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import datasets
import json
from PIL import Image
def train_data_format(json_to_dict: list):
final_list = []
count = 0
for item in json_to_dict:
count = count + 1
# print(item['annotations'])
test_dict = {"id": int, "tokens": [], "bboxes": [], "ner_tags": []}
# test_dict = {"tokens": [], "bboxes": [], "ner_tags": []}
test_dict["id"] = count
# test_dict["img_path"] = item["file_name"]
# print(item)
test_dict["image"] = Image.open(item["file_name"]).convert("RGB")
# test_dict["image"] = item["file_name"]
for cont in item["annotations"]:
test_dict["tokens"].append(cont["text"])
test_dict["bboxes"].append(cont["box"])
test_dict["ner_tags"].append(cont["label"])
final_list.append(test_dict)
# print(final_list)
return final_list
def read_json(json_path: str) -> dict:
with open(json_path, "r") as fp:
data = json.loads(fp.read())
return data
class MyDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
"image": datasets.Image(),
"tokens": datasets.Sequence(datasets.Value("string")),
"bboxes": datasets.Sequence(
datasets.Sequence(datasets.Value("int32"))
),
"ner_tags": datasets.Sequence(
datasets.ClassLabel(
num_classes=3,
names=["Other", "Patient_name", "Patient_address"],
)
),
}
)
)
def _split_generators(self, dl_manager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": "Training_layoutLMV3.json",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": "Training_layoutLMV3.json",
},
),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
# print(filepath)
# print(read_json(filepath))
# print(train_data_format(read_json(filepath)))
for id_, row in enumerate(train_data_format(read_json(filepath))):
yield id_, row
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