# Setting the Stage: What Are We Trying to Do?¶

In [48]:

    import numpy as np
    import scipy.misc
    import matplotlib.pyplot as pt

Let's load an image file and apply a simple image processing operation: Blurring. In mathematical terms, we will replace each pixel with a (weighted) average of its neighbors and itself.

In [49]:

    u = scipy.misc.imread("cat.jpeg").astype(np.float32).sum(axis=2)/(3*256)

    pt.imshow(u, cmap="gray")

    /usr/lib/python3/dist-packages/PIL/Image.py:665: ResourceWarning: unclosed file <_io.BufferedReader name='cat.jpeg'>
      self.load()

Out[49]:

    <matplotlib.image.AxesImage at 0x7f889862e1d0>

Now make `blurred_u`:

In [61]:

    blurred_u = np.zeros_like(u)
    blurred_u[1:-1, 1:-1] = (
        2*u[1:-1, 1:-1]
        + u[2:, 1:-1]
        + u[:-2, 1:-1]
        + u[1:-1, 2:]
        + u[1:-1, 2:]
        )/6
    u = blurred_u

In [62]:

    pt.imshow(blurred_u, cmap="gray")

Out[62]:

    <matplotlib.image.AxesImage at 0x7f889856a4e0>

- Now, one individual blurring process may be fast. (in the sense that it feels instantaneous)
- But it already takes a number of rounds (and therefore a short while) if we want the picture to be visibly blurry.
- It would take even longer if the picture were bigger than just 200x200 (0.04 Megapixels). For reference, my phone takes pictures with 13 Megapixels.
- It would take even longer if (say) we were a web service that needed to blur millions of images for customers.

As it turns out:

- What we did there was not efficient in lots of ways.
- Time to peel back the layers, see what's going on under the hood, and understand how to make this process very fast.
- While we're at it: Writing something fast that can just blur images might be nice. But it would be far easier to justify the effort to do that if what we wrote was fast and general, i.e. if it could apply lots of different image processing operations.

So here we go: Let's see what would go into designing our own image processing language.