Upload cvdataset-layoutlmv3.py (#5)

- Upload cvdataset-layoutlmv3.py (99ec04f5ef5054baf038d8285bc4b84c189701b5)

cvdataset-layoutlmv3.py

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+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{LayoutLmv3 for CV extractions,
+  title={LayoutLmv3for Key Information Extraction},
+  author={MisaR&D Team},
+  year={2022},
+}
+"""
+_DESCRIPTION = """\
+CV is a collection of receipts. It contains, for each photo about cv personal, a list of OCRs - with the bounding box, text, and class. 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]),
+    ]
+
+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/"),
+]
+
+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="CV Extractions", 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, 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']]
+            
+            boxes = [normalize_bbox(box, size) for box in boxes]
+            
+            flag=0
+            for i in boxes:
+              for j in i:
+                if j>1000:
+                  flag+=1
+                  pass
+            if flag>0: print(image_path)
+ 
+            yield guid, {"id": str(guid), "words": text, "bboxes": boxes, "ner_tags": label, "image_path": image_path}