.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_tif_gray_gradient_correction.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_plot_tif_gray_gradient_correction.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_tif_gray_gradient_correction.py:


SEM Gray-Value Gradient Correction
==================================

This example builds a small SEM-like grayscale image with an illumination
gradient and compares two correction methods used for cross-section images.

.. GENERATED FROM PYTHON SOURCE LINES 8-18

.. code-block:: Python


    from pathlib import Path
    from tempfile import TemporaryDirectory

    import matplotlib.pyplot as plt
    import numpy as np
    from PIL import Image

    from micromechanics.tif import Tif








.. GENERATED FROM PYTHON SOURCE LINES 19-22

Build a small synthetic SEM-like image. The sinusoidal term gives texture, the
Gaussian spot represents a brighter feature, and the linear term creates the
unwanted vertical illumination gradient.

.. GENERATED FROM PYTHON SOURCE LINES 22-29

.. code-block:: Python


    y, x = np.mgrid[0:160, 0:220]
    matrix = 90 + 40*np.sin(x/18.0)*np.sin(y/24.0)
    particles = 55*np.exp(-((x-80)**2+(y-70)**2)/(2*18**2))
    illumination = 0.42*y
    image_array = np.clip(matrix + particles + illumination, 0, 255).astype(np.uint8)








.. GENERATED FROM PYTHON SOURCE LINES 30-33

``Tif`` normally reads instrument files from disk, so the synthetic image is
written to a temporary TIF first. ``fileType="Conventional"`` skips vendor
metadata parsing and uses the given pixel size directly.

.. GENERATED FROM PYTHON SOURCE LINES 33-56

.. code-block:: Python


    with TemporaryDirectory() as tmp:
      file_name = Path(tmp) / "synthetic_sem.tif"
      Image.fromarray(image_array).save(file_name)

      original = Tif(str(file_name), fileType="Conventional", pixelSize=0.02)

      #############################################################################
      # ``gaussLevel`` estimates a smooth background using a Gaussian filter and
      # subtracts it. This is useful when the illumination variation is broad
      # compared with the microstructural features.

      leveled = Tif(str(file_name), fileType="Conventional", pixelSize=0.02)
      leveled.gaussLevel(level=18, plot=False, save=True)

      #############################################################################
      # ``removeGrayGradient`` fits a row-wise intensity trend and subtracts only
      # positive excess brightness. This targets cross-section images where one
      # direction carries most of the gray-value drift.

      row_corrected = Tif(str(file_name), fileType="Conventional", pixelSize=0.02)
      row_corrected.removeGrayGradient(save=True, plot=False)





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

 .. code-block:: none

    /home/runner/work/micromechanics/micromechanics/micromechanics/tif/processing.py:297: RankWarning: Polyfit may be poorly conditioned
      myFit = np.polyfit(x[maxYsum:], ysum[maxYsum:],2)




.. GENERATED FROM PYTHON SOURCE LINES 57-59

Compare the unprocessed image with both corrections. The best choice depends
on whether the gradient is broad and two-dimensional or primarily row-wise.

.. GENERATED FROM PYTHON SOURCE LINES 59-68

.. code-block:: Python


    fig, axes = plt.subplots(1, 3, figsize=(9, 3))
    for ax, title, data in zip(axes,
                              ["original", "Gaussian leveled", "row corrected"],
                              [original.image, leveled.image, row_corrected.image]):
      ax.imshow(data, cmap="gray")
      ax.set_title(title)
      ax.axis("off")
    plt.tight_layout()



.. image-sg:: /auto_examples/images/sphx_glr_plot_tif_gray_gradient_correction_001.png
   :alt: original, Gaussian leveled, row corrected
   :srcset: /auto_examples/images/sphx_glr_plot_tif_gray_gradient_correction_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.145 seconds)


.. _sphx_glr_download_auto_examples_plot_tif_gray_gradient_correction.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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