.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/demo_modulo.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_demo_modulo.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_demo_modulo.py:


Demo Modulo.
===================================================

.. GENERATED FROM PYTHON SOURCE LINES 8-10

Select Working Directory and Device
-----------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 10-34

.. code-block:: Python

    import os 
    os.chdir(os.path.dirname(os.getcwd()))
    print("Current Working Directory " , os.getcwd())

    import sys
    sys.path.append(os.path.join(os.getcwd()))


    #General imports
    import torch

    # Set random seed for reproducibility
    torch.manual_seed(0)

    manual_device = "cpu"
    # Check GPU support
    print("GPU support: ", torch.cuda.is_available())

    if manual_device:
        device = manual_device
    else:
        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")






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

 .. code-block:: none

    Current Working Directory  /home/runner/work/pycolibri/pycolibri
    GPU support:  False




.. GENERATED FROM PYTHON SOURCE LINES 35-37

Load libraries and data
-----------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 37-80

.. code-block:: Python

    from PIL import Image
    import requests
    from io import BytesIO

    import matplotlib.pyplot as plt
    import numpy as np

    from colibri.optics import Modulo
    from colibri.recovery.solvers import L2L2SolverModulo
    import torchvision.transforms as transforms

    np2tensor = lambda x: torch.tensor(x).permute(2, 0, 1).unsqueeze(0).float()

    def channel_norm(x):
        """ Normalize the input tensor to have values between 0 and 1
        Args:
            x (torch.Tensor): Input tensor with shape (B, C, H, W)
        """
        x -= x.min(dim=2, keepdim=True)[0].min(dim=3, keepdim=True)[0]
        x /= x.max(dim=2, keepdim=True)[0].max(dim=3, keepdim=True)[0]
        return x


    url = "https://optipng.sourceforge.net/pngtech/corpus/kodak/kodim23.png"
    response = requests.get(url)
    img = Image.open(BytesIO(response.content))
    img = np.array(img) / (255 + 1e-3)
    img = np2tensor(img)
    # Guarantee that the image has values between 0 and 1 by channel
    img = channel_norm(img)

    img_size = 512
    saturation_factor = 1.5
    blur_fn = transforms.GaussianBlur(7, sigma=(1, 1))

    img = torch.nn.functional.interpolate(img, size=img_size) * saturation_factor
    # apply blur
    img = blur_fn(img)


    modulo_sensing = Modulo()
    mod_img = modulo_sensing(img)








.. GENERATED FROM PYTHON SOURCE LINES 81-83

Setup and run reconstruction
-----------------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 83-109

.. code-block:: Python

    recons_fn = L2L2SolverModulo(img, modulo_sensing)
    xtilde    = None # initial guess
    rho       = 0.0  # regularization parameter

    recons_img  = recons_fn.solve(xtilde, rho)
    # Since the DCT solver returns a tensor with 0 mean
    # we need to normalize the image to have values between 0 and 1
    recons_img  = channel_norm(recons_img)


    fig, ax = plt.subplots(1, 3, figsize=(10, 10))


    ax[0].imshow(img[0].permute(1, 2, 0) / saturation_factor)
    ax[0].set_title("Original Image")
    ax[0].axis("off")

    ax[1].imshow(mod_img[0].permute(1, 2, 0))
    ax[1].set_title("Modulo Image")
    ax[1].axis("off")

    ax[2].imshow(recons_img[0].permute(1, 2, 0))
    ax[2].set_title("Reconstructed Image")
    ax[2].axis("off")

    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_demo_modulo_001.png
   :alt: Original Image, Modulo Image, Reconstructed Image
   :srcset: /auto_examples/images/sphx_glr_demo_modulo_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.440 seconds)


.. _sphx_glr_download_auto_examples_demo_modulo.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: demo_modulo.zip <demo_modulo.zip>`


.. only:: html

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

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