Spaces:
Sleeping
Sleeping
File size: 7,393 Bytes
6fc683c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 |
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)
|