November 2018
Intermediate to advanced
492 pages
12h 19m
English
Content preview from Hands-On Image Processing with Python
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Computing the morphological Beucher gradient
The morphological Beucher gradient can be computed as a difference image of the dilated version and the eroded version of an input grayscale image. SciPy ndimage provides a function for computing the morphological gradient of a grayscale image. The following code block shows how these two produce the same output for an Einstein image:
from scipy import ndimageim = rgb2gray(imread('../images/einstein.jpg'))im_d = ndimage.grey_dilation(im, size=(3,3))im_e = ndimage.grey_erosion(im, size=(3,3))im_bg = im_d - im_eim_g = ndimage.morphological_gradient(im, size=(3,3))pylab.gray()pylab.figure(figsize=(20,18))pylab.subplot(231), pylab.imshow(im), pylab.title('original', size=20),pylab.axis('off')pylab.subplot(232), ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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