Datasets:

biglam
/

loc_beyond_words

@@ -1,136 +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.
-"""Beyond Words"""
-import collections
-import json
-import os
-from typing import Any, Dict, List
-import datasets
-from pathlib import Path
-_CITATION = "TODO"
-_DESCRIPTION = "TODO"
-_HOMEPAGE = "TODO"
-_LICENSE = "Public Domain Mark 1.0"
-class BeyondWords(datasets.GeneratorBasedBuilder):
-    """Beyond Words Dataset"""
-    def _info(self):
-        features = datasets.Features(
-            {
-                "image_id": datasets.Value("int64"),
-                "image": datasets.Image(),
-                "width": datasets.Value("int32"),
-                "height": datasets.Value("int32"),
-            }
-        )
-        object_dict = {
-            "bw_id": datasets.Value("string"),
-            "category_id": datasets.ClassLabel(
-                names=[
-                    "Photograph",
-                    "Illustration",
-                    "Map",
-                    "Comics/Cartoon",
-                    "Editorial Cartoon",
-                    "Headline",
-                    "Advertisement",
-                ]
-            ),
-            "image_id": datasets.Value("string"),
-            "id": datasets.Value("int64"),
-            "area": datasets.Value("int64"),
-            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
-            "iscrowd": datasets.Value(
-                "bool"
-            ),  # always False for stuff segmentation task
-        }
-        features["objects"] = [object_dict]
-        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("data/images.zip")
-        training = dl_manager.download("data/train_80_percent.json")
-        validation = dl_manager.download("data/val_20_percent.json")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "annotations_file": Path(training),
-                    "image_dir": Path(images),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "annotations_file": Path(validation),
-                    "image_dir": Path(images),
-                },
-            ),
-        ]
-    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, "images", image),
-                "width": image_info["width"],
-                "height": image_info["height"],
-            }
-        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
-            )
-            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 annotation in annotations:
-                    objects.append(annotation)
-                example["objects"] = objects
-                yield (idx, example)