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prepare_scannet_data.py
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import argparse
import pprint
import time
import os.path as osp
import multiprocessing as mp
from tqdm import tqdm
import numpy as np
from referit3d.in_out.scannet_scan import ScannetScan, ScannetDataset
from referit3d.utils import immediate_subdirectories, create_dir, pickle_data, str2bool
"""
-top-scan-dir /home/e/scannet_dataset/scannet/scans -top-save-dir /home/e/scannet_dataset/scannet/scan_4_nr3d --load-dense False --save-jpg True --imgsize 128 --apply-global-alignment False --geo True --twoStreams True
"""
def parse_args():
parser = argparse.ArgumentParser(description='ReferIt3D')
parser.add_argument('-top-scan-dir', required=True, type=str,
help='the path to the downloaded ScanNet scans')
parser.add_argument('-top-save-dir', required=True, type=str,
help='the path of the directory to be saved preprocessed scans as a .pkl')
# Optional arguments.
parser.add_argument('--n-processes', default=-1, type=int,
help='the number of processes, -1 means use the available max')
parser.add_argument('--process-only-zero-view', default=True, type=str2bool,
help='00_view of scans are used')
parser.add_argument('--verbose', default=True, type=str2bool, help='')
parser.add_argument('--apply-global-alignment', default=True, type=str2bool,
help='rotate/translate entire scan globally to aligned it with other scans')
# Eslam
parser.add_argument('--load-dense', default=False, type=str2bool, help='Load dense version of point-clouds')
parser.add_argument('--save-jpg', default=False, type=str2bool, help='Save projected images directly')
parser.add_argument('--imgsize', default=32, type=int, help='Size of projected image')
parser.add_argument('--cocoon', default=False, type=str2bool, help='Save cocoon images for each object')
parser.add_argument('--twoStreams', default=False, type=str2bool, help='Save 2d images for each object and raw pc')
parser.add_argument('--geo', default=False, type=str2bool, help='Save 2d images for each object and raw pc')
parser.add_argument('--camaug', default=0, type=int, help='number of camera augmented frames.'
' set it to 0 to deactivate camaug')
parser.add_argument('--savepickle', default=True, type=str2bool, help='This flag indicates whether u wanna save'
' a pickle or not')
ret = parser.parse_args()
# Print the args
args_string = pprint.pformat(vars(ret))
print(args_string)
return ret
if __name__ == '__main__':
args = parse_args()
if args.process_only_zero_view:
tag = 'keep_all_points_00_view'
else:
tag = 'keep_all_points'
if args.apply_global_alignment:
tag += '_with_global_scan_alignment'
else:
tag += '_no_global_scan_alignment'
if args.load_dense:
tag += '_densePCLoaded'
if args.save_jpg:
tag += '_saveJPG'
if args.cocoon:
tag += '_cocoon'
if args.twoStreams:
tag += '_twoStreams'
if args.geo:
tag += '_GEO'
if args.camaug:
tag += '_camaug'
# Read all scan files.
all_scan_ids = [osp.basename(i) for i in immediate_subdirectories(args.top_scan_dir)]
print('{} scans found.'.format(len(all_scan_ids)))
kept_scan_ids = []
if args.process_only_zero_view:
for si in all_scan_ids:
if si.endswith('00'):
kept_scan_ids.append(si)
all_scan_ids = kept_scan_ids
print('Working with {} scans.'.format(len(all_scan_ids)))
if args.load_dense and False:
list_all_scans = np.array_split(np.array(all_scan_ids), 7)
else:
list_all_scans = [all_scan_ids]
# Prepare ScannetDataset
idx_to_semantic_class_file = 'referit3d/data/mappings/scannet_idx_to_semantic_class.json'
instance_class_to_semantic_class_file = 'referit3d/data/mappings/scannet_instance_class_to_semantic_class.json'
axis_alignment_info_file = 'referit3d/data/scannet/scans_axis_alignment_matrices.json'
scannet = ScannetDataset(args.top_scan_dir,
idx_to_semantic_class_file,
instance_class_to_semantic_class_file,
axis_alignment_info_file)
def scannet_loader(scan_id):
"""Helper function to load the scans in memory.
:param scan_id:
:return: the loaded scan.
"""
global scannet, args
print("scan_id = ", scan_id)
scan_i = ScannetScan(scan_id, scannet, args.apply_global_alignment, load_dense=args.load_dense,
save_jpg=args.save_jpg, img_size=args.imgsize, top_scan_dir=args.top_scan_dir,
cocoon=args.cocoon, camaug=args.camaug)
if args.load_dense:
scan_i.load_point_clouds_of_all_objects_dense()
else:
scan_i.load_point_clouds_of_all_objects()
if args.save_jpg:
scan_i.pc = None
scan_i.color = None
return scan_i
if args.verbose:
print('Loading scans in memory...')
start_time = time.time()
for id, all_scan_ids in enumerate(list_all_scans):
print("Start processing of id = ", id)
n_items = len(all_scan_ids)
if args.n_processes == -1:
n_processes = min(mp.cpu_count(), n_items)
pool = mp.Pool(n_processes)
chunks = int(n_items / n_processes)
all_scans = dict()
for i, data in enumerate(pool.imap(scannet_loader, all_scan_ids, chunksize=chunks)):
all_scans[all_scan_ids[i]] = data
pool.close()
pool.join()
if args.verbose:
print("Loading raw data took {:.4} minutes.".format((time.time() - start_time) / 60.0))
# Save data
if args.savepickle:
if args.verbose:
print('Saving the results.')
all_scans = list(all_scans.values())
save_dir = create_dir(osp.join(args.top_save_dir, tag))
if args.load_dense:
save_file = osp.join(save_dir, tag + "_" + str(id) + '.pkl')
else:
save_file = osp.join(save_dir, tag + '.pkl')
pickle_data(save_file, scannet, all_scans)
print("Finish processing of id = ", id)
if args.verbose:
print('All done.')