MaskClustering/preprocess/scannet/download-scannet.py

#!/usr/bin/env python
# Downloads ScanNet public data release
# Run with ./download-scannet.py (or python download-scannet.py on Windows)
# -*- coding: utf-8 -*-
import argparse
import os
import urllib.request 
import tempfile
from tqdm import tqdm

import ssl 
ssl._create_default_https_context = ssl._create_unverified_context

BASE_URL = 'http://kaldir.vc.in.tum.de/scannet/'
TOS_URL = BASE_URL + 'ScanNet_TOS.pdf'
FILETYPES = ['.aggregation.json', '.sens', '.txt', '_vh_clean.ply', '_vh_clean_2.0.010000.segs.json', '_vh_clean_2.ply', '_vh_clean.segs.json', '_vh_clean.aggregation.json', '_vh_clean_2.labels.ply', '_2d-instance.zip', '_2d-instance-filt.zip', '_2d-label.zip', '_2d-label-filt.zip']
FILETYPES_TEST = ['.sens', '.txt', '_vh_clean.ply', '_vh_clean_2.ply']
PREPROCESSED_FRAMES_FILE = ['scannet_frames_25k.zip', '5.6GB']
TEST_FRAMES_FILE = ['scannet_frames_test.zip', '610MB']
LABEL_MAP_FILES = ['scannetv2-labels.combined.tsv', 'scannet-labels.combined.tsv']
DATA_EFFICIENT_FILES = ['limited-reconstruction-scenes.zip', 'limited-annotation-points.zip', 'limited-bboxes.zip', '1.7MB']
GRIT_FILES = ['ScanNet-GRIT.zip']
RELEASES = ['v2/scans', 'v1/scans']
RELEASES_TASKS = ['v2/tasks', 'v1/tasks']
RELEASES_NAMES = ['v2', 'v1']
RELEASE = RELEASES[0]
RELEASE_TASKS = RELEASES_TASKS[0]
RELEASE_NAME = RELEASES_NAMES[0]
LABEL_MAP_FILE = LABEL_MAP_FILES[0]
RELEASE_SIZE = '1.2TB'
V1_IDX = 1


def get_release_scans(release_file):
    scan_lines = urllib.request.urlopen(release_file)
    scans = []
    for scan_line in scan_lines:
        scan_id = scan_line.decode('utf8').rstrip('\n')
        scans.append(scan_id)
    return scans


def download_release(release_scans, out_dir, file_types, use_v1_sens, skip_existing):
    if len(release_scans) == 0:
        return
    print('Downloading ScanNet ' + RELEASE_NAME + ' release to ' + out_dir + '...')
    for scan_id in release_scans:
        scan_out_dir = os.path.join(out_dir, scan_id)
        download_scan(scan_id, scan_out_dir, file_types, use_v1_sens, skip_existing)
    print('Downloaded ScanNet ' + RELEASE_NAME + ' release.')


def download_file(url, out_file):
    out_dir = os.path.dirname(out_file)
    if not os.path.isdir(out_dir):
        os.makedirs(out_dir)
    if not os.path.isfile(out_file):
        print('\t' + url + ' > ' + out_file)
        fh, out_file_tmp = tempfile.mkstemp(dir=out_dir)
        f = os.fdopen(fh, 'w')
        f.close()
        urllib.request.urlretrieve(url, out_file_tmp)
        os.rename(out_file_tmp, out_file)
    else:
        print('WARNING: skipping download of existing file ' + out_file)

def download_scan(scan_id, out_dir, file_types, use_v1_sens, skip_existing=False):
    print('Downloading ScanNet ' + RELEASE_NAME + ' scan ' + scan_id + ' ...')
    if not os.path.isdir(out_dir):
        os.makedirs(out_dir)
    for ft in file_types:
        v1_sens = use_v1_sens and ft == '.sens'
        url = BASE_URL + RELEASE + '/' + scan_id + '/' + scan_id + ft if not v1_sens else BASE_URL + RELEASES[V1_IDX] + '/' + scan_id + '/' + scan_id + ft
        out_file = out_dir + '/' + scan_id + ft
        if skip_existing and os.path.isfile(out_file):
            continue
        download_file(url, out_file)
    print('Downloaded scan ' + scan_id)


def download_task_data(out_dir):
    print('Downloading ScanNet v1 task data...')
    files = [
        LABEL_MAP_FILES[V1_IDX], 'obj_classification/data.zip',
        'obj_classification/trained_models.zip', 'voxel_labeling/data.zip',
        'voxel_labeling/trained_models.zip'
    ]
    for file in files:
        url = BASE_URL + RELEASES_TASKS[V1_IDX] + '/' + file
        localpath = os.path.join(out_dir, file)
        localdir = os.path.dirname(localpath)
        if not os.path.isdir(localdir):
          os.makedirs(localdir)
        download_file(url, localpath)
    print('Downloaded task data.')

def download_tfrecords(in_dir, out_dir):
    print('Downloading tf records (302 GB)...')
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)
    split_to_num_shards = {'train': 100, 'val': 25, 'test': 10}

    for folder_name in ['hires_tfrecords', 'lores_tfrecords']:
        folder_dir = '%s/%s' % (in_dir, folder_name)
        save_dir = '%s/%s' % (out_dir, folder_name)
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        for split, num_shards in split_to_num_shards.items():
            for i in range(num_shards):
                file_name = '%s-%05d-of-%05d.tfrecords' % (split, i, num_shards)
                url = '%s/%s' % (folder_dir, file_name)
                localpath = '%s/%s/%s' % (out_dir, folder_name, file_name)
                download_file(url, localpath)

def download_label_map(out_dir):
    print('Downloading ScanNet ' + RELEASE_NAME + ' label mapping file...')
    files = [ LABEL_MAP_FILE ]
    for file in files:
        url = BASE_URL + RELEASE_TASKS + '/' + file
        localpath = os.path.join(out_dir, file)
        localdir = os.path.dirname(localpath)
        if not os.path.isdir(localdir):
          os.makedirs(localdir)
        download_file(url, localpath)
    print('Downloaded ScanNet ' + RELEASE_NAME + ' label mapping file.')

def input(*args, **kwargs):
    return 'y'

def main():
    parser = argparse.ArgumentParser(description='Downloads ScanNet public data release.')
    parser.add_argument('-o', '--out_dir', required=True, help='directory in which to download')
    parser.add_argument('--task_data', action='store_true', help='download task data (v1)')
    parser.add_argument('--label_map', action='store_true', help='download label map file')
    parser.add_argument('--v1', action='store_true', help='download ScanNet v1 instead of v2')
    parser.add_argument('--ids', help='specific scan ids to download', nargs='+')
    parser.add_argument('--preprocessed_frames', action='store_true', help='download preprocessed subset of ScanNet frames (' + PREPROCESSED_FRAMES_FILE[1] + ')')
    parser.add_argument('--test_frames_2d', action='store_true', help='download 2D test frames (' + TEST_FRAMES_FILE[1] + '; also included with whole dataset download)')
    parser.add_argument('--data_efficient', action='store_true', help='download data efficient task files; also included with whole dataset download)')
    parser.add_argument('--tf_semantic', action='store_true', help='download google tensorflow records for 3D segmentation / detection')
    parser.add_argument('--grit', action='store_true', help='download ScanNet files for General Robust Image Task')
    parser.add_argument('--type', help='specific file type to download (.aggregation.json, .sens, .txt, _vh_clean.ply, _vh_clean_2.0.010000.segs.json, _vh_clean_2.ply, _vh_clean.segs.json, _vh_clean.aggregation.json, _vh_clean_2.labels.ply, _2d-instance.zip, _2d-instance-filt.zip, _2d-label.zip, _2d-label-filt.zip)')
    parser.add_argument('--skip_existing', action='store_true', help='skip download of existing files when downloading full release')
    args = parser.parse_args()

    print('By pressing any key to continue you confirm that you have agreed to the ScanNet terms of use as described at:')
    print(TOS_URL)
    print('***')
    print('Press any key to continue, or CTRL-C to exit.')
    key = input('')

    if args.v1:
        global RELEASE
        global RELEASE_TASKS
        global RELEASE_NAME
        global LABEL_MAP_FILE
        RELEASE = RELEASES[V1_IDX]
        RELEASE_TASKS = RELEASES_TASKS[V1_IDX]
        RELEASE_NAME = RELEASES_NAMES[V1_IDX]
        LABEL_MAP_FILE = LABEL_MAP_FILES[V1_IDX]
        assert((not args.tf_semantic) and (not args.grit)), "Task files specified invalid for v1"

    release_file = BASE_URL + RELEASE + '.txt'
    release_scans = get_release_scans(release_file)
    file_types = FILETYPES;
    release_test_file = BASE_URL + RELEASE + '_test.txt'
    release_test_scans = [] if args.v1 else get_release_scans(release_test_file)
    file_types_test = FILETYPES_TEST;
    out_dir_scans = os.path.join(args.out_dir, 'scans')
    out_dir_test_scans = os.path.join(args.out_dir, 'scans_test')
    out_dir_tasks = os.path.join(args.out_dir, 'tasks')

    if args.type:  # download file type
        file_type = args.type
        if file_type not in FILETYPES:
            print('ERROR: Invalid file type: ' + file_type)
            return
        file_types = [file_type]
        if file_type in FILETYPES_TEST:
            file_types_test = [file_type]
        else:
            file_types_test = []
    if args.task_data:  # download task data
        download_task_data(out_dir_tasks)
    elif args.label_map:  # download label map file
        download_label_map(args.out_dir)
    elif args.preprocessed_frames:  # download preprocessed scannet_frames_25k.zip file
        if args.v1:
            print('ERROR: Preprocessed frames only available for ScanNet v2')
        print('You are downloading the preprocessed subset of frames ' + PREPROCESSED_FRAMES_FILE[0] + ' which requires ' + PREPROCESSED_FRAMES_FILE[1] + ' of space.')
        download_file(os.path.join(BASE_URL, RELEASE_TASKS, PREPROCESSED_FRAMES_FILE[0]), os.path.join(out_dir_tasks, PREPROCESSED_FRAMES_FILE[0]))
    elif args.test_frames_2d:  # download test scannet_frames_test.zip file
        if args.v1:
            print('ERROR: 2D test frames only available for ScanNet v2')
        print('You are downloading the 2D test set ' + TEST_FRAMES_FILE[0] + ' which requires ' + TEST_FRAMES_FILE[1] + ' of space.')
        download_file(os.path.join(BASE_URL, RELEASE_TASKS, TEST_FRAMES_FILE[0]), os.path.join(out_dir_tasks, TEST_FRAMES_FILE[0]))
    elif args.data_efficient: # download data efficient task files
        print('You are downloading the data efficient task files' + ' which requires ' + DATA_EFFICIENT_FILES[-1] + ' of space.')
        for k in range(len(DATA_EFFICIENT_FILES)-1):
            download_file(os.path.join(BASE_URL, RELEASE_TASKS, DATA_EFFICIENT_FILES[k]), os.path.join(out_dir_tasks, DATA_EFFICIENT_FILES[k]))
    elif args.tf_semantic: # download google tf records
        download_tfrecords(os.path.join(BASE_URL, RELEASE_TASKS, 'tf3d'), os.path.join(out_dir_tasks, 'tf3d'))
    elif args.grit: # download GRIT file
        download_file(os.path.join(BASE_URL, RELEASE_TASKS, GRIT_FILES[0]), os.path.join(out_dir_tasks, GRIT_FILES[0]))
    elif args.ids:  # download single scan
        for scan_id in tqdm(args.ids):
            is_test_scan = scan_id in release_test_scans
            if scan_id not in release_scans and (not is_test_scan or args.v1):
                print('ERROR: Invalid scan id: ' + scan_id)
            else:
                out_dir = os.path.join(out_dir_scans, scan_id) if not is_test_scan else os.path.join(out_dir_test_scans, scan_id)
                scan_file_types = file_types if not is_test_scan else file_types_test
                use_v1_sens = not is_test_scan
                if not is_test_scan and not args.v1 and '.sens' in scan_file_types:
                    print('Note: ScanNet v2 uses the same .sens files as ScanNet v1: Press \'n\' to exclude downloading .sens files for each scan')
                    key = input('')
                    if key.strip().lower() == 'n':
                        scan_file_types.remove('.sens')
                download_scan(scan_id, out_dir, scan_file_types, use_v1_sens, skip_existing=args.skip_existing)
    else:  # download entire release
        if len(file_types) == len(FILETYPES):
            print('WARNING: You are downloading the entire ScanNet ' + RELEASE_NAME + ' release which requires ' + RELEASE_SIZE + ' of space.')
        else:
            print('WARNING: You are downloading all ScanNet ' + RELEASE_NAME + ' scans of type ' + file_types[0])
        print('Note that existing scan directories will be skipped. Delete partially downloaded directories to re-download.')
        print('***')
        print('Press any key to continue, or CTRL-C to exit.')
        key = input('')
        if not args.v1 and '.sens' in file_types:
            print('Note: ScanNet v2 uses the same .sens files as ScanNet v1: Press \'n\' to exclude downloading .sens files for each scan')
            key = input('')
            if key.strip().lower() == 'n':
                file_types.remove('.sens')
        download_release(release_scans, out_dir_scans, file_types, use_v1_sens=True, skip_existing=args.skip_existing)
        if not args.v1:
            download_label_map(args.out_dir)
            download_release(release_test_scans, out_dir_test_scans, file_types_test, use_v1_sens=False, skip_existing=args.skip_existing)
            download_file(os.path.join(BASE_URL, RELEASE_TASKS, TEST_FRAMES_FILE[0]), os.path.join(out_dir_tasks, TEST_FRAMES_FILE[0]))
            for k in range(len(DATA_EFFICIENT_FILES)-1):
                download_file(os.path.join(BASE_URL, RELEASE_TASKS, DATA_EFFICIENT_FILES[k]), os.path.join(out_dir_tasks, DATA_EFFICIENT_FILES[k]))


if __name__ == "__main__": main()