def graphcut_stawiaski(regions, gradient = False, foreground = False, background = False):
"""
Executes a Stawiaski label graph cut.
@param regions the regions image
@type regions scipy.ndarray (dtype=scipy.int?)
@param gradient the gradient image
@type gradient scipy.ndarray (dtype=scipy.float?)
@param foreground the foreground markers
@type foreground scipy.ndarray (dtype=scipy.bool_)
@param background the background markers
@type background scipy.ndarray (dtype=scipy.bool_)
@return the cut image as binary array
@rtype scipy.ndarray (dtype=scipy.bool_)
@raise ArgumentError when the supplied data is erroneous
"""
# initialize logger
logger = Logger.getInstance()
# unpack images if required
# !TODO: This is an ugly hack, especially since it can be seen inside the function definition
# How to overcome this, since I can not use a wrapper function as the whole thing must be pickable
if not gradient and not foreground and not background:
regions, gradient, foreground, background = regions
# ensure that input images are scipy arrays
img_region = scipy.asarray(regions)
img_gradient = scipy.asarray(gradient)
img_fg = scipy.asarray(foreground, dtype=scipy.bool_)
img_bg = scipy.asarray(background, dtype=scipy.bool_)
# ensure correctness of supplied images
if not (img_region.shape == img_gradient.shape == img_fg.shape == img_bg.shape): raise ArgumentError('All supplied images must be of the same shape.')
# recompute the label ids to start from id = 1
img_region = relabel(img_region)
# generate graph
gcgraph = graph_from_labels(img_region, img_fg, img_bg, boundary_term = boundary_stawiaski, boundary_term_args = (img_gradient))
# execute min-cut
maxflow = gcgraph.maxflow() # executes the cut and returns the maxflow value
logger.debug('Graph-cut terminated successfully with maxflow of {}.'.format(maxflow))
# apply results to the region image
mapping = [0] # no regions with id 1 exists in mapping, entry used as padding
mapping.extend(map(lambda x: 0 if gcgraph.termtype.SINK == gcgraph.what_segment(int(x) - 1) else 1,
scipy.unique(img_region)))
img_results = relabel_map(img_region, mapping)
return img_results.astype(scipy.bool_)
def fun(arr, start):
arr = relabel(arr, start)
return arr, arr.max() + 1
def fun(arr, start):
arr = relabel(arr, start)
return arr, arr.max() + 1