Image Processing/Python/Data Science

In this part, we will use linear combinations of two images to transform one image to another. Given two images A and B, define a linear combination of the images as: p.A + (1-p)B where p is a value (think of p as a probability) between 0 and 1, For example, if p = 0, then we only show image B, and if p=1, then we only show image A. For all other values of p, the two images are transposed on each other. When p is closer to 0, you will mostly notice image B, and if p is closer to 1, you will mostly notice image A. When p is in the middle, the intensity of the pixles will be equally divided between the two images. In this case, define the matrix p A as, each entry of A is multiplied (or scaled by) p Task 2.4 Interactive panel design Now we will use a widget to create an interactive panel that will allow you to experiment with the superimposed images. # Initialize the widget from ipywidgets import interact jupyter nbextension enable--py widgetsnbextension Define a function f The function f, takes alpha (which we called p before) and allow you to experiment with how the superimposed images changes as alpha changes. You will be able to drag the value between O and 1 to see how things change. With alpha 0, you will get only one image, and with alpha 1, you will get only the other image. The values of alpha between 0 and 1 will produce an image with varying domination by one image. Alpha = 0.5 is the equilibrium point, where each image is weighed equally ## BEGIN SOLUTION def f(alpha): ## END SOLUTION interact(f, alpha (0,1,0.1))