def Distance(famgs, fanms, fbmgs, fbnms, eigenmg, name):
vecfa = np.array(MapFaces(eigenmg, famgs))
vecfb = np.array(MapFaces(eigenmg, fbmgs))
#Writing files to plot array
plotarray = np.array([vecfa[:, 0:3], vecfb[:, 0:3]])
gnu.Save(name + '_fa.txt', plotarray[0])
gnu.Save(name + '_fb.txt', plotarray[1])
ss = 'set terminal png\n'
ss += 'set output "' + name + 'fa_fb.png"\n'
ss += 'set multiplot\n'
ss += 'set size 1,0.5\n'
ss += 'set origin 0,0.0\n'
ss += 'set view 0,0,1,2\n'
ss += 'splot "' + name + '_fa.txt", "' + name + '_fb.txt"\n'
ss += 'unset multiplot\n'
ss += 'unset size\n'
ss += 'unset origin\n'
fp1 = open('plot' + name + '_fa_fb.txt', 'w')
fp1.write(ss)
fp1.flush()
result = []
for fb in vecfb:
distance = []
for fa in vecfa:
dist = np.sqrt(((fb - fa)**2).sum())
distance.append(dist)
idx = np.array(distance).argsort()
result.append(list(idx))
return result
def TestLaws(mgnames, NJ=100):
# create laws filters
filts = texture.BuildLawsFilters()
# allocate for jets
NI = len(mgnames) # number of images
jets = np.zeros((NJ * NI, 25))
# for each image
for i in xrange(NI):
# load
# correlate
#corrs = BruteCorrelate( data, filts )
data = mgnames[i] + 0
corrs = map(lambda x: correlate2d(data, x), filts)
for j in range(25):
corrs[i] = cspline2d(abs(corrs[i]), 200)
corrs = np.array(corrs)
# extract random jets
V, H = data.shape
vs = range(V)
hs = range(H)
np.random.shuffle(vs)
np.random.shuffle(hs)
for j in range(NJ):
jets[i * NJ + j] = corrs[:, vs[j], hs[j]]
# k-means clustering
clust, mmb = kmeans.KMeans(NI, jets)
#return jets
cffs, evecs = pca.PCA(clust, 3)
cffs = pca.Map2PCA(clust, evecs)
gnu.Save('Laws_results.txt', cffs)
return clust, cffs
def Train(brodatz_f, brodatz_256_mgs):
#A container function that can be imported or run with user created arrays and filenames.
#Coded Just for convenience incase of code testing!!
qvecs = map(lambda x: basis.GaborCorr(x), brodatz_256_mgs)
qvecs = np.array(qvecs)
cffs, evecs = pca.PCA(qvecs, 3)
cffs = pca.Map2PCA(qvecs, evecs)
vecs_num = 0
for name in brodatz_f:
plot_vecs = cffs[vecs_num:vecs_num + 4]
vecs_num = vecs_num + 4
gnu.Save('graph_output\\' + name[0:-4] + '.txt', plot_vecs)
for i in brodatz_f:
print i + '\n'
ss = 'unset key\n'
ss += 'splot for [i=1:112] \'graph_output\\D\'.i.\'.txt\'\n'
fp1 = open('plot_brodatz_256_textures.txt', 'w')
fp1.write(ss)
fp1.flush()
return brodatz_f, cffs
eigenmg1 = eigenmg1[0:5]
eigenmg2 = eigenmg2[0:5]
for i in range(5):
smisc.imsave('output/' + groups[0][5:] + '_' + str(i) + '.png',
eigenmg1[i])
smisc.imsave('output/eface_group2' + groups[1][5:] + '_' + str(i) + '.png',
eigenmg2[i])
vecs1 = np.array(MapFaces(eigenmg1, mgs_grp1))
vecs2 = np.array(MapFaces(eigenmg2, mgs_grp2))
plotarray = np.array([vecs1[:, 0:3], vecs2[:, 0:3]])
#Writing files to plot array
gnu.Save('AsianFemale.txt', plotarray[0])
gnu.Save('WhiteMale.txt', plotarray[1])
ss = 'set terminal png\n'
ss += 'set output "diff_in_groups.png"\n'
ss += 'set multiplot\n'
ss += 'set size 1,0.5\n'
ss += 'set origin 0,0.0\n'
ss += 'set view 0,0,1,2\n'
ss += 'splot "AsianFemale.txt","WhiteMale.txt"\n"'
ss += 'unset multiplot\n'
ss += 'unset size\n'
ss += 'unset origin\n'
fp1 = open('plotaf.txt', 'w')
fp1.write(ss)
fnames = ReadFiles('data')
mgs = map(lambda x: basis.LoadGray(x), fnames)
nmgs = []
for i in range(len(mgs)):
nm = ip.Plop(mgs[i], 512, 512)
nmgs.append(nm)
fids = map(lambda x: basis.MakeGrid(x), nmgs)
fids = np.array(fids)
morph = warp.Morph(nmgs[1], fids[1], nmgs[2], fids[2], NI=10)
Imageset = []
Imageset.append(nmgs[1])
for i in morph:
Imageset.append(i)
Imageset.append(nmgs[2])
for i in range(len(Imageset)):
nm = 'output\\morph_' + str(i) + '.png'
smisc.imsave(nm, Imageset[i])
clust, cffs = TestLaws(Imageset)
qvecs, a = TestHaar(Imageset)
qvecs = qvecs.sum(3).sum(2)
cffs, evecs = pca.PCA(qvecs, 3)
cffs = pca.Map2PCA(qvecs, evecs)
gnu.Save('Haar_results.txt', cffs)
ss = 'unset key\n'
ss += 'splot for [i=1:112] \'graph_output\\D\'.i.\'.txt\'\n'
fp1 = open('plot_brodatz_256_textures.txt', 'w')
fp1.write(ss)
fp1.flush()
return brodatz_f, cffs
brodatz_f, brodatz_256_mgs = ReadBrodatz('brodatz')
qvecs = map(lambda x: basis.GaborCorr(x), brodatz_256_mgs)
qvecs = np.array(qvecs)
cffs, evecs = pca.PCA(qvecs, 3)
cffs = pca.Map2PCA(qvecs, evecs)
vecs_num = 0
for name in brodatz_f:
plot_vecs = cffs[vecs_num:vecs_num + 4]
vecs_num = vecs_num + 4
gnu.Save('graph_output\\' + name[0:-4] + '.txt', plot_vecs)
for i in brodatz_f:
print i + '\n'
ss = 'unset key\n'
ss += 'splot for [i=1:112] \'graph_output\\D\'.i.\'.plt\'\n'
fp1 = open('plot_brodatz_256_textures.txt', 'w')
fp1.write(ss)
fp1.flush()