mrcal-calibrate-cameras - Calibrate some synchronized, stationary cameras


$ mrcal-calibrate-cameras
    --corners-cache corners.vnl
    --lensmodel LENSMODEL_OPENCV8
    --focal 1700 --object-spacing 0.01 --object-width-n 10
    --outdir /tmp
    'left*.png' 'right*.png'

  ... lots of output as the solve runs ...
  RMS reprojection error: 1.9 pixels
  Worst reprojection error: 7.8 pixels
  Noutliers: 319 out of 17100 total points: 1.9% of the data

  Wrote /tmp/camera0-0.cameramodel
  Wrote /tmp/camera0-1.cameramodel


This tool uses the generic mrcal platform to solve a common specific problem of N-camera calibration using observations of a chessboard. Please see the mrcal documentation at for details.



images                A glob-per-camera for the images. Include a glob for
                      each camera. It is assumed that the image filenames in
                      each glob are of of the form xxxNNNyyy where xxx and
                      yyy are common to all images in the set, and NNN
                      varies. This NNN is a frame number, and identical
                      frame numbers across different globs signify a time-
                      synchronized observation. I.e. you can pass
                      'left*.jpg' and 'right*.jpg' to find images
                      'left0.jpg', 'left1.jpg', ..., 'right0.jpg',
                      'right1.jpg', ...


-h, --help            show this help message and exit
--lensmodel LENSMODEL
                      Which lens model we're using. This is a string
                      "LENSMODEL_....". Required unless we have a --seed.
                      See L<>
                      for notes about how to select a model
--focal FOCAL         Initial estimate of the focal length, in pixels.
                      Required unless --seed is given. See
                      L<> for
                      notes about how to estimate a focal length. This is
                      either a single value to use for all the cameras, or a
                      comma-separated whitespace-less list of values to use
                      for each camera. If such a list is given, it must
                      match the number of cameras being calibrated
                      Size of the imager. This is only required if we pass
                      --corners-cache AND if none of the image files on disk
                      actually exist and if we don't have a --seed. If we do
                      have a --seed, the --imagersize values must match the
                      --seed exactly
--outdir OUTDIR       Directory for the output camera models
--object-spacing OBJECT_SPACING
                      Width of each square in the calibration board, in
--object-width-n OBJECT_WIDTH_N
                      How many points the calibration board has per
                      horizontal side. If omitted we default to 10
--object-height-n OBJECT_HEIGHT_N
                      How many points the calibration board has per vertical
                      side. If omitted, we assume a square object, setting
--seed SEED           A comma-separated whitespace-less list of camera model
                      globs to use as a seed for the intrinsics and
                      extrinsics. The number of models must match the number
                      of cameras exactly. Expanded globs are sorted
                      alphanumerically. This is useful to bootstrap the
                      solve or to validate an existing set of models, or to
                      recompute just the extrinsics or just the intrinsics
                      of a solve. If omitted, we estimate a seed. Exclusive
                      with --focal. If given, --imagersize is omitted or it
                      must match EXACTLY with whatever is in the --seed
--jobs JOBS, -j JOBS  How much parallelization we want. Like GNU make.
                      Affects only the chessboard corner finder. If we are
                      reading a cache file, this does nothing
--corners-cache CORNERS_CACHE
                      Path to the corner-finder results. If this file
                      exists, I use the corners in this file. If it doesn't
                      exist, I invoke mrgingham to compute the corners, and
                      I write the results to that path. And THEN I compute
                      the calibration off those observations. This file is a
                      vnlog with legend "# filename x y level" (exactly what
                      mrgingham reports). Each rown is an observed corners.
                      If an image had no observations, a single row
                      "filename - - -" is expected. The "level" is the
                      decimation level used in detecting that corner. "0"
                      means "full-resolution", "1" means "half-resolution",
                      "2" means "quarter-resolution" and so on. A level of
                      "-" or <0 means "skip this point". This is how
                      incomplete board observations are specified. A file
                      with a missing "level" column will fill in "0" for all
                      corners. A non-mrgingham grid detector may be used by
                      running that separately, and using this option to read
                      the output. A detector may output weights instead of a
                      decimation level in the last column. Pass --corners-
                      cache-has-weights to interpret the data in that way
                      By default the corners we read in --corners-cache have
                      columns "filename x y level". If the last column is a
                      weight instead of a decimation level, pass this
                      option. This is useful to support non-mrgingham
                      chessboard detectors
--pairs               By default, we are calibrating a set of N independent
                      cameras. If we actually have a number of stereo pairs,
                      pass this argument. It changes the filename format of
                      the models written to disk (cameraPAIR-
                      INDEXINPAIR.cameramodel), and will report some
                      uncertainties about geometry inside each pair.
                      Consecutive cameras in the given list are paired up,
                      and an even number of cameras is required
                      By default we apply regularization in the solver in
                      the final optimization. This discourages obviously-
                      wrong solutions, but can introduce a bias. With this
                      option, regularization isn't applied
                      By default we throw out outliers. This option turns
                      that off
                      Keep the seeded extrinsics, if given. Allowed only if
                      Keep the seeded intrinsics, if given. Allowed only if
                      By default we assume the calibration target is
                      slightly deformed, and we compute this deformation. If
                      we want to assume that it is flat, pass this option.
                      For convenience we compute a valid-intrinsics region
                      to describe the results of the calibration. This is a
                      watered-down interpretation of the projection
                      uncertainty that is easy to interpret. The logic
                      computing this is somewhat crude, and may go away in
                      the future. The defaults should be reasonable, so if
                      in doubt, leave these alone. The intent is to produce
                      usable output even if we're using a lean lens model
                      where the computed uncertainty is always overly
                      optimistic. We bin the observations into a grid, and
                      use mrcal._report_regional_statistics() to get the
                      residual statistics in each bin. We then contour the
                      bins to produce the valid-intrinsics region. If we're
                      using a rich lens model (LENSMODEL_SPLINED_...), then
                      we only look at the uncertainty, and not at the other
                      statistics. This argument takes 5 parameters. The
                      uncertainty is computed at a range
                      valid_intrinsics_region_parameters[4]. If <= 0, I look
                      out to infinity. The default is 0. A region is valid
                      only if the projection uncertainty <
                      valid_intrinsics_region_parameters[0] *
                      observed_pixel_uncertainty. The default is 1. A region
                      is valid only if the mean-abs-residuals is <
                      valid_intrinsics_region_parameters[1] (only for lean
                      models). The default is 0.5. A region is valid only if
                      the residuals stdev is <
                      valid_intrinsics_region_parameters[2] *
                      observed_pixel_uncertainty (only for lean models). The
                      default is 1.5. A region is valid only if it contains
                      at least valid_intrinsics_region_parameters[3]
                      observations (only for lean models). The default is 3.
--verbose-solver      By default the final stage of the solver doesn't say
                      much. This option turns on verbosity to get lots of
                      diagnostics. This is generally not very useful to end
--explore             After the solve open an interactive shell to examine
                      the solution



Dima Kogan, <>


Copyright (c) 2017-2023 California Institute of Technology ("Caltech"). U.S. Government sponsorship acknowledged. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); You may obtain a copy of the License at