.. _image_tutorial:


**************
Image tutorial
**************

.. _imaging_startup:

Startup commands
===================

In this tutorial we will work interactively with images. To do so we will use
the IPython shell. You can start it with::
    
    $ipython

At the very least, you'll need to have access to the
:func:`~matplotlib.pyplot.imshow` function. The easy way for an interactive
environment: is to use the matplotlib API (see :ref:`artist-tutorial`) where
you use explicit 
`namespaces <http://bytebaker.com/2008/07/30/python-namespaces/>`_ and control 
object creation, etc...::

.. sourcecode:: ipython

   In [1]: import matplotlib.pyplot as plt
   In [2]: import matplotlib.image as mpimg
   In [3]: import numpy as np

You can now access functions like :func:`~matplotlib.pyplot.imshow` by using:
`plt.imshow(yourimage)`. You can learn more about these functions in the
:ref:`pyplot-tutorial`.


.. _importing_data:

Importing image data into Numpy arrays
===============================================

Plotting image data is supported by the `Pillow
<http://python-imaging.github.io/>`_).  Natively, matplotlib only
supports PNG images.  The commands shown below fall back on Pillow if the
native read fails.

The image used in this example is a PNG file, but keep that Pillow
requirement in mind for your own data.

Here's the image we're going to play with:

.. image:: ../_static/stinkbug.png

It's a 24-bit RGB PNG image (8 bits for each of R, G, B).  Depending
on where you get your data, the other kinds of image that you'll most
likely encounter are RGBA images, which allow for transparency, or
single-channel grayscale (luminosity) images.  You can right click on
it and choose "Save image as" to download it to your computer for the
rest of this tutorial.

And here we go...

.. sourcecode:: ipython

    In [4]: img=mpimg.imread('stinkbug.png')
    Out[4]:
    array([[[ 0.40784314,  0.40784314,  0.40784314],
            [ 0.40784314,  0.40784314,  0.40784314],
            [ 0.40784314,  0.40784314,  0.40784314],
            ...,
            [ 0.42745098,  0.42745098,  0.42745098],
            [ 0.42745098,  0.42745098,  0.42745098],
            [ 0.42745098,  0.42745098,  0.42745098]],

           [[ 0.41176471,  0.41176471,  0.41176471],
            [ 0.41176471,  0.41176471,  0.41176471],
            [ 0.41176471,  0.41176471,  0.41176471],
            ...,
            [ 0.42745098,  0.42745098,  0.42745098],
            [ 0.42745098,  0.42745098,  0.42745098],
            [ 0.42745098,  0.42745098,  0.42745098]],

           [[ 0.41960785,  0.41960785,  0.41960785],
            [ 0.41568628,  0.41568628,  0.41568628],
            [ 0.41568628,  0.41568628,  0.41568628],
            ...,
            [ 0.43137255,  0.43137255,  0.43137255],
            [ 0.43137255,  0.43137255,  0.43137255],
            [ 0.43137255,  0.43137255,  0.43137255]],

           ...,
           [[ 0.43921569,  0.43921569,  0.43921569],
            [ 0.43529412,  0.43529412,  0.43529412],
            [ 0.43137255,  0.43137255,  0.43137255],
            ...,
            [ 0.45490196,  0.45490196,  0.45490196],
            [ 0.4509804 ,  0.4509804 ,  0.4509804 ],
            [ 0.4509804 ,  0.4509804 ,  0.4509804 ]],

           [[ 0.44313726,  0.44313726,  0.44313726],
            [ 0.44313726,  0.44313726,  0.44313726],
            [ 0.43921569,  0.43921569,  0.43921569],
            ...,
            [ 0.4509804 ,  0.4509804 ,  0.4509804 ],
            [ 0.44705883,  0.44705883,  0.44705883],
            [ 0.44705883,  0.44705883,  0.44705883]],

           [[ 0.44313726,  0.44313726,  0.44313726],
            [ 0.4509804 ,  0.4509804 ,  0.4509804 ],
            [ 0.4509804 ,  0.4509804 ,  0.4509804 ],
            ...,
            [ 0.44705883,  0.44705883,  0.44705883],
            [ 0.44705883,  0.44705883,  0.44705883],
            [ 0.44313726,  0.44313726,  0.44313726]]], dtype=float32)

Note the dtype there - float32.  Matplotlib has rescaled the 8 bit
data from each channel to floating point data between 0.0 and 1.0.  As
a side note, the only datatype that Pillow can work with is uint8.
Matplotlib plotting can handle float32 and uint8, but image
reading/writing for any format other than PNG is limited to uint8
data.  Why 8 bits? Most displays can only render 8 bits per channel
worth of color gradation.  Why can they only render 8 bits/channel?
Because that's about all the human eye can see.  More here (from a
photography standpoint): `Luminous Landscape bit depth tutorial
<http://www.luminous-landscape.com/tutorials/bit-depth.shtml>`_.

Each inner list represents a pixel.  Here, with an RGB image, there
are 3 values.  Since it's a black and white image, R, G, and B are all
similar.  An RGBA (where A is alpha, or transparency), has 4 values
per inner list, and a simple luminance image just has one value (and
is thus only a 2-D array, not a 3-D array).  For RGB and RGBA images,
matplotlib supports float32 and uint8 data types.  For grayscale,
matplotlib supports only float32.  If your array data does not meet
one of these descriptions, you need to rescale it.

.. _plotting_data:

Plotting numpy arrays as images
===================================

So, you have your data in a numpy array (either by importing it, or by
generating it).  Let's render it.  In Matplotlib, this is performed
using the :func:`~matplotlib.pyplot.imshow` function.  Here we'll grab
the plot object.  This object gives you an easy way to manipulate the
plot from the prompt.

.. sourcecode:: ipython

    In [5]: imgplot = plt.imshow(img)

.. plot::

        import matplotlib.pyplot as plt
        import matplotlib.image as mpimg
        import numpy as np
        img = mpimg.imread('../_static/stinkbug.png')
        imgplot = plt.imshow(img)

You can also plot any numpy array - just remember that the datatype
must be float32 (and range from 0.0 to 1.0) or uint8.

.. _Pseudocolor:

Applying pseudocolor schemes to image plots
-------------------------------------------------

Pseudocolor can be a useful tool for enhancing contrast and
visualizing your data more easily.  This is especially useful when
making presentations of your data using projectors - their contrast is
typically quite poor.

Pseudocolor is only relevant to single-channel, grayscale, luminosity
images.  We currently have an RGB image.  Since R, G, and B are all
similar (see for yourself above or in your data), we can just pick one
channel of our data:

.. sourcecode:: ipython

    In [6]: lum_img = img[:,:,0]

This is array slicing.  You can read more in the `Numpy tutorial
<http://www.scipy.org/Tentative_NumPy_Tutorial>`_.

.. sourcecode:: ipython

    In [7]: imgplot = plt.imshow(lum_img)

.. plot::

    import matplotlib.pyplot as plt
    import matplotlib.image as mpimg
    import numpy as np
    img = mpimg.imread('../_static/stinkbug.png')
    lum_img = img[:,:,0]
    plt.imshow(lum_img)

Now, with a luminosity image, the default colormap (aka lookup table,
LUT), is applied.  The default is called jet.  There are plenty of
others to choose from.  Let's set some others using the
:meth:`~matplotlib.image.Image.set_cmap` method on our image plot
object:

.. sourcecode:: ipython

    In [8]: imgplot.set_cmap('hot')

.. plot::

        import matplotlib.pyplot as plt
        import matplotlib.image as mpimg
        import numpy as np
        img = mpimg.imread('../_static/stinkbug.png')
        lum_img = img[:,:,0]
        imgplot = plt.imshow(lum_img)
        imgplot.set_cmap('hot')

.. sourcecode:: ipython

    In [9]: imgplot.set_cmap('spectral')

.. plot::

        import matplotlib.pyplot as plt
        import matplotlib.image as mpimg
        import numpy as np
        img = mpimg.imread('../_static/stinkbug.png')
        lum_img = img[:,:,0]
        imgplot = plt.imshow(lum_img)
        imgplot.set_cmap('spectral')

There are many other colormap schemes available.  See the `list and
images of the colormaps
<../examples/color/colormaps_reference.html>`_.

.. _`Color Bars`:

Color scale reference
------------------------

It's helpful to have an idea of what value a color represents.  We can
do that by adding color bars.  It's as easy as one line:

.. sourcecode:: ipython

    In [10]: plt.colorbar()

.. plot::

            import matplotlib.pyplot as plt
            import matplotlib.image as mpimg
            import numpy as np
            img = mpimg.imread('../_static/stinkbug.png')
            lum_img = img[:,:,0]
            imgplot = plt.imshow(lum_img)
            imgplot.set_cmap('spectral')
            plt.colorbar()

This adds a colorbar to your existing figure.  This won't
automatically change if you change you switch to a different
colormap - you have to re-create your plot, and add in the colorbar
again.

.. _`Data ranges`:

Examining a specific data range
---------------------------------

Sometimes you want to enhance the contrast in your image, or expand
the contrast in a particular region while sacrificing the detail in
colors that don't vary much, or don't matter.  A good tool to find
interesting regions is the histogram.  To create a histogram of our
image data, we use the :func:`~matplotlib.pyplot.hist` function.

.. sourcecode:: ipython

    In[10]: plt.hist(lum_img.flatten(), 256, range=(0.0,1.0), fc='k', ec='k')

.. plot::

    import matplotlib.pyplot as plt
    import matplotlib.image as mpimg
    import numpy as np
    img = mpimg.imread('../_static/stinkbug.png')
    lum_img = img[:,:,0]
    plt.hist(lum_img.flatten(), 256, range=(0.0,1.0), fc='black', ec='black')

Most often, the "interesting" part of the image is around the peak,
and you can get extra contrast by clipping the regions above and/or
below the peak.  In our histogram, it looks like there's not much
useful information in the high end (not many white things in the
image).  Let's adjust the upper limit, so that we effectively "zoom in
on" part of the histogram.  We do this by calling the
:meth:`~matplotlib.image.Image.set_clim` method of the image plot
object.

.. sourcecode:: ipython

    In[11]: imgplot.set_clim(0.0,0.7)

.. plot::

    import matplotlib.pyplot as plt
    import matplotlib.image as mpimg
    import numpy as np
    fig = plt.figure()
    a=fig.add_subplot(1,2,1)
    img = mpimg.imread('../_static/stinkbug.png')
    lum_img = img[:,:,0]
    imgplot = plt.imshow(lum_img)
    a.set_title('Before')
    plt.colorbar(ticks=[0.1,0.3,0.5,0.7], orientation ='horizontal')
    a=fig.add_subplot(1,2,2)
    imgplot = plt.imshow(lum_img)
    imgplot.set_clim(0.0,0.7)
    a.set_title('After')
    plt.colorbar(ticks=[0.1,0.3,0.5,0.7], orientation='horizontal')

.. _Interpolation:

Array Interpolation schemes
---------------------------

Interpolation calculates what the color or value of a pixel "should"
be, according to different mathematical schemes.  One common place
that this happens is when you resize an image.  The number of pixels
change, but you want the same information.  Since pixels are discrete,
there's missing space.  Interpolation is how you fill that space.
This is why your images sometimes come out looking pixelated when you
blow them up.  The effect is more pronounced when the difference
between the original image and the expanded image is greater.  Let's
take our image and shrink it.  We're effectively discarding pixels,
only keeping a select few.  Now when we plot it, that data gets blown
up to the size on your screen.  The old pixels aren't there anymore,
and the computer has to draw in pixels to fill that space.

.. sourcecode:: ipython

    In [8]: from PIL import Image
    In [9]: img = Image.open('stinkbug.png')    # Open image as Pillow image object
    In [10]: rsize = img.resize((img.size[0]/10,img.size[1]/10)) # Use Pillow to resize
    In [11]: rsizeArr = np.asarray(rsize)  # Get array back
    In [12]: imgplot = plt.imshow(rsizeArr)

.. plot::

    import matplotlib.pyplot as plt
    import matplotlib.image as mpimg
    import numpy as np
    from PIL import Image
    img = Image.open('../_static/stinkbug.png')  # opens the file using Pillow - it's not an array yet
    rsize = img.resize((img.size[0]/10,img.size[1]/10))  # resize the image
    rsizeArr = np.asarray(rsize)
    lum_img = rsizeArr[:,:,0]
    imgplot = plt.imshow(rsizeArr)

Here we have the default interpolation, bilinear, since we did not
give :func:`~matplotlib.pyplot.imshow` any interpolation argument.

Let's try some others:

.. sourcecode:: ipython

    In [10]: imgplot.set_interpolation('nearest')

.. plot::

        import matplotlib.pyplot as plt
        import matplotlib.image as mpimg
        import numpy as np
        from PIL import Image
        img = Image.open('../_static/stinkbug.png')  # opens the file using Pillow - it's not an array yet
        rsize = img.resize((img.size[0]/10,img.size[1]/10))  # resize the image
        rsizeArr = np.asarray(rsize)
        lum_img = rsizeArr[:,:,0]
        imgplot = plt.imshow(rsizeArr)
        imgplot.set_interpolation('nearest')

.. sourcecode:: ipython

    In [10]: imgplot.set_interpolation('bicubic')

.. plot::

        import matplotlib.pyplot as plt
        import matplotlib.image as mpimg
        import numpy as np
        from PIL import Image
        img = Image.open('../_static/stinkbug.png')  # opens the file using Pillow - it's not an array yet
        rsize = img.resize((img.size[0]/10,img.size[1]/10))  # resize the image
        rsizeArr = np.asarray(rsize)
        lum_img = rsizeArr[:,:,0]
        imgplot = plt.imshow(rsizeArr)
        imgplot.set_interpolation('bicubic')

Bicubic interpolation is often used when blowing up photos - people
tend to prefer blurry over pixelated.
