with the following key-value pairs:
+	- `id`: a unique identifier for the source;
+	- `name`: the name of the source;
+	- `url`: the URL of the source;
+- `path`: the path to the image file;
+- `tags`: a list of tags associated with the image, with each tag having the following key-value pairs:
+	- `id`: a unique identifier for the tag;
+	- `name`: the name of the tag;
+	- `language`: the language of the tag (if available);
+	- `count`: the number of times the tag has been applied to the image;
+	- `regions`: the regions of the image to which the tag can be applied (if available);
+- `image`: the image.
+
+### Data Splits
+
+The dataset doesn't provide any predefined train, validation or test splits. 
+
+## Additional Information
+
+### Licensing Information
+
+[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
+
+### Citation Information
+
+```
+@dataset{bry_et_al_artigo,
+  author       = {Bry, François and
+                  Kohle, Hubertus and
+                  Krefeld, Thomas and 
+                  Riepl, Christian and 
+                  Schneider, Stefanie and 
+                  Schön, Gerhard and 
+                  Schulz, Klaus},
+  title        = {{ARTigo}: Social Image Tagging (Aggregated Data)},
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.8202331},
+  url          = {https://doi.org/10.5281/zenodo.8202331}}
+```

artigo.py

@@ -0,0 +1,168 @@
+# 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 the dataset of the 'ARTigo: Social Image Tagging' project."""
+
+import glob
+import json
+import datasets
+import requests
+
+from PIL import Image
+from pathlib import Path
+
+_CITATION = """\
+@dataset{bry_et_al_artigo,
+    author       = {Bry, François and
+                    Kohle, Hubertus and
+                    Krefeld, Thomas and 
+                    Riepl, Christian and 
+                    Schneider, Stefanie and 
+                    Schön, Gerhard and 
+                    Schulz, Klaus},
+    title        = {{ARTigo}: Social Image Tagging (Aggregated Data)},
+    publisher    = {Zenodo},
+    doi          = {10.5281/zenodo.8202331},
+    url          = {https://doi.org/10.5281/zenodo.8202331}}
+"""
+
+_DESCRIPTION = """\
+ARTigo (https://www.artigo.org/) is a Citizen Science project that has been jointly developed at the Institute for Art History and the Institute for Informatics at Ludwig Maximilian University of Munich since 2010. It enables participants to engage in the tagging of artworks, thus fostering knowledge accumulation and democratizing access to a traditionally elitist field. ARTigo is built as an interactive web application that offers Games With a Purpose: in them, players are presented with an image – and then challenged to communicate with one another using visual or textual annotations within a given time. Through this playful approach, the project aims to inspire greater appreciation for art and draw new audiences to museums and archives. It streamlines the discoverability of art-historical images, while promoting inclusivity, effective communication, and collaborative research practices. The project’s data are freely available to the wider research community for novel scientific investigations.
+"""
+
+_HOMEPAGE = "https://doi.org/10.5281/zenodo.8202331"
+
+_LICENSE = "Creative Commons Attribution Share Alike 4.0 International (CC BY-SA 4.0)"
+
+_URLS = "https://zenodo.org/api/records/8202331"
+
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+def create_annotations_dict(annotations_file):
+    annotations = {}
+
+    with open(annotations_file, encoding="utf-8") as annotations_obj:
+        for annotation_row in annotations_obj:
+            annotation_data = json.loads(annotation_row, strict=False)
+            for tag in annotation_data["tags"]:
+                if not tag.get("regions"):
+                    tag["regions"] = None
+            annotations[annotation_data["hash_id"]] = annotation_data
+
+    return annotations
+
+
+class ARTigo(datasets.GeneratorBasedBuilder):
+    """Dataset of the 'ARTigo: Social Image Tagging' project"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("int64"),
+                "hash_id": datasets.Value("string"),
+                "titles": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int64"),
+                        "name": datasets.Value("string"),
+                    }
+                ),
+                "creators": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int64"),
+                        "name": datasets.Value("string"),
+                    }
+                ),
+                "location": datasets.Value("string"),
+                "institution": datasets.Value("string"),
+                "source": {
+                    "id": datasets.Value("int64"),
+                    "name": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                },
+                "path": datasets.Value("string"),
+                "tags": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int64"),
+                        "name": datasets.Value("string"),
+                        "language": datasets.Value("string"),
+                        "count": datasets.Value("int64"),
+                        "regions": datasets.Sequence(
+                            {
+                                "x": datasets.Value("float64"),
+                                "y": datasets.Value("float64"),
+                                "width": datasets.Value("float64"),
+                                "height": datasets.Value("float64"),
+                            }
+                        ),
+                    }
+                ),
+                "image": datasets.Image(),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+    
+    def _split_generators(self, dl_manager):
+        zenodo_record = requests.get(_URLS).json()
+
+        image_urls = [
+            file["links"]["self"]
+            for file in zenodo_record['files']
+            if file["type"] == "zip"
+        ]
+        annotation_urls = [
+            file["links"]["self"]
+            for file in zenodo_record['files']
+            if file["type"] == "jsonl"
+        ]
+
+        image_directories = dl_manager.download_and_extract(image_urls)
+        annotations_file = dl_manager.download(annotation_urls)[0]
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "image_directories": image_directories,
+                    "annotations_file": annotations_file,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, image_directories, annotations_file):
+        annotations = create_annotations_dict(annotations_file)
+
+        for image_directory in image_directories:
+            for image_file in glob.glob(f"{image_directory}/**/*.jpg", recursive=True):
+                with Image.open(image_file) as image:
+                    try:
+                        hash_id, _ = Path(image_file).name.split('.', 1)
+                        image_data = annotations[hash_id]
+                        image_data["image"] = image
+
+                        yield image_data["id"], image_data
+                    except Exception:
+                        logger.warn(image_file.name)
+
+                        continue