视觉导航数据集下载

数据集下载

介绍:
https://blog.youkuaiyun.com/python_plus/article/details/144273135

1 RXR
https://github.com/google-research-datasets/RxR

2 R2R
https://bringmeaspoon.org/

下载:https://jacobkrantz.github.io/vlnce/data

3 matterport-3d 15GB的场景数据集
下载方法:
不需要发邮,直接拷贝代码按需下载:

1.conda创建python2.7虚拟环境

conda create -n py27 python=2.7

2.进入虚拟环境

conda activate py27

3.运行download_mp.py

python download_mp.py --task habitat -o ./data
# --task habitat：指定任务为 habitat，这是脚本支持的一个任务类型（具体作用需参考脚本的实现）。
# -o ./data：指定输出目录为当前路径下的 data 文件夹，下载的内容会保存到此目录。

download_mp.py的内容:

#!/usr/bin/env python
# Downloads MP public data release
# Run with ./download_mp.py (or python download_mp.py on Windows)
# -*- coding: utf-8 -*-
import argparse
import collections
import os
import tempfile
import urllib

BASE_URL = 'http://kaldir.vc.in.tum.de/matterport/'
RELEASE = 'v1/scans'
RELEASE_TASKS = 'v1/tasks/'
RELEASE_SIZE = '1.3TB'
TOS_URL = BASE_URL + 'MP_TOS.pdf'
FILETYPES = [
    'cameras',
    'matterport_camera_intrinsics',
    'matterport_camera_poses',
    'matterport_color_images',
    'matterport_depth_images',
    'matterport_hdr_images',
    'matterport_mesh',
    'matterport_skybox_images',
    'undistorted_camera_parameters',
    'undistorted_color_images',
    'undistorted_depth_images',
    'undistorted_normal_images',
    'house_segmentations',
    'region_segmentations',
    'image_overlap_data',
    'poisson_meshes',
    'sens'
]
TASK_FILES = {
    'keypoint_matching_data': ['keypoint_matching/data.zip'],
    'keypoint_matching_models': ['keypoint_matching/models.zip'],
    'surface_normal_data': ['surface_normal/data_list.zip'],
    'surface_normal_models': ['surface_normal/models.zip'],
    'region_classification_data': ['region_classification/data.zip'],
    'region_classification_models': ['region_classification/models.zip'],
    'semantic_voxel_label_data': ['semantic_voxel_label/data.zip'],
    'semantic_voxel_label_models': ['semantic_voxel_label/models.zip'],
    'minos': ['mp3d_minos.zip'],
    'gibson': ['mp3d_for_gibson.tar.gz'],
    'habitat': ['mp3d_habitat.zip'],
    'pixelsynth': ['mp3d_pixelsynth.zip'],
    'igibson': ['mp3d_for_igibson.zip'],
    'mp360': ['mp3d_360/data_00.zip', 'mp3d_360/data_01.zip', 'mp3d_360/data_02.zip', 'mp3d_360/data_03.zip', 'mp3d_360/data_04.zip', 'mp3d_360/data_05.zip', 'mp3d_360/data_06.zip']
}


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


def download_release(release_scans, out_dir, file_types):
    print('Downloading MP 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)
    print('Downloaded MP release.')


def download_file(url, out_file):
    out_dir = os.path.dirname(out_file)
    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.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):
    print('Downloading MP scan ' + scan_id + ' ...')
    if not os.path.isdir(out_dir):
        os.makedirs(out_dir)
    for ft in file_types:
        url = BASE_URL + RELEASE + '/' + scan_id + '/' + ft + '.zip'
        out_file = out_dir + '/' + ft + '.zip'
        download_file(url, out_file)
    print('Downloaded scan ' + scan_id)


def download_task_data(task_data, out_dir):
    print('Downloading MP task data for ' + str(task_data) + ' ...')
    for task_data_id in task_data:
        if task_data_id in TASK_FILES:
            file = TASK_FILES[task_data_id]
            for filepart in file:
                url = BASE_URL + RELEASE_TASKS + '/' + filepart
                localpath = os.path.join(out_dir, filepart)
                localdir = os.path.dirname(localpath)
                if not os.path.isdir(localdir):
                    os.makedirs(localdir)
                    download_file(url, localpath)
                    print('Downloaded task data ' + task_data_id)


def main():
    parser = argparse.ArgumentParser(description=
        '''
        Downloads MP public data release.
        Example invocation:
          python download_mp.py -o base_dir --id ALL --type object_segmentations --task_data semantic_voxel_label_data semantic_voxel_label_models
        The -o argument is required and specifies the base_dir local directory.
        After download base_dir/v1/scans is populated with scan data, and base_dir/v1/tasks is populated with task data.
        Unzip scan files from base_dir/v1/scans and task files from base_dir/v1/tasks/task_name.
        The --type argument is optional (all data types are downloaded if unspecified).
        The --id ALL argument will download all house data. Use --id house_id to download specific house data.
        The --task_data argument is optional and will download task data and model files.
        ''',
        formatter_class=argparse.RawTextHelpFormatter)
    parser.add_argument('-o', '--out_dir', required=True, help='directory in which to download')
    parser.add_argument('--task_data', default=[], nargs='+', help='task data files to download. Any of: ' + ','.join(TASK_FILES.keys()))
    parser.add_argument('--id', default='ALL', help='specific scan id to download or ALL to download entire dataset')
    parser.add_argument('--type', nargs='+', help='specific file types to download. Any of: ' + ','.join(FILETYPES))
    args = parser.parse_args()

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

    release_file = BASE_URL + RELEASE + '.txt'
    release_scans = get_release_scans(release_file)
    file_types = FILETYPES

    # download task data
    if args.task_data:
        print(args.task_data)
        if set(args.task_data) & set(TASK_FILES.keys()):  # download task data
            out_dir = os.path.join(args.out_dir, RELEASE_TASKS)
            download_task_data(args.task_data, out_dir)
        else:
            print('ERROR: Unrecognized task data id: ' + args.task_data)
        print('Done downloading task_data for ' + str(args.task_data))
        key = raw_input('Press any key to continue on to main dataset download, or CTRL-C to exit.')

    # download specific file types?
    if args.type:
        if not set(args.type) & set(FILETYPES):
            print('ERROR: Invalid file type: ' + file_type)
            return
        file_types = args.type

    if args.id and args.id != 'ALL':  # download single scan
        scan_id = args.id
        if scan_id not in release_scans:
            print('ERROR: Invalid scan id: ' + scan_id)
        else:
            out_dir = os.path.join(args.out_dir, RELEASE, scan_id)
            download_scan(scan_id, out_dir, file_types)
    elif 'minos' not in args.task_data and args.id == 'ALL' or args.id == 'all':  # download entire release
        if len(file_types) == len(FILETYPES):
            print('WARNING: You are downloading the entire MP release which requires ' + RELEASE_SIZE + ' of space.')
        else:
            print('WARNING: You are downloading all MP 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('')
        out_dir = os.path.join(args.out_dir, RELEASE)
        download_release(release_scans, out_dir, file_types)

if __name__ == "__main__": main()

可复制以下地址直接下载:
http://kaldir.vc.in.tum.de/matterport/v1/tasks//mp3d_habitat.zip