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https://github.com/elcorto/unfish

Correct fisheye distortions in images using OpenCV
https://github.com/elcorto/unfish

fisheye fisheye-lens-distortion fisheye-undistorting image-processing opencv

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Correct fisheye distortions in images using OpenCV

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README 

        
unfish -- correct fisheye distortions in images using OpenCV



about

-----

This is basically a packaged and polished version of the OpenCV tutorial_ (see

also hack_) with a command line interface. It shows how to correct lens

distortions in images using OpenCV, based on chessboard calibration images

taken with the same camera.



In my case, my mobile phone camera introduces a radial distortion (inverse

fisheye effect), hence the name.



Here is an example of a distorted and corrected image.



.. image:: examples/fish.jpg

   :width: 40%



.. image:: examples/unfish.jpg

   :width: 40%



The script ``bin/unfish`` does all this and a little more::



    usage:

        unfish prep [-f ] (-p  ...)

        unfish calib [-r  -f ] (-p  ...)

        unfish apply [-k ] ...



    commands:

        prep   optional preparation run, create rms_db.json

        calib  calibration run, calculate and write camera matrix and camera model

               coeffs using chessboard calibration images to ./unfish_data

        apply  apply correction model to images, images are written to

               ./corrected_images



    options:

        -p , --pattern-size   size of the chessboard

                (number of corners) in the calibration images, e.g. "9x6"

        -f , --fraction   fraction by which calibration files

                have been scaled down (see bin/resize.sh)

        -r , --max-rms   in calibration, use only files with

                rms reprojection error less than , uses rms_db.json

                written by "prep"

        -k   keep that many path levels from , e.g.

                files = /a/b/c/file1,/a/b/c/file2, and -k2, then store

                ./corrected_images/a/b/fileX instead of ./corrected_images/fileX  [default: 0]



In addition to the tutorial_, we added things like the ability to calculate the

RMS reprojection error per calibration image (``unfish prep``), in order to get

a feeling for the quality of the calibration per image.



workflow

--------



First, you print a chessboard and take a bunch of calibration images with the

affected camera, like this one:



.. image:: examples/calib_pattern.jpg

   :width: 40%



Next, a calibration run will calculate correction parameters (camera matrix and

lens model coefficients, written to ``./unfish_data/``). Finally, you apply the

correction to all affected images. Corrected images are written to

``./corrected_images``.



We found that it is a very good idea to scale down the chessboard calibration

images first. That makes the calibration part *a lot* faster (else the code

which searches for chessboard corners will run forever).



Here is what you need to do, using a 9x6 chessboard.



::



    $ ./bin/resize.sh 0.2 chess_pics/orig chess_pics/small

    $ unfish calib -f 0.2 -p 9x6 chess_pics/small/*

    $ unfish apply affected_pics/orig/*



tips & tricks

-------------



chessboard

    You can grab a 7x7 chessboard image from the `OpenCV repo `_,

    or a 9x6 from `older documentation `_. Remember: NxM are

    the number of *corners*. It's hard to say how many calibration images you

    need to take. We used around 100, but found that 5-10 good images have

    basically the same effect. Also, make sure that the paper with the printed

    chessboard is completely flat when you take photos.



````

    We found that excluding calibration images with a high per-image RMS

    reprojection error (``unfish calib -r  ...``) doesn't actually

    improve the overall calibration, not sure why yet.



.. _tutorial: http://docs.opencv.org/3.3.0/dc/dbb/tutorial_py_calibration.html

.. _hack: https://hackaday.io/project/12384-autofan-automated-control-of-air-flow/log/41862-correcting-for-lens-distortions

.. _chessboard: https://github.com/opencv/opencv/blob/master/samples/data/chessboard.png

.. _chessboard_old: http://docs.opencv.org/2.4/_downloads/pattern.png



install

-------

To let pip install all deps for you::



    $ git clone ...

    $ pip3 install -e .