Upload layoutlmv3.py

layoutlmv3.py

@@ -0,0 +1,143 @@
+import json
+import os
+import ast
+from pathlib import Path
+import datasets
+from PIL import Image
+import pandas as pd
+import glob
+
+logger = datasets.logging.get_logger(__name__)
+_CITATION = """\
+@article{,
+  title={},
+  author={},
+  journal={},
+  year={},
+  volume={}
+}
+"""
+_DESCRIPTION = """\
+This is a sample dataset for training layoutlmv3 model on custom annotated data.
+"""
+
+def load_image(image_path):
+    image = Image.open(image_path).convert("RGB")
+    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]),
+    ]
+
+_URLS = []
+data_dir = r"D:\Study\LayoutLMV3\data_ne"
+
+class DatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for InvoiceExtraction Dataset"""
+    def __init__(self, **kwargs):
+        """BuilderConfig for InvoiceExtraction Dataset.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(DatasetConfig, self).__init__(**kwargs)
+
+class InvoiceExtraction(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        DatasetConfig(name="InvoiceExtraction", version=datasets.Version("1.0.0"), description="InvoiceExtraction 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 = [ 'address', 'company_name', 'customer_id', 'invoice_id', 'invoice_date', 'invoice_total', 'sub_total', 'total_tax', 'item', 'amount',
+]
+                            )
+                    ),
+                    "image_path": datasets.Value("string"),
+                    "image": datasets.features.Image()
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+            homepage="",
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        """Uses local files located with data_dir"""
+        return [
+        datasets.SplitGenerator(
+            name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "dataset/training_data/")}
+        ),
+        datasets.SplitGenerator(
+            name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "dataset/testing_data/")}
+        ),
+    ]
+
+    def get_line_bbox(self, bboxs):
+        x = [bboxs[i][j] for i in range(len(bboxs)) for j in range(0, len(bboxs[i]), 2)]
+        y = [bboxs[i][j] for i in range(len(bboxs)) for j in range(1, len(bboxs[i]), 2)]
+
+        x0, y0, x1, y1 = min(x), min(y), max(x), max(y)
+
+        assert x1 >= x0 and y1 >= y0
+        bbox = [[x0, y0, x1, y1] for _ in range(len(bboxs))]
+        return bbox
+
+    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.replace('.json', '.png'))  # Adjust the file extension
+            print("Image Path:", image_path)  # Add this line
+            image, size = load_image(image_path)
+        
+            for item in data["form"]:
+                cur_line_bboxes = []
+                words, label = item["words"], item["label"]
+                words = [w for w in words if w["text"].strip() != ""]
+            
+                if len(words) == 0:
+                    continue
+            
+                if label == "other":
+                    for w in words:
+                        tokens.append(w["text"])
+                        ner_tags.append("O")
+                        cur_line_bboxes.append(normalize_bbox(w["box"], size))
+                else:
+                    tokens.append(words[0]["text"])
+                    ner_tags.append("B-" + label.upper())
+                    cur_line_bboxes.append(normalize_bbox(words[0]["box"], size))
+                
+                    for w in words[1:]:
+                        tokens.append(w["text"])
+                        ner_tags.append("I-" + label.upper())
+                        cur_line_bboxes.append(normalize_bbox(w["box"], size))
+            
+                cur_line_bboxes = self.get_line_bbox(cur_line_bboxes)
+                bboxes.extend(cur_line_bboxes)
+
+            yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags, "image": image}