def as_contours(self):
"""A dictionary of lists of contours keyed by byte_value"""
contours = dict()
for byte_value in self.__byte_values:
if byte_value == 0:
continue
mask = (self.__array == byte_value) * 255
found_contours = find_contours(
mask, 254, fully_connected='high') # a list of array
contours[byte_value] = ContourSet(found_contours)
return contours
def _as_contours(self):
"""A dictionary of lists of contours keyed by byte_value"""
contours = dict()
_maskbase = np.array([False, True])
_indexbase = np.zeros(self._array.shape, dtype=np.int8)
for byte_value in self._byte_values[self._byte_values != 0]:
mask = _maskbase[np.equal(self._array, byte_value, out=_indexbase)]
found_contours = find_contours(
mask, 254, fully_connected='high') # a list of array
contours[byte_value] = ContourSet(found_contours)
return contours
def feret_diameter(prop):
"""Determines the maximum Feret diameter.
Parameters
----------
prop : RegionProperties
Describes a labeled region.
Returns
-------
max_feret_diameter : float
Maximum Feret diameter of the region.
See also
--------
:func:`skimage.measure.regionprops` : Measure properties of labeled image regions
Examples
--------
>>> import numpy as np
>>> from skimage.measure import regionprops
>>> image = np.ones((2,2), dtype=np.int8)
>>> prop = regionprops(image)[0]
>>> feret_diameter(prop)
2.23606797749979
"""
identity_convex_hull = np.pad(prop.convex_image,
2,
mode='constant',
constant_values=0)
if prop._ndim == 2:
coordinates = np.vstack(
find_contours(identity_convex_hull, 0.5, fully_connected='high'))
elif prop._ndim == 3:
coordinates, _, _, _ = marching_cubes(identity_convex_hull, level=0.5)
distances = pdist(coordinates, 'sqeuclidean')
# TODO: allow computing the Feret diameter along a given direction (t,s)
# For this, restrict the maximum distance to those pairwise distances whose
# orientation (up to a given tolerance) is (t,s)
max_feret_diameter = sqrt(np.max(distances))
return max_feret_diameter