Add dataset loading script for train tickets dataset

train_tickets-yu2020pick.py

@@ -0,0 +1,191 @@
+# coding=utf-8
+import json
+import os
+import csv
+import re
+
+import datasets
+
+from PIL import Image
+import numpy as np
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{yu2021pick,
+               title={PICK: Processing key information extraction from documents using improved graph learning-convolutional networks},
+               author={Yu, Wenwen and Lu, Ning and Qi, Xianbiao and Gong, Ping and Xiao, Rong},
+               booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
+               pages={4363--4370},
+               year={2021},
+               organization={IEEE}
+}
+"""
+_DESCRIPTION = """\
+The train ticket is fixed layout dataset, however, it contains background noise and imaging distortions.
+It contains 1,530 synthetic images and 320 real images for training, and 80 real images for testing.
+Every train ticket has eight key text fields including ticket number, starting station, train number, destination station, date, ticket rates, seat category, and name.
+This dataset mainly consists of digits, English characters, and Chinese characters.
+"""
+
+_URL = """\
+https://drive.google.com/file/d/1o8JktPD7bS74tfjz-8dVcZq_uFS6YEGh/view?usp=sharing
+"""
+
+
+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]),
+    ]
+
+
+class TrainTicketsConfig(datasets.BuilderConfig):
+    """BuilderConfig for train_tickets"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for train_tickets.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TrainTicketsConfig, self).__init__(**kwargs)
+
+
+class TrainTickets(datasets.GeneratorBasedBuilder):
+    """train tickets"""
+
+    BUILDER_CONFIGS = [
+        TrainTicketsConfig(
+            name="train_tickets-yu2020pick",
+            version=datasets.Version("1.0.0"),
+            description="Chinese train tickets",
+        ),
+    ]
+
+    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=[
+                                "O",
+                                "S-DATE",
+                                "S-DESTINATION_STATION",
+                                "S-NAME",
+                                "S-SEAT_CATEGORY",
+                                "S-STARTING_STATION",
+                                "S-TICKET_NUM",
+                                "S-TICKET_RATES",
+                                "S-TRAIN_NUM",
+                            ]
+                        )
+                    ),
+                    "image_path": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/wenwenyu/PICK-pytorch",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(
+            "https://drive.google.com/uc?export=download&id=1o8JktPD7bS74tfjz-8dVcZq_uFS6YEGh"
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filelist": f"{downloaded_file}/train_tickets/synth1530_real320_baseline_trainset.csv"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filelist": f"{downloaded_file}/train_tickets/real80_baseline_testset.csv"
+                },
+            ),
+        ]
+
+    # based on https://github.com/wenwenyu/PICK-pytorch/blob/master/data_utils/documents.py#L229
+    def _read_gt_file_with_box_entity_type(self, filepath: str):
+        with open(filepath, "r", encoding="utf-8") as f:
+            document_text = f.read()
+
+        # match pattern in document: index,x1,y1,x2,y2,x3,y3,x4,y4,transcript,box_entity_type
+        regex = (
+            r"^\s*(-?\d+)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,"
+            r"\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,\s*(-?\d+\.?\d*)\s*,(.*),(.*)\n?$"
+        )
+
+        matches = re.finditer(regex, document_text, re.MULTILINE)
+
+        res = []
+        for _, match in enumerate(matches, start=1):
+            points = [int(match.group(i)) for i in range(2, 10)]
+            x = points[0:8:2]
+            y = points[1:8:2]
+            x1 = min(x)
+            y1 = min(y)
+            x2 = max(x)
+            y2 = max(y)
+            transcription = str(match.group(10))
+            entity_type = str(match.group(11))
+            res.append((x1, y1, x2, y2, transcription, entity_type))
+        return res
+
+    def _generate_examples(self, filelist):
+        logger.info("⏳ Generating examples from = %s", filelist)
+
+        ann_dir = os.path.join(os.path.dirname(filelist), "boxes_trans")
+        img_dir = os.path.join(os.path.dirname(filelist), "images1930")
+        print(ann_dir)
+
+        with open(filelist) as csv_file:
+            csv_reader = csv.reader(csv_file, delimiter=",")
+            for row in csv_reader:
+                guid = row[0]
+                # document_type = row[1]
+                filename = row[2]
+
+                words = []
+                bboxes = []
+                ner_tags = []
+                file_path = os.path.join(ann_dir, f"{filename}.tsv")
+                data = self._read_gt_file_with_box_entity_type(file_path)
+                image_path = os.path.join(img_dir, f"{filename}.jpg")
+                _, size = load_image(image_path)
+                for item in data:
+                    box = item[0:4]
+                    transcription, label = item[4:6]
+                    words.append(transcription)
+                    bboxes.append(normalize_bbox(box, size))
+                    if label == "other":
+                        ner_tags.append("O")
+                    else:
+                        ner_tags.append("S-" + label.upper())
+                yield guid, {
+                    "id": str(guid),
+                    "words": words,
+                    "bboxes": bboxes,
+                    "ner_tags": ner_tags,
+                    "image_path": image_path,
+                }