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95 lines
3.2 KiB
Plaintext
95 lines
3.2 KiB
Plaintext
Image Viewer
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============
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Quick Start
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-----------
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``skimage.viewer`` provides a matplotlib_-based canvas for displaying images and
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a Qt-based GUI-toolkit, with the goal of making it easy to create interactive
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image editors. You can simply use it to display an image:
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.. code-block:: python
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from skimage import data
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from skimage.viewer import ImageViewer
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image = data.coins()
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viewer = ImageViewer(image)
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viewer.show()
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Of course, you could just as easily use ``imshow`` from matplotlib_ (or
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alternatively, ``skimage.io.imshow`` which adds support for multiple
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io-plugins) to display images. The advantage of ``ImageViewer`` is that you can
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easily add plugins for manipulating images. Currently, only a few plugins are
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implemented, but it is easy to write your own. Before going into the details,
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let's see an example of how a pre-defined plugin is added to the viewer:
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.. code-block:: python
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from skimage.viewer.plugins.lineprofile import LineProfile
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viewer = ImageViewer(image)
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viewer += LineProfile(viewer)
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overlay, data = viewer.show()[0]
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The viewer's ``show`` method will return an overlay (of the same shape as the
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input image) and data (possibly ``None``) for each attached plugin. In this
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case, there is only one plugin, so we can directly name both. You can see the
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plugin class's ``output`` method to see what will be returned. Here,
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``overlay`` contains a drawing of the line (including its width), and ``data``
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contains the measured line profile.
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At the moment, there are not many plugins pre-defined, but there is a really
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simple interface for creating your own plugin. First, let us create a plugin to
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call the total-variation denoising function, ``denoise_tv_bregman``:
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.. code-block:: python
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from skimage.filter import denoise_tv_bregman
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from skimage.viewer.plugins.base import Plugin
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denoise_plugin = Plugin(image_filter=denoise_tv_bregman)
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.. note::
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The ``Plugin`` assumes the first argument given to the image filter is the
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image from the image viewer. In the future, this should be changed so you
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can pass the image to a different argument of the filter function.
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To actually interact with the filter, you have to add widgets that adjust the
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parameters of the function. Typically, that means adding a slider widget and
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connecting it to the filter parameter and the minimum and maximum values of the
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slider:
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.. code-block:: python
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from skimage.viewer.widgets import Slider
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from skimage.viewer.widgets.history import SaveButtons
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denoise_plugin += Slider('weight', 0.01, 0.5, update_on='release')
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denoise_plugin += SaveButtons()
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Here, we connect a slider widget to the filter's 'weight' argument. We also
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added some buttons for saving the image to file or to the ``scikit-image``
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image stack (see ``skimage.io.push`` and ``skimage.io.pop``).
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All that's left is to create an image viewer and add the plugin to that viewer.
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.. code-block:: python
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viewer = ImageViewer(image)
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viewer += denoise_plugin
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denoised = viewer.show()[0][0]
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When we close the viewer, ``denoised`` will contain the filtered image for the
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last used setting of ``weight``.
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.. image:: data/denoise_viewer_window.png
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.. image:: data/denoise_plugin_window.png
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.. _matplotlib: http://matplotlib.sourceforge.net/
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