draft dataset

README.md

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+---
+dataset_info:
+- config_name: raw
+  features:
+  - name: image
+    dtype: image
+  - name: source
+    dtype: string
+  - name: width
+    dtype: int16
+  - name: height
+    dtype: int16
+  - name: dept
+    dtype: int8
+  - name: segmented
+    dtype: int8
+  - name: objects
+    list:
+    - name: name
+      dtype:
+        class_label:
+          names:
+            '0': zebra
+            '1': tree
+            '2': nude
+            '3': crucifixion
+            '4': scroll
+            '5': head
+            '6': swan
+            '7': shield
+            '8': lily
+            '9': mouse
+            '10': knight
+            '11': dragon
+            '12': horn
+            '13': dog
+            '14': palm
+            '15': tiara
+            '16': helmet
+            '17': sheep
+            '18': deer
+            '19': person
+            '20': sword
+            '21': rooster
+            '22': bear
+            '23': halo
+            '24': lion
+            '25': monkey
+            '26': prayer
+            '27': crown of thorns
+            '28': elephant
+            '29': zucchetto
+            '30': unicorn
+            '31': holy shroud
+            '32': cat
+            '33': apple
+            '34': banana
+            '35': chalice
+            '36': bird
+            '37': eagle
+            '38': pegasus
+            '39': crown
+            '40': camauro
+            '41': saturno
+            '42': arrow
+            '43': dove
+            '44': centaur
+            '45': horse
+            '46': hands
+            '47': skull
+            '48': orange
+            '49': monk
+            '50': trumpet
+            '51': key of heaven
+            '52': fish
+            '53': cow
+            '54': angel
+            '55': devil
+            '56': book
+            '57': stole
+            '58': butterfly
+            '59': serpent
+            '60': judith
+            '61': mitre
+            '62': banner
+            '63': donkey
+            '64': shepherd
+            '65': boat
+            '66': god the father
+            '67': crozier
+            '68': jug
+            '69': lance
+    - name: pose
+      dtype:
+        class_label:
+          names:
+            '0': stand
+            '1': sit
+            '2': partial
+            '3': Unspecified
+            '4': squats
+            '5': lie
+            '6': bend
+            '7': fall
+            '8': walk
+            '9': push
+            '10': pray
+            '11': undefined
+            '12': kneel
+            '13': unrecognize
+            '14': unknown
+            '15': other
+            '16': ride
+    - name: diffult
+      dtype: int32
+    - name: xmin
+      dtype: float64
+    - name: ymin
+      dtype: float64
+    - name: xmax
+      dtype: float64
+    - name: ymax
+      dtype: float64
+  splits:
+  - name: train
+    num_bytes: 9046918
+    num_examples: 15156
+  download_size: 18160510195
+  dataset_size: 9046918
+- config_name: coco
+  features:
+  - name: image
+    dtype: image
+  - name: source
+    dtype: string
+  - name: width
+    dtype: int16
+  - name: height
+    dtype: int16
+  - name: dept
+    dtype: int8
+  - name: segmented
+    dtype: int8
+  - name: objects
+    list:
+    - name: category_id
+      dtype:
+        class_label:
+          names:
+            '0': zebra
+            '1': tree
+            '2': nude
+            '3': crucifixion
+            '4': scroll
+            '5': head
+            '6': swan
+            '7': shield
+            '8': lily
+            '9': mouse
+            '10': knight
+            '11': dragon
+            '12': horn
+            '13': dog
+            '14': palm
+            '15': tiara
+            '16': helmet
+            '17': sheep
+            '18': deer
+            '19': person
+            '20': sword
+            '21': rooster
+            '22': bear
+            '23': halo
+            '24': lion
+            '25': monkey
+            '26': prayer
+            '27': crown of thorns
+            '28': elephant
+            '29': zucchetto
+            '30': unicorn
+            '31': holy shroud
+            '32': cat
+            '33': apple
+            '34': banana
+            '35': chalice
+            '36': bird
+            '37': eagle
+            '38': pegasus
+            '39': crown
+            '40': camauro
+            '41': saturno
+            '42': arrow
+            '43': dove
+            '44': centaur
+            '45': horse
+            '46': hands
+            '47': skull
+            '48': orange
+            '49': monk
+            '50': trumpet
+            '51': key of heaven
+            '52': fish
+            '53': cow
+            '54': angel
+            '55': devil
+            '56': book
+            '57': stole
+            '58': butterfly
+            '59': serpent
+            '60': judith
+            '61': mitre
+            '62': banner
+            '63': donkey
+            '64': shepherd
+            '65': boat
+            '66': god the father
+            '67': crozier
+            '68': jug
+            '69': lance
+    - name: image_id
+      dtype: string
+    - name: area
+      dtype: int64
+    - name: bbox
+      sequence: float32
+      length: 4
+    - name: segmentation
+      list:
+        list: float32
+    - name: iscrowd
+      dtype: bool
+  - name: image_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 8285204
+    num_examples: 15156
+  download_size: 18160510195
+  dataset_size: 8285204
+---

european_art.py

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+# Copyright 2022 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 'You Actually Look Twice At it (YALTAi)' dataset."""
+
+
+import contextlib
+from typing import Dict
+import requests
+import datasets
+from PIL import Image
+from pathlib import Path
+import xml.etree.ElementTree as ET
+from xml.etree.ElementTree import Element
+from typing import Any, List
+from pathlib import PosixPath
+
+_CITATION = """\
+    @dataset{clerice_thibault_2022_6827706,
+  author       = {Clérice, Thibault},
+  title        = {YALTAi: Tabular Dataset},
+  month        = jul,
+  year         = 2022,
+  publisher    = {Zenodo},
+  version      = {1.0.0},
+  doi          = {10.5281/zenodo.6827706},
+  url          = {https://doi.org/10.5281/zenodo.6827706}
+}
+"""
+
+_DESCRIPTION = """Yalt AI Tabular Dataset"""
+
+_HOMEPAGE = "https://doi.org/10.5281/zenodo.6984525"
+
+_LICENSE = "Creative Commons Attribution Non Commercial Share Alike 2.0 Generic"
+
+ZENODO_API_URL = "https://zenodo.org/api/records/6984525"
+
+_CATEGORIES = [
+    "zebra",
+    "tree",
+    "nude",
+    "crucifixion",
+    "scroll",
+    "head",
+    "swan",
+    "shield",
+    "lily",
+    "mouse",
+    "knight",
+    "dragon",
+    "horn",
+    "dog",
+    "palm",
+    "tiara",
+    "helmet",
+    "sheep",
+    "deer",
+    "person",
+    "sword",
+    "rooster",
+    "bear",
+    "halo",
+    "lion",
+    "monkey",
+    "prayer",
+    "crown of thorns",
+    "elephant",
+    "zucchetto",
+    "unicorn",
+    "holy shroud",
+    "cat",
+    "apple",
+    "banana",
+    "chalice",
+    "bird",
+    "eagle",
+    "pegasus",
+    "crown",
+    "camauro",
+    "saturno",
+    "arrow",
+    "dove",
+    "centaur",
+    "horse",
+    "hands",
+    "skull",
+    "orange",
+    "monk",
+    "trumpet",
+    "key of heaven",
+    "fish",
+    "cow",
+    "angel",
+    "devil",
+    "book",
+    "stole",
+    "butterfly",
+    "serpent",
+    "judith",
+    "mitre",
+    "banner",
+    "donkey",
+    "shepherd",
+    "boat",
+    "god the father",
+    "crozier",
+    "jug",
+    "lance",
+]
+
+_POSES = [
+    "stand",
+    "sit",
+    "partial",
+    "Unspecified",
+    "squats",
+    "lie",
+    "bend",
+    "fall",
+    "walk",
+    "push",
+    "pray",
+    "undefined",
+    "kneel",
+    "unrecognize",
+    "unknown",
+    "other",
+    "ride",
+]
+
+
+logger = datasets.utils.logging.get_logger(__name__)
+
+
+def parse_annotation(annotations_object: Element) -> Dict[str, Any]:
+    with contextlib.suppress(ValueError):
+        name = annotations_object.find("name").text
+        pose = annotations_object.find("pose").text
+        diffult = int(annotations_object.find("difficult").text)
+        bndbox = annotations_object.find("bndbox")
+        xmin = float(bndbox.find("xmin").text)
+        ymin = float(bndbox.find("ymin").text)
+        xmax = float(bndbox.find("xmax").text)
+        ymax = float(bndbox.find("ymax").text)
+        return {
+            "name": name,
+            "pose": pose,
+            "diffult": diffult,
+            "xmin": xmin,
+            "ymin": ymin,
+            "xmax": xmax,
+            "ymax": ymax,
+        }
+
+
+def create_annotations_dict(xmls: List[PosixPath]) -> Dict[str, Any]:
+    annotations = {}
+    for xml in xmls:
+        tree = ET.parse(xml)
+        root = tree.getroot()
+        filename = root.find("filename").text
+        source = root.find("source/database").text
+        size = root.find("size")
+        width = int(size.find("width").text)
+        height = int(size.find("height").text)
+        depth = int(size.find("depth").text)
+        segmented = root.find("segmented")
+        segmented = int(segmented.text) if segmented else None
+        annotation_objects = root.findall("object")
+        annotation_objects = [
+            parse_annotation(annotation) for annotation in annotation_objects
+        ]
+        annotation_objects = [
+            annotation for annotation in annotation_objects if annotation is not None
+        ]
+        annotations[filename] = {
+            "source": source,
+            "width": width,
+            "height": height,
+            "dept": depth,
+            "segmented": segmented,
+            "objects": annotation_objects,
+        }
+    return annotations
+
+
+def get_coco_annotation_from_obj(
+    image_id, label, xmin, ymin, xmax, ymax
+):  # adapted from https://github.com/yukkyo/voc2coco/blob/abd05bbfa0740a04bb483862eccea2032bc80e24/voc2coco.py#L60
+    category_id = label
+    assert xmax > xmin and ymax > ymin, logger.warn(
+        f"Box size error !: (xmin, ymin, xmax, ymax): {xmin, ymin, xmax, ymax}"
+    )
+    o_width = xmax - xmin
+    o_height = ymax - ymin
+    ann = {
+        "image_id": image_id,
+        "area": o_width * o_height,
+        "iscrowd": 0,
+        "bbox": [xmin, ymin, o_width, o_height],
+        "category_id": category_id,
+        # "ignore": 0,
+        "segmentation": [],
+    }
+    return ann
+
+
+common_features = features = datasets.Features(
+    {
+        # "image_id": datasets.Value("int64"),
+        "image": datasets.Image(),
+        "source": datasets.Value("string"),
+        "width": datasets.Value("int16"),
+        "height": datasets.Value("int16"),
+        "dept": datasets.Value("int8"),
+        "segmented": datasets.Value("int8"),
+    }
+)
+
+
+class DeartDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for YaltAiTabularDataset."""
+
+    def __init__(self, name, **kwargs):
+        """BuilderConfig for YaltAiTabularDataset."""
+        super(DeartDatasetConfig, self).__init__(
+            version=datasets.Version("1.0.0"), name=name, description=None, **kwargs
+        )
+
+
+class DeartDataset(datasets.GeneratorBasedBuilder):
+    """Object Detection for historic manuscripts"""
+
+    BUILDER_CONFIGS = [
+        DeartDatasetConfig("raw"),
+        DeartDatasetConfig("coco"),
+    ]
+
+    def _info(self):
+        if self.config.name == "coco":
+            features = common_features
+            features["image_id"] = datasets.Value("string")
+            object_dict = {
+                "category_id": datasets.ClassLabel(names=_CATEGORIES),
+                "image_id": datasets.Value("string"),
+                "area": datasets.Value("int64"),
+                "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                "segmentation": [[datasets.Value("float32")]],
+                "iscrowd": datasets.Value("bool"),
+            }
+            features["objects"] = [object_dict]
+        if self.config.name == "raw":
+            features = common_features
+
+            object_dict = {
+                "name": datasets.ClassLabel(names=_CATEGORIES),
+                "pose": datasets.ClassLabel(names=_POSES),
+                "diffult": datasets.Value("int32"),
+                "xmin": datasets.Value("float64"),
+                "ymin": datasets.Value("float64"),
+                "xmax": datasets.Value("float64"),
+                "ymax": datasets.Value("float64"),
+            }
+            features["objects"] = [object_dict]
+        return datasets.DatasetInfo(
+            features=features,
+            supervised_keys=None,
+            description=_DESCRIPTION,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        zenodo_record = requests.get(ZENODO_API_URL).json()
+        urls = sorted(
+            [
+                file["links"]["self"]
+                for file in zenodo_record["files"]
+                if file["type"] == "zip"
+            ]
+        )
+        annotation_data = urls.pop(0)
+        annotation_data = dl_manager.download_and_extract(annotation_data)
+
+        image_data = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotations_data": Path(annotation_data),
+                    "image_data": image_data,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, annotations_data, image_data):
+        xmls = list(annotations_data.rglob("*.xml"))
+        annotations_data = create_annotations_dict(xmls)
+        count = 0
+        for directory in image_data:
+            for file in Path(directory).glob("*.jpg"):
+                with Image.open(file) as image:
+                    try:
+                        if self.config.name == "raw":
+                            example = annotations_data[file.name]
+                            example["image"] = image
+                            count += 1
+                            yield count, example
+                        if self.config.name == "coco":
+                            updated_annotations = []
+                            example = annotations_data[file.name]
+                            annotations = example["objects"]
+                            for annotation in annotations:
+                                label = annotation["name"]
+                                xmin, ymin = annotation["xmin"], annotation["ymin"]
+                                xmax, ymax = annotation["xmax"], annotation["ymax"]
+                                updated_annotations.append(
+                                    get_coco_annotation_from_obj(
+                                        count, label, xmin, ymin, xmax, ymax
+                                    ),
+                                )
+                            example["image"] = image
+                            example["objects"] = updated_annotations
+                            example["image_id"] = str(count)
+                            count += 1
+                            yield count, example
+                    except Exception:
+                        logger.warn(file.name)
+                        continue