def test_particle_feature_learner():
from and_or_images.tools.image_iterators.aloi_iterator import AloiIterator
from and_or_images.structures.gabor_region import GaborRegion
from and_or_images.algorithms.window_samplers.grid_window_sampler import GridWindowSampler
num_cats = 10
images_per_category = 1
image_shape = (144,192)
feature_shape = (72, 96)
global image_iter
image_iter = AloiIterator(num_cats, images_per_category)
gabor = GaborRegion(image_shape, rotations=3,
initial_wavelength=3,
num_wavelengths=2)
win_sampler = GridWindowSampler(image_shape, feature_shape)
global gabor_acts
gabor_acts = gabor.precompute_image_activations(image_iter)
# strip off the categories
acts = [act for act,img_idx in gabor_acts]
global fl
fl = ParticleFeatureLearner(gabor, win_sampler, agreement_percent = 0.90)
feature_assignments, features = fl.compute_features(acts)
def test_greedy_feature_learner():
from and_or_images.tools.image_iterators.aloi_iterator import AloiIterator
from and_or_images.structures.gabor_region import GaborRegion
global fl
fl = GreedyFeatureLearner()
num_cats = 500
images_per_category = 1
image_shape = (144,192)
global image_iter
image_iter = AloiIterator(num_cats, images_per_category)
gabor = GaborRegion(image_shape, rotations=3,
initial_wavelength=3,
num_wavelengths=2)
global gabor_acts
gabor_acts = gabor.precompute_image_activations(image_iter)
# strip off the categories
acts = [act for act,img_idx in gabor_acts]
feature_assignments, features = fl.compute_features(acts,
agreement_threshold = 0.35,
analyze=True)
def train(self):
""" Store a copy of every image in the iterator. """
Experiment.train(self)
start = time.time()
self.image_shape = (144, 192)
gabor = GaborRegion(self.image_shape, rotations=3,
initial_wavelength=3,
num_wavelengths=2)
# Regions = [ GaborRegion, AndRegion, OrRegion (classifier) ]
self.network = AndOrNetwork((144,192), num_regions=2,
input_region = gabor)
f1 = self.network.regions[1]
f2 = self.network.regions[2]
classifier = self.network.get_classifier()
self.gabor_acts = gabor.precompute_image_activations(self.image_iter)
windows = self.get_windows()
for window in windows:
self.network.prepare_for_inference(1)
elapsed = (time.time() - start)
total_cxns = 0
for i, r in enumerate(self.network.regions[1:]):
num_cxns = r.get_num_cxns()
print "Region %s cxns: %s" % (i, num_cxns)
total_cxns += num_cxns
print "Total connections:", total_cxns
print "Training time:", elapsed
print "Time per category:", (elapsed / i)
print colored("Training complete", "green")
def test(self):
""" Test that every image is correctly recognized. """
Experiment.test(self)
start = time.time()
classifier = self.network.get_classifier()
i = 0
while self.image_iterator.has_next():
image, category = self.image_iterator.next()
recognized = self.network.do_inference(numpy.array(image), category)
if not recognized:
active_cats = classifier.get_active_categories()
print colored("Failed: " + category + " recognized as "+repr(active_cats), 'red')
i += 1
if i % self.PRINT_INCR == 0: print "Iter:", i
elapsed = (time.time() - start)
print "Testing time:", elapsed
print "Time per category:", (elapsed / i)
print colored("Testing complete", "green")
def test_particle_feature_learner():
from and_or_images.tools.image_iterators.aloi_iterator import AloiIterator
from and_or_images.structures.gabor_region import GaborRegion
from and_or_images.algorithms.window_samplers.grid_window_sampler import GridWindowSampler
num_cats = 10
images_per_category = 10
image_shape = (144,192)
# feature_shape = (72, 96) # 2 x 2
# feature_shape = (36, 48) # 4 x 4
# feature_shape = (18, 24) # 8 x 8
# feature_shape = (9, 12) # 16 x 16
#
# feature_shape = (48, 48) # 3 x 4
# feature_shape = (24, 24) # 6 x 8
feature_shape = (12, 12) # 12 x 16 = 192
# feature_shape = (6, 6) # 24 x 32 = 768
global image_iter
image_iter = AloiIterator(num_cats, images_per_category)
gabor = GaborRegion(image_shape, rotations=3,
initial_wavelength=3,
num_wavelengths=2)
win_sampler = GridWindowSampler(image_shape, feature_shape, overlap=False)
global gabor_acts
gabor_acts = gabor.precompute_image_activations(image_iter)
# strip off the categories
acts = [act for act,img_idx in gabor_acts]
global fl
fl = ParticleFeatureLearner(gabor, win_sampler, agreement_percent = 0.40)
feature_assignments, features = fl.compute_features(acts)
