File size: 1,511 Bytes
e9ed9dc | 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 | # encoding: utf-8
# a = input("please input a number:")
import pdb
import torch
import json
import pdb
vigil = json.load(open('/mnt/petrelfs/wangzehan/LLaVA-Next-3D/data/processed/intent3d_id_v3_val_llava_style.json', 'r'))
# vigil = json.load(open('/mnt/petrelfs/wangzehan/LLaVA-Next-3D/data/processed/vigil3d_scannet_id_v2_val_llava.json', 'r'))
scanrefer = json.load(open('/mnt/petrelfs/wangzehan/LLaVA-Next-3D/data/processed/scanrefer_vg_val_llava_style.json', 'r'))
box_info = json.load(open('/mnt/petrelfs/wangzehan/LLaVA-Next-3D/data/metadata/scannet_val_gt_box.json', 'r'))
num_id = 0
vigil_val = []
for vigil_item in vigil:
obj_ids = vigil_item['metadata']['object_id']
if len(obj_ids) == 0:
box = []
else:
box = [box_info[vigil_item['video']][int(obj_id)] for obj_id in obj_ids]
desc = vigil_item['conversations'][0]['value'].split('\n')[1]
new_item = {"id": num_id, "video": vigil_item['video'], "conversations": [
{"value": f"<image>Identify the object according to the following description.\n{desc}", "from": "human"},
{"value": "<ground>", "from": "gpt"}],
"box": box,
"metadata": {"dataset": "vigil", "question_type": "multiple", "ann_id": num_id, "object_id": obj_ids}}
vigil_val.append(new_item)
num_id += 1
with open('/mnt/petrelfs/wangzehan/LLaVA-Next-3D/extra_data/Other_VG/intent3d_full_vg_val_llava_style.json', 'w') as f:
json.dump(vigil_val, f, indent=4)
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