Python utils.download方法代码示例

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_vg(path, overwrite=False):
    _DOWNLOAD_URLS = [
        ('https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip',
         'a055367f675dd5476220e9b93e4ca9957b024b94'),
        ('https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip',
         '2add3aab77623549e92b7f15cda0308f50b64ecf'),
    ]
    makedirs(path)
    for url, checksum in _DOWNLOAD_URLS:
        filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
        # extract
        if filename.endswith('zip'):
            with zipfile.ZipFile(filename) as zf:
                zf.extractall(path=path)
    # move all images into folder `VG_100K`
    vg_100k_path = os.path.join(path, 'VG_100K')
    vg_100k_2_path = os.path.join(path, 'VG_100K_2')
    files_2 = os.listdir(vg_100k_2_path)
    for fl in files_2:
        shutil.move(os.path.join(vg_100k_2_path, fl),
                    os.path.join(vg_100k_path, fl)) 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def read_data(opt):
    """
    Pre-process data
    --------------------

    Next we need a video or video frame
    if you want to test video frame, you can change opt.video_loader to False
    and opt.data-dir is your video frame path.
    meanwhile you need first frame object coordinates in opt.gt-bbox
    gt_bbox is first frame object coordinates, and it is bbox(center_x,center_y,weight,height)
    """
    video_frames = []
    if opt.video_loader:
        im_video = utils.download(opt.video_path)
        cap = cv2.VideoCapture(im_video)
        while(True):
            ret, img = cap.read()
            if not ret:
                break
            video_frames.append(img)
    else:
        for data in sorted(os.listdir(opt.data_dir)):
            video_frames.append(cv2.imread(os.path.join(opt.data_dir, data)))
    return video_frames 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def main():
    args = parse_args()
    name = "Market-1501-v15.09.15"
    url = "http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/{name}.zip".format(name=name)
    root = osp.expanduser(args.download_dir)
    makedirs(root)
    fpath = osp.join(root, name + '.zip')
    exdir = osp.join(root, name)
    if not osp.exists(fpath) and not osp.isdir(exdir) and args.no_download:
        raise ValueError(('{} dataset archive not found, make sure it is present.'
                          ' Or you should not disable "--no-download" to grab it'.format(fpath)))
    # Download by default
    if not args.no_download:
        print('Downloading dataset')
        download(url, fpath, overwrite=False)
        print('Dataset downloaded')
    # Extract dataset if fresh copy downloaded or existing archive is yet to be extracted
    if not args.no_download or not osp.isdir(exdir):
        extract(fpath, root)
        make_list(exdir) 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_city(path, overwrite=False):
    _CITY_DOWNLOAD_URLS = [
        ('gtFine_trainvaltest.zip', '99f532cb1af174f5fcc4c5bc8feea8c66246ddbc'),
        ('leftImg8bit_trainvaltest.zip', '2c0b77ce9933cc635adda307fbba5566f5d9d404')]
    download_dir = os.path.join(path, 'downloads')
    makedirs(download_dir)
    for filename, checksum in _CITY_DOWNLOAD_URLS:
        if not check_sha1(filename, checksum):
            raise UserWarning('File {} is downloaded but the content hash does not match. ' \
                              'The repo may be outdated or download may be incomplete. ' \
                              'If the "repo_url" is overridden, consider switching to ' \
                              'the default repo.'.format(filename))
        # extract
        with zipfile.ZipFile(filename,"r") as zip_ref:
            zip_ref.extractall(path=path)
        print("Extracted", filename) 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_voc(path, overwrite=False):
    _DOWNLOAD_URLS = [
        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
         '34ed68851bce2a36e2a223fa52c661d592c66b3c'),
        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
         '41a8d6e12baa5ab18ee7f8f8029b9e11805b4ef1'),
        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
         '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')]
    makedirs(path)
    for url, checksum in _DOWNLOAD_URLS:
        filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
        # extract
        with tarfile.open(filename) as tar:
            tar.extractall(path=path)


#####################################################################################
# Download and extract the VOC augmented segmentation dataset into ``path`` 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_aug(path, overwrite=False):
    _AUG_DOWNLOAD_URLS = [
        ('http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz', '7129e0a480c2d6afb02b517bb18ac54283bfaa35')]
    makedirs(path)
    for url, checksum in _AUG_DOWNLOAD_URLS:
        filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
        # extract
        with tarfile.open(filename) as tar:
            tar.extractall(path=path)
            shutil.move(os.path.join(path, 'benchmark_RELEASE'),
                        os.path.join(path, 'VOCaug'))
            filenames = ['VOCaug/dataset/train.txt', 'VOCaug/dataset/val.txt']
            # generate trainval.txt
            with open(os.path.join(path, 'VOCaug/dataset/trainval.txt'), 'w') as outfile:
                for fname in filenames:
                    fname = os.path.join(path, fname)
                    with open(fname) as infile:
                        for line in infile:
                            outfile.write(line) 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_coco(args, overwrite=False):
    """download COCO dataset and Unzip to download_dir"""
    _DOWNLOAD_URLS = [
        ('http://images.cocodataset.org/zips/train2017.zip',
         '10ad623668ab00c62c096f0ed636d6aff41faca5'),
        ('http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
         '8551ee4bb5860311e79dace7e79cb91e432e78b3'),
        ('http://images.cocodataset.org/zips/val2017.zip',
         '4950dc9d00dbe1c933ee0170f5797584351d2a41'),
    ]
    if not os.path.isdir(args.download_dir):
        makedirs(args.download_dir)
    for url, checksum in _DOWNLOAD_URLS:
        filename = download(url, path=args.download_dir, overwrite=overwrite, sha1_hash=checksum)
        with zipfile.ZipFile(filename) as zf:
            zf.extractall(path=args.download_dir) 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def parse_args():
    parser = argparse.ArgumentParser(
        description='Setup the ImageNet dataset.',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--download-dir', required=True,
                        help="The directory that contains downloaded tar files")
    parser.add_argument('--target-dir', default=_TARGET_DIR,
                        help="The directory to store extracted images")
    parser.add_argument('--checksum', action='store_true',
                        help="If check integrity before extracting.")
    parser.add_argument('--with-rec', action='store_true',
                        help="If build image record files.")
    parser.add_argument('--num-thread', type=int, default=1,
                        help="Number of threads to use when building image record file.")
    args = parser.parse_args()
    return args 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def main(args):
    # download VID dataset
    download_VID(args)
    print('VID dataset has already download completed')
    VID_base_path = os.path.join(args.download_dir, 'ILSVRC2015')
    ann_base_path = os.path.join(VID_base_path, 'Annotations/VID/train/')
    symlink(args)
    # Format XML and save it in JSON
    parse_vid(ann_base_path, args)
    print('VID dataset json has already generat completed')
    # crop VID dataset for prepare for tracking
    par_crop(args, ann_base_path)
    print('VID dataset has already crop completed')
    # generat VID json for prepare for tracking
    gen_json(args)
    print('VID dataset has already generat completed') 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_coco(path, overwrite=False):
    _DOWNLOAD_URLS = [
        ('http://images.cocodataset.org/zips/train2017.zip',
         '10ad623668ab00c62c096f0ed636d6aff41faca5'),
        ('http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
         '8551ee4bb5860311e79dace7e79cb91e432e78b3'),
        ('http://images.cocodataset.org/zips/val2017.zip',
         '4950dc9d00dbe1c933ee0170f5797584351d2a41'),
        # ('http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip',
         # '46cdcf715b6b4f67e980b529534e79c2edffe084'),
        # test2017.zip, for those who want to attend the competition.
        # ('http://images.cocodataset.org/zips/test2017.zip',
        #  '4e443f8a2eca6b1dac8a6c57641b67dd40621a49'),
    ]
    makedirs(path)
    for url, checksum in _DOWNLOAD_URLS:
        filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
        # extract
        with zipfile.ZipFile(filename) as zf:
            zf.extractall(path=path) 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_voc(path, overwrite=False):
    _DOWNLOAD_URLS = [
        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
         '34ed68851bce2a36e2a223fa52c661d592c66b3c'),
        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
         '41a8d6e12baa5ab18ee7f8f8029b9e11805b4ef1'),
        ('http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar',
         '4e443f8a2eca6b1dac8a6c57641b67dd40621a49')]
    makedirs(path)
    for url, checksum in _DOWNLOAD_URLS:
        filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
        # extract
        with tarfile.open(filename) as tar:
            tar.extractall(path=path)


#####################################################################################
# Download and extract the VOC augmented segementation dataset into ``path`` 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_coco(path, overwrite=False):
    _DOWNLOAD_URLS = [
        ('http://images.cocodataset.org/zips/train2017.zip',
         '10ad623668ab00c62c096f0ed636d6aff41faca5'),
        ('http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
         '8551ee4bb5860311e79dace7e79cb91e432e78b3'),
        ('http://images.cocodataset.org/zips/val2017.zip',
         '4950dc9d00dbe1c933ee0170f5797584351d2a41'),
        ('http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip',
         'e7aa0f7515c07e23873a9f71d9095b06bcea3e12'),
        # test2017.zip, for those who want to attend the competition.
        # ('http://images.cocodataset.org/zips/test2017.zip',
        #  '4e443f8a2eca6b1dac8a6c57641b67dd40621a49'),
    ]
    makedirs(path)
    for url, checksum in _DOWNLOAD_URLS:
        filename = download(url, path=path, overwrite=overwrite, sha1_hash=checksum)
        # extract
        with zipfile.ZipFile(filename) as zf:
            zf.extractall(path=path) 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def parse_args():
    parser = argparse.ArgumentParser(
        description='Initialize Visual Genome dataset.',
        epilog='Example: python visualgenome.py --download-dir ~/visualgenome',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--download-dir', type=str, default='~/visualgenome/',
                        help='dataset directory on disk')
    parser.add_argument('--no-download', action='store_true', help='disable automatic download if set')
    parser.add_argument('--overwrite', action='store_true', help='overwrite downloaded files if set, in case they are corrupted')
    args = parser.parse_args()
    return args 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def download_json(path, overwrite=False):
    url = 'https://data.dgl.ai/dataset/vg.zip'
    output = 'vg.zip'
    download(url, path=path)
    with zipfile.ZipFile(output) as zf:
        zf.extractall(path=path)
    json_path = os.path.join(path, 'vg')
    json_files = os.listdir(json_path)
    for fl in json_files:
        shutil.move(os.path.join(json_path, fl),
                    os.path.join(path, fl))
    os.rmdir(json_path) 

# 需要导入模块: from gluoncv import utils [as 别名]
# 或者: from gluoncv.utils import download [as 别名]
def parse_args():
    parser = argparse.ArgumentParser(
        description='Initialize ADE20K dataset.',
        epilog='Example: python setup_ade20k.py',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--download-dir', default=None, help='dataset directory on disk')
    args = parser.parse_args()
    return args