Delete nls_chapbook_images.py

nls_chapbook_images.py

@@ -1,175 +0,0 @@
-# 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