Create food.py

food.py

@@ -0,0 +1,123 @@
+# coding=utf-8
+# Copyright 2021 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.
+"""Food dataset."""
+
+
+import collections
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\none"""
+
+_DESCRIPTION = """\
+A simple food dataset for personal study use. Structure follows the CPPE-5 dataset.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "Unknown"
+
+_URL = "https://drive.google.com/uc?id=1fXfOU8EyGn0oiZFclM-fe8FoCigDL41l"
+
+_CATEGORIES = ["Broccoli", "Tomato", "Potato"]
+
+
+class Food(datasets.GeneratorBasedBuilder):
+    """Food Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "image": datasets.Image(),
+                "width": datasets.Value("int32"),
+                "height": datasets.Value("int32"),
+                "objects": datasets.Sequence(
+                    {
+                        "id": datasets.Value("int64"),
+                        "area": datasets.Value("int64"),
+                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                        "category": datasets.ClassLabel(names=_CATEGORIES),
+                    }
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotation_file_path": "annotations/train.json",
+                    "files": dl_manager.iter_archive(archive),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "annotation_file_path": "annotations/test.json",
+                    "files": dl_manager.iter_archive(archive),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, annotation_file_path, files):
+        def process_annot(annot, category_id_to_category):
+            return {
+                "id": annot["id"],
+                "area": annot["area"],
+                "bbox": annot["bbox"],
+                "category": category_id_to_category[annot["category_id"]],
+            }
+
+        image_id_to_image = {}
+        idx = 0
+        # This loop relies on the ordering of the files in the archive:
+        # Annotation files come first, then the images.
+        for path, f in files:
+            file_name = os.path.basename(path)
+            if path == annotation_file_path:
+                annotations = json.load(f)
+                category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
+                image_id_to_annotations = collections.defaultdict(list)
+                for annot in annotations["annotations"]:
+                    image_id_to_annotations[annot["image_id"]].append(annot)
+                image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
+            elif file_name in image_id_to_image:
+                image = image_id_to_image[file_name]
+                objects = [
+                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
+                ]
+                yield idx, {
+                    "image_id": image["id"],
+                    "image": {"path": path, "bytes": f.read()},
+                    "width": image["width"],
+                    "height": image["height"],
+                    "objects": objects,
+                }
+                idx += 1