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prepare_nr3d_annos.py
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prepare_nr3d_annos.py
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import numpy as np
import json
import sys
sys.path.append('.')
import torch
import random
from tqdm import tqdm
from collections import defaultdict
import argparse
from utils.box_utils import get_box3d_min_max, box3d_iou, construct_bbox_corners
from prompts.prompts import grounding_prompt
import csv
import string
def is_explicitly_view_dependent(tokens):
"""
:param df: pandas dataframe with "tokens" columns
:return: a boolean mask
"""
target_words = {'front', 'behind', 'back', 'right', 'left', 'facing', 'leftmost', 'rightmost',
'looking', 'across'}
return len(set(tokens).intersection(target_words)) > 0
parser = argparse.ArgumentParser()
parser.add_argument('--segmentor', required=True, type=str)
parser.add_argument('--version', type=str, default='')
parser.add_argument('--train_iou_thres', type=float, default=0.75)
parser.add_argument('--max_obj_num', type=int, default=150)
args = parser.parse_args()
segmentor = args.segmentor
version = args.version
train_scenes = [x.strip() for x in open('annotations/scannet/scannetv2_train.txt').readlines()]
val_scenes = [x.strip() for x in open('annotations/scannet/scannetv2_val.txt').readlines()]
scene_lists = {
'train': train_scenes,
'val': val_scenes
}
raw_annos = []
with open('annotations/referit3d/nr3d.csv') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
raw_annos.append({
'scene_id': row['scan_id'],
'obj_id': int(row['target_id']),
'description': row['utterance'],
'stimulus_id': row['stimulus_id'],
'tokens': row['tokens']
})
# split = 'train'
# annos = [anno for anno in raw_annos if anno['scene_id'] in scene_lists[split]]
# new_annos = []
# if segmentor == 'deva':
# seg_gt_ious = torch.load(f"annotations/scannet_{segmentor}_seg_gt_ious.pt", map_location='cpu')
# else:
# instance_attribute_file = f"annotations/scannet_{segmentor}_{split}_attributes{version}.pt"
# scannet_attribute_file = f"annotations/scannet_{split}_attributes.pt"
# instance_attrs = torch.load(instance_attribute_file, map_location='cpu')
# scannet_attrs = torch.load(scannet_attribute_file, map_location='cpu')
# iou25_count = 0
# iou50_count = 0
# count = [0] * args.max_obj_num
# for anno in tqdm(annos):
# scene_id = anno['scene_id']
# obj_id = anno['obj_id']
# desc = anno['description']
# if desc[-1] in string.punctuation:
# desc = desc[:-1]
# prompt = random.choice(grounding_prompt).replace('<description>', desc)
# if segmentor == 'deva':
# if scene_id not in seg_gt_ious:
# continue
# seg_gt_iou = seg_gt_ious[scene_id]
# if obj_id >= seg_gt_iou.shape[1]:
# continue
# max_iou, max_id = seg_gt_iou[:, obj_id].max(0)
# max_iou = float(max_iou)
# max_id = int(max_id)
# else:
# if scene_id not in instance_attrs:
# continue
# instance_locs = instance_attrs[scene_id]['locs']
# scannet_locs = scannet_attrs[scene_id]['locs']
# max_iou, max_id = -1, -1
# for pred_id in range(instance_locs.shape[0]):
# pred_locs = instance_locs[pred_id].tolist()
# gt_locs = scannet_locs[obj_id].tolist()
# pred_corners = construct_bbox_corners(pred_locs[:3], pred_locs[3:])
# gt_corners = construct_bbox_corners(gt_locs[:3], gt_locs[3:])
# iou = box3d_iou(pred_corners, gt_corners)
# if iou > max_iou:
# max_iou = iou
# max_id = pred_id
# if max_iou >= 0.25:
# iou25_count += 1
# if max_iou >= 0.5:
# iou50_count += 1
# count[max_id] += 1
# if split == 'train':
# if max_iou >= args.train_iou_thres:
# new_annos.append({
# 'scene_id': scene_id,
# 'obj_id': max_id,
# 'prompt': prompt,
# 'caption': f"<OBJ{max_id:03}>."
# })
# else:
# new_annos.append({
# 'scene_id': scene_id,
# 'obj_id': obj_id,
# 'prompt': prompt,
# 'ref_captions': [f"<OBJ{max_id:03}>."]
# })
# print(len(new_annos))
# print(count)
# print(f"max [email protected]: {iou25_count / len(new_annos)}")
# print(f"max [email protected]: {iou50_count / len(new_annos)}")
# with open(f"annotations/nr3d_{segmentor}_{split}{version}.json", 'w') as f:
# json.dump(new_annos, f, indent=4)
for split in ['train', 'val']:
annos = [anno for anno in raw_annos if anno['scene_id'] in scene_lists[split]]
new_annos = []
easy_num = 0
dep_num = 0
count = [0] * args.max_obj_num
for anno in tqdm(annos):
scene_id = anno['scene_id']
obj_id = anno['obj_id']
if scene_id == "scene0217_00" and obj_id > 30:
obj_id -= 31
desc = anno['description']
if desc[-1] in string.punctuation:
desc = desc[:-1]
stimulus_id = anno['stimulus_id']
tokens = anno['tokens']
tokens = tokens[2:-2].split("', '")
is_easy = int(stimulus_id.split('-')[2]) <= 2
is_view_dep = is_explicitly_view_dependent(tokens)
if is_easy:
easy_num += 1
if is_view_dep:
dep_num += 1
easy_hard_str = 'easy' if is_easy else 'hard'
dep_indep_str = 'dep' if is_view_dep else 'indep'
type_info = f"{easy_hard_str}_{dep_indep_str}"
prompt = random.choice(grounding_prompt).replace('<description>', desc)
try:
count[obj_id] += 1
except:
print(f"{obj_id} excceed max obj num")
if split == "train":
continue
if split == "train":
new_annos.append({
'scene_id': scene_id,
'obj_id': obj_id,
'prompt': prompt,
'caption': f"<OBJ{obj_id:03}>."
})
else:
new_annos.append({
'scene_id': scene_id,
'obj_id': obj_id,
'prompt': prompt,
'ref_captions': [f"<OBJ{obj_id:03}>."],
'type_info': type_info
})
print(easy_num, dep_num)
print(len(new_annos))
print(count)
with open(f"annotations/nr3d_{split}{version}.json", 'w') as f:
json.dump(new_annos, f, indent=4)