#!/usr/bin/python import sys import numpy as np import cPickle import descritores diretorio = sys.argv[1] bins = int(round(float(sys.argv[2]))) rmin = float(sys.argv[3]) rmax = float(sys.argv[4]) s = float(sys.argv[5]) #print "cd",bins,rmin,rmax f = open(diretorio+"classes.txt","r") cl = cPickle.load(f) f.close() db = {} for im_file in cl.keys(): tmp = descritores.cd(diretorio+im_file,sigma = s) h = np.histogram(tmp,bins = 40,range = (0.1,1.)) h = h[0].astype(float)/float(h[0].sum()) db[im_file] = np.hstack((cl[im_file],h)) # print im_file,db[im_file] cPickle.dump(db,open(sys.argv[6],"a"))
#!/usr/bin/python import sys import scipy import cPickle import descritores import numpy as np diretorio = sys.argv[1] f = open(diretorio+"classes.txt","r") cl = cPickle.load(f) f.close() db = {} for im_file in cl.keys(): tmp = descritores.cd(diretorio+im_file,method = 'octave') db[im_file] = scipy.hstack((cl[im_file],tmp)) if np.isnan(db[im_file]).any(): print im_file cPickle.dump(db,open(sys.argv[2],"wb"))
#!/usr/bin/python import sys import scipy import cPickle import descritores import numpy as np diretorio = sys.argv[1] bins = 150 range = (0.05,1.) f = open(diretorio+"classes.txt","r") cl = cPickle.load(f) f.close() db = {} for im_file in cl.keys(): tmp = descritores.cd(diretorio+im_file) tmp_h = np.histogram(tmp,bins = bins,range = range)[0] tmp_h = tmp_h.astype(float)/tmp_h.sum() db[im_file] = scipy.hstack((cl[im_file],tmp_h)) if np.isnan(db[im_file]).any(): print im_file,tmp_h cPickle.dump(db,open(sys.argv[2],"wb"))