Add loading script and dataset

UCMerced_LandUse.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06c539ef28703a58fb07bd2837991ac7c48b813b00bb12ac197efd813a18daeb
+size 332468434

uc_merced_land_use.py

@@ -0,0 +1,166 @@
+# Copyright 2020 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.
+"""UC Merced Land Use Dataset"""
+
+
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{yang2010bagofvisualwords,
+  author    = {Yi Yang and Shawn Newsam},
+  title     = {Bag-Of-Visual-Words and Spatial Extensions for Land-Use Classification},
+  booktitle = {ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS)},
+  year      = {2010}
+}
+"""
+
+_DESCRIPTION = """\
+ This is a 21 class land use image dataset meant for research purposes.
+
+There are 100 images for each of the following classes:
+
+- agricultural
+- airplane
+- baseballdiamond
+- beach
+- buildings
+- chaparral
+- denseresidential
+- forest
+- freeway
+- golfcourse
+- harbor
+- intersection
+- mediumresidential
+- mobilehomepark
+- overpass
+- parkinglot
+- river
+- runway
+- sparseresidential
+- storagetanks
+- tenniscourt
+
+Each image measures 256x256 pixels.
+
+The images were manually extracted from large images from the
+USGS National Map Urban Area Imagery collection for various urban areas around
+the country. The pixel resolution of this public domain imagery is 1 foot.
+
+For more information about the original UC Merced Land Use dataset,
+please visit the official dataset page:
+
+http://weegee.vision.ucmerced.edu/datasets/landuse.html
+
+Please refer to the original dataset source for any additional details,
+citations, or specific usage guidelines provided by the dataset creators.
+"""
+
+_HOMEPAGE = "http://weegee.vision.ucmerced.edu/datasets/landuse.html"
+
+_LICENSE = "cc0-1.0"
+
+_DATA_URL = "http://weegee.vision.ucmerced.edu/datasets/UCMerced_LandUse.zip"
+
+_LABEL_NAMES = [
+    "agricultural",
+    "airplane",
+    "baseballdiamond",
+    "beach",
+    "buildings",
+    "chaparral",
+    "denseresidential",
+    "forest",
+    "freeway",
+    "golfcourse",
+    "harbor",
+    "intersection",
+    "mediumresidential",
+    "mobilehomepark",
+    "overpass",
+    "parkinglot",
+    "river",
+    "runway",
+    "sparseresidential",
+    "storagetanks",
+    "tenniscourt",
+]
+
+
+class UCMercedLandUse(datasets.GeneratorBasedBuilder):
+    """A 21 class land use image dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="ucmerced_landuse",
+            version=VERSION,
+            description="UC Merced Land Use Dataset",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "ucmerced_landuse"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "img": datasets.Image(),
+                    "label": datasets.features.ClassLabel(names=_LABEL_NAMES),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # data_dir = dl_manager.download_and_extract(_DATA_URL)
+
+        script_dir = os.path.dirname(os.path.abspath(__file__))
+        archive_path = os.path.join(script_dir, "UCMerced_LandUse.zip")
+        data_dir = dl_manager.extract(archive_path)
+
+        class_dirs = [
+            os.path.join(data_dir, "UCMerced_LandUse/Images", label)
+            for label in _LABEL_NAMES
+        ]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "class_dirs": class_dirs,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, class_dirs, split):
+        for key, class_dir in enumerate(class_dirs):
+            class_label = os.path.basename(class_dir)
+
+            # Iterate through the images in the class directory
+            for image_filename in os.listdir(class_dir):
+                image_path = os.path.join(class_dir, image_filename)
+
+                yield key, {
+                    "img": image_path,
+                    "label": class_label,
+                }