* SYNOPSIS
Detect calibration boards in observed camera images

#+BEGIN_EXAMPLE
$ mrgingham /tmp/image*.jpg

# filename x y level
/tmp/image1.jpg - -
/tmp/image2.jpg 1385.433000 1471.719000 0
/tmp/image2.jpg 1483.597000 1469.825000 0
/tmp/image2.jpg 1582.086000 1467.561000 1
...


$ mrgingham /tmp/image.jpg |
  vnl-filter -p x,y |
  feedgnuplot --domain --lines --points --image /tmp/image.jpg

[ image pops up with the detected grid plotted on top ]


$ mrgingham /tmp/image.jpg |
  vnl-filter -p x,y,level |
  feedgnuplot --domain
              --with 'linespoints pt 7 ps 2 palette'
              --tuplesizeall 3 --image /tmp/image.jpg

[ fancy image pops up with the detected grid plotted on top, detections
  colored by their decimation level ]
#+END_EXAMPLE

* DESCRIPTION
Both chessboard and a non-offset grid of circles are supported. Chessboard are
the /strongly/ preferred choice, since the circles cannot produce accurate
results: we care about the center point, which we are not directly observing.
Thus with closeup and oblique views, the reported circle center and the real
circle center could be very far away from each other. Because of this, more work
was put into the chessboard detector. Use that one. Really.

These are both nominally supported by OpenCV, but those implementations are slow
and not at all robust, in my experience. The implementations here are much
faster and work much better. I /do/ use OpenCV, but only for some core
functionality.

Currently mrgingham looks for a square grid of points, with some
user-requestable width. The default is a 10x10 grid.

** Approach
These tools work in two passes:

1. Look for "interesting" points in the image. The goal is to find all the
   points we care about, in any order. It is assumed that

   - there will be many outliers
   - there will be no outliers interspersed throughout the points we do care
     about (this isn't an unreasonable requirement: areas between chessboard
     corners have a solid color)

2. Run a geometric analysis to find a grid in this set of "interesting" points.
   This will throw out the outliers and it will order the output

If we return /any/ data, that means we found a full grid. The geometric search
is fairly anal, so if we found a full grid, it's extremely likely that it is
"right".

*** Chessboards
This is based on the feature detector described in this paper:
https://arxiv.org/abs/1301.5491

The authors provide a simple MIT-licensed implementation here:
http://www-sigproc.eng.cam.ac.uk/Main/SB476Chess

This produces an image of detector response. /This/ library then aggregates
these responses by looking at local neighborhoods of high responses, and
computing the mean of the position of the points in each candidate neighborhood,
weighted by the detector response.

As noted earlier, I look for a square grid, 10x10 points by default. Here that
means 10x10 /internal corners/, meaning a chessboard with 11 /squares/ per side.
To ensure robust detections, it is recommended to make the outer squares of the
chessboard wider than the inner squares. This would ensure that we see exactly
10 points in a row with the expected spacing. If the outer squares have the same
size, the edge of the board might be picked up, and we would see 11 or 12 points
instead.

A recommended 10x10 pattern can be printed from this file: [[chessboard.10x10.pdf]].
And a recommended 14x14 pattern can be printed from this file:
[[chessboard.14x14.pdf]]. The denser chessboard containts more data, so fewer
observations will be required for convergence of the calibration algorithm. But
a higher-res camera is required to reliably detect the corners.

*** Circles
*This isn't recommended, and exists for legacy compatibility only*

The circle finder does mostly what the first stage of the OpenCV circle detector
does:

1. Find a reasonable intensity threshold
2. Threshold the image
3. Find blobs
4. Return centroid of the blobs

This is relatively slow, can get confused by uneven lighting (although CLAHE can
take care of that), and is inaccurate: nothing says that the centroid of a blob
came from the center of the circle on the calibration board.

** API
The user-facing functions live in =mrgingham.hh=. Everything is in C++, mostly
because some of the underlying libraries are in C++. All functions return a
=bool= to indicate success/failure. All functions put the destination arguments
/first/. All functions return the output points in
=std::vector<mrgingham::PointDouble& points_out>=, an ordered list of found
points. The inputs are one of

- An image filename
- An OpenCV matrix: =cv::Mat& image=
- A set of detected points, that are unordered, and are a superset of the points
  we're seeking

The prototypes:

#+BEGIN_SRC C++
namespace mrgingham
{
    bool find_circle_grid_from_image_array( std::vector<mrgingham::PointDouble>& points_out,
                                            const cv::Mat& image );

    bool find_circle_grid_from_image_file( std::vector<mrgingham::PointDouble>& points_out,
                                           const char* filename );

    bool find_chessboard_from_image_array( std::vector<mrgingham::PointDouble>& points_out,
                                           const cv::Mat& image,
                                           int image_pyramid_level = -1 );

    int  find_chessboard_from_image_file( std::vector<mrgingham::PointDouble>& points_out,
                                          const char* filename,
                                          int image_pyramid_level = -1 );

    bool find_grid_from_points( std::vector<mrgingham::PointDouble>& points_out,
                                const std::vector<mrgingham::Point>& points );
};
#+END_SRC

The arguments should be clear. The only one that needs an explanation is
=image_pyramid_level=:

- if =image_pyramid_level= is 0 then we just use the image as is.

- if =image_pyramid_level= > 0 then we cut down the image by a factor of 2 that
  many times. So for example, level 3 means each dimension is cut down by a
  factor of 2^3 = 8

- if =image_pyramid_level= < 0 then we try several levels, taking the first one
  that produces results

** Applications
There're several included applications that exercise the library.
=mrgingham-...= are distributed, and their manpages appear below.

- =mrgingham= takes in images as globs (with some optional
  manipulation given on the cmdline), finds the grids, and returns them on
  stdout, as a vnlog

- =mrgingham-observe-pixel-uncertainty= evaluates the distribution of corner
  detections from repeated observations of a stationary scene

- =test-find-grid-from-points= takes in a file that contains an unordered set of
  points with outliers. It the finds the grid, and returns it on stdout

- =test-dump-chessboard-corners= is a lower-level tool that just finds the
  chessboard corner features and returns them on stdout. No geometric search is
  done.

- =test-dump-chessboard-corners= similarly is a lower-level tool that just finds the blob
  center features and returns them on stdout. No geometric search is done.

The =mrgingham...= tools are distributed in the package, while the others are
internal.

** Tests
There's a test suite in =test/test.sh=. It checks all images in =test/data/*=,
and reports which ones produced no data. Currently I don't ship any actual data.
I will at some point.

* MANPAGES
** mrgingham
#+BEGIN_EXAMPLE
NAME
    mrgingham - Extract chessboard corners from a set of images

SYNOPSIS
     $ mrgingham /tmp/image*.jpg

     # filename x y level
     /tmp/image1.jpg - -
     /tmp/image2.jpg 1385.433000 1471.719000 0
     /tmp/image2.jpg 1483.597000 1469.825000 0
     /tmp/image2.jpg 1582.086000 1467.561000 1
     ...


     $ mrgingham /tmp/image.jpg |
       vnl-filter -p x,y |
       feedgnuplot --domain --lines --points --image /tmp/image.jpg

     [ image pops up with the detected grid plotted on top ]


     $ mrgingham /tmp/image.jpg |
       vnl-filter -p x,y,level |
       feedgnuplot --domain
                   --with 'linespoints pt 7 ps 2 palette'
                   --tuplesizeall 3 --image /tmp/image.jpg

     [ fancy image pops up with the detected grid plotted on top, detections
       colored by their decimation level ]

DESCRIPTION
    This tool uses the "mrgingham" library to detect chessboard corners from
    images stored on disk. Images are given on the commandline, as globs.
    Each glob is expanded, and each image is processed (possibly in parallel
    if "-j" was given). The output is a vnlog
    <https://www.github.com/dkogan/vnlog> containing the filename,
    coordinates of the chessboard corners and the decimation level used to
    compute each corner. For diagnostics, pass in "--debug". This produces a
    number of self-plotting files that describe the results of the
    intermediate steps. Each diagnostic file is reported on the console when
    it is written.

    Both chessboard and a non-offset grid of circles are supported.
    Chessboard are the *strongly* preferred choice; the circle detector is
    mostly here for compatibility. Both are nominally supported by OpenCV,
    but those implementations are slow and not at all robust, in my
    experience. The implementations here are much faster and work much
    better. I *do* use OpenCV here, but only for some core functionality.

    Currently mrgingham looks for a square grid of points, with some
    user-requestable width. The default is a 10x10 grid.

  Approach
    This tool works in two passes:

    *   Look for "interesting" points in the image. The goal is to find all
        the points we care about, in any order. It is assumed that

        *   there will be many outliers

        *   there will be no outliers interspersed throughout the points we
            do care about (this isn't an unreasonable requirement: areas
            between chessboard corners have a solid color)

    *   Run a geometric analysis to find a grid in this set of "interesting"
        points. This will throw out the outliers and it will order the
        output

    If we return *any* data, that means we found a full grid. The geometric
    search is fairly anal, so if we found a full grid, it's extremely likely
    that it is "right".

   Chessboards
    This is based on the feature detector described in this paper:
    <https://arxiv.org/abs/1301.5491>

    The authors provide a simple MIT-licensed implementation here:
    <http://www-sigproc.eng.cam.ac.uk/Main/SB476Chess>

    This produces an image of detector response. *This* library then
    aggregates these responses by looking at local neighborhoods of high
    responses, and computing the mean of the position of the points in each
    candidate neighborhood, weighted by the detector response.

    As noted earlier, I look for a square grid, 10x10 points by default.
    Here that means 10x10 *internal corners*, meaning a chessboard with 11
    *squares* per side. A recommended pattern is available in
    "chessboard.10x10.pdf" and "chessboard.14fx14.pdf" in the "mrgingham"
    sources.

   Circles
    This isn't recommended, and exists for legacy compatibility only*

    The circle finder does mostly what the first stage of the OpenCV circle
    detector does:

    *   Find a reasonable intensity threshold

    *   Threshold the image

    *   Find blobs

    *   Return centroid of the blobs

    This is relatively slow, can get confused by uneven lighting (although
    CLAHE can take care of that), and is inaccurate: nothing says that the
    centroid of a blob came from the center of the circle on the calibration
    board.

ARGUMENTS
    The general usage is

     mrgingham [--debug] [--jobs N] [--noclahe] [--blur radius]
               [--level l] [--blobs] imageglobs imageglobs ...

    By default we look for a chessboard. By default we apply adaptive
    histogram equalization, then blur with a radius of 1. We then use an
    adaptive level of downsampling when looking for the chessboard.

    The arguments are

    "--noclahe"
        Optional argument to control image preprocessing. Unless given we
        will apply adaptive histogram equalization (CLAHE algorithm) to the
        images. This is *extremely* helpful if the images aren't lit evenly;
        which is most of them.

    "--blur RADIUS"
        Optional argument to control image preprocessing. This will apply a
        gaussian blur to the image (after the histogram equalization). A
        light blurring is very helpful with CLAHE, since that makes noisy
        images. By default we will blur with radius = 1. Set to <= 0 to
        disable

    "--level L"
        Optional argument to control image preprocessing. Applies a
        downsampling to the image (after CLAHE and "--blur", if those are
        given). Level 0 means 'use the original image'. Level > 0 means
        downsample by 2**level. Level < 0 means 'try several different
        levels until we find one that works. This is the default.

    "--jobs N"
        Parallelizes the processing N-ways. "-j" is a synonym. This is just
        like GNU make, except you're required to explicitly specify a job
        count.

        The images are given as (multiple) globs. The output is a vnlog with
        columns "filename","x","y". All filenames matched in the glob will
        appear in the output. Images for which no chessboard pattern was
        found appear as a single record with null "x" and "y".

    "--debug"
        If given, "mrgingham" will dump various intermediate results into
        "/tmp" and it will report more stuff on the console. The output is
        self-documenting

    "--blobs"
        Find circle centers instead of chessboard corners. Not recommended


#+END_EXAMPLE

** mrgingham-observe-pixel-uncertainty
#+BEGIN_EXAMPLE
NAME
    mrgingham-observe-pixel-uncertainty - Evaluate observed point
    distribution from stationary observations

SYNOPSIS
      $ observe-pixel-uncertainty '*.png'
        Evaluated 49 observations
        mean 1-sigma for independent x,y: 0.26

      $ mrcal-calibrate-cameras --observed-pixel-uncertainty 0.26 .....
      [ mrcal computes a camera calibration ]

DESCRIPTION
    mrgingham has finite precision, so repeated observations of the same
    board will produce slightly different corner coordinates. This tool
    takes in a set of images (assumed observing a chessboard, with both the
    camera and board stationary). It then outputs the 1-standard-deviation
    statistic for the distribution of detected corners. This can then be
    passed in to mrcal: 'mrcal-calibrate-cameras
    --observed-pixel-uncertainty ...'

    The distribution of the detected corners is assumed to be gaussian, and
    INDEPENDENT in the horizontal and vertical directions. If the x and y
    distributions are each s, then the LENGTH of the deviation of each pixel
    is a Rayleigh distribution with expected value s*sqrt(pi/2) ~ s*1.25

    THIS TOOL PERFORMS VERY LIGHT OUTLIER REJECTION; IT IS ASSUMED THAT THE
    SCENE IS STATIONARY

OPTIONS
  POSITIONAL ARGUMENTS
      input                 Either 1: A glob that matches images observing a
                            stationary calibration target. This must be a GLOB. So
                            in the shell pass in '*.png' and NOT *.png. These are
                            processed by 'mrgingham' and the arguments passed in
                            with --mrgingham. Or 2: a vnlog representing corner
                            detections from these images. This is assumed to be a
                            file with a filename ending in .vnl, formatted like
                            'mrgingham' output: 3 columns: filename,x,y

  OPTIONAL ARGUMENTS
      -h, --help            show this help message and exit
      --show {geometry,histograms}
                            Visualize something. Arguments can be: "geometry":
                            show the 1-stdev ellipses of the distribution for each
                            chessboard corner separately. "histograms": show the
                            distribution of all the x- and y-deviations off the
                            mean
      --mrgingham MRGINGHAM
                            If we're processing images, these are the arguments
                            given to mrgingham. If we are reading a pre-computed
                            file, this does nothing
      --num-corners NUM_CORNERS
                            How many corners to expect in each image. If this is
                            wrong I will throw an error. Defaults to 100
      --imagersize IMAGERSIZE IMAGERSIZE
                            Optional imager dimensions: width and height. This is
                            optional. If given, we use it to size the "--show
                            geometry" plot


#+END_EXAMPLE

* MAINTAINER
This is maintained by Dima Kogan <dima@secretsauce.net>. Please let Dima know if
something is unclear/broken/missing.
* LICENSE AND COPYRIGHT

This library is free software; you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License as published by the Free
Software Foundation; either version 2.1 of the License, or (at your option) any
later version.

Copyright 2017-2021 California Institute of Technology

Copyright 2017-2021 Dima Kogan (=dima@secretsauce.net=)
