def main():
parser = argparse.ArgumentParser(
description='Tools for hamming distance-based image retrieval by cuda')
parser.add_argument('-f',
help='The filename of image raw features (SIFT).')
parser.add_argument('-v',
default='fvecs',
help='The format of image raw features.')
parser.add_argument('-s',
default='dict',
help='The method of indexing storage.')
parser.add_argument('-d',
default='128',
help='Dimensions of raw image feature.')
parser.add_argument('-o',
default='0',
help='Offset of accessing raw image features.')
parser.add_argument('-n',
default='1',
help='Number of raw image features to read.')
parser.add_argument('-i',
default='n',
help='Whether to perform indexing step.')
parser.add_argument('-e', help='The dirname of indexing folder.')
parser.add_argument('-k', default='10', help='Number of retrieved images.')
parser.add_argument('-r',
default='32',
help='Number of dimensions randomly sampled.')
parser.add_argument('-c',
default='n',
help='Whether to perform compressing step.')
parser.add_argument('-q',
default='n',
help='Whether to sequentially sampling.')
parser.add_argument(
'-p',
default='n',
help='Whether to perform querying in compressed domain.')
parser.add_argument('-g', default='y', help='GPU mode. default is "yes".')
parser.add_argument(
'-l',
default='n',
help='VLQ base64 mode. Load VLQ base64 encoding compressed dict.')
parser.add_argument('-b',
default='1',
help='Expanding level of search buckets.')
parser.add_argument('-t',
default='int32',
help='FastDict type (int32, int8, string).')
parser.add_argument('-u',
default='local',
help='CUDA client type (local, net).')
parser.add_argument('-host',
default='localhost',
help='CUDA server address.')
args = parser.parse_args()
d = int(args.d)
nuse = int(args.n)
off = int(args.o)
random_dims = int(args.r)
random_sampling = True
if args.q == 'y':
random_sampling = False
lsh = LSHash(64,
d,
random_sampling,
args.t,
args.u,
args.host,
random_dims,
1,
storage_config=args.s,
matrices_filename='project_plane.npz')
np_feature_vecs = load_features(args.f, args.v, nuse, d, lsh, args.e, off,
args.i)
if args.c == 'y':
if args.e != None and args.s == 'random':
lsh.load_index(args.e)
print "compressing index..."
lsh.compress_index(args.e)
print "compressing done."
else:
print "Please specify generated indexing file."
sys.exit(0)
if args.c != 'y' and args.i != 'y' and args.e != None and args.s == 'random':
if args.p == 'y':
print "loading compressed index."
lsh.load_compress_index(args.e, (args.l == 'y'))
print "loading done."
else:
print "loading index."
lsh.load_index(args.e)
print "loading done."
if args.p != 'y':
retrived = lsh.query(np_feature_vecs[1],
num_results=int(args.k),
expand_level=int(args.b),
distance_func='hamming')
else:
retrived = lsh.query_in_compressed_domain(np_feature_vecs[1],
num_results=int(args.k),
expand_level=int(args.b),
distance_func='hamming',
gpu_mode=args.g,
vlq_mode=args.l)
print retrived
def main():
parser = argparse.ArgumentParser(description = 'Tools for hamming distance-based image retrieval by cuda')
parser.add_argument('-f', help = 'The filename of image raw features (SIFT).')
parser.add_argument('-v', default = 'fvecs', help = 'The format of image raw features.')
parser.add_argument('-s', default = 'dict', help = 'The method of indexing storage.')
parser.add_argument('-d', default = '128', help = 'Dimensions of raw image feature.')
parser.add_argument('-o', default = '0', help = 'Offset of accessing raw image features.')
parser.add_argument('-n', default = '1', help = 'Number of raw image features to read.')
parser.add_argument('-i', default = 'n', help = 'Whether to perform indexing step.')
parser.add_argument('-e', help = 'The dirname of indexing folder.')
parser.add_argument('-k', default = '10', help = 'Number of retrieved images.')
parser.add_argument('-r', default = '32', help = 'Number of dimensions randomly sampled.')
parser.add_argument('-c', default = 'n', help = 'Whether to perform compressing step.')
parser.add_argument('-q', default = 'n', help = 'Whether to sequentially sampling.')
parser.add_argument('-p', default = 'n', help = 'Whether to perform querying in compressed domain.')
parser.add_argument('-g', default = 'y', help = 'GPU mode. default is "yes".')
parser.add_argument('-l', default = 'n', help = 'VLQ base64 mode. Load VLQ base64 encoding compressed dict.')
parser.add_argument('-b', default = '1', help = 'Expanding level of search buckets.')
parser.add_argument('-t', default = 'int32', help = 'FastDict type (int32, int8, string).')
parser.add_argument('-u', default = 'local', help = 'CUDA client type (local, net).')
parser.add_argument('-host', default = 'localhost', help = 'CUDA server address.')
args = parser.parse_args()
d = int(args.d)
nuse = int(args.n)
off = int(args.o)
random_dims = int(args.r)
random_sampling = True
if args.q == 'y':
random_sampling = False
lsh = LSHash(64, d, random_sampling, args.t, args.u, args.host, random_dims, 1, storage_config = args.s, matrices_filename = 'project_plane.npz')
np_feature_vecs = load_features(args.f, args.v, nuse, d, lsh, args.e, off, args.i)
if args.c == 'y':
if args.e != None and args.s == 'random':
lsh.load_index(args.e)
print "compressing index..."
lsh.compress_index(args.e)
print "compressing done."
else:
print "Please specify generated indexing file."
sys.exit(0)
if args.c != 'y' and args.i != 'y' and args.e != None and args.s == 'random':
if args.p == 'y':
print "loading compressed index."
lsh.load_compress_index(args.e, (args.l == 'y'))
print "loading done."
else:
print "loading index."
lsh.load_index(args.e)
print "loading done."
if args.p != 'y':
retrived = lsh.query(np_feature_vecs[1], num_results = int(args.k), expand_level = int(args.b), distance_func = 'hamming')
else:
retrived = lsh.query_in_compressed_domain(np_feature_vecs[1], num_results = int(args.k), expand_level = int(args.b), distance_func = 'hamming', gpu_mode = args.g, vlq_mode = args.l)
print retrived
