|
|
|
import json |
|
import os |
|
|
|
import datasets |
|
|
|
from PIL import Image |
|
import numpy as np |
|
|
|
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]), |
|
] |
|
|
|
class TTFormLMConfig(datasets.BuilderConfig): |
|
"""BuilderConfig for TTForm""" |
|
|
|
def __init__(self, **kwargs): |
|
"""BuilderConfig for TTForm. |
|
|
|
Args: |
|
**kwargs: keyword arguments forwarded to super. |
|
""" |
|
super(TTFormLMConfig, self).__init__(**kwargs) |
|
|
|
class TTFormLM(datasets.GeneratorBasedBuilder): |
|
"""TTForm dataset.""" |
|
|
|
BUILDER_CONFIGS = [ |
|
TTFormLMConfig(name="ttform", version=datasets.Version("1.0.0"), description="TTFormLM dataset"), |
|
] |
|
|
|
def _info(self): |
|
return datasets.DatasetInfo( |
|
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", "B-HEADER", "I-HEADER", "B-QUESTION", "I-QUESTION", "B-ANSWER", "I-ANSWER"] |
|
) |
|
), |
|
"image_path": datasets.Value("string"), |
|
} |
|
), |
|
supervised_keys=None |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
"""Returns SplitGenerators.""" |
|
downloaded_file = dl_manager.download_and_extract("https://drive.google.com/uc?export=download&id=18ytJQIAE4wFtE5fDhnlFW5zcRWI_tJjR") |
|
return [ |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_data/"} |
|
), |
|
datasets.SplitGenerator( |
|
name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/testing_data/"} |
|
), |
|
] |
|
|
|
def _generate_examples(self, filepath): |
|
logger.info("⏳ Generating examples from = %s", filepath) |
|
ann_dir = os.path.join(filepath, "annotations") |
|
img_dir = os.path.join(filepath, "images") |
|
for guid, file in enumerate(sorted(os.listdir(ann_dir))): |
|
words = [] |
|
bboxes = [] |
|
ner_tags = [] |
|
file_path = os.path.join(ann_dir, file) |
|
with open(file_path, "r", encoding="utf8") as f: |
|
data = json.load(f) |
|
image_path = os.path.join(img_dir, file) |
|
image_path = image_path.replace("json", "png") |
|
image, size = load_image(image_path) |
|
for item in data["form"]: |
|
words_example, label = item["words"], item["label"] |
|
words_example = [w for w in words_example if w["text"].strip() != ""] |
|
if len(words_example) == 0: |
|
continue |
|
if label == "other": |
|
for w in words_example: |
|
words.append(w["text"]) |
|
ner_tags.append("O") |
|
bboxes.append(normalize_bbox(w["box"], size)) |
|
else: |
|
words.append(words_example[0]["text"]) |
|
ner_tags.append("B-" + label.upper()) |
|
bboxes.append(normalize_bbox(words_example[0]["box"], size)) |
|
for w in words_example[1:]: |
|
words.append(w["text"]) |
|
ner_tags.append("I-" + label.upper()) |
|
bboxes.append(normalize_bbox(w["box"], size)) |
|
yield guid, {"id": str(guid), "words": words, "bboxes": bboxes, "ner_tags": ner_tags, "image_path": image_path} |
|
|
