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import json
from pycocotools.coco import COCO
from tqdm import tqdm
import string
import re
def extract_object_name(text):
parts = text.split("is")
if len(parts) > 1:
return parts[1].strip()
return None
text_pth = "/home/yuqian_fu/Projects/DAVIS_test_gap20.json"
save_path = "/home/yuqian_fu/Projects/DAVIS_test_gap20_instruction.json"
new_data = []
sent_id = 0
with open(text_pth, "r") as fp:
datas = json.load(fp)
# data是一帧帧图片
for data in datas:
instruct_list = []
# new_annos = []
for anno in data["first_frame_anns"]:
text = anno["text"]
# 提取is之后的句子
# raw = extract_object_name(text)
#将raw变小写
raw_lower = text.lower()
# 删除 "green" 并去掉多余的空格
# result = raw_lower.replace("green", "").strip()
# 删除objname中的序号：ball_0 --> ball
result = re.sub(r'_\d+$', '', raw_lower)
# 删除所有标点符号
sent = result.translate(str.maketrans('', '', string.punctuation))
tokens = sent.split()
sample = {
"tokens": tokens,
"raw": text,
"sent_id": sent_id,
"sent": sent
}
# anno["llava_text"] = sent
# new_annos.append(anno)
sent_id += 1
instruct_list.append(sample)
# del anno["text"] #debug
data["instruction"] = instruct_list
# data["first_frame_anns"] = new_annos
# del data["instruction"] #debug
new_data.append(data)
print(sent_id)
print("len of new_data: ", len(new_data))
with open(save_path, "w") as fp:
json.dump(new_data, fp)
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