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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_registration_plot_register_translation.py>`
        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_registration_plot_register_translation.py:


=====================================
Image Registration
=====================================

In this example, we use phase cross-correlation to identify the
relative shift between two similar-sized images.

The ``phase_cross_correlation`` function uses cross-correlation in
Fourier space, optionally employing an upsampled matrix-multiplication
DFT to achieve arbitrary subpixel precision [1]_.

.. [1] Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup,
       "Efficient subpixel image registration algorithms," Optics Letters 33,
       156-158 (2008). :DOI:`10.1364/OL.33.000156`

.. GENERATED FROM PYTHON SOURCE LINES 18-92



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/registration/images/sphx_glr_plot_register_translation_001.png
         :alt: Reference image, Offset image, Cross-correlation
         :srcset: /auto_examples/registration/images/sphx_glr_plot_register_translation_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/registration/images/sphx_glr_plot_register_translation_002.png
         :alt: Reference image, Offset image, Supersampled XC sub-area
         :srcset: /auto_examples/registration/images/sphx_glr_plot_register_translation_002.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Known offset (y, x): (-22.4, 13.32)
    Detected pixel offset (y, x): [ 22. -13.]
    Detected subpixel offset (y, x): [ 22.4  -13.32]






|

.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    from skimage import data
    from skimage.registration import phase_cross_correlation
    from skimage.registration._phase_cross_correlation import _upsampled_dft
    from scipy.ndimage import fourier_shift

    image = data.camera()
    shift = (-22.4, 13.32)
    # The shift corresponds to the pixel offset relative to the reference image
    offset_image = fourier_shift(np.fft.fftn(image), shift)
    offset_image = np.fft.ifftn(offset_image)
    print(f'Known offset (y, x): {shift}')

    # pixel precision first
    shift, error, diffphase = phase_cross_correlation(image, offset_image)

    fig = plt.figure(figsize=(8, 3))
    ax1 = plt.subplot(1, 3, 1)
    ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1)
    ax3 = plt.subplot(1, 3, 3)

    ax1.imshow(image, cmap='gray')
    ax1.set_axis_off()
    ax1.set_title('Reference image')

    ax2.imshow(offset_image.real, cmap='gray')
    ax2.set_axis_off()
    ax2.set_title('Offset image')

    # Show the output of a cross-correlation to show what the algorithm is
    # doing behind the scenes
    image_product = np.fft.fft2(image) * np.fft.fft2(offset_image).conj()
    cc_image = np.fft.fftshift(np.fft.ifft2(image_product))
    ax3.imshow(cc_image.real)
    ax3.set_axis_off()
    ax3.set_title("Cross-correlation")

    plt.show()

    print(f'Detected pixel offset (y, x): {shift}')

    # subpixel precision
    shift, error, diffphase = phase_cross_correlation(
        image, offset_image, upsample_factor=100
    )

    fig = plt.figure(figsize=(8, 3))
    ax1 = plt.subplot(1, 3, 1)
    ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1)
    ax3 = plt.subplot(1, 3, 3)

    ax1.imshow(image, cmap='gray')
    ax1.set_axis_off()
    ax1.set_title('Reference image')

    ax2.imshow(offset_image.real, cmap='gray')
    ax2.set_axis_off()
    ax2.set_title('Offset image')

    # Calculate the upsampled DFT, again to show what the algorithm is doing
    # behind the scenes.  Constants correspond to calculated values in routine.
    # See source code for details.
    cc_image = _upsampled_dft(image_product, 150, 100, (shift * 100) + 75).conj()
    ax3.imshow(cc_image.real)
    ax3.set_axis_off()
    ax3.set_title("Supersampled XC sub-area")


    plt.show()

    print(f'Detected subpixel offset (y, x): {shift}')


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

   **Total running time of the script:** (0 minutes 0.774 seconds)


.. _sphx_glr_download_auto_examples_registration_plot_register_translation.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/v0.24.0?filepath=notebooks/auto_examples/registration/plot_register_translation.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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