Thresholding¶

The example in this section is present in the source under mahotas/demos/thresholding.py.

We start with an image, a grey-scale image:

import mahotas.demos
photo = mahotas.demos.load('luispedro')
photo = photo.astype(np.uint8)

Before Oct 2013, the mahotas.demos.load function did not exist and you needed to specify the path explicitly:

luispedro_image = '../../mahotas/demos/data/luispedro.jpg'
photo = mahotas.imread(luispedro_image, as_grey=True)

The reason we convert to np.uint8 is because as_grey returns floating point images (there are good reasons for this and good reasons against it, since it’s easier to truncate than to go back, it returns np.uint8).

import mahotas
import mahotas.demos
import numpy as np
from pylab import imshow, gray, show
from os import path

photo = mahotas.demos.load('luispedro', as_grey=True)
photo = photo.astype(np.uint8)

gray()
imshow(photo)
show()

(Source code, png, hires.png, pdf)

Thresholding functions have a trivial interface: they take an image and return a value. One of the most well-known thresholding methods is Otsu’s method:

T_otsu = mahotas.otsu(photo)
print(T_otsu)
imshow(photo > T_otsu)
show()

prints 115.

import mahotas
import mahotas.demos
import numpy as np
from pylab import imshow, gray, show
from os import path

photo = mahotas.demos.load('luispedro', as_grey=True)
photo = photo.astype(np.uint8)


T_otsu = mahotas.otsu(photo)
print(T_otsu)
gray()
imshow(photo > T_otsu)
show()

(Source code, png, hires.png, pdf)

An alternative is the Riddler-Calvard method:

T_rc = mahotas.rc(photo)
print(T_rc)
imshow(photo > T_rc)
show()

In this image, it prints almost the same as Otsu: 115.68. The thresholded image is exactly the same:

import mahotas
import mahotas.demos
import numpy as np
from pylab import imshow, gray, show
from os import path

photo = mahotas.demos.load('luispedro', as_grey=True)
photo = photo.astype(np.uint8)


T_rc = mahotas.rc(photo)
print(T_rc)
gray()
imshow(photo > T_rc)
show()

(Source code, png, hires.png, pdf)

See also the labeled documentation which can be very helpful in combination with thresholding.

API Documentation¶

The mahotas.thresholding module contains the thresholding functions, but they are also available in the main mahotas namespace.

Thresholding Module¶

Thresholding functions.

These functions return the numeric threshold. In order to obtain a thresholded image, you can do the following:

T_otsu = mh.otsu(image)
binarized_image = (image > T_otsu)

Functions which have an ignore_zeros parameters will only consider non-zero pixels when computing the thresholding.

otsu():	Otsu method
rc():	Riddler-Calvard’s method
bernsen:	Bernsen thresholding
gbernsen:	Generalized Bernsen thresholding

mahotas.thresholding.otsu(img, ignore_zeros=False)

Calculate a threshold according to the Otsu method.

Example:

import mahotas as mh
import mahotas.demos

im = mahotas.demos.nuclear_image()
# im is stored as RGB, let's convert to single 2D format:
im = im.max(2)

#Now, we compute Otsu:
t = mh.otsu(im)

# finally, we use the value to form a binary image:
bin = (im > t)

See Wikipedia for details on methods: http://en.wikipedia.org/wiki/Otsu’s_method

Parameters:

img : an image as a numpy array.

This should be of an unsigned integer type.

ignore_zeros : Boolean

whether to ignore zero-valued pixels (default: False)

Returns:

T : integer

the threshold

mahotas.thresholding.rc(img, ignore_zeros=False)

Calculate a threshold according to the Riddler-Calvard method.

Example:

import mahotas as mh
import mahotas.demos

im = mahotas.demos.nuclear_image()
# im is stored as RGB, let's convert to single 2D format:
im = im.max(2)

#Now, we compute a threshold:
t = mh.rc(im)

# finally, we use the value to form a binary image:
bin = (im > t)

Parameters:

img : ndarray

Image of any type

ignore_zeros : boolean, optional

Whether to ignore zero valued pixels (default: False)

Returns:

T : float

threshold

mahotas.thresholding.soft_threshold(f, tval)

Soft threshold function:

                    ^
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                    |        /
                    |       /
- - - - - - - - - - - - - - - - - ->
             /      |
            /       |
           /        |
          /         |
         /          |
        /           |

Parameters:

f : ndarray

tval : scalar

Returns:

thresholded : ndarray

mahotas.thresholding.bernsen(f, radius, contrast_threshold, gthresh={128})

Bernsen local thresholding

Parameters:

f : ndarray

input image

radius : integer

radius of circle (to consider “local”)

contrast_threshold : integer

contrast threshold

gthresh : numeric, optional

global threshold to fall back in low contrast regions

Returns:

thresholded : binary ndarray