Update hugging_face.py

hugging_face.py

@@ -7,6 +7,8 @@ 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
@@ -34,73 +36,118 @@ _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)
-_URL = "https://data.mendeley.com/datasets/5ty2wb6gvg/1"
 _URLS = {
-    "train": _URL + "train-v1.1.json",
-    "dev": _URL + "dev-v1.1.json",
+    "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 SquadDataset(datasets.GeneratorBasedBuilder):
+class RDD2020_Dataset(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
     _URLS = _URLS
     VERSION = datasets.Version("1.1.0")
 
     def _info(self):
-        raise ValueError('woops!')
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "title": datasets.Value("string"),
-                    "context": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answers": datasets.features.Sequence(
-                        {"text": datasets.Value("string"), "answer_start": datasets.Value("int32"), }
-                    ),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both question
-            # and context as input).
-            supervised_keys=None,
-            homepage="https://rajpurkar.github.io/SQuAD-explorer/",
+            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: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        urls_to_download = self._URLS
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+    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={"filepath": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            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, filepath):
-        """This function returns the examples in the raw (text) form."""
-        logging.info("generating examples from = %s", filepath)
-        with open(filepath) as f:
-            squad = json.load(f)
-            for article in squad["data"]:
-                title = article.get("title", "").strip()
-                for paragraph in article["paragraphs"]:
-                    context = paragraph["context"].strip()
-                    for qa in paragraph["qas"]:
-                        question = qa["question"].strip()
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"].strip() for answer in qa["answers"]]
-
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield id_, {
-                            "title": title,
-                            "context": context,
-                            "question": question,
-                            "id": id_,
-                            "answers": {"answer_start": answer_starts, "text": answers, },
-                        }
+    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,
+            }
+
+