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<li><a class="reference internal" href="#">Image tutorial</a><ul>
<li><a class="reference internal" href="#startup-commands">Startup commands</a></li>
<li><a class="reference internal" href="#importing-image-data-into-numpy-arrays">Importing image data into Numpy arrays</a></li>
<li><a class="reference internal" href="#plotting-numpy-arrays-as-images">Plotting numpy arrays as images</a><ul>
<li><a class="reference internal" href="#applying-pseudocolor-schemes-to-image-plots">Applying pseudocolor schemes to image plots</a></li>
<li><a class="reference internal" href="#color-scale-reference">Color scale reference</a></li>
<li><a class="reference internal" href="#examining-a-specific-data-range">Examining a specific data range</a></li>
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<div class="section" id="image-tutorial">
<span id="id1"></span><h1>Image tutorial<a class="headerlink" href="#image-tutorial" title="Permalink to this headline">¶</a></h1>
<div class="section" id="startup-commands">
<span id="imaging-startup"></span><h2>Startup commands<a class="headerlink" href="#startup-commands" title="Permalink to this headline">¶</a></h2>
<p>First, let’s start IPython. It is a most excellent enhancement to the
standard Python prompt, and it ties in especially well with
Matplotlib. Start IPython either at a shell, or the IPython Notebook now.</p>
<p>With IPython started, we now need to connect to a GUI event loop. This
tells IPython where (and how) to display plots. To connect to a GUI
loop, execute the <strong>%matplotlib</strong> magic at your IPython prompt. There’s more
detail on exactly what this does at <a class="reference external" href="http://ipython.org/ipython-doc/2/interactive/reference.html#gui-event-loop-support">IPython’s documentation on GUI
event loops</a>.</p>
<p>If you’re using IPython Notebook, the same commands are available, but
people commonly use a specific argument to the %matplotlib magic:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="o">%</span><span class="k">matplotlib</span> inline
</pre></div>
</div>
<p>This turns on inline plotting, where plot graphics will appear in your
notebook. This has important implications for interactivity. For inline plotting, commands in
cells below the cell that outputs a plot will not affect the plot. For example,
changing the color map is not possible from cells below the cell that creates a plot.
However, for other backends, such as qt4, that open a separate window,
cells below those that create the plot will change the plot - it is a
live object in memory.</p>
<p>This tutorial will use matplotlib’s imperative-style plotting
interface, pyplot. This interface maintains global state, and is very
useful for quickly and easily experimenting with various plot
settings. The alternative is the object-oriented interface, which is also
very powerful, and generally more suitable for large application
development. If you’d like to learn about the object-oriented
interface, a great place to start is our <a class="reference external" href="http://matplotlib.org/faq/usage_faq.html">FAQ on usage</a>. For now, let’s get on
with the imperative-style approach:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [2]: </span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="gp">In [3]: </span><span class="kn">import</span> <span class="nn">matplotlib.image</span> <span class="kn">as</span> <span class="nn">mpimg</span>
<span class="gp">In [4]: </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
</pre></div>
</div>
</div>
<div class="section" id="importing-image-data-into-numpy-arrays">
<span id="importing-data"></span><h2>Importing image data into Numpy arrays<a class="headerlink" href="#importing-image-data-into-numpy-arrays" title="Permalink to this headline">¶</a></h2>
<p>Loading image data is supported by the <a class="reference external" href="http://python-imaging.github.io/">Pillow</a> library. Natively, matplotlib only
supports PNG images. The commands shown below fall back on Pillow if the
native read fails.</p>
<p>The image used in this example is a PNG file, but keep that Pillow
requirement in mind for your own data.</p>
<p>Here’s the image we’re going to play with:</p>
<img alt="../_images/stinkbug.png" src="../_images/stinkbug.png" />
<p>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.</p>
<p>And here we go...</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="n">img</span><span class="o">=</span><span class="n">mpimg</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">'stinkbug.png'</span><span class="p">)</span>
<span class="gh">Out[5]:</span>
<span class="go">array([[[ 0.40784314, 0.40784314, 0.40784314],</span>
<span class="go"> [ 0.40784314, 0.40784314, 0.40784314],</span>
<span class="go"> [ 0.40784314, 0.40784314, 0.40784314],</span>
<span class="go"> ...,</span>
<span class="go"> [ 0.42745098, 0.42745098, 0.42745098],</span>
<span class="go"> [ 0.42745098, 0.42745098, 0.42745098],</span>
<span class="go"> [ 0.42745098, 0.42745098, 0.42745098]],</span>
<span class="go"> ...,</span>
<span class="go"> [[ 0.44313726, 0.44313726, 0.44313726],</span>
<span class="go"> [ 0.4509804 , 0.4509804 , 0.4509804 ],</span>
<span class="go"> [ 0.4509804 , 0.4509804 , 0.4509804 ],</span>
<span class="go"> ...,</span>
<span class="go"> [ 0.44705883, 0.44705883, 0.44705883],</span>
<span class="go"> [ 0.44705883, 0.44705883, 0.44705883],</span>
<span class="go"> [ 0.44313726, 0.44313726, 0.44313726]]], dtype=float32)</span>
</pre></div>
</div>
<p>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): <a class="reference external" href="http://www.luminous-landscape.com/tutorials/bit-depth.shtml">Luminous Landscape bit depth tutorial</a>.</p>
<p>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.</p>
</div>
<div class="section" id="plotting-numpy-arrays-as-images">
<span id="plotting-data"></span><h2>Plotting numpy arrays as images<a class="headerlink" href="#plotting-numpy-arrays-as-images" title="Permalink to this headline">¶</a></h2>
<p>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 <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow"><code class="xref py py-func docutils literal"><span class="pre">imshow()</span></code></a> function. Here we’ll grab
the plot object. This object gives you an easy way to manipulate the
plot from the prompt.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-1.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-1.png">png</a>, <a class="reference external" href="../users/image_tutorial-1.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-1.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-1.png" src="../_images/image_tutorial-1.png" />
</div>
<p>You can also plot any numpy array.</p>
<div class="section" id="applying-pseudocolor-schemes-to-image-plots">
<span id="pseudocolor"></span><h3>Applying pseudocolor schemes to image plots<a class="headerlink" href="#applying-pseudocolor-schemes-to-image-plots" title="Permalink to this headline">¶</a></h3>
<p>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.</p>
<p>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:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [7]: </span><span class="n">lum_img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[:,:,</span><span class="mi">0</span><span class="p">]</span>
</pre></div>
</div>
<p>This is array slicing. You can read more in the <a class="reference external" href="http://www.scipy.org/Tentative_NumPy_Tutorial">Numpy tutorial</a>.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [8]: </span><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-2.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-2.png">png</a>, <a class="reference external" href="../users/image_tutorial-2.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-2.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-2.png" src="../_images/image_tutorial-2.png" />
</div>
<p>Now, with a luminosity (2D, no color) image, the default colormap (aka lookup table,
LUT), is applied. The default is called jet. There are plenty of
others to choose from.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [9]: </span><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">"hot"</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-3.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-3.png">png</a>, <a class="reference external" href="../users/image_tutorial-3.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-3.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-3.png" src="../_images/image_tutorial-3.png" />
</div>
<p>Note that you can also change colormaps on existing plot objects using the
<code class="xref py py-meth docutils literal"><span class="pre">set_cmap()</span></code> method:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>
<span class="gp">In [11]: </span><span class="n">imgplot</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="s1">'spectral'</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-4.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-4.png">png</a>, <a class="reference external" href="../users/image_tutorial-4.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-4.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-4.png" src="../_images/image_tutorial-4.png" />
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">However, remember that in the IPython notebook with the inline backend,
you can’t make changes to plots that have already been rendered. If you
create imgplot here in one cell, you cannot call set_cmap() on it in a later
cell and expect the earlier plot to change. Make sure that you enter these
commands together in one cell. plt commands will not change plots from earlier
cells.</p>
</div>
<p>There are many other colormap schemes available. See the <a class="reference external" href="../examples/color/colormaps_reference.html">list and
images of the colormaps</a>.</p>
</div>
<div class="section" id="color-scale-reference">
<span id="color-bars"></span><h3>Color scale reference<a class="headerlink" href="#color-scale-reference" title="Permalink to this headline">¶</a></h3>
<p>It’s helpful to have an idea of what value a color represents. We can
do that by adding color bars.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [12]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">)</span>
<span class="gp">In [13]: </span><span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-5.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-5.png">png</a>, <a class="reference external" href="../users/image_tutorial-5.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-5.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-5.png" src="../_images/image_tutorial-5.png" />
</div>
<p>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.</p>
</div>
<div class="section" id="examining-a-specific-data-range">
<span id="data-ranges"></span><h3>Examining a specific data range<a class="headerlink" href="#examining-a-specific-data-range" title="Permalink to this headline">¶</a></h3>
<p>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 <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist"><code class="xref py py-func docutils literal"><span class="pre">hist()</span></code></a> function.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [14]: </span><span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">lum_img</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">bins</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="n">fc</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-6.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-6.png">png</a>, <a class="reference external" href="../users/image_tutorial-6.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-6.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-6.png" src="../_images/image_tutorial-6.png" />
</div>
<p>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 passing the clim argument to
imshow. You could also do this by calling the
<code class="xref py py-meth docutils literal"><span class="pre">set_clim()</span></code> method of the image plot
object, but make sure that you do so in the same cell as your plot
command when working with the IPython Notebook - it will not change
plots from earlier cells.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [15]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">lum_img</span><span class="p">,</span> <span class="n">clim</span><span class="o">=</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">))</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-7.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-7.png">png</a>, <a class="reference external" href="../users/image_tutorial-7.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-7.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-7.png" src="../_images/image_tutorial-7.png" />
</div>
</div>
<div class="section" id="array-interpolation-schemes">
<span id="interpolation"></span><h3>Array Interpolation schemes<a class="headerlink" href="#array-interpolation-schemes" title="Permalink to this headline">¶</a></h3>
<p>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.</p>
<p>We’ll use the Pillow library that we used to load the image also to resize
the image.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [16]: </span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="gp">In [17]: </span><span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../_static/stinkbug.png'</span><span class="p">)</span>
<span class="gp">In [18]: </span><span class="n">img</span><span class="o">.</span><span class="n">thumbnail</span><span class="p">((</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">),</span> <span class="n">Image</span><span class="o">.</span><span class="n">ANTIALIAS</span><span class="p">)</span> <span class="c1"># resizes image in-place</span>
<span class="gp">In [19]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-8.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-8.png">png</a>, <a class="reference external" href="../users/image_tutorial-8.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-8.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-8.png" src="../_images/image_tutorial-8.png" />
</div>
<p>Here we have the default interpolation, bilinear, since we did not
give <a class="reference internal" href="../api/pyplot_api.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow"><code class="xref py py-func docutils literal"><span class="pre">imshow()</span></code></a> any interpolation argument.</p>
<p>Let’s try some others:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [20]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">"nearest"</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-9.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-9.png">png</a>, <a class="reference external" href="../users/image_tutorial-9.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-9.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-9.png" src="../_images/image_tutorial-9.png" />
</div>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [21]: </span><span class="n">imgplot</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">interpolation</span><span class="o">=</span><span class="s2">"bicubic"</span><span class="p">)</span>
</pre></div>
</div>
<p>(<a class="reference external" href="../users/image_tutorial-10.py">Source code</a>, <a class="reference external" href="../users/image_tutorial-10.png">png</a>, <a class="reference external" href="../users/image_tutorial-10.hires.png">hires.png</a>, <a class="reference external" href="../users/image_tutorial-10.pdf">pdf</a>)</p>
<div class="figure">
<img alt="../_images/image_tutorial-10.png" src="../_images/image_tutorial-10.png" />
</div>
<p>Bicubic interpolation is often used when blowing up photos - people
tend to prefer blurry over pixelated.</p>
</div>
</div>
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