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import argparse
import random
import json
from pathlib import Path
import numpy as np
import torch
import open3d as o3d
def convert_pc_to_box(obj_pc):
xmin = np.min(obj_pc[:,0])
ymin = np.min(obj_pc[:,1])
zmin = np.min(obj_pc[:,2])
xmax = np.max(obj_pc[:,0])
ymax = np.max(obj_pc[:,1])
zmax = np.max(obj_pc[:,2])
center = [(xmin+xmax)/2, (ymin+ymax)/2, (zmin+zmax)/2]
box_size = [xmax-xmin, ymax-ymin, zmax-zmin]
return center, box_size
def load_scan(pcd_path, inst2label_path, scene_name):
pcd_data = torch.load(pcd_path / f'{scene_name}.pth')
inst_to_label = torch.load(inst2label_path / f"{scene_name}.pth")
points, colors, instance_labels = pcd_data[0], pcd_data[1], pcd_data[-1]
pcds = np.concatenate([points, colors], 1)
return points, colors, pcds, instance_labels, inst_to_label
def visualize_one_scene(obj_pcds, points, colors, caption):
# visualize scene
o3d_pcd = o3d.geometry.PointCloud()
o3d_pcd.points = o3d.utility.Vector3dVector(points)
o3d_pcd.colors = o3d.utility.Vector3dVector(colors / 255.0)
# visualize gt
for idx, (obj, obj_label) in enumerate(obj_pcds):
if idx > 3:
break
gt_center, gt_size = convert_pc_to_box(obj)
gt_o3d_box = o3d.geometry.OrientedBoundingBox(gt_center, np.eye(3,3), gt_size)
gt_o3d_box.color = [0, 1, 0]
mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.6, origin=[-0, -0, -0])
o3d.visualization.draw_geometries([o3d_pcd, gt_o3d_box, mesh_frame], window_name=obj_label+'_'+caption)
def visualize_data(save_root, scene_name, vis_obj=True):
inst2label_path = save_root / 'instance_id_to_label'
pcd_path = save_root / 'pcd_with_global_alignment'
points, colors, pcds, instance_labels, inst_to_label = load_scan(pcd_path, inst2label_path, scene_name)
if not vis_obj:
o3d_pcd = o3d.geometry.PointCloud()
mesh_frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.6, origin=[-0, -0, -0])
o3d_pcd.points = o3d.utility.Vector3dVector(points)
o3d_pcd.colors = o3d.utility.Vector3dVector(colors / 255.0)
o3d.visualization.draw_geometries([mesh_frame, o3d_pcd])
return
obj_pcds = []
for i in inst_to_label.keys():
mask = instance_labels == i # time consuming
if np.sum(mask) == 0:
continue
obj_pcds.append((pcds[mask], inst_to_label[i]))
visualize_one_scene(obj_pcds, points, colors, scene_name)
def visualize_refer(save_root, anno_file):
inst2label_path = save_root / 'instance_id_to_label'
pcd_path = save_root / 'pcd_with_global_alignment'
json_data = json.load(open(anno_file, 'r', encoding='utf8'))
for item in json_data:
scan_id = item['scan_id']
inst2label_path = save_root / 'instance_id_to_label'
pcd_path = save_root / 'pcd_with_global_alignment'
inst_to_label = torch.load(inst2label_path / f"{scan_id}.pth")
pcd_data = torch.load(pcd_path / f'{scan_id}.pth')
points, colors, instance_labels = pcd_data[0], pcd_data[1], pcd_data[-1]
pcds = np.concatenate([points, colors], 1)
target_id = int(item['target_id'])
mask = instance_labels == target_id
if np.sum(mask) == 0:
continue
obj_pcds = [(pcds[mask], inst_to_label[target_id])]
visualize_one_scene(obj_pcds, points, colors, scan_id+'-'+item['utterance'])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-r", "--root", required=True, type=str, help="path of dataset dir")
parser.add_argument("-d", "--dataset", type=str,
help="available datasets in ['ARKitScenes', 'HM3D', 'MultiScan', 'ProcThor', \
'Structured3D', 'ScanNet', '3RScan']")
parser.add_argument("--vis_refer", action="store_true",
help="visualize reference data")
parser.add_argument("-a", "--anno", type=str, default="ssg_ref_rel2_template.json",
help="the annotation file for reference")
args = parser.parse_args()
dataset = args.dataset
assert dataset in ['ARKitScenes', 'HM3D', 'MultiScan', 'ProcThor', 'Structured3D', 'ScanNet', '3RScan']
print(dataset)
data_root = Path(args.root) / dataset
if args.vis_refer:
if dataset == 'ScanNet':
anno_file = data_root / 'annotations/refer' / args.anno
else:
anno_file = data_root / 'annotations' / args.anno
visualize_refer(data_root / 'scan_data', anno_file)
else:
all_scans = (data_root / 'scan_data' / 'pcd_with_global_alignment').glob('*.pth')
scene_id = Path(random.choice(list(all_scans))).stem
visualize_data(data_root / 'scan_data', scene_id)
# from transformers import BertConfig, BertModel, BertTokenizer
# hidden_size=768
# num_hidden_layers=4
# num_attention_heads=12
# type_vocab_size=2
# weights="bert-base-uncased"
# tok = BertTokenizer.from_pretrained("bert-base-uncased")
# print(tok.convert_tokens_to_ids("all_good")) # should print an int, not crash
# bert_config = BertConfig(
# hidden_size=hidden_size,
# num_hidden_layers=num_hidden_layers,
# num_attention_heads=num_attention_heads,
# type_vocab_size=type_vocab_size
# )
# model = BertModel.from_pretrained(
# weights, config=bert_config
# )
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