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<!DOCTYPE html>

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to download the full example code</p>

</div>

<span id="sphx-glr-tutorials-images-py"></span><span id="id1"></span><h1>Image tutorial<a class="headerlink" href="#image-tutorial" title="Link to this heading">#</a></h1>

<p>A short tutorial on plotting images with Matplotlib.</p>

<span id="imaging-startup"></span><h2>Startup commands<a class="headerlink" href="#startup-commands" title="Link to this heading">#</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 directly at a shell, or with the Jupyter

Notebook (where IPython as a running kernel).</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="https://ipython.readthedocs.io/en/stable/interactive/reference.html#gui-event-loop-support">IPython's documentation on GUI

event loops</a>.</p>

<p>If you're using Jupyter Notebook, the same commands are available, but

people commonly use a specific argument to the %matplotlib magic:</p>

<div class="highlight-ipython notranslate"><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 colormap is not possible from cells below the cell that creates a plot.

However, for other backends, such as Qt, 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 implicit 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 explicit,

which is more suitable for large application development. For an explanation

of the tradeoffs between the implicit and explicit interfaces see

<a class="reference internal" href="../users/explain/figure/api_interfaces.html#api-interfaces"><span class="std std-ref">Matplotlib Application Interfaces (APIs)</span></a> and the <a class="reference internal" href="../users/explain/quick_start.html#quick-start"><span class="std std-ref">Quick start guide</span></a> to start using the explicit interface.

For now, let's get on with the implicit approach:</p>

<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>

<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

</pre></div>

</div>

</section>

<span id="importing-data"></span><h2>Importing image data into Numpy arrays<a class="headerlink" href="#importing-image-data-into-numpy-arrays" title="Link to this heading">#</a></h2>

<p>Matplotlib relies on the <a class="reference external" href="https://pillow.readthedocs.io/en/latest/">Pillow</a> library to load image data.</p>

<p>Here's the image we're going to play with:</p>

<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. Download <a class="reference external" href="https://raw.githubusercontent.com/matplotlib/matplotlib/main/doc/_static/stinkbug.png">stinkbug.png</a>

to your computer for the rest of this tutorial.</p>

<p>We use Pillow to open an image (with <a class="reference external" href="https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.open" title="(in Pillow (PIL Fork) v10.0.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PIL.Image.open</span></code></a>), and immediately

convert the <a class="reference external" href="https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image" title="(in Pillow (PIL Fork) v10.0.0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PIL.Image.Image</span></code></a> object into an 8-bit (<code class="docutils literal notranslate"><span class="pre">dtype=uint8</span></code>) numpy

array.</p>

<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">img</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.asarray.html#numpy.asarray" title="numpy.asarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">asarray</span></a><span class="p">(</span><span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../../doc/_static/stinkbug.png'</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="nb">repr</span><span class="p">(</span><span class="n">img</span><span class="p">))</span>

</pre></div>

</div>

<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>array([[[104, 104, 104],

[104, 104, 104],

...,

[109, 109, 109],

[109, 109, 109]],

[[105, 105, 105],

[105, 105, 105],

...,

[109, 109, 109],

[109, 109, 109]],

[[107, 107, 107],

[106, 106, 106],

...,

[110, 110, 110],

[110, 110, 110]],

...,

[[112, 112, 112],

[111, 111, 111],

[110, 110, 110],

...,

[116, 116, 116],

[115, 115, 115],

[115, 115, 115]],

[[113, 113, 113],

[113, 113, 113],

[112, 112, 112],

...,

[115, 115, 115],

[114, 114, 114],

[114, 114, 114]],

[[113, 113, 113],

[115, 115, 115],

...,

[114, 114, 114],

[113, 113, 113]]], dtype=uint8)

</pre></div>

</div>

<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>

</section>

<span id="plotting-data"></span><h2>Plotting numpy arrays as images<a class="headerlink" href="#plotting-numpy-arrays-as-images" title="Link to this heading">#</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/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow"><code class="xref py py-func docutils literal notranslate"><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-default notranslate"><div class="highlight"><pre><span></span><a href="../api/image_api.html#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage" class="sphx-glr-backref-module-matplotlib-image sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">imgplot</span></a> <span class="o">=</span> <a href="../api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span></a><span class="p">(</span><span class="n">img</span><span class="p">)</span>

</pre></div>

</div>

<img src="../_images/sphx_glr_images_001.png" srcset="../_images/sphx_glr_images_001.png, ../_images/sphx_glr_images_001_2_00x.png 2.00x" alt="images" class = "sphx-glr-single-img"/><p>You can also plot any numpy array.</p>

<span id="pseudocolor"></span><h3>Applying pseudocolor schemes to image plots<a class="headerlink" href="#applying-pseudocolor-schemes-to-image-plots" title="Link to this heading">#</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 using array slicing (you can read more in the

<a class="reference external" href="https://numpy.org/doc/stable/user/quickstart.html#indexing-slicing-and-iterating">Numpy tutorial</a>):</p>

<a href="../api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lum_img</span></a><span class="p">)</span>

</pre></div>

</div>

<img src="../_images/sphx_glr_images_002.png" srcset="../_images/sphx_glr_images_002.png, ../_images/sphx_glr_images_002_2_00x.png 2.00x" alt="images" class = "sphx-glr-single-img"/><p>Now, with a luminosity (2D, no color) image, the default colormap (aka lookup table,

LUT), is applied. The default is called viridis. There are plenty of

others to choose from.</p>

<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lum_img</span></a><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>

<img src="../_images/sphx_glr_images_003.png" srcset="../_images/sphx_glr_images_003.png, ../_images/sphx_glr_images_003_2_00x.png 2.00x" alt="images" class = "sphx-glr-single-img"/><p>Note that you can also change colormaps on existing plot objects using the

<a class="reference internal" href="../api/cm_api.html#matplotlib.cm.ScalarMappable.set_cmap" title="matplotlib.cm.ScalarMappable.set_cmap"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_cmap()</span></code></a> method:</p>

<a href="../api/cm_api.html#matplotlib.cm.ScalarMappable.set_cmap" title="matplotlib.cm.ScalarMappable.set_cmap" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-method"><span class="n">imgplot</span><span class="o">.</span><span class="n">set_cmap</span></a><span class="p">(</span><span class="s1">'nipy_spectral'</span><span class="p">)</span>

</pre></div>

</div>

<p>However, remember that in the Jupyter 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="../colors/colormaps.html">list and

images of the colormaps</a>.</p>

</section>

<span id="color-bars"></span><h3>Color scale reference<a class="headerlink" href="#color-scale-reference" title="Link to this heading">#</a></h3>

<p>It's helpful to have an idea of what value a color represents. We can

do that by adding a color bar to your figure:</p>

<a href="../api/_as_gen/matplotlib.pyplot.colorbar.html#matplotlib.pyplot.colorbar" title="matplotlib.pyplot.colorbar" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span></a><span class="p">()</span>

</pre></div>

</div>

<span id="data-ranges"></span><h3>Examining a specific data range<a class="headerlink" href="#examining-a-specific-data-range" title="Link to this heading">#</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/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist"><code class="xref py py-func docutils literal notranslate"><span class="pre">hist()</span></code></a> function.</p>

<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../api/_as_gen/matplotlib.pyplot.hist.html#matplotlib.pyplot.hist" title="matplotlib.pyplot.hist" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">hist</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lum_img</span></a><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">bins</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">256</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>

<img src="../_images/sphx_glr_images_006.png" srcset="../_images/sphx_glr_images_006.png, ../_images/sphx_glr_images_006_2_00x.png 2.00x" alt="images" class = "sphx-glr-single-img"/><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 setting <em>clim</em>, the colormap

limits.</p>

<p>This can be done by passing a <em>clim</em> keyword argument in the call to

<code class="docutils literal notranslate"><span class="pre">imshow</span></code>.</p>

<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span></a><span class="p">(</span><a href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">lum_img</span></a><span class="p">,</span> <span class="n">clim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">175</span><span class="p">))</span>

</pre></div>

</div>

<img src="../_images/sphx_glr_images_007.png" srcset="../_images/sphx_glr_images_007.png, ../_images/sphx_glr_images_007_2_00x.png 2.00x" alt="images" class = "sphx-glr-single-img"/><p>This can also be done by calling the

<a class="reference internal" href="../api/cm_api.html#matplotlib.cm.ScalarMappable.set_clim" title="matplotlib.cm.ScalarMappable.set_clim"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_clim()</span></code></a> method of the returned image

plot object, but make sure that you do so in the same cell as your plot

command when working with the Jupyter Notebook - it will not change

plots from earlier cells.</p>

<a href="../api/cm_api.html#matplotlib.cm.ScalarMappable.set_clim" title="matplotlib.cm.ScalarMappable.set_clim" class="sphx-glr-backref-module-matplotlib-cm sphx-glr-backref-type-py-method"><span class="n">imgplot</span><span class="o">.</span><span class="n">set_clim</span></a><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">175</span><span class="p">)</span>

</pre></div>

</div>

<span id="interpolation"></span><h3>Array Interpolation schemes<a class="headerlink" href="#array-interpolation-schemes" title="Link to this heading">#</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-default notranslate"><div class="highlight"><pre><span></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">'../../doc/_static/stinkbug.png'</span><span class="p">)</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="c1"># resizes image in-place</span>

<a href="../api/image_api.html#matplotlib.image.AxesImage" title="matplotlib.image.AxesImage" class="sphx-glr-backref-module-matplotlib-image sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">imgplot</span></a> <span class="o">=</span> <a href="../api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span></a><span class="p">(</span><span class="n">img</span><span class="p">)</span>

</pre></div>

</div>

<img src="../_images/sphx_glr_images_009.png" srcset="../_images/sphx_glr_images_009.png, ../_images/sphx_glr_images_009_2_00x.png 2.00x" alt="images" class = "sphx-glr-single-img"/><p>Here we use the default interpolation ("nearest"), since we did not

give <a class="reference internal" href="../api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="matplotlib.pyplot.imshow"><code class="xref py py-func docutils literal notranslate"><span class="pre">imshow()</span></code></a> any interpolation argument.</p>

<p>Let's try some others. Here's "bilinear":</p>

</pre></div>

</div>

<img src="../_images/sphx_glr_images_010.png" srcset="../_images/sphx_glr_images_010.png, ../_images/sphx_glr_images_010_2_00x.png 2.00x" alt="images" class = "sphx-glr-single-img"/><p>and bicubic:</p>

</pre></div>

</div>

<img src="../_images/sphx_glr_images_011.png" srcset="../_images/sphx_glr_images_011.png, ../_images/sphx_glr_images_011_2_00x.png 2.00x" alt="images" class = "sphx-glr-single-img"/><p>Bicubic interpolation is often used when blowing up photos - people

tend to prefer blurry over pixelated.</p>

<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> (0 minutes 6.717 seconds)</p>

<p><a class="reference download internal" download="" href="../_downloads/6da5ecb585bca3fd57ae9c5b9f7c8699/images.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">images.py</span></code></a></p>

</div>

<p><a class="reference download internal" download="" href="../_downloads/33cdbcfd1b016e6cea1c03ad960b40ce/images.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">images.ipynb</span></code></a></p>

</div>

<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>

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<i class="fa-solid fa-list"></i> On this page

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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#startup-commands">Startup commands</a></li>

<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#importing-image-data-into-numpy-arrays">Importing image data into Numpy arrays</a></li>

<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#plotting-numpy-arrays-as-images">Plotting numpy arrays as images</a><ul class="nav section-nav flex-column">

<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#applying-pseudocolor-schemes-to-image-plots">Applying pseudocolor schemes to image plots</a></li>

<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#color-scale-reference">Color scale reference</a></li>

<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#examining-a-specific-data-range">Examining a specific data range</a></li>

<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#array-interpolation-schemes">Array Interpolation schemes</a></li>

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