Upload sroie.py

sroie.py

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+# coding=utf-8
+import json
+import os
+from pathlib import Path
+import datasets
+from PIL import Image
+# import torch
+# from detectron2.data.transforms import ResizeTransform, TransformList
+logger = datasets.logging.get_logger(__name__)
+_CITATION = """\
+@article{2019,
+   title={ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction},
+   url={http://dx.doi.org/10.1109/ICDAR.2019.00244},
+   DOI={10.1109/icdar.2019.00244},
+   journal={2019 International Conference on Document Analysis and Recognition (ICDAR)},
+   publisher={IEEE},
+   author={Huang, Zheng and Chen, Kai and He, Jianhua and Bai, Xiang and Karatzas, Dimosthenis and Lu, Shijian and Jawahar, C. V.},
+   year={2019},
+   month={Sep}
+}
+"""
+_DESCRIPTION = """\
+https://arxiv.org/abs/2103.10213
+"""
+
+
+def load_image(image_path):
+    image = Image.open(image_path)
+    w, h = image.size
+    return image, (w, h)
+def normalize_bbox(bbox, size):
+    return [
+        int(1000 * bbox[0] / size[0]),
+        int(1000 * bbox[1] / size[1]),
+        int(1000 * bbox[2] / size[0]),
+        int(1000 * bbox[3] / size[1]),
+    ]
+
+def _get_drive_url(url):
+    base_url = 'https://drive.google.com/uc?id='
+    split_url = url.split('/')
+    return base_url + split_url[5]
+_URLS = [
+    _get_drive_url("https://drive.google.com/file/d/1ZyxAw1d-9UvhgNLGRvsJK4gBCMf0VpGD/view?usp=sharing"),
+]
+class SroieConfig(datasets.BuilderConfig):
+    """BuilderConfig for SROIE"""
+    def __init__(self, **kwargs):
+        """BuilderConfig for SROIE.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SroieConfig, self).__init__(**kwargs)
+class Sroie(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        SroieConfig(name="sroie", version=datasets.Version("1.0.0"), description="SROIE dataset"),
+    ]
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "words": datasets.Sequence(datasets.Value("string")),
+                    "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=["O","B-COMPANY", "I-COMPANY", "B-DATE", "I-DATE", "B-ADDRESS", "I-ADDRESS", "B-TOTAL", "I-TOTAL"]
+                        )
+                    ),
+                    #"image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
+                    "image_path": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+            homepage="https://arxiv.org/abs/2103.10213",
+        )
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        """Uses local files located with data_dir"""
+        downloaded_file = dl_manager.download_and_extract(_URLS)
+        # move files from the second URL together with files from the first one.
+        dest = Path(downloaded_file[0])/"sroie"
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train"}
+            ),            
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test"}
+            ),
+        ]
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        ann_dir = os.path.join(filepath, "tagged")
+        img_dir = os.path.join(filepath, "images")
+        for guid, fname in enumerate(sorted(os.listdir(img_dir))):
+            name, ext = os.path.splitext(fname)
+            file_path = os.path.join(ann_dir, name + ".json")
+            with open(file_path, "r", encoding="utf8") as f:
+                data = json.load(f)
+            image_path = os.path.join(img_dir, fname)
+            
+            image, size = load_image(image_path)
+            
+            boxes = [normalize_bbox(box, size) for box in data["bbox"]]
+
+
+            yield guid, {"id": str(guid), "words": data["words"], "bboxes": boxes, "ner_tags": data["labels"], "image_path": image_path}