笔者CV小白选手获取ScanNet数据集是真的闹心，好在最后获取成功啦！这里将这两天获取数据集的详细过程记录一下，希望能够帮助到更多人，一起学习进步！(o( ￣▽￣ )ブ)

1.数据集介绍

ScanNet是一个RGB-D视频数据集，包含超过1500次扫描的250万个视图，使用3D相机姿势、曲面重建和实例级语义分割进行注释。

官网： https://github.com/ScanNet/ScanNet

2.数据集获取

申请数据集，申请方式：ScanNet Terms of Use to [email protected]
申请之后会获得两份代码。这里为了能够节省大家的申请时间，索性就将代码贴出来。
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 (for python3)
import urllib
import tempfile
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']
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)
    scan_lines = urllib.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):
    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)
    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)
        urllib.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):
    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
        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_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 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('--id', help='specific scan id to download')
    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('--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)')
    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 = raw_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]
    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 = 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.id:  # download single scan
        scan_id = args.id
        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 = raw_input('')
                if key.strip().lower() == 'n':
                    scan_file_types.remove('.sens')
            download_scan(scan_id, out_dir, scan_file_types, use_v1_sens)
    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 = raw_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 = raw_input('')
            if key.strip().lower() == 'n':
                file_types.remove('.sens')
        download_release(release_scans, out_dir_scans, file_types, use_v1_sens=True)
        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)
            download_file(os.path.join(BASE_URL, RELEASE_TASKS, TEST_FRAMES_FILE[0]), os.path.join(out_dir_tasks, TEST_FRAMES_FILE[0]))
if __name__ == "__main__": main()

download_scannetv2.py




    

 

#coding:utf-8
#!/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      #(for python3)
# import urllib
import tempfile
BASE_URL = 'http://kaldir.vc.in.tum.de/scannet/'
TOS_URL = BASE_URL + 'ScanNet_TOS.pdf'
FILETYPES = ['.sens', '.txt',
             '_vh_clean.ply', '_vh_clean_2.ply',
             '_vh_clean.segs.json', '_vh_clean_2.0.010000.segs.json',
             '.aggregation.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']
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)
    # scan_lines = urllib.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):
    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)
    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)
        # urllib.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):
    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
        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_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 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('--id', help='specific scan id to download')
    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('--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)')
    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]
    release_file = BASE_URL + RELEASE + '.txt'  # 存放场景ID的文件
    release_scans = get_release_scans(release_file)  # 所有场景的ID
    file_types = FILETYPES;  # 所有文件的后缀名
    release_test_file = BASE_URL + RELEASE + '_test.txt'  # 存放测试场景ID的文件
    release_test_scans = get_release_scans(release_test_file)  # 测试场景的ID
    file_types_test = FILETYPES_TEST;  # 测试相关文件的后缀名
    out_dir_scans = os.path.join(args.out_dir, 'scans')  # 下载文件的子文件夹
    out_dir_test_scans = os.path.join(args.out_dir, 'scans_test')  # 下载文件的子文件夹
    out_dir_tasks = os.path.join(args.out_dir, 'tasks')  # 下载文件的子文件夹
    # 指定下载的文件类型
    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.id:  # download single scan
        scan_id = args.id
        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)
    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)
        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)
            download_file(os.path.join(BASE_URL, RELEASE_TASKS, TEST_FRAMES_FILE[0]),
                          os.path.join(out_dir_tasks, TEST_FRAMES_FILE[0]))
if __name__ == "__main__": main()

上述两份代码可以根据自己的需求复制代码。
 开始下载数据集
 

#-o 保存文件路径
python download_scannet.py -o data

由于整份数据较大，作者也提供了较小子集的选项scannet_frames_25k。
 

#下载scannet_frames_25k
python download-scannet.py -o data --preprocessed_frames 

两份代码根据自己实际需求选取下载。（这里作者下载的是全部的）
 注意python环境。
 在这里插入图片描述
 如果下载的速度很慢的话，也可以根据下方提供的网址复制进浏览器下载，这样的话能快一些。
 

3.导出数据集
 

由于下载下来的文件是.sens形式，因此需要进一步导出数据。官方提供的导出代码：https://github.com/ScanNet/ScanNet/tree/master/SensReader/python
 文件目录
 在这里插入图片描述
 开始导出
 

python reader.py --filename scene0000_00.sens --output_path image 

1.注意：将所需导出的文件从False设置为True
 在这里插入图片描述
 

2.为了更加直观的查看导出的进程，这里参考RGB-D数据集：ScanNet
 修改了SensorData.py部分代码
 

from tqdm import tqdm 
#更换71行代码：for i in range(num_frames): 为：
for i in tqdm(range(num_frames),ncols=80):
#相应的81行、93行 也可以相应更换为：
for f in tqdm(range(0, len(self.frames), frame_skip),ncols=80):
for f in tqdm(range(0, len(self.frames), frame_skip),ncols=80):

导出后文件目录
 在这里插入图片描述
 

参考博客
 RGB-D数据集：ScanNet
 关于ScanNet数据集
# python # 计算机视觉 # 深度学习
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