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SYNOPSIS

Detect calibration boards in observed camera images

$ 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 ]

INSTALLATION

As of today (2022-02-27), mrgingham is included in the bleeding-edge versions of Debian and Ubuntu. So if you’re running at least Debian/testing or the not-yet-published Debian 12 (bookworm) or Ubuntu 22.04 (jammy), you can simply

apt install mrgingham libmrgingham-dev

to install the standalone tool and the development library respectively. For older Debian and Ubuntu distros, packages are available in the mrcal repositories. Please see the mrcal installation page.

If running any other distro, you should build mrgingham from source. First, make sure all the build-time dependencies are installed. These are listed in the =debian/control= file:

Build-Depends: ...,
 libopencv-dev,
 libboost-dev,
 pkg-config,
 mawk,
 perl,
 python3-all,
 python3-all-dev,
 python3-numpy

On a Debian-based distro you can:

sudo apt install \
 libopencv-dev   \
 libboost-dev    \
 pkg-config      \
 mawk            \
 perl            \
 python3-all     \
 python3-all-dev \
 python3-numpy

Replace the package names with their analogues on other distros. Once the dependencies are installed, you

make

and then the tool can be invoked with

./mrgingham

DESCRIPTION

Both chessboard and a square 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 here, but only for some core functionality.

Currently mrgingham looks for a square grid of points, with some user-requestable width. Rectangular grids are not supported at this time.

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”.

Once again: the current implementation of mrgingham detects only complete chessboards.

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. A simple tool to generate these .pdf files is included in the mrgingham repository. To make an NxM chessboard figure do make chessboard.NxM.pdf in the mrgingham source tree. N and M must be even integers. Note: today mrgingham requires that N = M=, but eventually this may be lifted, so this tool does not have this requirement.

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.

Output representation

The mrgingham tool produces its output in a vnlog text table. The columns are:

  • filename: path to the image on disk
  • x, y: detected pixel coordinates of the chessboard corner
  • level: image level used in detecting this corner. Level 0 means “full-resolution”. Level 1 means “half-resolution” and that the noise on this detection has double the standard deviation. Level 2 means “quarter-resolution” and so on.

If no chessboard was found in an image, a single record is output:

filename - - -

The corners are output in a consistent order: starting at the top-left, traversing the grid, in the horizontal direction first. Usually, the chessboard is observed by multiple cameras mounted at a similar orientation, so this consistent order is consistent across cameras.

However, if some cameras in the set are rotated, their observed chessboard corners will not be consistent anymore: the first corner will be “top-left” in pixel coordinates for both, which is at the top of the chessboard for rightside-up cameras, but the bottom of the chessboard for upside-down cameras. This situation is resolved with the mrgingham-rotate-corners tool. It post-processes mrgingham output to reorder detections from rotated cameras. See the manpage of that tool for more detail. Eventually the implementation could be extended to be able to uniquely identify each corner, obviating the need for mrgingham-rotate-corners, but we’re not there today.

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:

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 );
};

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. The mrgingham-... tools are distributed, and their manpages appear below. The test-... tools are internal.

  • 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
  • mrgingham-rotate-corners corrects chessboard detections produced by rotated cameras by reordering the points in the detection stream
  • test-find-grid-from-points ingests 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.

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

xxx-manpage-mrgingham-xxx

mrgingham-observe-pixel-uncertainty

xxx-manpage-mrgingham-observe-pixel-uncertainty-xxx

mrgingham-rotate-corners

xxx-manpage-mrgingham-rotate-corners-xxx

MAINTAINER

This is maintained by Dima Kogan <[email protected]>. 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 ([email protected])