Update renovation.py

renovation.py

@@ -1,36 +1,56 @@
-import csv
+# 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
-import requests
-from PIL import Image
-from io import BytesIO
 from datasets.tasks import ImageClassification
-import pyarrow as pa
 
 
-_HOMEPAGE = "https://huggingface.co/datasets/rshrott/renovation"
+_HOMEPAGE = "https://github.com/AI-Lab-Makerere/ibean/"
 
 _CITATION = """\
-@ONLINE {renovationquality,
-    author="Your Name",
-    title="Renovation Quality Dataset",
-    month="Your Month",
-    year="Your Year",
-    url="https://huggingface.co/datasets/rshrott/renovation"
+@ONLINE {beansdata,
+    author="Makerere AI Lab",
+    title="Bean disease dataset",
+    month="January",
+    year="2020",
+    url="https://github.com/AI-Lab-Makerere/ibean/"
 }
 """
 
 _DESCRIPTION = """\
-This dataset contains images of various properties, along with labels indicating the quality of renovation - 'cheap', 'average', 'expensive'.
+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.
 """
 
-_URL = "https://huggingface.co/datasets/rshrott/renovation/raw/main/labels.csv"
+_URLS = {
+    "train": "https://huggingface.co/datasets/beans/resolve/main/data/train.zip",
+    "validation": "https://huggingface.co/datasets/beans/resolve/main/data/validation.zip",
+    "test": "https://huggingface.co/datasets/beans/resolve/main/data/test.zip",
+}
 
-_NAMES = ["cheap", "average", "expensive"]
+_NAMES = ["angular_leaf_spot", "bean_rust", "healthy"]
 
-class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
-    """Renovation Quality Dataset."""
 
-    VERSION = datasets.Version("1.0.0")
+class Beans(datasets.GeneratorBasedBuilder):
+    """Beans plant leaf images dataset."""
 
     def _info(self):
         return datasets.DatasetInfo(
@@ -38,71 +58,45 @@ class RenovationQualityDataset(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "image_file_path": datasets.Value("string"),
-                    "image": datasets.Value("string"),
-                    "label": datasets.features.ClassLabel(names=_NAMES),
+                    "image": datasets.Image(),
+                    "labels": datasets.features.ClassLabel(names=_NAMES),
                 }
             ),
-            supervised_keys=("image", "label"),
+            supervised_keys=("image", "labels"),
             homepage=_HOMEPAGE,
             citation=_CITATION,
-            task_templates=[ImageClassification(image_column="image", label_column="label")],
+            task_templates=[ImageClassification(image_column="image", label_column="labels")],
         )
 
-
     def _split_generators(self, dl_manager):
-        csv_path = dl_manager.download(_URL)
+        data_files = dl_manager.download_and_extract(_URLS)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "filepath": csv_path,
-                    "split": "train",
+                    "files": dl_manager.iter_files([data_files["train"]]),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "filepath": csv_path,
-                    "split": "validation",
+                    "files": dl_manager.iter_files([data_files["validation"]]),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "filepath": csv_path,
-                    "split": "test",
+                    "files": dl_manager.iter_files([data_files["test"]]),
                 },
             ),
         ]
 
-    def _generate_examples(self, filepath, split):
-        def url_to_image(url):
-            response = requests.get(url)
-            img = Image.open(BytesIO(response.content))
-            return img
-    
-        with open(filepath, "r") as f:
-            reader = csv.reader(f)
-            next(reader)  # skip header
-            rows = list(reader)
-            if split == 'train':
-                rows = rows[:int(0.8 * len(rows))]
-            elif split == 'validation':
-                rows = rows[int(0.8 * len(rows)):int(0.9 * len(rows))]
-            else: # test
-                rows = rows[int(0.9 * len(rows)):]
-    
-            for id_, row in enumerate(rows):
-                if len(row) < 2:
-                    print(f"Row with id {id_} has less than 2 elements: {row}")
-                else:
-                    image_file_path = str(row[0])
-                    image = url_to_image(image_file_path)
-                    image_bytes = image.tobytes()
-                    yield id_, {
-                        'image_file_path': image_file_path,
-                        'image': image_bytes,
-                        'label': row[1],
-                    }
-
-
+    def _generate_examples(self, files):
+        for i, path in enumerate(files):
+            file_name = os.path.basename(path)
+            if file_name.endswith(".jpg"):
+                yield i, {
+                    "image_file_path": path,
+                    "image": path,
+                    "labels": os.path.basename(os.path.dirname(path)).lower(),
+                }