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

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

.. _sphx_glr_auto_examples_generatedata.py:


Supplementary 01: Generate data for examples
============================================

This script generates 100 training images, 25 validation images, and
10 testing images to train MS-D networks with.


.. code-block:: default


    import numpy as np
    import tifffile
    import os

    n = 256
    nit = 24

    np.random.seed(12345)

    sz = n//8

    def generate():
        im = np.zeros((n,n),dtype=np.float32)
        l = np.zeros((n,n),dtype=np.uint8)

        tmpl = np.zeros((4,sz,sz),dtype=np.float32)
        tmpl[0] = 1
        tmpl[1] = 1
        tmpl[1][sz//4:-sz//4,sz//4:-sz//4]=0
        xx,yy = np.mgrid[-1:1:1j*sz,-1:1:1j*sz]
        tmpl[2] = xx**2+yy**2<1
        tmpl[3] = xx**2+yy**2<1
        tmpl[3][xx**2+yy**2<0.25]=0

        i = 0
        tp = 0
        while i<nit:
            found=False
            while found==False:
                x, y = (np.random.random(2)*(n-sz)).astype(np.int)
                if l[x:x+sz,y:y+sz].max()==0:
                    found=True
            vl = np.random.random()*0.8+0.2
            im[x:x+sz,y:y+sz] = tmpl[tp]*vl
            l[x:x+sz,y:y+sz] = tmpl[2*(tp//2)]*(tp+1)
            tp+=1
            if tp==4:
                tp=0
            i+=1

        imn = im+np.random.normal(size=im.shape)
        return imn, im, l

    os.makedirs('train',exist_ok=True)
    os.makedirs('train/noisy',exist_ok=True)
    os.makedirs('train/noiseless',exist_ok=True)
    os.makedirs('train/label',exist_ok=True)
    for i in range(100):
        imn, im, l = generate()
        tifffile.imsave('train/noisy/{:05d}.tiff'.format(i), imn.astype(np.float32))
        tifffile.imsave('train/noiseless/{:05d}.tiff'.format(i), im.astype(np.float32))
        tifffile.imsave('train/label/{:05d}.tiff'.format(i), l.astype(np.uint8))
    
    os.makedirs('val',exist_ok=True)
    os.makedirs('val/noisy',exist_ok=True)
    os.makedirs('val/noiseless',exist_ok=True)
    os.makedirs('val/label',exist_ok=True)
    for i in range(25):
        imn, im, l = generate()
        tifffile.imsave('val/noisy/{:05d}.tiff'.format(i), imn.astype(np.float32))
        tifffile.imsave('val/noiseless/{:05d}.tiff'.format(i), im.astype(np.float32))
        tifffile.imsave('val/label/{:05d}.tiff'.format(i), l.astype(np.uint8))
    
    os.makedirs('test',exist_ok=True)
    os.makedirs('test/noisy',exist_ok=True)
    os.makedirs('test/noiseless',exist_ok=True)
    os.makedirs('test/label',exist_ok=True)
    for i in range(10):
        imn, im, l = generate()
        tifffile.imsave('test/noisy/{:05d}.tiff'.format(i), imn.astype(np.float32))
        tifffile.imsave('test/noiseless/{:05d}.tiff'.format(i), im.astype(np.float32))
        tifffile.imsave('test/label/{:05d}.tiff'.format(i), l.astype(np.uint8))
    


.. _sphx_glr_download_auto_examples_generatedata.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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