Create new file

nls_chapbook_illustrations.py

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+# Copyright 2022 Daniel van Strien.
+#
+# 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.
+"""NLS Chapbook Images"""
+
+import collections
+import json
+import os
+from typing import Any, Dict, List
+
+import datasets
+
+
+_CITATION = "TODO"
+
+
+_DESCRIPTION = "TODO"
+
+
+_HOMEPAGE = "TODO"
+
+
+_LICENSE = "Public Domain Mark 1.0"  # TODO confirm licence terms for annotations
+
+
+_IMAGES_URL = "https://nlsfoundry.s3.amazonaws.com/data/nls-data-chapbooks.zip"
+
+# TODO update url if this is merged upstream
+_ANNOTATIONS_URL = "https://gitlab.com/davanstrien/nls-chapbooks-illustrations/-/raw/master/data/annotations/step5-manual-verification-image-0-47329_train_coco.json"
+
+
+class NationalLibraryScotlandChapBooksConfig(datasets.BuilderConfig):
+    """BuilderConfig for National Library of Scotland Chapbooks dataset."""
+
+    def __init__(self, name, **kwargs):
+        super(NationalLibraryScotlandChapBooksConfig, self).__init__(
+            version=datasets.Version("1.0.0"),
+            name=name,
+            description="TODO",
+            **kwargs,
+        )
+
+
+class NationalLibraryScotlandChapBooks(datasets.GeneratorBasedBuilder):
+    """National Library of Scotland Chapbooks dataset."""
+
+    BUILDER_CONFIGS = [
+        NationalLibraryScotlandChapBooksConfig("illustration_detection"),
+        NationalLibraryScotlandChapBooksConfig("image_classification"),
+    ]
+
+    def _info(self):
+        if self.config.name == "illustration_detection":
+            features = datasets.Features(
+                {
+                    "image_id": datasets.Value("int64"),
+                    "image": datasets.Image(),
+                    "width": datasets.Value("int32"),
+                    "height": datasets.Value("int32"),
+                    "url": datasets.Value("string"),
+                    "date_captured": datasets.Value("string"),
+                }
+            )
+            object_dict = {
+                "category_id": datasets.ClassLabel(
+                    names=["early_printed_illustration"]
+                ),
+                "image_id": datasets.Value("string"),
+                "id": datasets.Value("int64"),
+                "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 == "image_classification":
+            features = datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(
+                        num_classes=2, names=["not-illustrated", "illustrated"]
+                    ),
+                }
+            )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images = dl_manager.download_and_extract(_IMAGES_URL)
+        annotations = dl_manager.download(_ANNOTATIONS_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotations_file": os.path.join(annotations),
+                    "image_dir": os.path.join(images, "nls-data-chapbooks"),
+                },
+            )
+        ]
+
+    def _get_image_id_to_annotations_mapping(
+        self, annotations: List[Dict]
+    ) -> Dict[int, List[Dict[Any, Any]]]:
+        """
+        A helper function to build a mapping from image ids to annotations.
+        """
+        image_id_to_annotations = collections.defaultdict(list)
+        for annotation in annotations:
+            image_id_to_annotations[annotation["image_id"]].append(annotation)
+        return image_id_to_annotations
+
+    def _generate_examples(self, annotations_file, image_dir):
+        def _image_info_to_example(image_info, image_dir):
+            image = image_info["file_name"]
+            return {
+                "image_id": image_info["id"],
+                "image": os.path.join(image_dir, image),
+                "width": image_info["width"],
+                "height": image_info["height"],
+                "url": image_info.get("url"),
+                "date_captured": image_info["date_captured"],
+            }
+
+        with open(annotations_file, encoding="utf8") as f:
+            annotation_data = json.load(f)
+            images = annotation_data["images"]
+            annotations = annotation_data["annotations"]
+
+            image_id_to_annotations = self._get_image_id_to_annotations_mapping(
+                annotations
+            )
+            if self.config.name == "illustration_detection":
+                for idx, image_info in enumerate(images):
+                    example = _image_info_to_example(
+                        image_info,
+                        image_dir,
+                    )
+                    annotations = image_id_to_annotations[image_info["id"]]
+                    objects = []
+                    for annot in annotations:
+                        category_id = annot["category_id"]
+                        if category_id == 1:
+                            annot["category_id"] = 0
+                        object_ = annot
+                        objects.append(object_)
+                    example["objects"] = objects
+                    yield idx, example
+            if self.config.name == "image_classification":
+                for idx, image_info in enumerate(images):
+                    example = _image_info_to_example(image_info, image_dir)
+                    annotations = image_id_to_annotations[image_info["id"]]
+                    if len(annotations) < 1:
+                        label = 0
+                    else:
+                        label = 1
+                    example = {
+                        "image": os.path.join(image_dir, image_info["file_name"]),
+                        "label": label,
+                    }
+                    yield idx, example