# Image Manipulation with skimage

    z = "hello world"
    print(z)

Before docing anything else, we need to install some libraries we'll be using:

    %%bash
    pip install scikit-image

Once it's installed, we cab build a simple UI for performing basic image manipulation with scikit-image.

    from IPython.html.widgets import interact, interactive, fixed
    from IPython.display import display

    import skimage
    from skimage import data, filter, io

    i = data.coffee()

    io.Image(i)

    def edit_image(image, sigma=0.1, r=1.0, g=1.0, b=1.0):
        new_image = filter.gaussian_filter(image, sigma=sigma, multichannel=True)
        new_image[:,:,0] = r*new_image[:,:,0]
        new_image[:,:,1] = g*new_image[:,:,1]
        new_image[:,:,2] = b*new_image[:,:,2]
        new_image = io.Image(new_image)
        display(new_image)
        return new_image

    lims = (0.0,1.0,0.01)
    w = interactive(edit_image, image=fixed(i), sigma=(0.0,10.0,0.1), r=lims, g=lims, b=lims)
    display(w)

    w.result

# Python 3 only: Function annotations

In Python 3, you can use the new function annotation syntax to describe widgets for interact:

    lims = (0.0,1.0,0.01)

    @interact
    def edit_image(image: fixed(i), sigma:(0.0,10.0,0.1)=0.1, r:lims=1.0, g:lims=1.0, b:lims=1.0):
        new_image = filter.gaussian_filter(image, sigma=sigma, multichannel=True)
        new_image[:,:,0] = r*new_image[:,:,0]
        new_image[:,:,1] = g*new_image[:,:,1]
        new_image[:,:,2] = b*new_image[:,:,2]
        new_image = io.Image(new_image)
        display(new_image)
        return new_image