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layoutlmv3.py

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+import json
+import os
+import ast
+from pathlib import Path
+import datasets
+from PIL import Image
+import pandas as pd
+
+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 = []
+
+'''Edit your working directory folder path here if required. 
+If this file is in the same folder as the "layoutlmv3" folder keep it as it is.
+'''
+data_path = r'./' 
+
+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 = ['num_facture','date_facture','fournisseur','client','mat_client','mat_fournisseur','tva','pourcentage_tva','remise','pourcentage_remise','timbre','fodec','ttc','devise','net_ht'] #Enter the list of labels that you have here.
+                            )
+                    ),
+                    "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"""
+        dest = os.path.join(data_path, 'layoutlmv3')
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(dest, "train.txt"), "dest": dest}
+            ),            
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(dest, "test.txt"), "dest": dest}
+            ),
+        ]
+
+    def _generate_examples(self, filepath, dest):
+
+        df = pd.read_csv(os.path.join(dest, 'class_list.txt'), delimiter=',', header=None)
+        id2labels = dict(zip(df[0].tolist(), df[1].tolist()))
+
+
+        logger.info("⏳ Generating examples from = %s", filepath)
+
+        item_list = []
+        with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
+            for line in f:
+                item_list.append(line.rstrip('\n\r'))
+        print(item_list)
+        for guid, fname in enumerate(item_list):
+            print(fname)
+            data = ast.literal_eval(fname)
+            image_path = os.path.join(dest, data['file_name'])
+            image, size = load_image(image_path)
+            boxes = data['bboxes']
+
+            text = data['tokens']
+            label = data['ner_tags']
+            
+            #print(boxes)
+            #for i in boxes:
+            #  print(i)
+            boxes = [normalize_bbox(box, size) for box in boxes]
+            flag=0
+            #print(image_path)
+            for i in boxes:
+              #print(i)
+              for j in i:
+                if j>1000:
+                  flag+=1
+                  #print(j)
+                  pass
+            if flag>0: print(image_path)
+ 
+            yield guid, {"id": str(guid), "tokens": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path, "image": image}