

.. _sphx_glr_auto_examples_transform_plot_pyramid.py:


====================
Build image pyramids
====================

The ``pyramid_gaussian`` function takes an image and yields successive images
shrunk by a constant scale factor. Image pyramids are often used, e.g., to
implement algorithms for denoising, texture discrimination, and scale-
invariant detection.





.. image:: /auto_examples/transform/images/sphx_glr_plot_pyramid_001.png
    :align: center





.. code-block:: python

    import numpy as np
    import matplotlib.pyplot as plt

    from skimage import data
    from skimage.transform import pyramid_gaussian


    image = data.astronaut()
    rows, cols, dim = image.shape
    pyramid = tuple(pyramid_gaussian(image, downscale=2))

    composite_image = np.zeros((rows, cols + cols // 2, 3), dtype=np.double)

    composite_image[:rows, :cols, :] = pyramid[0]

    i_row = 0
    for p in pyramid[1:]:
        n_rows, n_cols = p.shape[:2]
        composite_image[i_row:i_row + n_rows, cols:cols + n_cols] = p
        i_row += n_rows

    fig, ax = plt.subplots()
    ax.imshow(composite_image)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.168 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
