|
|
import json |
|
|
import random |
|
|
from tqdm import tqdm |
|
|
DEFAULT_IMAGE_TOKEN = '<image>' |
|
|
|
|
|
SHORT_QUESTION = [ |
|
|
"Can you segment the {class_name} in this image?", |
|
|
"Please segment the {class_name} in this image.", |
|
|
"What is {class_name} in this image? Please respond with segmentation mask.", |
|
|
"What is {class_name} in this image? Please output segmentation mask.", |
|
|
"Could you identify and segment the {class_name} in this image?", |
|
|
"Would you be able to segment the {class_name} in this image?", |
|
|
"Can you provide a segmentation mask for the {class_name} in this image?", |
|
|
"Please provide a segmentation mask for the {class_name} in this image.", |
|
|
"Could you please segment the {class_name} in this image for me?", |
|
|
"What {class_name} is present in this image? Kindly respond with a segmentation mask.", |
|
|
"Which part of this image contains {class_name}? Please output with segmentation mask.", |
|
|
"Is there a {class_name} in this image? If so, please provide the segmentation mask.", |
|
|
"Can you segment out the {class_name} visible in this image?", |
|
|
"Would you identify and provide a segmentation mask for the {class_name} in this image?", |
|
|
] |
|
|
|
|
|
ANSWER_LIST = [ |
|
|
"It is [SEG].", |
|
|
"Sure, [SEG].", |
|
|
"Sure, it is [SEG].", |
|
|
"Sure, the segmentation result is [SEG].", |
|
|
"[SEG].", |
|
|
] |
|
|
|
|
|
|
|
|
|
|
|
json_data = json.load(open('refcoco+.json')) |
|
|
final_data = [] |
|
|
idx = 0 |
|
|
for data in tqdm(json_data): |
|
|
if len(data['masks'])!=len(data['cat']): |
|
|
print('err') |
|
|
indices = list(range(len(data['masks']))) |
|
|
|
|
|
|
|
|
random.shuffle(indices) |
|
|
|
|
|
data['masks'] = [data['masks'][i] for i in indices] |
|
|
data['cat'] = [data['cat'][i] for i in indices] |
|
|
|
|
|
s = 0 |
|
|
while s<len(data['masks']): |
|
|
num = random.randint(2, 5) |
|
|
dic = {} |
|
|
dic['id'] = f'refcoco+_{idx}' |
|
|
idx+=1 |
|
|
dic['image'] = data['image'] |
|
|
dic['height'] = data['height'] |
|
|
dic['width'] = data['width'] |
|
|
dic['conversations'] = [] |
|
|
dic['masks'] = [] |
|
|
for i in range(num): |
|
|
if i+s>=len(data['masks']): |
|
|
break |
|
|
dic['conversations'].append({'from': 'human', 'value': random.choice(SHORT_QUESTION).format(class_name=data['cat'][i+s].lower())}) |
|
|
dic['conversations'].append({'from': 'gpt', 'value': random.choice(ANSWER_LIST)}) |
|
|
dic['masks'].append(data['masks'][i+s]) |
|
|
dic['conversations'][0]['value'] = '<image>\n' + dic['conversations'][0]['value'] |
|
|
s+=num |
|
|
final_data.append(dic) |
|
|
print(len(final_data)) |
|
|
|
|
|
with open('refcoco+_seg.json', 'w') as f: |
|
|
f.write(json.dumps(final_data, indent=4)) |
|
|
|
