.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples\02_single_channel_image.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_02_single_channel_image.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_02_single_channel_image.py:


====================
Single-Channel Image
====================

In fluorescence imaging, we work with single-channel images. A single channel image simply
means that each pixel has a single intensity value. In fluorescence images, each value represents
how much light (or fluorescence) was detected at that point. `This website <https://bioimagebook.github.io/chapters/3-fluorescence/1-formation_overview/formation_overview.html>`_
gives a very good simple explanation on how the light detected is transformed into pixels.

Grayscale
=========
A single channel image is also often referred to as a grayscale image.
A grayscale image simply maps the intensity values to shades of gray—from
black (0 intensity) to white (maximum intensity).

As an example, we will use this 100 × 100 pixel image, `blobs.jpeg`,
which shows, well, blobs. This image actually shows a magnified view of
lipid droplets in HeLa cells that I extracted from
`this image <https://biotium.com/wp-content/uploads/2017/08/LipidSpot-610-NucSpot-470-fix-perm-detail.gif>`_.

.. GENERATED FROM PYTHON SOURCE LINES 23-38

.. code-block:: Python


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

    # Open the image and convert it to a NumPy array
    blobs = Image.open("images/blobs.jpeg")
    blobs = np.array(blobs)

    plt.figure()
    plt.imshow(blobs, cmap='gray')
    plt.colorbar()  # Add a color scale bar to see the intensity range
    plt.title("Blobs (Grayscale)")
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_02_single_channel_image_001.png
   :alt: Blobs (Grayscale)
   :srcset: /auto_examples/images/sphx_glr_02_single_channel_image_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 39-44

In this grayscale representation, every pixel's value is mapped onto
a scale from black to white. Black pixels have the lowest intensity,
and white pixels have the highest intensity. Intermediate values appear
as different shades of gray.


.. GENERATED FROM PYTHON SOURCE LINES 46-50

Other Representations of Single-Channel Images
==============================================
We can also replace the generally used grayscale gradient with a different
color gradient (colormap).

.. GENERATED FROM PYTHON SOURCE LINES 50-76

.. code-block:: Python


    plt.figure(figsize=(17,3.8))

    plt.subplot(141)
    plt.title("Blobs (binary)")
    plt.imshow(blobs, cmap='binary')
    plt.colorbar()

    plt.subplot(142)
    plt.title("Blobs (Blues)")
    plt.imshow(blobs, cmap='Blues')
    plt.colorbar()

    plt.subplot(143)
    plt.title("Blobs (viridis)")
    plt.imshow(blobs, cmap='viridis')
    plt.colorbar()

    plt.subplot(144)
    plt.title("Blobs (jet)")
    plt.imshow(blobs, cmap='jet')
    plt.colorbar()

    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_02_single_channel_image_002.png
   :alt: Blobs (binary), Blobs (Blues), Blobs (viridis), Blobs (jet)
   :srcset: /auto_examples/images/sphx_glr_02_single_channel_image_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 77-87

As you can see, not only can we assign different values to different
colors, but the general rule that *a larger value means a brighter pixel and a smaller value means a darker pixel*
also doesn't really apply to the above image representations.

In this case, the image can also be thought of as a heatmap. This is
often the case with scientific images. Because the regular grayscale
gradient cannot always represent the information we want to
show from an image data.

So it is up to the researchers to pick the appropriate representation.


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

   **Total running time of the script:** (0 minutes 0.486 seconds)


.. _sphx_glr_download_auto_examples_02_single_channel_image.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 02_single_channel_image.ipynb <02_single_channel_image.ipynb>`

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

      :download:`Download Python source code: 02_single_channel_image.py <02_single_channel_image.py>`

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

      :download:`Download zipped: 02_single_channel_image.zip <02_single_channel_image.zip>`


.. only:: html

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

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