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216 lines (196 loc) · 7.22 KB
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import argparse
import json
import os
from PIL import Image
from transformers import AutoTokenizer
def bbox_string(box, width, length):
return (
str(int(1000 * (box[0] / width)))
+ " "
+ str(int(1000 * (box[1] / length)))
+ " "
+ str(int(1000 * (box[2] / width)))
+ " "
+ str(int(1000 * (box[3] / length)))
)
def actual_bbox_string(box, width, length):
return (
str(box[0])
+ " "
+ str(box[1])
+ " "
+ str(box[2])
+ " "
+ str(box[3])
+ "\t"
+ str(width)
+ " "
+ str(length)
)
def convert(args):
with open(
os.path.join(args.output_dir, args.data_split + ".txt.tmp"),
"w",
encoding="utf8",
) as fw, open(
os.path.join(args.output_dir, args.data_split + "_box.txt.tmp"),
"w",
encoding="utf8",
) as fbw, open(
os.path.join(args.output_dir, args.data_split + "_image.txt.tmp"),
"w",
encoding="utf8",
) as fiw:
for file in os.listdir(args.data_dir):
file_path = os.path.join(args.data_dir, file)
with open(file_path, "r", encoding="utf8") as f:
data = json.load(f)
image_path = file_path.replace("annotations", "images")
image_path = image_path.replace("json", "png")
file_name = os.path.basename(image_path)
image = Image.open(image_path)
width, length = image.size
for item in data["form"]:
words, label = item["words"], item["label"]
words = [w for w in words if w["text"].strip() != ""]
if len(words) == 0:
continue
if label == "other":
for w in words:
fw.write(w["text"] + "\tO\n")
fbw.write(
w["text"]
+ "\t"
+ bbox_string(w["box"], width, length)
+ "\n"
)
fiw.write(
w["text"]
+ "\t"
+ actual_bbox_string(w["box"], width, length)
+ "\t"
+ file_name
+ "\n"
)
else:
if len(words) == 1:
fw.write(words[0]["text"] + "\tS-" + label.upper() + "\n")
fbw.write(
words[0]["text"]
+ "\t"
+ bbox_string(words[0]["box"], width, length)
+ "\n"
)
fiw.write(
words[0]["text"]
+ "\t"
+ actual_bbox_string(words[0]["box"], width, length)
+ "\t"
+ file_name
+ "\n"
)
else:
fw.write(words[0]["text"] + "\tB-" + label.upper() + "\n")
fbw.write(
words[0]["text"]
+ "\t"
+ bbox_string(words[0]["box"], width, length)
+ "\n"
)
fiw.write(
words[0]["text"]
+ "\t"
+ actual_bbox_string(words[0]["box"], width, length)
+ "\t"
+ file_name
+ "\n"
)
for w in words[1:-1]:
fw.write(w["text"] + "\tI-" + label.upper() + "\n")
fbw.write(
w["text"]
+ "\t"
+ bbox_string(w["box"], width, length)
+ "\n"
)
fiw.write(
w["text"]
+ "\t"
+ actual_bbox_string(w["box"], width, length)
+ "\t"
+ file_name
+ "\n"
)
fw.write(words[-1]["text"] + "\tE-" + label.upper() + "\n")
fbw.write(
words[-1]["text"]
+ "\t"
+ bbox_string(words[-1]["box"], width, length)
+ "\n"
)
fiw.write(
words[-1]["text"]
+ "\t"
+ actual_bbox_string(words[-1]["box"], width, length)
+ "\t"
+ file_name
+ "\n"
)
fw.write("\n")
fbw.write("\n")
fiw.write("\n")
def seg_file(file_path, tokenizer, max_len):
subword_len_counter = 0
output_path = file_path[:-4]
with open(file_path, "r", encoding="utf8") as f_p, open(
output_path, "w", encoding="utf8"
) as fw_p:
for line in f_p:
line = line.rstrip()
if not line:
fw_p.write(line + "\n")
subword_len_counter = 0
continue
token = line.split("\t")[0]
current_subwords_len = len(tokenizer.tokenize(token))
# Token contains strange control characters like \x96 or \x95
# Just filter out the complete line
if current_subwords_len == 0:
continue
if (subword_len_counter + current_subwords_len) > max_len:
fw_p.write("\n" + line + "\n")
subword_len_counter = current_subwords_len
continue
subword_len_counter += current_subwords_len
fw_p.write(line + "\n")
def seg(args):
tokenizer = AutoTokenizer.from_pretrained(
args.model_name_or_path, do_lower_case=True
)
seg_file(
os.path.join(args.output_dir, args.data_split + ".txt.tmp"),
tokenizer,
args.max_len,
)
seg_file(
os.path.join(args.output_dir, args.data_split + "_box.txt.tmp"),
tokenizer,
args.max_len,
)
seg_file(
os.path.join(args.output_dir, args.data_split + "_image.txt.tmp"),
tokenizer,
args.max_len,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--data_dir", type=str, default="data/training_data/annotations"
)
parser.add_argument("--data_split", type=str, default="train")
parser.add_argument("--output_dir", type=str, default="data")
parser.add_argument("--model_name_or_path", type=str, default="bert-base-uncased")
parser.add_argument("--max_len", type=int, default=510)
args = parser.parse_args()
convert(args)
seg(args)