draft dataset

data/images.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dcd764a5335d62b6dda2bb85d372d39c33c56f4c90ad19b4dd3cae638bd2d294
+size 1193066001

loc_beyond_words.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.
+"""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"] = datasets.Sequence(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)