Labeled Image Functions¶
Labeled images are integer images where the values correspond to different regions. I.e., region 1 is all of the pixels which have value 1, region two is the pixels with value 2, and so on. By convention, region 0 is the background and processed differently.
Labeling Images¶
New in version 0.6.5.
The first step is obtaining a labeled function from a binary function:
import mahotas
import numpy as np
from pylab import imshow, show
regions = np.zeros((8,8), bool)
regions[:3,:3] = 1
regions[6:,6:] = 1
labeled, nr_objects = mahotas.label(regions)
imshow(labeled, interpolation='nearest')
show()
(Source code, png, hires.png, pdf)
This results in an image with 3 values:
- background, where the original image was 0
- for the first region: (0:3, 0:3);
- for the second region: (6:, 6:).
There is an extra argument to label: the structuring element, which defaults to a 3x3 cross (or, 4-neighbourhood).
Borders¶
A border pixel is one where there is more than one region in its neighbourhood (one of those regions can be the background).
You can retrieve border pixels with either the borders() function, which gets all the borders or the border() (note the singular) which gets only the border between a single pair of regions. As usual, what neighbour means is defined by a structuring element, defaulting to a 3x3 cross.
API Documentation¶
The mahotas.labeled submodule contains the functions mentioned above. label() is also available as mahotas.label.
- mahotas.labeled.borders(labeled, Bc={3x3 cross}, output={np.zeros(labeled.shape, bool)})
Compute border pixels
A pixel is on a border if it has value i and a pixel in its neighbourhood (defined by Bc) has value j, with i != j.
Parameters : labeled : ndarray of integer type
input labeled array
Bc : structure element, optional
output : ndarray of same shape as labeled, dtype=bool, optional
where to store the output. If None, a new array is allocated
Returns : border_img : boolean ndarray
Pixels are True exactly where there is a border in labeled
- mahotas.labeled.border(labeled, i, j, Bc={3x3 cross}, output={np.zeros(labeled.shape, bool)}, always_return=True)
Compute the border region between i and j regions.
A pixel is on the border if it has value i (or j) and a pixel in its neighbourhood (defined by Bc) has value j (or i).
Parameters : labeled : ndarray of integer type
input labeled array
i : integer
j : integer
Bc : structure element, optional
output : ndarray of same shape as labeled, dtype=bool, optional
where to store the output. If None, a new array is allocated
always_return : bool, optional
if false, then, in the case where there is no pixel on the border, returns None. Otherwise (the default), it always returns an array even if it is empty.
Returns : border_img : boolean ndarray
Pixels are True exactly where there is a border between i and j in labeled
- mahotas.labeled.bwperim(bw, n=4)
Find the perimeter of objects in binary images.
A pixel is part of an object perimeter if its value is one and there is at least one zero-valued pixel in its neighborhood.
By default the neighborhood of a pixel is 4 nearest pixels, but if n is set to 8 the 8 nearest pixels will be considered.
Parameters : bw : ndarray
A black-and-white image (any other image will be converted to black & white)
n : int, optional
Connectivity. Must be 4 or 8 (default: 4)
Returns : perim : ndarray
A boolean image
See also
- borders
- function This is a more generic function
- mahotas.labeled.remove_bordering(labeled, rsize=1, output={np.empty_like(im)})
Remove objects that are touching the border.
Pass im as output to achieve in-place operation.
Parameters : labeled : ndarray
Labeled array
rsize : int, optional
Minimum distance to the border (in Manhatan distance) to allow an object to survive.
output : ndarray, optional
If im is passed as output, then it operates inline.
Returns : slabeled : ndarray
Subset of labeled
- mahotas.labeled.label(array, Bc={3x3 cross}, output=None)
Label the array
Parameters : array : ndarray
This will be interpreted as an integer array
Bc : ndarray, optional
This is the structuring element to use
output : ndarray, optional
Output array. Must be a C-array, of type np.int32
Returns : labeled : ndarray
Labeled result
nr_objects : int
Number of objects
- mahotas.labeled.labeled_sum(array, labeled)
Labeled sum. sum will be an array of size labeled.max() + 1, where sum[i] is equal to np.sum(array[labeled == i]).
Parameters : array : ndarray of any type
labeled : int ndarray
Label map. This is the same type as returned from mahotas.label()
Returns : sums : 1-d ndarray of array.dtype
- mahotas.labeled.labeled_size(labeled)
Equivalent to:
for i in xrange(...): sizes[i] = np.sum(labeled == i)
but, naturally, much faster.
Parameters : labeled : int ndarray Returns : sizes : 1-d ndarray of int