.. _sphx_glr_build_examples_datasets_ade20k.py:

Prepare ADE20K dataset.
========================

`ADE20K <http://sceneparsing.csail.mit.edu/>`_ is a scene-centric containing
20 thousands images annotated with 150 object categories.
This tutorial help you to download ADE20K and set it up for later experiments.

.. image:: http://groups.csail.mit.edu/vision/datasets/ADE20K/assets/images/examples.png
   :width: 600 px

.. hint::

   You need 2.3 GB free disk space to download and extract this dataset.
   SSD harddrives are recommended for faster speed.
   The time it takes to prepare the dataset depends on your Internet connection
   and disk speec. For example, it takes around 25 min on an AWS EC2 instance
   with EBS.

Prepare the dataset
-------------------

We will download and unzip the following files:

+-------------------------------------------------------------------------------------------------------+--------+
| File name                                                                                             | Size   |
+=======================================================================================================+========+
| `ADEChallengeData2016.zip <http://data.csail.mit.edu/places/ADEchallenge/ADEChallengeData2016.zip>`_  | 923 MB |
+-------------------------------------------------------------------------------------------------------+--------+
| `release_test.zip <http://data.csail.mit.edu/places/ADEchallenge/release_test.zip>`_                  | 202 MB |
+-------------------------------------------------------------------------------------------------------+--------+

The easiest way is to run this script:

 :download:`Download script: ade20k.py<../../../scripts/datasets/ade20k.py>`

.. code-block:: bash

   python ade20k.py

If you have already downloaded the above files and unziped them,
you can set the folder name through ``--download-dir`` to avoid
downloading them again. For example

.. code-block:: python

   python ade20k.py --download-dir ~/ade_downloads



How to load the dataset
-----------------------

Loading images and labels from ADE20K is straight-forward
with GluonCV's dataset utility:



.. code-block:: python


    from gluoncv.data import ADE20KSegmentation
    train_dataset = ADE20KSegmentation(split='train')
    val_dataset = ADE20KSegmentation(split='val')
    print('Training images:', len(train_dataset))
    print('Validation images:', len(val_dataset))






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

 Out::

    Training images: 20210
    Validation images: 2000


Get the first sample
--------------------




.. code-block:: python

    import numpy as np
    img, mask = val_dataset[0]
    # get pallete for the mask
    from gluoncv.utils.viz import get_color_pallete
    mask = get_color_pallete(mask.asnumpy(), dataset='ade20k')
    mask.save('mask.png')








Visualize data and label
------------------------




.. code-block:: python

    from matplotlib import pyplot as plt
    import matplotlib.image as mpimg
    # subplot 1 for img
    fig = plt.figure()
    fig.add_subplot(1,2,1)
    plt.imshow(img.asnumpy().astype('uint8'))
    # subplot 2 for the mask
    mmask = mpimg.imread('mask.png')
    fig.add_subplot(1,2,2)
    plt.imshow(mmask)
    # display
    plt.show()



.. image:: /build/examples_datasets/images/sphx_glr_ade20k_001.png
    :align: center




**Total running time of the script:** ( 0 minutes  0.350 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: ade20k.py <ade20k.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: ade20k.ipynb <ade20k.ipynb>`


.. only:: html

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

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