We will be flipping the SciPy Lena image—all in the name of science, of course, or at least as a demo. In addition to flipping the image, we will slice it and apply a mask to it.
The steps to follow are listed below:
- Plot the flipped image.
Flip the Lena array around the vertical axis using the following code:
matplotlib.pyplot.imshow(lena[:,::-1])
- Plot a slice of the image.
Take a slice out of the image and plot it. In this step, we will have a look at the shape of the Lena array. The shape is a tuple representing the dimensions of the array. The following code effectively selects the left-upper quadrant of the Playboy picture.
matplotlib.pyplot.imshow(lena[:lena.shape[0]/2,:lena.shape[1]/2])
- Apply a mask to the image.
Apply a mask to the image by finding all the values in the Lena array that are even (this is just arbitrary for demo purposes). Copy the array and change the even values to
0. This has the effect of putting lots of blue dots (dark spots if you are looking at a black and white image) on the image:mask = lena % 2 == 0 masked_lena = lena.copy() masked_lena[mask] = 0
All these efforts result in a 2 by 2 image grid, as shown in the following screenshot:
The following is the complete code for this recipe:
import scipy.misc import matplotlib.pyplot # Load the Lena array lena = scipy.misc.lena() # Plot the Lena array matplotlib.pyplot.subplot(221) matplotlib.pyplot.imshow(lena) #Plot the flipped array matplotlib.pyplot.subplot(222) matplotlib.pyplot.imshow(lena[:,::-1]) #Plot a slice array matplotlib.pyplot.subplot(223) matplotlib.pyplot.imshow(lena[:lena.shape[0]/2,:lena.shape[1]/2]) # Apply a mask mask = lena % 2 == 0 masked_lena = lena.copy() masked_lena[mask] = 0 matplotlib.pyplot.subplot(224) matplotlib.pyplot.imshow(masked_lena) matplotlib.pyplot.show()
- The Installing Matplotlib recipe in Chapter 1, Winding Along with IPython
- The Installing SciPy recipe
- The Installing PIL recipe