import sys import argparse import re import os from numpy import array, zeros, mean, std, sort, add, subtract, divide, dot, sqrt from numpy import linalg as la from scipy.cluster.vq import vq, kmeans, whiten import vlad parser = argparse.ArgumentParser( description='K-means clustering util for image feature processing.') parser.add_argument('-d', help='The directory of vlad feature files.') parser.add_argument('-c', help='The apc clustering result file.') parser.add_argument('-o', help='The output file.') parser.add_argument('-t', help='The threshold for cluster size, optionally.') args = parser.parse_args() photos = vlad.list_files(args.d) clusters = vlad.load_clustering(args.c) for idx, cluster in enumerate(clusters): if args.t == None or len(cluster['member']) >= int(args.t): vlad.write_out_vlad_matrix_libsvm_format( photos, args.o + ".cluster." + str(idx), cluster)
parser.add_argument('-d', help='The directory of vlad feature files.') parser.add_argument('-o', help='The output file.') parser.add_argument('-s', help='The number of samples, optionally.') parser.add_argument('-f', help='The output format, optionally.') parser.add_argument('-l', help='The data label, optionally.') args = parser.parse_args() photos = vlad.list_files(args.d) data_label = '' if args.l != None: data_label = args.l if args.s == None: if args.f == None: vlad.write_out_vlad_matrix(photos, args.o) elif args.f == "libsvm": vlad.write_out_vlad_matrix_libsvm_format(photos, args.o, label=data_label) else: sampled_sets = vlad.random_sample_photos(photos, int(args.s)) for idx, photo_set in enumerate(sampled_sets): if args.f == None: vlad.write_out_vlad_matrix(photo_set, args.o + "." + str(idx)) elif args.f == "libsvm": vlad.write_out_vlad_matrix_libsvm_format(photo_set, args.o + "." + str(idx), label=data_label)
import sys import argparse import re import os from numpy import array, zeros, mean, std, sort, add, subtract, divide, dot, sqrt from numpy import linalg as la from scipy.cluster.vq import vq, kmeans, whiten import vlad parser = argparse.ArgumentParser(description = 'K-means clustering util for image feature processing.') parser.add_argument('-d', help = 'The directory of vlad feature files.') parser.add_argument('-c', help = 'The apc clustering result file.') parser.add_argument('-o', help = 'The output file.') parser.add_argument('-t', help = 'The threshold for cluster size, optionally.') args = parser.parse_args() photos = vlad.list_files(args.d) clusters = vlad.load_clustering(args.c) for idx, cluster in enumerate(clusters): if args.t == None or len(cluster['member']) >= int(args.t): vlad.write_out_vlad_matrix_libsvm_format(photos, args.o + ".cluster." + str(idx), cluster)
parser.add_argument('-d', help = 'The directory of vlad feature files.') parser.add_argument('-o', help = 'The output file.') parser.add_argument('-s', help = 'The number of samples, optionally.') parser.add_argument('-f', help = 'The output format, optionally.') parser.add_argument('-l', help = 'The data label, optionally.') args = parser.parse_args() photos = vlad.list_files(args.d) data_label = '' if args.l != None: data_label = args.l if args.s == None: if args.f == None: vlad.write_out_vlad_matrix(photos, args.o) elif args.f == "libsvm": vlad.write_out_vlad_matrix_libsvm_format(photos, args.o, label = data_label) else: sampled_sets = vlad.random_sample_photos(photos, int(args.s)) for idx, photo_set in enumerate(sampled_sets): if args.f == None: vlad.write_out_vlad_matrix(photo_set, args.o + "." + str(idx)) elif args.f == "libsvm": vlad.write_out_vlad_matrix_libsvm_format(photo_set, args.o + "." + str(idx), label = data_label)