Delete cvdataset-layoutlmv3.py

cvdataset-layoutlmv3.py

@@ -1,130 +0,0 @@
-import os
-from pathlib import Path
-import datasets
-from PIL import Image
-import pandas as pd
-import json
-
-logger = datasets.logging.get_logger(__name__)
-
-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]),
-    ]
-
-def _get_drive_url(url):
-    base_url = 'https://drive.google.com/uc?id='
-    split_url = url.split("/")
-    
-    return base_url + split_url[5]
-
-_URLS = [
-    _get_drive_url("https://drive.google.com/file/d/1KdDBmGP96lFc7jv2Bf4eqrO121ST-TCh/"),
-]
-
-_CITATION = """\
-@article{liharding-nguyen,
-  title={CVDS: A Dataset for CV Form Understanding},
-  author={MISA - employees},
-  year={2022},
-}
-"""
-
-_DESCRIPTION = """\
-Dataset for key information extraction with cv form understanding
-"""
-
-class DatasetConfig(datasets.BuilderConfig):
-    """BuilderConfig for CV Dataset"""
-    def __init__(self, **kwargs):
-        """BuilderConfig for CV Dataset.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(DatasetConfig, self).__init__(**kwargs)
-        
-class CVDS(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        DatasetConfig(name="CVDS", version=datasets.Version("1.0.0"), description="CV 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=['person_name', 'dob_key', 'dob_value', 'gender_key', 'gender_value', 'phonenumber_key', 'phonenumber_value', 'email_key', 'email_value', 'address_key', 'address_value', 'socical_address_value', 'education', 'education_name', 'education_time', 'experience', 'experience_name', 'experience_time', 'information', 'undefined']
-                        )
-                    ),
-                    "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])/'data1'
-
-        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, file_path, dest):
-        df = pd.read_csv(dest/"class_list.txt", delimiter="\s", header=None)
-        id2label = dict(zip(df[0].tolist(), df[1].tolist()))
-        
-        logger.info("⏳ Generating examples from = %s", file_path)
-        
-        item_list = []
-        with open(file_path, "r", encoding="utf8") 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)
-            
-            bboxes = [[i["box"][6], i["box"][7], i["box"][2]. i["box"][3]] for i in data["annotations"]]
-            word = [i['text'] for i in data["annotations"]]
-            label = [id2label[i["label"]] for i in data["annotations"]]
-            
-            bboxes = [normalize_bbox(box, size) for box in bboxes]
-            
-            flag=0
-            for i in bboxes:
-                for j in i:
-                    if j > 1000:
-                        flag+=1
-                        pass
-            
-            if flag > 0:
-                print(image_path)
-                
-            yield guid, {"id": str(guid), "words": word, "bboxes": bboxes, "ner_tags": label, "image_path": image_path}