def lbp_combine_best_patches(patch_image_directory,
context_image,
prediction_fn,
min_stride=1,
max_iters=1,
max_stride=3):
# open the context image
context = Image.open(context_image)
# get smallest pw and images as np arrays
smallest_pw, patch_images = get_stylized_images(patch_image_directory)
# get context image scaled down so that it's the same size as the stylized imgs
patchR, patchC, _ = patch_images[0].shape
context = context.resize((patchC, patchR), Image.ANTIALIAS)
context = np.array(context)
lbf_folder = 'lbf_output/'
if not os.path.exists(lbf_folder):
os.makedirs(lbf_folder)
inner_folder = lbf_folder + prediction_fn + '_folder/'
if not os.path.exists(inner_folder):
os.makedirs(inner_folder)
stride = min_stride
while stride <= max_stride:
evidence, factors = construct_graph(patch_images, context, smallest_pw,
stride)
model = Model(factors)
# Get some feedback on how inference is converging by listening in on some of the label beliefs.
def reporter(infe, orde):
print('{:3}'.format(orde.total_iterations))
order = FloodingProtocol(model, max_iterations=max_iters)
inference = LoopyBeliefUpdateInference(model, order, callback=reporter)
inference.calibrate(evidence)
K = len(patch_images)
rrange = range(0, patchR - smallest_pw + 1, stride)
crange = range(0, patchC - smallest_pw + 1, stride)
num_r = len(rrange) # number of patches vertically
num_c = len(crange) # number of patches horizontally
ff_labels = [[None for i in range(num_c)] for j in range(num_r)]
ff_r = 0
for r in rrange:
ff_c = 0
for c in crange:
variable_name = 'label_{}_{}'.format(r, c)
# first factor is the context-style factor tha we want
label_factor = inference.get_marginals(variable_name)[0]
# save the actual patch location to make it easier to remap them later on
ff_labels[ff_r][ff_c] = [[r, c], label_factor.normalized_data]
ff_c += 1
ff_r += 1
# save the labels so they can be easily reused
ff_labels = np.array(ff_labels)
np.save(
"%s%s_stride_%d_first_factor_label_data" %
(inner_folder, prediction_fn, stride), ff_labels)
stride += 1
