.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/registration/plot_opticalflow.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_opticalflow.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_opticalflow.py:

Registration using optical flow
==================================

Demonstration of image registration using optical flow.

By definition, the optical flow is the vector field *(u, v)* verifying
*image1(x+u, y+v) = image0(x, y)*, where (image0, image1) is a couple of
consecutive 2D frames from a sequence. This vector field can then be
used for registration by image warping.

To display registration results, an RGB image is constructed by
assigning the result of the registration to the red channel and the
target image to the green and blue channels. A perfect registration
results in a gray level image while misregistred pixels appear colored
in the constructed RGB image.

.. GENERATED FROM PYTHON SOURCE LINES 18-80

.. code-block:: Python


    import numpy as np
    from matplotlib import pyplot as plt
    from skimage.color import rgb2gray
    from skimage.data import stereo_motorcycle, vortex
    from skimage.transform import warp
    from skimage.registration import optical_flow_tvl1, optical_flow_ilk

    # --- Load the sequence
    image0, image1, disp = stereo_motorcycle()

    # --- Convert the images to gray level: color is not supported.
    image0 = rgb2gray(image0)
    image1 = rgb2gray(image1)

    # --- Compute the optical flow
    v, u = optical_flow_tvl1(image0, image1)

    # --- Use the estimated optical flow for registration

    nr, nc = image0.shape

    row_coords, col_coords = np.meshgrid(np.arange(nr), np.arange(nc), indexing='ij')

    image1_warp = warp(image1, np.array([row_coords + v, col_coords + u]), mode='edge')

    # build an RGB image with the unregistered sequence
    seq_im = np.zeros((nr, nc, 3))
    seq_im[..., 0] = image1
    seq_im[..., 1] = image0
    seq_im[..., 2] = image0

    # build an RGB image with the registered sequence
    reg_im = np.zeros((nr, nc, 3))
    reg_im[..., 0] = image1_warp
    reg_im[..., 1] = image0
    reg_im[..., 2] = image0

    # build an RGB image with the registered sequence
    target_im = np.zeros((nr, nc, 3))
    target_im[..., 0] = image0
    target_im[..., 1] = image0
    target_im[..., 2] = image0

    # --- Show the result

    fig, (ax0, ax1, ax2) = plt.subplots(3, 1, figsize=(5, 10))

    ax0.imshow(seq_im)
    ax0.set_title("Unregistered sequence")
    ax0.set_axis_off()

    ax1.imshow(reg_im)
    ax1.set_title("Registered sequence")
    ax1.set_axis_off()

    ax2.imshow(target_im)
    ax2.set_title("Target")
    ax2.set_axis_off()

    fig.tight_layout()




.. image-sg:: /auto_examples/registration/images/sphx_glr_plot_opticalflow_001.png
   :alt: Unregistered sequence, Registered sequence, Target
   :srcset: /auto_examples/registration/images/sphx_glr_plot_opticalflow_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 81-88

The estimated vector field *(u, v)* can also be displayed with a
quiver plot.

In the following example, Iterative Lukas-Kanade algorithm (iLK) is
applied to images of particles in the context of particle image
velocimetry (PIV). The sequence is the Case B from the
`PIV challenge 2001 <http://www.pivchallenge.org/>`_

.. GENERATED FROM PYTHON SOURCE LINES 88-123

.. code-block:: Python


    image0, image1 = vortex()

    # --- Compute the optical flow
    v, u = optical_flow_ilk(image0, image1, radius=15)

    # --- Compute flow magnitude
    norm = np.sqrt(u**2 + v**2)

    # --- Display
    fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 4))

    # --- Sequence image sample

    ax0.imshow(image0, cmap='gray')
    ax0.set_title("Sequence image sample")
    ax0.set_axis_off()

    # --- Quiver plot arguments

    nvec = 20  # Number of vectors to be displayed along each image dimension
    nl, nc = image0.shape
    step = max(nl // nvec, nc // nvec)

    y, x = np.mgrid[:nl:step, :nc:step]
    u_ = u[::step, ::step]
    v_ = v[::step, ::step]

    ax1.imshow(norm)
    ax1.quiver(x, y, u_, v_, color='r', units='dots', angles='xy', scale_units='xy', lw=3)
    ax1.set_title("Optical flow magnitude and vector field")
    ax1.set_axis_off()
    fig.tight_layout()

    plt.show()



.. image-sg:: /auto_examples/registration/images/sphx_glr_plot_opticalflow_002.png
   :alt: Sequence image sample, Optical flow magnitude and vector field
   :srcset: /auto_examples/registration/images/sphx_glr_plot_opticalflow_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 5.905 seconds)


.. _sphx_glr_download_auto_examples_registration_plot_opticalflow.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_opticalflow.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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