Upload RDD_2020.py

RDD_2020.py

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+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+import csv
+import json
+import os
+from typing import List
+import datasets
+import logging
+import xml.etree.ElementTree as ET
+import os
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={Shixuan An
+},
+year={2024}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# 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 = {
+    "dataset": "https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/5ty2wb6gvg-1.zip"
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class RDD2020_Dataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    _URLS = _URLS
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "image_id": datasets.Value("string"),
+                "country": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "image_resolution": datasets.Features({
+                    "width": datasets.Value("int32"),
+                    "height": datasets.Value("int32"),
+                    "depth": datasets.Value("int32"),
+                }),
+                "image_path": datasets.Value("string"),
+                "pics_array": datasets.Array3D(shape=(None, None, 3), dtype="uint8"),
+                "crack_type": datasets.Sequence(datasets.Value("string")),
+                "crack_coordinates": datasets.Sequence(datasets.Features({
+                    "x_min": datasets.Value("int32"),
+                    "x_max": datasets.Value("int32"),
+                    "y_min": datasets.Value("int32"),
+                    "y_max": datasets.Value("int32"),
+                })),
+            }),
+            homepage='https://data.mendeley.com/datasets/5ty2wb6gvg/1',
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """This method downloads/extracts the data and defines the splits."""
+        data_dir = dl_manager.download_and_extract(_URLS["dataset"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images_dir": os.path.join(data_dir, "train"),
+                    "annotations_dir": os.path.join(data_dir, "train", "annotations"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images_dir": os.path.join(data_dir, "test1"),
+                    "annotations_dir": os.path.join(data_dir, "test1", "annotations"),
+                    "split": "test1",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images_dir": os.path.join(data_dir, "test2"),
+                    "annotations_dir": os.path.join(data_dir, "test2", "annotations"),
+                    "split": "test2",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, images_dir, annotations_dir, split):
+        """Yields examples as (key, example) tuples."""
+        for image_file in os.listdir(images_dir):
+            if not image_file.endswith('.jpg'):
+                continue
+            image_id = image_file.split('.')[0]
+            annotation_file = image_id + '.xml'
+            annotation_path = os.path.join(annotations_dir, annotation_file)
+
+            if not os.path.exists(annotation_path):
+                continue
+
+            tree = ET.parse(annotation_path)
+            root = tree.getroot()
+
+            country = split.capitalize()
+            image_path = os.path.join(images_dir, image_file)
+            crack_type = []
+            crack_coordinates = []
+
+            for obj in root.findall('object'):
+                crack_type.append(obj.find('name').text)
+                bndbox = obj.find('bndbox')
+                coordinates = {
+                    "x_min": int(bndbox.find('xmin').text),
+                    "x_max": int(bndbox.find('xmax').text),
+                    "y_min": int(bndbox.find('ymin').text),
+                    "y_max": int(bndbox.find('ymax').text),
+                }
+                crack_coordinates.append(coordinates)
+
+            # Assuming images are of uniform size, you might want to adjust this or extract from image directly
+            image_resolution = {"width": 600, "height": 600, "depth": 3} if country != "India" else {"width": 720,
+                                                                                                     "height": 720,
+                                                                                                    "depth": 3}
+            yield image_id, {
+                "image_id": image_id,
+                "country": country,
+                "type": split,
+                "image_resolution": image_resolution,
+                "image_path": image_path,
+                "crack_type": crack_type,
+                "crack_coordinates": crack_coordinates,
+            }
+
+