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dtcluster.py
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dtcluster.py
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################################################################################
#
# Library: pydstk
#
# Copyright 2010 Kitware Inc. 28 Corporate Drive,
# Clifton Park, NY, 12065, USA.
#
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 ( the "License" );
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
################################################################################
import os
import cv2
import sys
import time
import copy
import pickle
import numpy as np
import cv2.cv as cv
from optparse import OptionParser
import dscore.dsdist as dsdist
import dsutil.dsutil as dsutil
import dsutil.dsinfo as dsinfo
from dscore.system import LinearDS
from dscore.dsdist import ldsMartinDistance
"""Application #3: Dynamic Texture Clustering
"""
__license__ = "Apache License, Version 2.0"
__author__ = "Roland Kwitt, Kitware Inc., 2013"
__email__ = "E-Mail: roland.kwitt@kitware.com"
__status__ = "Development"
def usage():
"""Print usage information"""
print("""
Dynamic Texture Clusering (using MDS).
USAGE:
{0} [OPTIONS]
{0} -h
OPTIONS (Overview):
-i ARG -- Input file (list of N pickled DT's)
-k ARG -- Number of K cluster centers
-b ARG -- Base directory of input DT's
-o ARG -- (Pickled) Output list of length N
NOTE: The output list contains N entries, where only those
entries which were identified as representatives contain
original input DT filenames and "Dummy" otherwise.
[-d ARG] -- Already available distance matrix
[-v] -- Verbose output (default: False)
AUTHOR: Roland Kwitt, Kitware Inc., 2013
roland.kwitt@kitware.com
""".format(sys.argv[0]))
sys.exit(-1)
def main(argv=None):
if argv is None:
argv = sys.argv
parser = OptionParser(add_help_option=False)
parser.add_option("-i", dest="iListFile")
parser.add_option("-o", dest="oListFile")
parser.add_option("-d", dest="dMatFile")
parser.add_option("-b", dest="iBase")
parser.add_option("-k", dest="kCenter", type="int", default=5)
parser.add_option("-h", dest="shoHelp", action="store_true", default=False)
parser.add_option("-v", dest="verbose", action="store_true", default=False)
options, args = parser.parse_args()
if options.shoHelp:
usage()
iBase = options.iBase
dMatFile = options.dMatFile
iListFile = options.iListFile
oListFile = options.oListFile
kCenter = options.kCenter
verbose = options.verbose
assert kCenter > 0, "Oops (kCenter < 1) ..."
iList = pickle.load(open(iListFile))
if verbose:
dsinfo.info("Loaded list with %d DT models!" % len(iList))
# load DT's
dts = []
for dtFile in iList:
dts.append(pickle.load(open(os.path.join(iBase, dtFile))))
if verbose:
dsinfo.info("Running DT clustering with %d clusters ..." % kCenter)
D = None
if not dMatFile is None:
if os.path.exists(dMatFile):
if verbose:
dsinfo.info("Try loading distance matrix %s!" % dMatFile)
D = pickle.load(open(dMatFile))
if D is None:
if verbose:
dsinfo.info("Computing pairwise distances ...")
nDTs = len(dts)
D = np.zeros((nDTs, nDTs))
for i in range(nDTs):
for j in range(nDTs):
D[i,j] = ldsMartinDistance(dts[i], dts[j], 50)
# run clustering
(eData, ids) = LinearDS.cluster(D, kCenter, verbose)
ids = list(ids)
#write list of DT representatives
oList = []
for j, dtFile in enumerate(iList):
if j in ids:
oList.append(dtFile)
else:
oList.append("Dummy")
pickle.dump(oList, open(oListFile, "w"))
if verbose:
dsinfo.info("Wrote list of representative DS's to %s!" % oListFile)
if not dMatFile is None:
if verbose:
dsinfo.info("Writing distance matrix to %s!" % dMatFile)
pickle.dump(D, open(dMatFile, "w"))
if __name__ == "__main__":
sys.exit(main())