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

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

.. _sphx_glr_auto_examples_generatedata_tomography.py:


Supplementary 02: Generate data for tomography examples
=======================================================


This script generates tomographic reconstructions of phantom samples:
1 sample for training, 1 for validation, and 1 for testing


.. code-block:: default


    import numpy as np
    import tifffile
    import os
    import astra

    n = 256
    nang = 256
    ang = np.linspace(0, np.pi, nang, False)
    np.random.seed(12345)

    # ASTRA objects
    proj_geom = astra.create_proj_geom('parallel', 1, int(np.sqrt(2)*n), ang)
    vol_geom = astra.create_vol_geom((n,n))
    pid = astra.create_projector('strip', proj_geom, vol_geom)
    w = astra.OpTomo(pid)

    for tpe in ['tomo_train', 'tomo_val', 'tomo_test']:
        xfac = 0.75 + np.random.random()*1.25
        yfac = 0.75 + np.random.random()*1.25
        zfac = 0.75 + np.random.random()*1.25

        xx,yy,zz = np.mgrid[-1.5*xfac:1.5*xfac:1j*n, -1.5*yfac:1.5*yfac:1j*n, -1.5*zfac:1.5*zfac:1j*n]
        ph = np.zeros((n,n,n),dtype=np.float32)
        ph_label = np.zeros((n,n,n),dtype=np.uint8)
        msk = np.logical_and(np.abs(xx)<=1,np.logical_and(np.abs(zz)<=1,np.abs(yy)<=1))
        ph[msk]=1
        ph_label[msk]=1
        msk = xx**2+yy**2+zz**2<=1
        ph[msk]=0
        ph_label[msk]=2
        q = 1/np.sqrt(3)
        msk = np.logical_and(np.abs(xx)<=q,np.logical_and(np.abs(zz)<=q,np.abs(yy)<=q))
        ph[msk]=1
        ph_label[msk]=3

        os.makedirs(tpe, exist_ok=True)
        os.makedirs(os.path.join(tpe,'lowqual'), exist_ok=True)
        os.makedirs(os.path.join(tpe,'highqual'), exist_ok=True)
        os.makedirs(os.path.join(tpe,'label'), exist_ok=True)

        for j in range(n):
            sinogram = w*ph[j]
            sinogram_hq = sinogram + np.random.normal(size=sinogram.shape, scale=n/1000)
            sinogram_lq = sinogram + np.random.normal(size=sinogram.shape, scale=n/10)
            rec_hq = w.reconstruct('FBP', sinogram_hq)
            rec_lq = w.reconstruct('FBP', sinogram_lq)
            tifffile.imsave(os.path.join(os.path.join(tpe,'lowqual'),'{:05d}.tiff'.format(j)), rec_lq)
            tifffile.imsave(os.path.join(os.path.join(tpe,'highqual'),'{:05d}.tiff'.format(j)), rec_hq)
            tifffile.imsave(os.path.join(os.path.join(tpe,'label'),'{:05d}.tiff'.format(j)), ph_label[j])
        


.. _sphx_glr_download_auto_examples_generatedata_tomography.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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