help='actual descriptors to save then')
parser.add_argument('--ds',
'--descriptor_suffix',
help='suffix for descriptor folder')
parser.add_argument('--cluster',
required=True,
help='points to the cluster file')
parser.add_argument('--ratio',
type=float,
help='max ratio 1st to 2nd nearest cluster')
return parser
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Clustering - Index')
parser = pc.commonArguments(parser)
parser = addArguments(parser)
args = parser.parse_args()
np.random.seed(42)
if not os.path.exists(args.outputfolder):
pc.mkdir_p(args.outputfolder)
print args.max_descriptors
files, labels = pc.getFiles(args.inputfolder,
args.suffix,
args.labelfile,
concat=True)
print 'n-files:', len(files)
print "NN {:10} TOP-1: {:7} mAP: {:12}".format(name, top1, mAP)
return top1, mAP
def runNN(descriptors, labels, parallel, nprocs):
"""
compute nearest neighbor from specific descriptors, given labels
"""
distance_method = { "cosine": 'cosine' }
ret_matrix = None
for name, method in distance_method.iteritems():
dist_matrix = computeDistances(descriptors, method,
parallel, nprocs)
computeStats(name, dist_matrix, labels, parallel)
ret_matrix = dist_matrix
return ret_matrix
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Evaluate stuff")
parser = pc.commonArguments(parser)
args = parser.parse_args()
descr_files, labels = pc.getFiles(args.inputfolder, args.suffix, args.labelfile,
exact=True)
descriptors = pc.loadDescriptors(descr_files)
ret_matrix = runNN( descriptors, labels, args.parallel )
