.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_parrot.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_parrot.py:


Image-colored wordcloud with boundary map
=========================================
A slightly more elaborate version of an image-colored wordcloud
that also takes edges in the image into account.
Recreating an image similar to the parrot example.



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


    *

      .. image:: /auto_examples/images/sphx_glr_parrot_001.png
          :alt: parrot
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_parrot_002.png
          :alt: parrot
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_parrot_003.png
          :alt: Original Image
          :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/images/sphx_glr_parrot_004.png
          :alt: Edge map
          :class: sphx-glr-multi-img






.. code-block:: default


    import os
    from PIL import Image

    import numpy as np
    import matplotlib.pyplot as plt
    from scipy.ndimage import gaussian_gradient_magnitude

    from wordcloud import WordCloud, ImageColorGenerator

    # get data directory (using getcwd() is needed to support running example in generated IPython notebook)
    d = os.path.dirname(__file__) if "__file__" in locals() else os.getcwd()

    # load wikipedia text on rainbow
    text = open(os.path.join(d, 'wiki_rainbow.txt'), encoding="utf-8").read()

    # load image. This has been modified in gimp to be brighter and have more saturation.
    parrot_color = np.array(Image.open(os.path.join(d, "parrot-by-jose-mari-gimenez2.jpg")))
    # subsample by factor of 3. Very lossy but for a wordcloud we don't really care.
    parrot_color = parrot_color[::3, ::3]

    # create mask  white is "masked out"
    parrot_mask = parrot_color.copy()
    parrot_mask[parrot_mask.sum(axis=2) == 0] = 255

    # some finesse: we enforce boundaries between colors so they get less washed out.
    # For that we do some edge detection in the image
    edges = np.mean([gaussian_gradient_magnitude(parrot_color[:, :, i] / 255., 2) for i in range(3)], axis=0)
    parrot_mask[edges > .08] = 255

    # create wordcloud. A bit sluggish, you can subsample more strongly for quicker rendering
    # relative_scaling=0 means the frequencies in the data are reflected less
    # acurately but it makes a better picture
    wc = WordCloud(max_words=2000, mask=parrot_mask, max_font_size=40, random_state=42, relative_scaling=0)

    # generate word cloud
    wc.generate(text)
    plt.imshow(wc)

    # create coloring from image
    image_colors = ImageColorGenerator(parrot_color)
    wc.recolor(color_func=image_colors)
    plt.figure(figsize=(10, 10))
    plt.imshow(wc, interpolation="bilinear")
    wc.to_file("parrot_new.png")

    plt.figure(figsize=(10, 10))
    plt.title("Original Image")
    plt.imshow(parrot_color)

    plt.figure(figsize=(10, 10))
    plt.title("Edge map")
    plt.imshow(edges)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  21.403 seconds)


.. _sphx_glr_download_auto_examples_parrot.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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