colour-science / colour-demosaicing

CFA (Colour Filter Array) Demosaicing Algorithms for Python

Colour - Demosaicing

A Python package implementing various CFA (Colour Filter Array) demosaicing algorithms and related utilities.

It is open source and freely available under the BSD-3-Clause terms.

Table of Contents

Features

The following CFA (Colour Filter Array) demosaicing algorithms are implemented:

• Bilinear
• Malvar (2004)
• DDFAPD - Menon (2007)

Examples

Various usage examples are available from the examples directory.

User Guide

Installation

Because of their size, the resources dependencies needed to run the various examples and unit tests are not provided within the Pypi package. They are separately available as Git Submodules when cloning the repository.

Primary Dependencies

Colour - Demosaicing requires various dependencies in order to run:

• python >= 3.9,
• colour-science >= 4.3
• imageio >= 2,
• numpy >= 1.22,
• scipy >= 1.8,

Pypi

Once the dependencies are satisfied, Colour - Demosaicing can be installed from the Python Package Index by issuing this command in a shell:

pip install --user colour-demosaicing

The overall development dependencies are installed as follows:

pip install --user 'colour-demosaicing[development]'

Contributing

If you would like to contribute to Colour - Demosaicing, please refer to the following Contributing guide for Colour.

Bibliography

The bibliography is available in the repository in BibTeX format.

API Reference

The main technical reference for Colour - Demosaicing is the API Reference.

Code of Conduct

The Code of Conduct, adapted from the Contributor Covenant 1.4, is available on the Code of Conduct page.

Contact & Social

The Colour Developers can be reached via different means:

• Email
• Facebook
• Github Discussions
• Gitter
• Twitter

About

Colour - Demosaicing by Colour Developers
Copyright 2015 Colour Developers – [email protected]
This software is released under terms of BSD-3-Clause: https://opensource.org/licenses/BSD-3-Clause
https://github.com/colour-science/colour-demosaicing

colour-demosaicing's Issues

Colour shift issue for with Malvar (2004) demosaicing.

Hi,
I met a problem that using Malvar04 can not convert white space correctly.
.
The left image is the originally space image, the middle image is the bayer image, the right is the output of the Malvar04. Can you help me to solve this problem?
Also, the result of the bluid-in code in matlab works fine. Matlab results:

To note that, the bayer images are the same in matlab and python.

Git fatal message upon import.

Getting the following message upon importing:

fatal: Not a git repository (or any of the parent directories): .git

Started ~ a month ago.

Why is brightness changing when using the Malvar algorithm?

Hi,
When using the Malvar algorithm I get an output image which is much brighter than the original input image.
This change is different for different images. What is the reason this happens?

Is "RCCB" pattern supported?

Does current code support RCCB bayer pattern?

Thanks,

Visible clipping artifacts in highlights with Malvar (2004) and Menon (2007).

My apologies ahead of time for not posting images as I am uncertain whether I have the appropriate rights.

I just tried demosaicing_CFA_Bayer_Malvar2004() and demosaicing_CFA_Bayer_Menon2007() on the standard MATLAB test image "mandi.tif" (see https://www.mathworks.com/help/images/ref/demosaic.html).

With the Malvar method, I see distinctive speckles of pure red and blue in the output near highlights. Besides that, the results are basically identical to that of MATLAB.

With the Menon method, there are pure red, green, and blue everywhere.

I suspect these are some bugs in the way overflow and underflow are handled.

How to save the image correctly?

hi
thanks for this lib. I have a question about saving img.
I can use the plot to plot a correct color img, however, I don't know how to save it correctly. The color in the saved img is quite different from the plotting. Will you please show a example of img saving?

Sphinx documentation API section is empty.

The primary README points an API reference in Sphinx. However if one goes there, there is only a list of packages and modules, with no actual content. Is something missing?

How to make demosaicing function return RGB values in [0,1] range?

Hi, @KelSolaar , thanks a lot for the lib.
I noticed that the demosaicing function does not return the output in [0,1] range, how can I make it in [0,1] range?

def demosaicing_CFA_Bayer_bilinear(CFA, pattern='RGGB'):
Notes
    -----
    -   The definition output is not clipped in range [0, 1] : this allows for
        direct HDRI / radiance image generation on *Bayer* CFA data and post
        demosaicing of the high dynamic range data as showcased in this
        `Jupyter Notebook <https://github.com/colour-science/colour-hdri/\
blob/develop/colour_hdri/examples/\
examples_merge_from_raw_files_with_post_demosaicing.ipynb>`__.

Error when using colour import on Win10 Python 3.7.9

Hi,
Trying to use your library for demosaic RAW16 -> RGB images and for some reason getting 'The specified module could not be found', despite the fact that installed all prerequisites listed and VC code can see colour package as locally installed, any help will be appreciated.

Investigate potential overflows in the complex demosaicing definitions.

I am using this with a point grey camera, and I get some weird artifacts while using the demosaicing_CFA_Bayer_DDFAPD, demosaicing_CFA_Bayer_Malvar2004, and demosaicing_CFA_Bayer_Menon2007 methods.
The method demosaicing_CFA_Bayer_bilinear doesn't seem to have that problem.

They look like these blue dots. I am thinking that the colors are overflowing the byte.

Question regarding Cok (1987) "Constant Hue-Based Interpolation" method.

Hello, so glad to see your repo here, recently I also try to study demosaicing and I have a question about constant hue - based interpolation. If you have time, can you take a look here, thank you so much.

Default convolution mode introduces edge effects

The default image extension mode used in scipy.ndimage.convolve is 'reflect', which reflects the image about the edge of the pixels. This introduces artefacts in the edge of the resulting RGB layers when demosaicing:

# Constant ones as image
orig = np.ones((2,2)) 
R, G, B = demosaicing_CFA_Bayer_bilinear(orig)

# R, G and B should also be constant 1 (at least in the measured pixels), but instead
# R: [[2.25, 0.75], G: [[0.5, 1.5], B: [[0.25, 0.75],
#     [0.75, 0.25]]     [1.5, 0.5]]     [0.75, 2.25]]

Passing mode='mirror' - which reflects about pixel centers (doesn't repeat the edge values) - to convolve in demosaicing_CFA_Bayer_bilinear returns the expected values. I figure this should be the default for demosaicing?

Menon demosaicing optimization.

The convolutions can be optimized in the referenced code snippet. Two convolutions are sufficient using h0 + h1 as filter.
https://github.com/colour-science/colour-demosaicing/blob/develop/colour_demosaicing/bayer/demosaicing/menon2007.py#L113-L114

Demosaicing does not return clipped values

Demosaicing a normalized bayer image with float values between 0 and 1 using the demosaicing_CFA_Bayer_Malvar2004 method does not return a clipped image. This should either be mentioned in the documentation or demosaicing methods should finish with an appropriate clipping step since without noticing this behaviour the resulting PSNRs are significantly worse.

The dev dependency `pytest-xdist` is always installed

Description

The dependency pytest-xdist is not listed in the extra development and is always installed since version 0.2.2. This brings even more dependencies not needed at runtime.

Code for Reproduction

No response

Exception Message

No response

Environment Information

No response

[BUG]: Exit without throwing an exception

Description

Hey folks,

I am a frequent user of your library and made it even part of my own project PlenoptiCam, where I recently encountered a problem, which I believe is due to the colour-demosaicing software library. Raw images passed to PlenoptiCam are relativey large in terms of their spatial resolution, thus requiring a large amount of memory. I observed that occasionally my software quit at some point without throwing an error or exception. When debugging, I noticed that this behaviour occurs at a point of my pipeline where the rather costly demosaicing_CFA_Bayer_Menon2007 function is called. I attempted to reproduce the problem with a synthetic image having a very large image resolution and observed the same problem: program abort without any message. Below you can find a short code example causing this issue on my machine.

While it is obvious why this error occurs, I would like to have a little control about the case when it happens. Would it be possible to throw an exception just before the function call crashes? This would allow users to keep programs running and let them know about the cause of the problem.

Code for Reproduction

import numpy as np
from colour_demosaicing import demosaicing_CFA_Bayer_Menon2007

mockup_image = np.ones([int(1e4), int(1e4)])
res = demosaicing_CFA_Bayer_Menon2007(CFA=mockup_image, pattern="RGGB", refining_step=True)

Exception Message

An Exception would be desired, but unfortunately there is no.

Environment Information

===============================================================================
*                                                                             *
*   Interpreter :                                                             *
*       python : 3.8.10 (default, Mar 15 2022, 12:22:08)                      *
*                [GCC 9.4.0]                                                  *
*                                                                             *
*   colour-science.org :                                                      *
*       colour : 0.4.1                                                        *
*       colour-demosaicing : 0.2.1                                            *
*                                                                             *
*   Runtime :                                                                 *
*       imageio : 2.16.1                                                      *
*       matplotlib : 3.5.1                                                    *
*       networkx : 2.7.1                                                      *
*       numpy : 1.22.3                                                        *
*       scipy : 1.8.0                                                         *
*                                                                             *
===============================================================================

Is "colour-demosaicing" available in Anaconda?

I would like to use colour_demosaicing in my python script as follows:
from colour_demosaicing import demosaicing_CFA_Bayer_bilinear as demosaic
I installed colour-science with conda install -c conda-forge colour-science but I get the following error:

Traceback (most recent call last):
  File "robotcar.py", line 12, in <module>
    from colour_demosaicing import demosaicing_CFA_Bayer_bilinear as demosaic
ImportError: No module named colour_demosaicing

Is there any other package library I should install in conda env to import colour_demosaicing ?