44.4: Advanced Python Indexing

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This one is a little long and reviews some of the information from the last video. However, I really like using images as a way to talk about array and matrix indexing in Numpy.

Direct Link to the Youtube video.

from IPython.display import YouTubeVideo
YouTubeVideo("XSyiafkKerQ",width=640,height=360, cc_load_policy=True)

%matplotlib inline
import matplotlib.pylab as plt
import numpy as np
import imageio

#from urllib.request import urlopen, urlretrieve
#from scipy.misc import imsave

url = 'https://res.cloudinary.com/miles-extranet-dev/image/upload/ar_16:9,c_fill,w_1000,g_face,q_50/Michigan/migration_photos/G21696/G21696-msubeaumonttower01.jpg'
im = imageio.imread(url)

im[10,10,0] = 255
im[10,10,1] = 255
im[10,10,2] = 255

#Show the image
plt.imshow(im);

im[20,20,:] = 255
plt.imshow(im)

cropped = im[0:50,0:50,:]
plt.imshow(cropped)

cropped = im[50:,350:610,:]
plt.imshow(cropped)

red = im[:,:,0]
plt.imshow(red)
plt.colorbar()

#Note python changed slightly since the making of the video.  
# We added the astype funciton to ensure that values are between 0-255
red_only = np.zeros(im.shape).astype(int)
red_only[:,:,0] = red

plt.imshow(red_only)

green_only = np.zeros(im.shape).astype(int)
green_only[:,:,1] = im[:,:,1]

plt.imshow(green_only)

blue_only = np.zeros(im.shape).astype(int)
blue_only[:,:,2] = im[:,:,2]

plt.imshow(blue_only)

Do This

Modify the following code to set all of the values in the blue channel to zero using only one simple line of indexing code.

no_blue = im.copy()
#####Start your code here #####

#####End of your code here#####  
plt.imshow(no_blue)

Question

What was the command you use to set all of the values of blue inside no_blue to zero?