mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-08 19:28:49 +08:00
emmanuelle
a5a771a8e4
background=0 by default. Modified label function so that background pixels are labeled with 0, and background=0 by default. All tests of _ccomp.pyx pass Modified a couple of files to be consistent with the new behavior of measure.label Modified doctring of label to pass doctest Modified TODO.txt as well as release notes to mention the new behavior of label. Typo in docstring Typo in docstring Changed default value of kw argument background in measure.label Removed unnecessary and outdated comment
125 lines
3.8 KiB
Python
125 lines
3.8 KiB
Python
"""Convex Hull."""
|
|
import numpy as np
|
|
from ..measure._pnpoly import grid_points_in_poly
|
|
from ._convex_hull import possible_hull
|
|
from ..measure._label import label
|
|
from ..util import unique_rows
|
|
|
|
__all__ = ['convex_hull_image', 'convex_hull_object']
|
|
|
|
try:
|
|
from scipy.spatial import Delaunay
|
|
except ImportError:
|
|
Delaunay = None
|
|
|
|
|
|
def convex_hull_image(image):
|
|
"""Compute the convex hull image of a binary image.
|
|
|
|
The convex hull is the set of pixels included in the smallest convex
|
|
polygon that surround all white pixels in the input image.
|
|
|
|
Parameters
|
|
----------
|
|
image : (M, N) array
|
|
Binary input image. This array is cast to bool before processing.
|
|
|
|
Returns
|
|
-------
|
|
hull : (M, N) array of bool
|
|
Binary image with pixels in convex hull set to True.
|
|
|
|
References
|
|
----------
|
|
.. [1] http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/
|
|
|
|
"""
|
|
if image.ndim > 2:
|
|
raise ValueError("Input must be a 2D image")
|
|
|
|
if Delaunay is None:
|
|
raise ImportError("Could not import scipy.spatial.Delaunay, "
|
|
"only available in scipy >= 0.9.")
|
|
|
|
# Here we do an optimisation by choosing only pixels that are
|
|
# the starting or ending pixel of a row or column. This vastly
|
|
# limits the number of coordinates to examine for the virtual hull.
|
|
coords = possible_hull(image.astype(np.uint8))
|
|
N = len(coords)
|
|
|
|
# Add a vertex for the middle of each pixel edge
|
|
coords_corners = np.empty((N * 4, 2))
|
|
for i, (x_offset, y_offset) in enumerate(zip((0, 0, -0.5, 0.5),
|
|
(-0.5, 0.5, 0, 0))):
|
|
coords_corners[i * N:(i + 1) * N] = coords + [x_offset, y_offset]
|
|
|
|
# repeated coordinates can *sometimes* cause problems in
|
|
# scipy.spatial.Delaunay, so we remove them.
|
|
coords = unique_rows(coords_corners)
|
|
|
|
# Subtract offset
|
|
offset = coords.mean(axis=0)
|
|
coords -= offset
|
|
|
|
# Find the convex hull
|
|
chull = Delaunay(coords).convex_hull
|
|
v = coords[np.unique(chull)]
|
|
|
|
# Sort vertices clock-wise
|
|
v_centred = v - v.mean(axis=0)
|
|
angles = np.arctan2(v_centred[:, 0], v_centred[:, 1])
|
|
v = v[np.argsort(angles)]
|
|
|
|
# Add back offset
|
|
v += offset
|
|
|
|
# For each pixel coordinate, check whether that pixel
|
|
# lies inside the convex hull
|
|
mask = grid_points_in_poly(image.shape[:2], v)
|
|
|
|
return mask
|
|
|
|
|
|
def convex_hull_object(image, neighbors=8):
|
|
"""Compute the convex hull image of individual objects in a binary image.
|
|
|
|
The convex hull is the set of pixels included in the smallest convex
|
|
polygon that surround all white pixels in the input image.
|
|
|
|
Parameters
|
|
----------
|
|
image : (M, N) array
|
|
Binary input image.
|
|
neighbors : {4, 8}, int
|
|
Whether to use 4- or 8-connectivity.
|
|
|
|
Returns
|
|
-------
|
|
hull : ndarray of bool
|
|
Binary image with pixels in convex hull set to True.
|
|
|
|
Notes
|
|
-----
|
|
This function uses skimage.morphology.label to define unique objects,
|
|
finds the convex hull of each using convex_hull_image, and combines
|
|
these regions with logical OR. Be aware the convex hulls of unconnected
|
|
objects may overlap in the result. If this is suspected, consider using
|
|
convex_hull_image separately on each object.
|
|
|
|
"""
|
|
if image.ndim > 2:
|
|
raise ValueError("Input must be a 2D image")
|
|
|
|
if neighbors != 4 and neighbors != 8:
|
|
raise ValueError('Neighbors must be either 4 or 8.')
|
|
|
|
labeled_im = label(image, neighbors, background=0)
|
|
convex_obj = np.zeros(image.shape, dtype=bool)
|
|
convex_img = np.zeros(image.shape, dtype=bool)
|
|
|
|
for i in range(1, labeled_im.max() + 1):
|
|
convex_obj = convex_hull_image(labeled_im == i)
|
|
convex_img = np.logical_or(convex_img, convex_obj)
|
|
|
|
return convex_img
|