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 datetime
import os

import numpy as np
from load_scannet_data import export

SCANNET_DIR = 'scans'
TRAIN_SCAN_NAMES = [
    line.rstrip() for line in open('meta_data/scannet_train.txt')
]
LABEL_MAP_FILE = 'meta_data/scannetv2-labels.combined.tsv'
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])
MAX_NUM_POINT = 50000
OUTPUT_FOLDER = './scannet_train_detection_data'


def export_one_scan(scan_name, output_filename_prefix):
    mesh_file = os.path.join(SCANNET_DIR, scan_name,
                             scan_name + '_vh_clean_2.ply')
    agg_file = os.path.join(SCANNET_DIR, scan_name,
                            scan_name + '.aggregation.json')
    seg_file = os.path.join(SCANNET_DIR, scan_name,
                            scan_name + '_vh_clean_2.0.010000.segs.json')
    meta_file = os.path.join(
        SCANNET_DIR, scan_name, scan_name +
        '.txt')  # includes axisAlignment info for the train set scans.
    mesh_vertices, semantic_labels, instance_labels, instance_bboxes, \
        instance2semantic = export(mesh_file, agg_file, seg_file,
                                   meta_file, LABEL_MAP_FILE, None)

    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('Num of instances: ', num_instances)

    bbox_mask = np.in1d(instance_bboxes[:, -1], OBJ_CLASS_IDS)
    instance_bboxes = instance_bboxes[bbox_mask, :]
    print('Num of care instances: ', instance_bboxes.shape[0])

    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, :]
        semantic_labels = semantic_labels[choices]
        instance_labels = instance_labels[choices]

    np.save(output_filename_prefix + '_vert.npy', mesh_vertices)
    np.save(output_filename_prefix + '_sem_label.npy', semantic_labels)
    np.save(output_filename_prefix + '_ins_label.npy', instance_labels)
    np.save(output_filename_prefix + '_bbox.npy', instance_bboxes)


def batch_export():
    if not os.path.exists(OUTPUT_FOLDER):
        print('Creating new data folder: {}'.format(OUTPUT_FOLDER))
        os.mkdir(OUTPUT_FOLDER)

    for scan_name in TRAIN_SCAN_NAMES:
        print('-' * 20 + 'begin')
        print(datetime.datetime.now())
        print(scan_name)
        output_filename_prefix = os.path.join(OUTPUT_FOLDER, scan_name)
        if os.path.isfile(output_filename_prefix + '_vert.npy'):
            print('File already exists. skipping.')
            print('-' * 20 + 'done')
            continue
        try:
            export_one_scan(scan_name, output_filename_prefix)
        except Exception:
            print('Failed export scan: %s' % (scan_name))
        print('-' * 20 + 'done')


if __name__ == '__main__':
    batch_export()