.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/transform/plot_swirl.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_transform_plot_swirl.py: ===== Swirl ===== Image swirling is a non-linear image deformation that creates a whirlpool effect. This example describes the implementation of this transform in ``skimage``, as well as the underlying warp mechanism. Image warping ------------- When applying a geometric transformation on an image, we typically make use of a reverse mapping, i.e., for each pixel in the output image, we compute its corresponding position in the input. The reason is that, if we were to do it the other way around (map each input pixel to its new output position), some pixels in the output may be left empty. On the other hand, each output coordinate has exactly one corresponding location in (or outside) the input image, and even if that position is non-integer, we may use interpolation to compute the corresponding image value. Performing a reverse mapping ---------------------------- To perform a geometric warp in ``skimage``, you simply need to provide the reverse mapping to the :py:func:`skimage.transform.warp` function. E.g., consider the case where we would like to shift an image 50 pixels to the left. The reverse mapping for such a shift would be:: def shift_left(xy): xy[:, 0] += 50 return xy The corresponding call to warp is:: from skimage.transform import warp warp(image, shift_left) The swirl transformation ------------------------ Consider the coordinate :math:`(x, y)` in the output image. The reverse mapping for the swirl transformation first computes, relative to a center :math:`(x_0, y_0)`, its polar coordinates, .. math:: \theta = \arctan((y-y0)/(x-x0)) \rho = \sqrt{(x - x_0)^2 + (y - y_0)^2}, and then transforms them according to .. math:: r = \ln(2) \, \mathtt{radius} / 5 \phi = \mathtt{rotation} s = \mathtt{strength} \theta' = \phi + s \, e^{-\rho / r} + \theta where ``radius`` indicates the swirl extent in pixels, ``rotation`` adds a rotation angle, and ``strength`` is a parameter for the amount of swirl. The transformation of ``radius`` into :math:`r` is to ensure that the transformation decays to :math:`\approx 1/1000^{\mathsf{th}}` within the specified radius. .. GENERATED FROM PYTHON SOURCE LINES 68-88 .. image-sg:: /auto_examples/transform/images/sphx_glr_plot_swirl_001.png :alt: plot swirl :srcset: /auto_examples/transform/images/sphx_glr_plot_swirl_001.png :class: sphx-glr-single-img .. code-block:: Python import matplotlib.pyplot as plt from skimage import data from skimage.transform import swirl image = data.checkerboard() swirled = swirl(image, rotation=0, strength=10, radius=120) fig, (ax0, ax1) = plt.subplots( nrows=1, ncols=2, figsize=(8, 3), sharex=True, sharey=True ) ax0.imshow(image, cmap=plt.cm.gray) ax0.axis('off') ax1.imshow(swirled, cmap=plt.cm.gray) ax1.axis('off') plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.134 seconds) .. _sphx_glr_download_auto_examples_transform_plot_swirl.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-image/scikit-image/v0.23.2?filepath=notebooks/auto_examples/transform/plot_swirl.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_swirl.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_swirl.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_