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This is a library for finding calibration boards in observed camera images:

$ mrgingham --chessboard --clahe --level 1 /tmp/image*.jpg

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

$ mrgingham --chessboard --clahe --level 1 /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 ]


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 a 10x10 grid of points is hard-coded into the implementation. Talk to Dima, if this is a problem for you.


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”.


This is based on the feature detector described in this paper:

The authors provide a simple MIT-licensed implementation here:

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 hard-coded 10x10 grid. Here that means 10x10 internal corners, meaning an 11x11 chessboard. It probably doesn’t matter, but if the outer squares have a different width than the inner squares, the detector is less likely to fail. This would ensure that we see exactly 10 points in a row with the expected spacing, not 12. I haven’t tried with an even 10x10 board, so I don’t know if this is a real issue.

The recommended pattern can be printed from this file: chessboard.pdf


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.


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 );

    bool 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


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.


There’s a test suite in test/ 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.







This is maintained by Dima Kogan <>. Please let Dima know if something is unclear/broken/missing.


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 2017-2018 California Institute of Technology

Copyright 2017-2018 Dima Kogan (