Upload batch123.py

batch123.py

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+# coding=utf-8
+import json
+import os
+import datasets
+from PIL import Image
+import numpy as np
+logger = datasets.logging.get_logger(__name__)
+_CITATION = """\\n@article{Jaume2019FUNSDAD,
+  title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
+  author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
+  journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
+  year={2019},
+  volume={2},
+  pages={1-6}
+}
+"""
+_DESCRIPTION = """\\nhttps://guillaumejaume.github.io/FUNSD/
+"""
+def load_image(image_path):
+    image = Image.open(image_path).convert("RGB")
+    w, h = image.size
+    # resize image to 224x224
+    image = image.resize((224, 224))
+    image = np.asarray(image)  
+    image = image[:, :, ::-1] # flip color channels from RGB to BGR
+    image = image.transpose(2, 0, 1) # move channels to first dimension
+    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]),
+    ]
+class FunsdConfig(datasets.BuilderConfig):
+    """BuilderConfig for FUNSD"""
+    def __init__(self, **kwargs):
+        """BuilderConfig for FUNSD.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FunsdConfig, self).__init__(**kwargs)
+class Funsd(datasets.GeneratorBasedBuilder):
+    """FUNSD dataset."""
+    BUILDER_CONFIGS = [
+        FunsdConfig(name="funsd", version=datasets.Version("1.0.0"), description="FUNSD dataset"),
+    ]
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "bboxes": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=["O", 'S-HOSPITAL-NAME', 'S-MRN', 
+                                   'S-PAID-AMOUNT', 'I-HOSPITAL-NAME', 'S-PATIENT-NAME', 
+                                   'S-PATIENT-NRIC', 'S-RECEIPT-DATE', 'S-RECEIPT-NO', 'B-MRN',
+                                   'S-TOTAL', 'S-TREATING-DOCTOR', 'S-TREATMENT-DATE', 
+                                   'B-HOSPITAL-NAME', 'I-MRN', 'B-PATIENT-NRIC', 
+                                   'I-PATIENT-NRIC', 'B-PAID-AMOUNT', 'I-PAID-AMOUNT', 
+                                   'B-PATIENT-NAME', 'I-PATIENT-NAME', 'B-RECEIPT-DATE', 
+                                   'I-RECEIPT-DATE', 'B-TOTAL', 'I-TOTAL', 'B-TREATING-DOCTOR', 
+                                   'I-TREATING-DOCTOR', 'B-TREATMENT-DATE', 'B-RECEIPT-NO', 
+                                   'I-RECEIPT-NO', 'I-TREATMENT-DATE']
+                        )
+                    ),
+                    #"image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
+                    "image_path": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://guillaumejaume.github.io/FUNSD/",
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        url = 'https://transfer.sh/h1YqN8/datafiles.zip'
+        downloaded_file = dl_manager.download_and_extract(url)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/data/training_data/"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/data/testing_data/"}
+            ),
+        ]
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        ann_dir = os.path.join(filepath, "annotations")
+        img_dir = os.path.join(filepath, "images")
+        for guid, file in enumerate(sorted(os.listdir(ann_dir))):
+            tokens = []
+            bboxes = []
+            ner_tags = []
+            file_path = os.path.join(ann_dir, file)
+            with open(file_path, "r", encoding="utf8") as f:
+                data = json.load(f)
+            image_path = os.path.join(img_dir, file)
+            image_path = image_path.replace("json", "png")
+            image, size = load_image(image_path)
+            for item in data["form"]:
+                words, label = item["words"], item["label"]
+                words = [w for w in words if w["text"].strip() != ""]
+                if len(words) == 0:
+                    continue
+                if label == "others":
+                    for w in words:
+                        tokens.append(w["text"])
+                        ner_tags.append("O")
+                        bboxes.append(normalize_bbox(w["box"], size))
+                else:
+                    tokens.append(words[0]["text"])
+                    ner_tags.append("B-" + label.upper())
+                    bboxes.append(normalize_bbox(words[0]["box"], size))
+                    for w in words[1:]:
+                        tokens.append(w["text"])
+                        ner_tags.append("I-" + label.upper())
+                        bboxes.append(normalize_bbox(w["box"], size))
+            yield guid, {"id": str(guid), "tokens": tokens, 
+                         "bboxes": bboxes, "ner_tags": ner_tags, 
+                         "image_path": image_path}