def run(self):
iterator = "Aloi"
if iterator=='Bikes':
num_cats = 365
image_shape = (480, 640)
images_per_category = 1
image_iter = ImageListIterator("bikes", num_cats)
if iterator=='Flowers':
num_cats = 500
image_shape = (500,500)
images_per_category = 1
image_iter = ImageListIterator("flowers", num_cats, randomize=True)
if iterator=='Aloi':
num_cats = 500
images_per_category = 1
image_shape = (144,192)
image_iter = AloiIterator(num_cats, images_per_category)
self.pickle_name = "%s-%s-%s.pkl" % (iterator, num_cats, images_per_category)
print "Creating gabor region..."
gabor = GaborRegion(image_shape, rotations=3,
initial_wavelength=3,
num_wavelengths=2 )
# precompute all gabor activations
print colored("Computing gabor activations...", 'green')
images = []
i = 0
while image_iter.has_next():
image, category, img_idx = image_iter.next()
gabor.do_inference(numpy.array(image))
active = gabor.node_values.copy()
images.append((active, active.sum(), (category, img_idx)))
i += 1
if i%10==0: print i
print colored("Computing overlap...", 'green')
self.num_merges = num_merges = 10
overlap_results = [[] for i in range(num_merges)]
cxn_results = [[] for i in range(num_merges)]
i = 0
for active, size, idx in images:
merged_idxs = [idx]
cum_size = size
for merge_idx in range(num_merges):
max_overlap = 0
max_percent = 0
max_merge = None
max_cidx = None
max_size = None
num_parents = merge_idx + 2
for comp_active, csize, cidx in images:
if cidx in merged_idxs: continue
merge = numpy.logical_and(active, comp_active)
overlap = merge.sum()
if overlap > max_overlap:
max_overlap = overlap
max_merge = merge
max_cidx = cidx
max_size = csize
active = max_merge
cum_size += max_size
merged_idxs.append(max_cidx)
overlap_results[merge_idx].append(max_overlap)
cxn_results[merge_idx].append(max_overlap * (num_parents-1) - num_parents)
i += 1
if i%10==0: print i
fig = plt.figure(1)
fig.set_size_inches(5, 10)
plt.subplot(1,2,1)
colors = '#008800 #880000 #000088 #888800'.split()
start, end = 1000000, 0
for color, merge_idx in zip(colors, range(num_merges)):
start = min(start, min(overlap_results[merge_idx]))
end = max(end, max(overlap_results[merge_idx]))
for color, merge_idx in zip(colors, reversed(range(num_merges))):
plt.hist(overlap_results[merge_idx], 20, range=(start, end),
facecolor=color, alpha=0.75)
plt.xlabel("# shared features")
plt.ylabel("# occurrences")
plt.title("Component sharing\n%s categories, %s images." % \
(num_cats, images_per_category))
plt.grid(True)
plt.subplot(1,2,2)
start, end = 1000000, 0
for color, merge_idx in zip(colors, range(num_merges)):
start = min(start, min(cxn_results[merge_idx]))
end = max(end, max(cxn_results[merge_idx]))
for color, merge_idx in zip(colors, reversed(range(num_merges))):
plt.hist(cxn_results[merge_idx], numpy.arange(start, end, (end-start)/20),
facecolor=color, alpha=0.75)
plt.xlabel("# fewer connections")
plt.ylabel("# occurrences")
plt.title("Component sharing\n%s categories, %s images." % \
(num_cats, images_per_category))
plt.grid(True)
plt.show()
print colored("Analysis complete", "green")
self.cxn_results = cxn_results
self.overlap_results = overlap_results
self.save_results()
self.report()
