add loading script

Oxford-IIIT-Pet.py

@@ -0,0 +1,201 @@
+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# 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.
+"""Oxford_IIT pet loading script."""
+
+
+import xml.etree.ElementTree as ET
+from pathlib import Path
+
+import datasets
+
+_CITATION = """\
+@InProceedings{parkhi12a,
+  author       = "Parkhi, O. M. and Vedaldi, A. and Zisserman, A. and Jawahar, C.~V.",
+  title        = "Cats and Dogs",
+  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition",
+  year         = "2012",
+}
+
+"""
+
+_DESCRIPTION = """\
+37 category pet dataset with roughly 200 images for each class. The images have a large variations in scale, pose and lighting. All images have an associated ground truth annotation of breed, head ROI, and pixel level trimap segmentation.
+"""
+
+_HOMEPAGE = "https://www.robots.ox.ac.uk/~vgg/data/pets/"
+
+_LICENSE = "CC BY-SA 4.0"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "images": "https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz",
+    "annotations": "https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz",
+}
+
+
+_LABEL_CLASSES = [
+    "Abyssinian",
+    "american_bulldog",
+    "american_pit_bull_terrier",
+    "basset_hound",
+    "beagle",
+    "Bengal",
+    "Birman",
+    "Bombay",
+    "boxer",
+    "British_Shorthair",
+    "chihuahua",
+    "Egyptian_Mau",
+    "english_cocker_spaniel",
+    "english_setter",
+    "german_shorthaired",
+    "great_pyrenees",
+    "havanese",
+    "japanese_chin",
+    "keeshond",
+    "leonberger",
+    "Maine_Coon",
+    "miniature_pinscher",
+    "newfoundland",
+    "Persian",
+    "pomeranian",
+    "pug",
+    "Ragdoll",
+    "Russian_Blue",
+    "saint_bernard",
+    "samoyed",
+    "scottish_terrier",
+    "shiba_inu",
+    "Siamese",
+    "Sphynx",
+    "staffordshire_bull_terrier",
+    "wheaten_terrier",
+    "yorkshire_terrier",
+]
+_SPECIES_CLASSES = ["Cat", "Dog"]
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "label": datasets.features.ClassLabel(names=_LABEL_CLASSES),
+                "species": datasets.features.ClassLabel(names=_SPECIES_CLASSES),
+                "segmentation_mask": datasets.Image(),
+                # "bbox": datasets.features.Array2D(shape=(1, 4), dtype="int64"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images_dir": Path(data_dir["images"]) / "images",
+                    "annotations_dir": Path(data_dir["annotations"]) / "annotations",
+                    "images_list": Path(data_dir["annotations"])
+                    / "annotations"
+                    / "trainval.txt",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images_dir": Path(data_dir["images"]) / "images",
+                    "annotations_dir": Path(data_dir["annotations"]) / "annotations",
+                    "images_list": Path(data_dir["annotations"])
+                    / "annotations"
+                    / "test.txt",
+                },
+            ),
+        ]
+
+    # Not used because missing xml file for Abyssinian_104
+    # def _get_data_from_xml_file(self, xlm_annotation_file):
+    #     # From https://huggingface.co/datasets/fuliucansheng/pascal_voc/blob/main/pascal_voc.py
+    #     anno_tree = ET.parse(xlm_annotation_file)
+    #     objects = []
+    #     for obj in anno_tree.findall("./object"):
+    #         info = {
+    #             "class": obj.findall("./name")[0].text,
+    #             "bbox": [[
+    #                 int(float(obj.findall("./bndbox/xmin")[0].text)),
+    #                 int(float(obj.findall("./bndbox/ymin")[0].text)),
+    #                 int(float(obj.findall("./bndbox/xmax")[0].text)),
+    #                 int(float(obj.findall("./bndbox/ymax")[0].text)),]
+    #             ],
+    #         }
+
+    #         if obj.findall("./pose"):
+    #             info["pose"] = obj.findall("./pose")[0].text
+    #         if obj.findall("./truncated"):
+    #             info["truncated"] = int(obj.findall("./truncated")[0].text)
+    #         if obj.findall("./difficult"):
+    #             info["difficult"] = int(obj.findall("./difficult")[0].text)
+    #         else:
+    #             info["difficult"] = 0
+    #         if obj.findall("./occluded"):
+    #             info["occluded"] = int(obj.findall("./occluded")[0].text)
+
+    #         if obj.findall("./actions"):
+    #             info["action"] = [
+    #                 action.tag
+    #                 for action in obj.findall("./actions/")
+    #                 if int(action.text) == 1
+    #             ][0]
+
+    #         objects.append(info)
+    #     print(len(objects))
+    #     return objects
+
+    def _generate_examples(self, images_dir, annotations_dir, images_list):
+        bounding_box_dir = annotations_dir / "xmls"
+        trimaps_dir = annotations_dir / "trimaps"
+
+        with open(images_list, encoding="utf-8") as f:
+            for row in f:
+                image_name, label, species, _ = row.strip().split(" ")
+
+                trimap_name = f"{image_name}.png"
+                # bbox_name = f"{image_name}.xml"
+                image_name = f"{image_name}.jpg"
+
+                label = _LABEL_CLASSES[int(label) - 1]
+                species = _SPECIES_CLASSES[int(species) - 1]
+                # objects = self._get_data_from_xml_file(str(bounding_box_dir / bbox_name))
+
+                record = {
+                    "image": str(images_dir / image_name),
+                    "label": label,
+                    "species": species,
+                    "segmentation_mask": str(trimaps_dir / trimap_name),
+                    # "bbox": [[ymin, ymax, xmin, xmax]]
+                }
+                yield image_name, record