.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/scene/image_custom_kernel.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_gallery_scene_image_custom_kernel.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_scene_image_custom_kernel.py:


Custom image sampling
=====================

Use custom interpolation kernels for image sampling.

Press k to switch kernel.

.. GENERATED FROM PYTHON SOURCE LINES 15-94



.. image-sg:: /gallery/scene/images/sphx_glr_image_custom_kernel_001.png
   :alt: image custom kernel
   :srcset: /gallery/scene/images/sphx_glr_image_custom_kernel_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import sys
    from itertools import cycle

    from vispy import scene
    from vispy import app
    from vispy.io import load_data_file, read_png

    from scipy.signal.windows import gaussian
    from scipy.ndimage import gaussian_filter
    import numpy as np

    canvas = scene.SceneCanvas(keys='interactive')
    canvas.size = 800, 600
    canvas.show()

    # Set up a viewbox to display the image with interactive pan/zoom
    view = canvas.central_widget.add_view()

    # Load the image with a slight blur (so we can later show the sharpening filter)
    img_data = gaussian_filter(
        read_png(load_data_file('mona_lisa/mona_lisa_sm.png')),
        sigma=1,
    )

    # build gaussian kernel
    small_gaussian_window = gaussian(5, 1)
    small_gaussian_kernel = np.outer(small_gaussian_window, small_gaussian_window)
    # normalize
    small_gaussian_kernel = small_gaussian_kernel / small_gaussian_kernel.sum()

    # do the same but larget and with bigger sigma
    big_gaussian_window = gaussian(20, 10)
    big_gaussian_kernel = np.outer(big_gaussian_window, big_gaussian_window)
    big_gaussian_kernel = big_gaussian_kernel / big_gaussian_kernel.sum()

    # sharpening kernel
    sharpen_kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])

    kernels = {
        'null': np.ones((1, 1)),
        'small gaussian': small_gaussian_kernel,
        'big gaussian': big_gaussian_kernel,
        'sharpening': sharpen_kernel,
    }

    k_names = cycle(kernels.keys())

    k = next(k_names)

    image = scene.visuals.Image(
        img_data,
        interpolation='custom',
        custom_kernel=kernels[k],
        parent=view.scene,
    )

    canvas.title = f'Custom sampling with {k} kernel'

    # Set 2D camera (the camera will scale to the contents in the scene)
    view.camera = scene.PanZoomCamera(aspect=1)
    # flip y-axis to have correct aligment
    view.camera.flip = (0, 1, 0)
    view.camera.set_range()


    # Implement key presses
    @canvas.events.key_press.connect
    def on_key_press(event):
        if event.key == 'k':
            k = next(k_names)
            image.custom_kernel = kernels[k]
            canvas.title = f'Custom sampling with {k} kernel'
            canvas.update()


    if __name__ == '__main__' and sys.flags.interactive == 0:
        app.run()
        print(__doc__)


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.996 seconds)


.. _sphx_glr_download_gallery_scene_image_custom_kernel.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: image_custom_kernel.ipynb <image_custom_kernel.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: image_custom_kernel.py <image_custom_kernel.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_