Boolean indexing is indexing based on a boolean array and falls in the category fancy indexing.
We will apply this indexing technique to an image:
- Image with dots on the diagonal.
This is in some way similar to the Fancy indexing recipe, in this chapter. This time we select modulo
4points on the diagonal of the image:def get_indices(size): arr = numpy.arange(size) return arr % 4 == 0
Then we just apply this selection and plot the points:
lena1 = lena.copy() xindices = get_indices(lena.shape[0]) yindices = get_indices(lena.shape[1]) lena1[xindices, yindices] = 0 matplotlib.pyplot.subplot(211) matplotlib.pyplot.imshow(lena1)
- Set to
0based on value.Select array values between quarter and three-quarters of the maximum value and set them to
0:lena2[(lena > lena.max()/4) & (lena < 3 * lena.max()/4)] = 0
The plot with the two new images will look like the following screenshot:
The following is the complete code for this recipe:
import scipy.misc import matplotlib.pyplot import numpy # Load the Lena array lena = scipy.misc.lena() def get_indices(size): arr = numpy.arange(size) return arr % 4 == 0 # Plot Lena lena1 = lena.copy() xindices = get_indices(lena.shape[0]) yindices = get_indices(lena.shape[1]) lena1[xindices, yindices] = 0 matplotlib.pyplot.subplot(211) matplotlib.pyplot.imshow(lena1) lena2 = lena.copy() # Between quarter and 3 quarters of the max value lena2[(lena > lena.max()/4) & (lena < 3 * lena.max()/4)] = 0 matplotlib.pyplot.subplot(212) matplotlib.pyplot.imshow(lena2) matplotlib.pyplot.show()
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