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prepare_scannet_seg_data.py
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#!/usr/bin/python3
'''Prepare Data for ScanNet Segmentation Task.'''
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import math
import h5py
import pickle
import argparse
import numpy as np
from datetime import datetime
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import data_utils
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--folder', '-f', help='Path to data folder')
parser.add_argument('--max_point_num', '-m', help='Max point number of each sample', type=int, default=8192)
parser.add_argument('--block_size', '-b', help='Block size', type=float, default=1.5)
parser.add_argument('--grid_size', '-g', help='Grid size', type=float, default=0.03)
parser.add_argument('--save_ply', '-s', help='Convert .pts to .ply', action='store_true')
args = parser.parse_args()
print(args)
root = args.folder if args.folder else '../../data/scannet/seg'
max_point_num = args.max_point_num
batch_size = 2048
data = np.zeros((batch_size, max_point_num, 3))
data_num = np.zeros((batch_size), dtype=np.int32)
label = np.zeros((batch_size), dtype=np.int32)
label_seg = np.zeros((batch_size, max_point_num), dtype=np.int32)
indices_split_to_full = np.zeros((batch_size, max_point_num, 2), dtype=np.int32)
datasets = ['train', 'test']
for dataset_idx, dataset in enumerate(datasets):
filename = os.path.abspath(os.path.join(root, 'scannet_%s.pickle' % dataset))
print('{}-Loading {}...'.format(datetime.now(), filename))
file_pickle = open(filename, 'rb')
xyz_all = pickle.load(file_pickle, encoding='latin1')
labels_all = pickle.load(file_pickle, encoding='latin1')
file_pickle.close()
offsets = [('zero', 0.0), ('half', args.block_size / 2)]
for offset_name, offset in offsets:
idx_h5 = 0
idx = 0
for room_idx, xyz in enumerate(xyz_all):
# align to room bottom center
xyz_min = np.amin(xyz, axis=0, keepdims=True)
xyz_max = np.amax(xyz, axis=0, keepdims=True)
xyz_center = (xyz_min + xyz_max) / 2
xyz_center[0][-1] = xyz_min[0][-1]
xyz = xyz - xyz_center
labels = labels_all[room_idx]
print('{}-Computing block id of {} points...'.format(datetime.now(), xyz.shape[0]))
xyz_min = np.amin(xyz, axis=0, keepdims=True) - offset
xyz_max = np.amax(xyz, axis=0, keepdims=True)
block_size = (args.block_size, args.block_size, 2 * (xyz_max[0, -1] - xyz_min[0, -1]))
xyz_blocks = np.floor((xyz - xyz_min) / block_size).astype(np.int)
print('{}-Collecting points belong to each block...'.format(datetime.now(), xyz.shape[0]))
blocks, point_block_indices, block_point_counts = np.unique(xyz_blocks, return_inverse=True,
return_counts=True, axis=0)
block_point_indices = np.split(np.argsort(point_block_indices), np.cumsum(block_point_counts[:-1]))
print('{}-{} is split into {} blocks.'.format(datetime.now(), dataset, blocks.shape[0]))
block_to_block_idx_map = dict()
for block_idx in range(blocks.shape[0]):
block = (blocks[block_idx][0], blocks[block_idx][1])
block_to_block_idx_map[(block[0], block[1])] = block_idx
# merge small blocks into one of their big neighbors
block_point_count_threshold = max_point_num / 10
nbr_block_offsets = [(0, 1), (1, 0), (0, -1), (-1, 0), (-1, 1), (1, 1), (1, -1), (-1, -1)]
block_merge_count = 0
for block_idx in range(blocks.shape[0]):
if block_point_counts[block_idx] >= block_point_count_threshold:
continue
block = (blocks[block_idx][0], blocks[block_idx][1])
for x, y in nbr_block_offsets:
nbr_block = (block[0] + x, block[1] + y)
if nbr_block not in block_to_block_idx_map:
continue
nbr_block_idx = block_to_block_idx_map[nbr_block]
if block_point_counts[nbr_block_idx] < block_point_count_threshold:
continue
block_point_indices[nbr_block_idx] = np.concatenate(
[block_point_indices[nbr_block_idx], block_point_indices[block_idx]], axis=-1)
block_point_indices[block_idx] = np.array([], dtype=np.int)
block_merge_count = block_merge_count + 1
break
print('{}-{} of {} blocks are merged.'.format(datetime.now(), block_merge_count, blocks.shape[0]))
idx_last_non_empty_block = 0
for block_idx in reversed(range(blocks.shape[0])):
if block_point_indices[block_idx].shape[0] != 0:
idx_last_non_empty_block = block_idx
break
# uniformly sample each block
for block_idx in range(idx_last_non_empty_block + 1):
point_indices = block_point_indices[block_idx]
if point_indices.shape[0] == 0:
continue
block_points = xyz[point_indices]
block_min = np.amin(block_points, axis=0, keepdims=True)
xyz_grids = np.floor((block_points - block_min) / args.grid_size).astype(np.int)
grids, point_grid_indices, grid_point_counts = np.unique(xyz_grids, return_inverse=True,
return_counts=True, axis=0)
grid_point_indices = np.split(np.argsort(point_grid_indices), np.cumsum(grid_point_counts[:-1]))
grid_point_count_avg = int(np.average(grid_point_counts))
point_indices_repeated = []
for grid_idx in range(grids.shape[0]):
point_indices_in_block = grid_point_indices[grid_idx]
repeat_num = math.ceil(grid_point_count_avg / point_indices_in_block.shape[0])
if repeat_num > 1:
point_indices_in_block = np.repeat(point_indices_in_block, repeat_num)
np.random.shuffle(point_indices_in_block)
point_indices_in_block = point_indices_in_block[:grid_point_count_avg]
point_indices_repeated.extend(list(point_indices[point_indices_in_block]))
block_point_indices[block_idx] = np.array(point_indices_repeated)
block_point_counts[block_idx] = len(point_indices_repeated)
for block_idx in range(idx_last_non_empty_block + 1):
point_indices = block_point_indices[block_idx]
if point_indices.shape[0] == 0:
continue
block_point_num = point_indices.shape[0]
block_split_num = int(math.ceil(block_point_num * 1.0 / max_point_num))
point_num_avg = int(math.ceil(block_point_num * 1.0 / block_split_num))
point_nums = [point_num_avg] * block_split_num
point_nums[-1] = block_point_num - (point_num_avg * (block_split_num - 1))
starts = [0] + list(np.cumsum(point_nums))
np.random.shuffle(point_indices)
block_points = xyz[point_indices]
block_labels = labels[point_indices]
x, y, z = np.split(block_points, (1, 2), axis=-1)
block_xzy = np.concatenate([x, z, y], axis=-1)
for block_split_idx in range(block_split_num):
start = starts[block_split_idx]
point_num = point_nums[block_split_idx]
end = start + point_num
idx_in_batch = idx % batch_size
data[idx_in_batch, 0:point_num, ...] = block_xzy[start:end, :]
data_num[idx_in_batch] = point_num
label[idx_in_batch] = dataset_idx # won't be used...
label_seg[idx_in_batch, 0:point_num] = block_labels[start:end]
ind_in_room = point_indices[start:end]
indices_split_to_full[idx_in_batch, 0:point_num] = np.stack([np.zeros_like(ind_in_room) + room_idx ,ind_in_room], -1)
if ((idx + 1) % batch_size == 0) \
or (room_idx == len(xyz_all) - 1
and block_idx == idx_last_non_empty_block
and block_split_idx == block_split_num - 1):
item_num = idx_in_batch + 1
filename_h5 = os.path.join(root, dataset, '%s_%d.h5' % (offset_name, idx_h5))
os.makedirs(os.path.dirname(filename_h5), exist_ok=True)
print('{}-Saving {}...'.format(datetime.now(), filename_h5))
file = h5py.File(filename_h5, 'w')
file.create_dataset('data', data=data[0:item_num, ...])
file.create_dataset('data_num', data=data_num[0:item_num, ...])
file.create_dataset('label', data=label[0:item_num, ...])
file.create_dataset('label_seg', data=label_seg[0:item_num, ...])
file.create_dataset('indices_split_to_full', data=indices_split_to_full[0:item_num, ...])
file.close()
if args.save_ply:
print('{}-Saving ply of {}...'.format(datetime.now(), filename_h5))
filepath_label_ply = os.path.join(root, dataset, 'ply_label',
'label_%s_%d' % (offset_name, idx_h5))
data_utils.save_ply_property_batch(data[0:item_num, :, 0:3],
label_seg[0:item_num, ...],
filepath_label_ply, data_num[0:item_num, ...], 22)
idx_h5 = idx_h5 + 1
idx = idx + 1
if __name__ == '__main__':
main()
print('{}-Done.'.format(datetime.now()))