add data builder

README.md

@@ -22,8 +22,24 @@ dataset_info:
     dtype:
       class_label:
         names:
-          0: 'bokeh'
-          1: 'no-bokeh'
+          0: bokeh
+          1: no-bokeh
+- config_name: default
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          0: '0'
+          1: '1'
+  splits:
+  - name: train
+    num_bytes: 38833123
+    num_examples: 1200
+  download_size: 39731200
+  dataset_size: 38833123
 ---
 ## Dataset Summary
 
@@ -44,4 +60,4 @@ Depth-of-Field(DoF) dataset is comprised of 1200 annotated images, binary annota
   year={2021}
 }
 Note that each DoF dataset has its own citation. Please see the source to
-get the correct citation for each contained dataset.
+get the correct citation for each contained dataset.

dof.py

@@ -0,0 +1,83 @@
+# 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.
+"""The depth-of-field dataset"""
+
+import os
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_URL = (
+    "https://drive.google.com/uc?export=download&id=1baxZd7X_pSZ7e9_15s5weFY5AH_sPSNi"
+)
+
+_HOMEPAGE = "https://github.com/sniafas/photography-style-analysis"
+
+_DESCRIPTION = "A set of annotated images in shallow and deep depth of field"
+
+_CITATION = """\
+@article{sniafas2021,
+  title={DoF: An image dataset for depth of field classification},
+  author={Niafas, Stavros},
+  doi= {10.13140/RG.2.2.29880.62722},
+  url= {https://www.researchgate.net/publication/364356051_DoF_depth_of_field_datase}
+  year={2021}
+}
+"""
+
+
+class DoF(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.features.ClassLabel(names=["0", "1"]),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            task_templates=[
+                ImageClassification(image_column="image", label_column="label")
+            ],
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(archive_path),
+                },
+            )
+        ]
+
+    def _generate_examples(self, images):
+        """Generate images and labels for splits."""
+        for file_path, file_obj in images:
+            print(file_path)
+            label = file_path.split("/")[0]
+            yield file_path, {
+                "image": {"path": file_path, "bytes": file_obj.read()},
+                "label": label,
+            }