Delete wildreceipt-layoutlmv3.py

wildreceipt-layoutlmv3.py

@@ -1,133 +0,0 @@
-import json
-import os
-from pathlib import Path
-import datasets
-from PIL import Image
-import pandas as pd
-
-logger = datasets.logging.get_logger(__name__)
-_CITATION = """\
-@article{Sun2021SpatialDG,
-  title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction},
-  author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang},
-  journal={ArXiv},
-  year={2021},
-  volume={abs/2103.14470}
-}
-"""
-_DESCRIPTION = """\
-WildReceipt is a collection of receipts. It contains, for each photo, a list of OCRs - with the bounding box, text, and class. It contains 1765 photos, with 25 classes, and 50000 text boxes. The goal is to benchmark "key information extraction" - extracting key information from documents
-https://arxiv.org/abs/2103.14470
-
-"""
-
-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]),
-    ]
-
-
-_URLS = ["https://download.openmmlab.com/mmocr/data/wildreceipt.tar"]
-
-class DatasetConfig(datasets.BuilderConfig):
-    """BuilderConfig for WildReceipt Dataset"""
-    def __init__(self, **kwargs):
-        """BuilderConfig for WildReceipt Dataset.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(DatasetConfig, self).__init__(**kwargs)
-
-
-class WildReceipt(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        DatasetConfig(name="WildReceipt", version=datasets.Version("1.0.0"), description="WildReceipt 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 = ['Ignore',  'Store_name_value', 'Store_name_key', 'Store_addr_value', 'Store_addr_key', 'Tel_value', 'Tel_key', 'Date_value', 'Date_key', 'Time_value', 'Time_key', 'Prod_item_value', 'Prod_item_key', 'Prod_quantity_value', 'Prod_quantity_key', 'Prod_price_value', 'Prod_price_key', 'Subtotal_value', 'Subtotal_key', 'Tax_value', 'Tax_key', 'Tips_value', 'Tips_key', 'Total_value', 'Total_key', 'Others']
-                            )
-                    ),
-                    "image_path": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            citation=_CITATION,
-            homepage="",
-        )
-
-
-
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        """Uses local files located with data_dir"""
-        downloaded_file = dl_manager.download_and_extract(_URLS)
-        dest = Path(downloaded_file[0])/'wildreceipt'
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": dest/"train.txt", "dest": dest}
-            ),            
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": dest/"test.txt", "dest": dest}
-            ),
-        ]
-
-    def _generate_examples(self, filepath, dest):
-
-        df = pd.read_csv(dest/'class_list.txt', delimiter='\s', header=None)
-        id2labels = dict(zip(df[0].tolist(), df[1].tolist()))
-
-
-        logger.info("⏳ Generating examples from = %s", filepath)
-
-        item_list = []
-        with open(filepath, 'r') as f:
-            for line in f:
-                item_list.append(line.rstrip('\n\r'))
-        
-        for guid, fname in enumerate(item_list):
-
-            data = json.loads(fname)
-            image_path = dest/data['file_name']
-            image, size = load_image(image_path)
-            boxes = [[i['box'][6], i['box'][7], i['box'][2], i['box'][3]] for i in data['annotations']]
-
-            text = [i['text'] for i in data['annotations']]
-            label = [id2labels[i['label']] for i in data['annotations']]
-            
-            #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), "words": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path}