def polylinesFromBinImage(img, minimum_cluster_size=6,
remove_small_obj_size=3,
reconnect_size=3,
max_n_contours=None, max_len_contour=None,
copy=True):
'''
return a list of arrays of un-branching contours
img -> (boolean) array
optional:
---------
minimum_cluster_size -> minimum number of pixels connected together to build a contour
##search_kernel_size -> TODO
##min_search_kernel_moment -> TODO
numeric:
-------------
max_n_contours -> maximum number of possible contours in img
max_len_contour -> maximum contour length
'''
assert minimum_cluster_size > 1
assert reconnect_size % 2, 'ksize needs to be odd'
# assert search_kernel_size == 0 or search_kernel_size > 2 and search_kernel_size%2, 'kernel size needs to be odd'
# assume array size parameters, is not given:
if max_n_contours is None:
max_n_contours = max(img.shape)
if max_len_contour is None:
max_len_contour = sum(img.shape[:2])
# array containing coord. of all contours:
contours = np.zeros(shape=(max_n_contours, max_len_contour, 2),
dtype=np.uint16) # if not search_kernel_size else np.float32)
if img.dtype != np.bool:
img = img.astype(bool)
elif copy:
img = img.copy()
if remove_small_obj_size:
remove_small_objects(img, remove_small_obj_size,
connectivity=2, in_place=True)
if reconnect_size:
# remove gaps
maximum_filter(img, reconnect_size, output=img)
# reduce contour width to 1
img = skeletonize(img)
n_contours = _populateContoursArray(img, contours, minimum_cluster_size)
contours = contours[:n_contours]
l = []
for c in contours:
ind = np.zeros(shape=len(c), dtype=bool)
_getValidInd(c, ind)
# remove all empty spaces:
l.append(c[ind])
return l
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