def split(image, prob, segmentation, n=1, min_pixels=100):
'''
'''
ugly_segmentation = np.array(segmentation)
hist = Util.get_histogram(ugly_segmentation.astype(np.uint64))
labels = len(hist)
for l in range(labels):
if l == 0:
continue
for s in range(n):
binary_mask = Util.threshold(ugly_segmentation, l)
splitted_label, border = Patch.split_label(image, binary_mask)
# check if splitted_label is large enough
if len(splitted_label[splitted_label == 1]) < min_pixels:
continue
ugly_segmentation[splitted_label ==
1] = ugly_segmentation.max() + 1
return ugly_segmentation
def generate_split_error(image, prob, segmentation, n=3):
hist = Util.get_histogram(segmentation.astype(np.uint64))
labels = range(1, len(hist)) # do not include zeros
np.random.shuffle(labels)
for l in labels:
binary_mask = Util.threshold(segmentation, l)
labeled_parts = skimage.measure.label(binary_mask)
labeled_parts += 1 # avoid the 0
labeled_parts[binary_mask == 0] = 0
labeled_parts, no_labeled_parts = mh.labeled.relabel(labeled_parts)
for i in range(n):
for part in range(1, no_labeled_parts + 1):
binary_part = Util.threshold(labeled_parts, part)
split_binary_mask, split_isolated_border = Patch.split_label(
image, binary_part)
if split_binary_mask.max() == 0:
# the strange err (label too small or sth)
print 'Caught empty..'
continue
patches = Patch.analyze_border(image, prob,
split_binary_mask,
binary_mask.invert(),
split_isolated_border)
for s in patches:
yield s
yield Patch.fliplr(s)
yield Patch.flipud(s)
yield Patch.rotate(s, 90)
yield Patch.rotate(s, 180)
yield Patch.rotate(s, 270)
