def vl_test_hikmeans():
""" VL_TEST_HIKMEANS Test VL_HIKMEANS function
"""
K = 3
nleaves = 100
data = numpy.array(numpy.random.rand(2, 1000) * 255, 'uint8')
datat = numpy.array(numpy.random.rand(2, 10000) * 255, 'uint8')
[tree, A] = vlfeat.vl_hikmeans(data, K, nleaves, verb=1)
AT = vlfeat.vl_hikmeanspush(tree, datat)
pylab.figure()
plottree(tree)
pylab.xlim(0, 255)
pylab.ylim(0, 255)
print('hikmeans-tree')
pylab.figure()
gen = color_gen()
for k in range(K * K):
color = next(gen)
sel = pylab.find(A[-1, :] == k)
pylab.plot(data[0, sel], data[1, sel], '.', color=color)
sel = pylab.find(AT[-1, :] == k)
pylab.plot(datat[0, sel], datat[1, sel], '+', color=color)
plottree(tree, linewidth=4)
pylab.xlim(0, 255)
pylab.ylim(0, 255)
print('hikmeans-clusters')
def vl_test_hikmeans():
""" VL_TEST_HIKMEANS Test VL_HIKMEANS function
"""
K = 3
nleaves = 100
data = numpy.array(numpy.random.rand(2, 1000) * 255, 'uint8')
datat = numpy.array(numpy.random.rand(2, 10000) * 255, 'uint8')
[tree, A] = vlfeat.vl_hikmeans(data, K, nleaves, verb=1)
AT = vlfeat.vl_hikmeanspush(tree, datat)
pylab.figure()
plottree(tree)
pylab.xlim(0, 255)
pylab.ylim(0, 255)
print('hikmeans-tree') ;
pylab.figure()
gen = color_gen()
for k in range(K*K):
color = next(gen)
sel = pylab.find(A[-1, :] == k)
pylab.plot(data[0, sel], data[1, sel], '.', color=color)
sel = pylab.find(AT[-1, :] == k)
pylab.plot(datat[0, sel], datat[1, sel], '+', color=color)
plottree(tree, linewidth=4)
pylab.xlim(0, 255)
pylab.ylim(0, 255)
print('hikmeans-clusters') ;
def generate_tree(self, data, K, nleaves, file_dir, file_name):
self.K = K
self.nleaves = nleaves
[tr, A] = vl.vl_hikmeans(data, self.K, self.nleaves, verb=1)
tr.save(os.path.join(file_dir, file_name))
def vl_test_hikmeans_io():
""" VL_TEST_HIKMEANS_IO Test VL_HIKMEANS function
"""
K = 3
nleaves = 100
data = numpy.array(numpy.random.rand(2, 1000) * 255, 'uint8')
[tree, A] = vlfeat.vl_hikmeans(data, K, nleaves, verb=1)
tree.save('/tmp/test.vlhkm')
tree2 = vlfeat._vlfeat.VlHIKMTree(0, 0)
tree2.load('/tmp/test.vlhkm')
pylab.figure()
plottree(tree)
pylab.xlim(0, 255)
pylab.ylim(0, 255)
pylab.figure()
plottree(tree2)
pylab.xlim(0, 255)
pylab.ylim(0, 255)
print('hikmeans-tree-io')
def vl_test_hikmeans_io():
""" VL_TEST_HIKMEANS_IO Test VL_HIKMEANS function
"""
K = 3
nleaves = 100
data = numpy.array(numpy.random.rand(2, 1000) * 255, 'uint8')
[tree, A] = vlfeat.vl_hikmeans(data, K, nleaves, verb=1)
tree.save('/tmp/test.vlhkm')
tree2 = vlfeat._vlfeat.VlHIKMTree(0, 0)
tree2.load('/tmp/test.vlhkm')
pylab.figure()
plottree(tree)
pylab.xlim(0, 255)
pylab.ylim(0, 255)
pylab.figure()
plottree(tree2)
pylab.xlim(0, 255)
pylab.ylim(0, 255)
print('hikmeans-tree-io')
def learn(bk, descr):
if bk['dictionary'] == 'ikm':
print('block_dictionary: === Running IKM ===')
print('block_dictionary: num words (K): %d'%(bk['ikm_nwords']))
dict_ = vlfeat.vl_ikmeans(descr,
bk['ikm_nwords'],
verbosity=1,
method='elkan')
print('block_dictionary: IKM done')
elif bk['dictionary'] == 'hikm':
print('block_dictionary: === Runnining HIKM ===')
print('block_dictionary: num leaves: %d'%(bk['hikm_nleaves']))
print('block_dictionary: branching (K): %d'%(bk['hikm_K']))
print('block_dictionary: only_leaves: %d'%(bk['hikm_only_leaves']))
dict_,asgn = vlfeat.vl_hikmeans(descr,
bk['hikm_K'],
bk['hikm_nleaves'],
verbosity=1 ,
method='elkan')
print('block_dictionary: HIKM done')
else:
raise TypeError('block_dictionary.py :learn() unknown dictionary type')
return dict_
