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mortars_test.py

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+# 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.
+"""Beans leaf dataset with images of diseased and health leaves."""
+
+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_HOMEPAGE = ""
+
+_CITATION = ""
+
+_DESCRIPTION = """\
+Beans is a dataset of images of beans taken in the field using smartphone
+cameras. It consists of 3 classes: 2 disease classes and the healthy class.
+Diseases depicted include Angular Leaf Spot and Bean Rust. Data was annotated
+by experts from the National Crops Resources Research Institute (NaCRRI) in
+Uganda and collected by the Makerere AI research lab.
+"""
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/apetulante/mortars_test/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/apetulante/mortars_test/main/data/validation.zip",
+    "test": "https://huggingface.co/datasets/apetulante/mortars_test/main/data/test.zip",
+}
+
+_NAMES = ["Obsidian", "Chert", "Sand"]
+
+
+class Mortars(datasets.GeneratorBasedBuilder):
+    """Beans plant leaf images dataset."""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image_file_path": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "labels": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("image", "labels"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            task_templates=[ImageClassification(image_column="image", label_column="labels")],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["train"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["validation"]]),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.iter_files([data_files["test"]]),
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".bmp"):
+                yield i, {
+                    "image_file_path": path,
+                    "image": path,
+                    "labels": os.path.basename(os.path.dirname(path)).lower(),
+                }