.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here ` to download the full example code
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_examples_imaging_contrast_enhancement.py:
Re-reimplement histogram stretching, matching, and equalization and hist/pdf/cdf
================================================================================
1) Require ip.image
2) Build image histogram, PDF, and CDF blocks
2) Build LUT application block
4) Build a contrast enhancement block
5) Display comparison of original vs equalized
* We should definitely have a more robust block for doing these with pooling too
NOTE: AMONG US ($5 on steam)
First let's import our basic libraries
.. code-block:: default
import numpy as np
import matplotlib.pyplot as plt
import imagepypelines as ip
ip.require('image')
We'll also need to set up a few blocks
.. code-block:: default
# A block for computing the histogram, pdf, and cdf of an image
@ip.blockify()
def compute_stats(image, bit_depth=8):
hist, bins = np.histogram(image, bins=np.linspace(0, (2**bit_depth)+1, (2**bit_depth)+1))
pdf = hist / image.size
cdf = np.cumsum(pdf)
return hist, pdf, cdf
# A quick block for building a histogram equalization look-up table (LUT)
@ip.blockify()
def equalization_LUT(image_cdf, bit_depth=8):
target_cdf = np.cumsum(np.ones(2**bit_depth) / 2**bit_depth)
lut = np.searchsorted(target_cdf, image_cdf)
return lut
# A quick block for applying a LUT to an image
@ip.blockify()
def apply_LUT(image, lut):
return lut[image].astype(image.dtype)
# A quick block for plotting original versus stretched histograms
@ip.blockify(batch_type="each", void=True)
def compare_stats(original, equalized):
fig = plt.figure()
plt.plot(np.linspace(0,len(original)-1,len(original)), original, color='r')
plt.plot(np.linspace(0,len(equalized)-1,len(equalized)), equalized, color='b')
plt.ion()
plt.show()
# stats = compute_stats(ip.image.giza())
# eq_stats = compute_stats(apply_LUT(ip.image.giza(), equalization_LUT(stats[2])))
# compare_stats(stats[0], eq_stats[0])
tasks = {
# set an entry point for images into the pipeline
'images': ip.Input(),
# compute image stats
('hists', 'pdfs', 'cdfs'): (compute_stats, 'images'),
# construct equalization LUT
'LUTs': (equalization_LUT, 'cdfs'),
# apply LUT
'applied': (apply_LUT, 'images', 'LUTs'),
# Scale the images, cast dtype, and view the stretched images
# in sequence!
'safe': (ip.image.DisplaySafe(), 'applied'),
'viewing' : (ip.image.CompareView(pause_for=1000), 'images','safe'),
# show graph of hist (red) and eq_hist (blue),
('eq_hists', 'eq_pdfs', 'eq_cdfs'): (compute_stats, 'applied'),
'plotted': (compare_stats, 'hists', 'eq_hists')
}
# Pipeline setup
contrast = ip.Pipeline(tasks)
# Let's grab some example data from the ImagePypelines standard set
images = [ip.image.giza(), ip.image.crowd()]
# And let's see some results!
contrast(images)
.. rst-class:: sphx-glr-horizontal
*
.. image:: /examples/imaging/images/sphx_glr_contrast_enhancement_001.png
:alt: contrast enhancement
:class: sphx-glr-multi-img
*
.. image:: /examples/imaging/images/sphx_glr_contrast_enhancement_002.png
:alt: contrast enhancement
:class: sphx-glr-multi-img
*
.. image:: /examples/imaging/images/sphx_glr_contrast_enhancement_003.png
:alt: Image1, Image2
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
{'images': [array([[ 76, 90, 97, ..., 81, 82, 78],
[ 86, 98, 102, ..., 83, 80, 78],
[ 92, 100, 102, ..., 83, 81, 84],
...,
[142, 143, 140, ..., 77, 73, 73],
[138, 141, 140, ..., 74, 72, 72],
[133, 138, 138, ..., 73, 73, 74]], dtype=uint8), array([[ 49, 52, 64, ..., 234, 233, 234],
[ 48, 48, 56, ..., 212, 211, 211],
[ 51, 48, 50, ..., 196, 197, 197],
...,
[149, 120, 71, ..., 152, 154, 153],
[143, 118, 72, ..., 154, 157, 154],
[144, 118, 72, ..., 154, 157, 153]], dtype=uint8)], 'cdfs': (array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
7.62939453e-06, 6.86645508e-05, 3.28063965e-04, 9.95635986e-04,
2.88391113e-03, 6.70242310e-03, 1.31950378e-02, 2.27241516e-02,
3.50990295e-02, 5.02586365e-02, 6.70547485e-02, 8.38317871e-02,
1.01882935e-01, 1.18560791e-01, 1.35112762e-01, 1.51741028e-01,
1.68613434e-01, 1.85802460e-01, 2.04189301e-01, 2.22629547e-01,
2.41489410e-01, 2.59677887e-01, 2.77946472e-01, 2.95696259e-01,
3.12442780e-01, 3.28109741e-01, 3.42876434e-01, 3.56536865e-01,
3.68343353e-01, 3.78852844e-01, 3.88496399e-01, 3.96915436e-01,
4.04430389e-01, 4.11140442e-01, 4.17003632e-01, 4.22477722e-01,
4.27440643e-01, 4.31854248e-01, 4.36115265e-01, 4.40158844e-01,
4.44313049e-01, 4.48329926e-01, 4.52354431e-01, 4.56317902e-01,
4.60422516e-01, 4.64336395e-01, 4.68463898e-01, 4.72484589e-01,
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1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
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.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 2.596 seconds)
.. _sphx_glr_download_examples_imaging_contrast_enhancement.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: contrast_enhancement.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: contrast_enhancement.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_