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batch_load_scannet_data.py
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batch_load_scannet_data.py
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# Modified from
# https://github.com/facebookresearch/votenet/blob/master/scannet/batch_load_scannet_data.py
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Batch mode in loading Scannet scenes with vertices and ground truth labels
for semantic and instance segmentations.
Usage example: python ./batch_load_scannet_data.py
"""
import argparse
import datetime
import os
from os import path as osp
import numpy as np
from load_scannet_data import export
DONOTCARE_CLASS_IDS = np.array([])
OBJ_CLASS_IDS = np.array(
[3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39])
def export_one_scan(scan_name,
output_filename_prefix,
max_num_point,
label_map_file,
scannet_dir,
test_mode=False):
mesh_file = osp.join(scannet_dir, scan_name, scan_name + '_vh_clean_2.ply')
agg_file = osp.join(scannet_dir, scan_name,
scan_name + '.aggregation.json')
seg_file = osp.join(scannet_dir, scan_name,
scan_name + '_vh_clean_2.0.010000.segs.json')
# includes axisAlignment info for the train set scans.
meta_file = osp.join(scannet_dir, scan_name, f'{scan_name}.txt')
mesh_vertices, semantic_labels, instance_labels, unaligned_bboxes, \
aligned_bboxes, instance2semantic, axis_align_matrix = export(
mesh_file, agg_file, seg_file, meta_file, label_map_file, None,
test_mode)
if not test_mode:
mask = np.logical_not(np.in1d(semantic_labels, DONOTCARE_CLASS_IDS))
mesh_vertices = mesh_vertices[mask, :]
semantic_labels = semantic_labels[mask]
instance_labels = instance_labels[mask]
num_instances = len(np.unique(instance_labels))
print(f'Num of instances: {num_instances}')
bbox_mask = np.in1d(unaligned_bboxes[:, -1], OBJ_CLASS_IDS)
unaligned_bboxes = unaligned_bboxes[bbox_mask, :]
bbox_mask = np.in1d(aligned_bboxes[:, -1], OBJ_CLASS_IDS)
aligned_bboxes = aligned_bboxes[bbox_mask, :]
assert unaligned_bboxes.shape[0] == aligned_bboxes.shape[0]
print(f'Num of care instances: {unaligned_bboxes.shape[0]}')
if max_num_point is not None:
max_num_point = int(max_num_point)
N = mesh_vertices.shape[0]
if N > max_num_point:
choices = np.random.choice(N, max_num_point, replace=False)
mesh_vertices = mesh_vertices[choices, :]
if not test_mode:
semantic_labels = semantic_labels[choices]
instance_labels = instance_labels[choices]
np.save(f'{output_filename_prefix}_vert.npy', mesh_vertices)
if not test_mode:
np.save(f'{output_filename_prefix}_sem_label.npy', semantic_labels)
np.save(f'{output_filename_prefix}_ins_label.npy', instance_labels)
np.save(f'{output_filename_prefix}_unaligned_bbox.npy',
unaligned_bboxes)
np.save(f'{output_filename_prefix}_aligned_bbox.npy', aligned_bboxes)
np.save(f'{output_filename_prefix}_axis_align_matrix.npy',
axis_align_matrix)
def batch_export(max_num_point,
output_folder,
scan_names_file,
label_map_file,
scannet_dir,
test_mode=False):
if test_mode and not os.path.exists(scannet_dir):
# test data preparation is optional
return
if not os.path.exists(output_folder):
print(f'Creating new data folder: {output_folder}')
os.mkdir(output_folder)
scan_names = [line.rstrip() for line in open(scan_names_file)]
for scan_name in scan_names:
print('-' * 20 + 'begin')
print(datetime.datetime.now())
print(scan_name)
output_filename_prefix = osp.join(output_folder, scan_name)
if osp.isfile(f'{output_filename_prefix}_vert.npy'):
print('File already exists. skipping.')
print('-' * 20 + 'done')
continue
try:
export_one_scan(scan_name, output_filename_prefix, max_num_point,
label_map_file, scannet_dir, test_mode)
except Exception:
print(f'Failed export scan: {scan_name}')
print('-' * 20 + 'done')
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--max_num_point',
default=None,
help='The maximum number of the points.')
parser.add_argument(
'--output_folder',
default='./scannet_instance_data',
help='output folder of the result.')
parser.add_argument(
'--train_scannet_dir', default='scans', help='scannet data directory.')
parser.add_argument(
'--test_scannet_dir',
default='scans_test',
help='scannet data directory.')
parser.add_argument(
'--label_map_file',
default='meta_data/scannetv2-labels.combined.tsv',
help='The path of label map file.')
parser.add_argument(
'--train_scan_names_file',
default='meta_data/scannet_train.txt',
help='The path of the file that stores the scan names.')
parser.add_argument(
'--test_scan_names_file',
default='meta_data/scannetv2_test.txt',
help='The path of the file that stores the scan names.')
args = parser.parse_args()
batch_export(
args.max_num_point,
args.output_folder,
args.train_scan_names_file,
args.label_map_file,
args.train_scannet_dir,
test_mode=False)
batch_export(
args.max_num_point,
args.output_folder,
args.test_scan_names_file,
args.label_map_file,
args.test_scannet_dir,
test_mode=True)
if __name__ == '__main__':
main()