Upload LayoutLM_dataset.py

LayoutLM_dataset.py

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+# coding=utf-8
+'''
+Reference: https://huggingface.co/datasets/nielsr/funsd/blob/main/funsd.py
+'''
+import ast
+import os
+import random
+
+import datasets
+import matplotlib.pyplot as plt
+import pandas as pd
+from pdf2image import convert_from_path
+from PIL import Image
+
+
+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]),
+    ]
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@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 = """\
+https://guillaumejaume.github.io/FUNSD/
+"""
+
+
+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):
+    """Conll2003 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"))),
+                    "segment_class": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=["O", "B-PARTES","I-PARTES", "B-EMENTA","I-EMENTA", "B-ACORDAO","I-ACORDAO", "B-RELATORIO","I-RELATORIO", "B-VOTO", "I-VOTO"]
+                            #names=["OUTROS", "PARTES", "EMENTA","ACORDAO","RELATORIO", "VOTO"]
+                        )
+                    ),
+                    "image": datasets.features.Image(),
+                }
+            ),
+            supervised_keys=None,
+            #homepage="https://guillaumejaume.github.io/FUNSD/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract("http://direitodigital.ufms.br:8000/direitodigital_dev.zip")
+        """Returns SplitGenerators."""
+        return [
+             datasets.SplitGenerator(
+                name=datasets.NamedSplit('trainmini_stf'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/stf"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit('dev_stf'), gen_kwargs={"filepath": f"{downloaded_file}/dev/stf"}
+            ),
+             datasets.SplitGenerator(
+                name=datasets.NamedSplit('trainmini_stj'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/stj"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit('dev_stj'), gen_kwargs={"filepath": f"{downloaded_file}/dev/stj"}
+            ),
+             datasets.SplitGenerator(
+                name=datasets.NamedSplit('trainmini_trf2'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/trf2"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit('dev_trf2'), gen_kwargs={"filepath": f"{downloaded_file}/dev/trf2"}
+            ),
+             datasets.SplitGenerator(
+                name=datasets.NamedSplit('trainmini_tjpb'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/tjpb"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit('dev_tjpb'), gen_kwargs={"filepath": f"{downloaded_file}/dev/tjpb"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit('trainmini_tjmg'), gen_kwargs={"filepath": f"{downloaded_file}/trainmini/tjmg"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.NamedSplit('dev_tjmg'), gen_kwargs={"filepath": f"{downloaded_file}/dev/tjmg"}
+            )
+        ]
+
+    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():
+        guid = 0
+
+        file_paths = [
+            os.path.join(root, filename)
+            for root, dirs, files in os.walk(dataset_path)
+            for filename in files
+            if filename.endswith('.tsv')
+        ]
+        random.shuffle(file_paths)
+        
+        
+        #for dir_path, _, file_names in os.walk(filepath):
+        for tsv_name in file_paths:
+            #for file in file_names:
+                #tsv_name = os.path.join(dir_path, file)
+                
+                pdf_name = tsv_name.replace('/LayoutLM_dataset/', '/pdfs/').replace('.tsv', '.pdf')
+                
+                img_path = tsv_name.replace('.tsv','')
+                pages_img = convert_from_path(pdf_name, size=(762,1000),fmt="png")
+
+                dataframe = pd.read_csv(tsv_name ,delimiter='\t', keep_default_na=False).replace(["None","SUMULA","CERTIDAO_DE_JULGAMENTO","AUTUACAO","CERTIDAO","EXTRATO_DE_ATA"], 'OUTROS')
+                for page in dataframe['page'].unique():
+                    #image, size = load_image(os.path.join(img_path, str(page-1)+'.png'))
+                    image, size = pages_img[page-1], pages_img[page-1].size
+                    
+                    data = (dataframe[dataframe["page"] == page])
+                    form = []
+                    for index,  row in data.iterrows():
+                        tokens = []
+                        for token in ast.literal_eval(row['tokens']):
+
+                            tokens.append({
+                                    'box' : 
+                                        [token['x'], token['y'], token['x']+token['width'], token['y'] + token['height']],
+                                    'text' : token['text']
+                            })
+                        line_dict = {
+                            'text': row['text'],
+                            'box': [row['x'], row['y'], row['x']+row['width'], row['y'] + row['height']],
+                            'label': row['label'],
+                            'words': tokens
+                        }
+                        form.append(line_dict)
+                    yield from self.get_form(guid, image, size, form)
+                    guid += 1   
+        
+    
+    def get_form(self, guid, image, size, form):
+        tokens = []
+        bboxes = []
+        segment_class = []
+
+        for item in 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 == "OUTROS":
+                for w in words:
+                    tokens.append(w["text"])
+                    segment_class.append("O")
+                    #segment_class.append(label)
+                    cur_line_bboxes.append(normalize_bbox(w["box"], size))
+            else:
+                tokens.append(words[0]["text"])
+                segment_class.append("B-" + label.upper())
+                cur_line_bboxes.append(normalize_bbox(words[0]["box"], size))
+                for w in words[1:]:
+                    tokens.append(w["text"])
+                    segment_class.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, "segment_class": segment_class,
+                         "image": image}
+
+def main():
+    dataset = Funsd()
+    for example in dataset._generate_examples('/home/marlon/LayoutLM_dataset/trainmini'):
+        print(example)
+        
+
+
+
+
+if __name__ == '__main__':
+    main()