Calibration in Braid

What is a calibration?

In our usage here, we refer to a calibration as a model of a camera which allows us to compute the 2D image location of a given 3D point. Braid can use calibrations of multiple cameras to calculate the 3D position of a given point when viewed in 2D camera images.

For a given camera, the calibration is divided into two parts. The "extrinsics", or extrinsic parameters, define the pose of the camera. This is the 3D position and orientation of the camera. The "intrinsics", or intrinsic parameters, define the projection of 3D coordinates relative to the camera to an image point in pixels. In Braid, the camera model is a pinhole model with warping distortion. The intrinsic parameters include focal length, the pixel coordinates of the optical axis, and the radial and tangential parameters of a "plumb bob" distortion model (also called the Brown-Conrady distortion model).

XML calibration files in Braid

The XML calibration files used in Braid are backwards-compatible with those from Braid's predecessor, Flydra. Several of the steps described here also use tools from Flydra.

Step 0: setup cameras (zoom, focus, aperture, gain) and lights

Setup camera position, zoom, focus (using an object in the tracking volume) and aperture (slightly stopped down from wide-open). Exposure times and gains are set in Strand Cam for each camera individually. Note that if you intend to run at 100 frames per second, exposure times must be less than 10 milliseconds. These settings (exposure time and gain) are (unfortunately) currently not saved in any file, and can be set only in the camera settings GUI (in the browser). The camera keeps these values persistently when it is on, but if it has been power cycled, it will reset to new values.

Try to a luminance distribution which extends across the entire dynamic range of your sensor (from intensity values 0 to 255) with very little clipping.

Methods for calibration

The "traditional" calibration method, inherited from flydra, uses the MultiCamSelfCal MCSC library.

Other methods of calibration are in development. For example, the Braid April Tag Calibration Tool. There is also a tutorial Jupyter notebook for a manual approach involving April Tags. These April Tag approaches may be particularly interesting in a setting where Braid is used for tracking in a Virtual Reality setup where computer displays can be used to show April Tags.

MCSC Method, Step 1: run "Checkerboard Calibration" to get the camera intrinsic parameters

(There is a script to draw checkerboards as SVG files: draw_checkerboard_svg.py.)

In Strand Cam, there is a region called "Checkerboard Calibration" which allows you to calibrate the camera intrinsic parameters. Show a checkerboard to the camera. You must enter your the checkerboard parameters into the user interface. For example, a standard 8x8 checkerboard would have 7x7 corners. Try to show the checkerboard at different distances and angles. Do not forget to show the checkerboard corners in the corners of the camera field of view. There is a field which shows the number of checkerboard collected - this should increase as the system detects checkerboards. When you have gathered a good set of (say, at least 10) checkerboards, click the "Perform and Save Calibration" button. The results of this calibration are saved to the directory $HOME/.config/strand-cam/camera_info.

Repeat this for all cameras before proceeding to the next step.

MCSC Method, Step 2: collect calibration data and run MultiCamSelfCal (MCSC)

To calibrate Braid, we use MultiCamSelfCal. This is a software package which takes simultaneously captured images of an LED moving through a volume to create a single calibration for all cameras.

Acquire a dataset for MultiCamSelfCal

Room lights should be off.

Synchronize your cameras and start saving data and begin to collect data by moving a LED in the arena (try move the LED in the whole arena volume, also turn it off and on sometimes to validate synchronization). When you are done, stop saving.

Convert `.braidz` file to flydra mainbrain `.h5` file

Note: from here in the document, there are many commands used from Flydra. While Braid itself runs without needing Flydra in any way, performing calibration and other data analysis steps currently requires the use of Flydra. Specifically, the flydra_analysis package is required. Please see here for instructions about Flydra installation.

By converting from .braidz to a Flydra mainbrain .h5 file, you can use the wide range of analysis programs from flydra.

For example, let's say you have the file 20190924_161153.braidz saved by the Braid program. We will use the script convert_braidz_to_flydra_h5.py to do this conversion:

python ~/src/strand-braid/strand-braid-user/scripts/convert_braidz_to_flydra_h5.py --no-delete 20190924_161153.braidz

Upon success, there will be a new file saved with the suffix .h5. In this case, it will be named 20190924_161153.braidz.h5.

We can do the above but making use of bash variables to save typing later BRAIDZ_FILE.

BRAIDZ_FILE=20190924_161153.braidz
DATAFILE="$BRAIDZ_FILE.h5"
python ~/src/strand-braid/strand-braid-user/scripts/convert_braidz_to_flydra_h5.py --no-delete $BRAIDZ_FILE

Note that this conversion requires the program compute-flydra1-compat (this should be installed with Strand Camera and Braid by default and is part of the the braid-offline rust crate) to be on your path if you are converting 3D trajectories.

Run MultiCamSelfCal on data collected with Braid

You can collect data and calibrate similar to the description for Flydra

If your mainbrain .h5 file is in the location $DATAFILE, you can run the MultiCamSelfCal program on this data to generate a multiple camera calibration.

flydra_analysis_generate_recalibration --2d-data $DATAFILE --disable-kalman-objs $DATAFILE --undistort-intrinsics-yaml=$HOME/.config/strand-cam/camera_info  --run-mcsc --use-nth-observation=4

This will print various pieces of information to the console when it runs. First it will print something like this:

851 points
by camera id:
 Basler_40022057: 802
 Basler_40025037: 816
 Basler_40025042: 657
 Basler_40025383: 846
by n points:
 3: 283
 4: 568

This means that 851 frames were acquired in which 3 or more cameras detected exactly one point. The contribution from each camera is listed. In this example, all cameras had between 657 and 846 points. Then, the number of points detected by exactly 3 cameras is shown (283 such frames) and exactly 4 cameras (568 frames).

Important things to watch for are listed here. These may be useful, but are rather rough guidelines to provide some orientation:

No camera should have very few points, otherwise this camera will not contribute much to the overall calibration and will likely have a bad calibration itself.
The number of points used should be somewhere between 300 and 1000. Fewer than 300 points often results in poor quality calibrations. More than 1000 points results in the calibration procedure being very slow. The --use-nth-observation command line argument can be used to change the number of points used.

After running successfully, the console output should look something like this:

********** After 0 iteration *******************************************
RANSAC validation step running with tolerance threshold: 10.00 ...
RANSAC: 1 samples, 811 inliers out of 811 points
RANSAC: 2 samples, 811 inliers out of 811 points
RANSAC: 2 samples, 762 inliers out of 762 points
RANSAC: 1 samples, 617 inliers out of 617 points
811 points/frames have survived validations so far
Filling of missing points is running ...
Repr. error in proj. space (no fact./fact.) is ...  0.386139 0.379033
************************************************************
Number of detected outliers:   0
About cameras (Id, 2D reprojection error, #inliers):
CamId    std       mean  #inliers
  1      0.41      0.41    762
  2      0.33      0.33    811
  3      0.49      0.41    617
  4      0.33      0.37    811
***************************************************************
**************************************************************
Refinement by using Bundle Adjustment
Repr. error in proj. space (no fact./fact./BA) is ...  0.388949 0.381359 0.358052
2D reprojection error
All points: mean  0.36 pixels, std is 0.31

finished: result in  /home/strawlab/20190924_161153.braidz.h5.recal/result

Important things to watch for:

There should only be one iteration (numbered 0).
The mean reprojection error should be low. Certainly less than 1 pixel and ideally less than 0.5 pixels as shown here.
The reprojection error should be low across all cameras.

The above example calibration is a good one.

Convert your new calibration to an XML file which can be used by Braid

Convert this to XML:

flydra_analysis_calibration_to_xml ${DATAFILE}.recal/result > new-calibration-name.xml

You may now use this new calibration, saved as an XML file, as the calibration for Braid. Specify the filename of your new XML file as cal_fname in the [mainbrain] section of your Braid configuration .toml file.

With the new calibration, perform offline tracking the data used to calibrate.

Now you have a working calibration, which is NOT aligned or scaled to any coordinate system, but an (undefined) coordinate system that from MCSC code picked. We can use this calibration to do tracking, although in general having correct scaling is important for good tracking. The reason correct scaling is important for good quality tracking is because Braid tracks using a dynamic model of movement in which maneuverability is parameterized and tracking performance is best when the actual maneuverability statistics match the expected statistic.

So, using the calibration from above, perform 3D tracking of the data with:

flydra_kalmanize ${DATAFILE} -r ${DATAFILE}.recal/result

You can view these results with:

DATAFILE_RETRACKED=`python -c "print('${DATAFILE}'[:-3])"`.kalmanized.h5
flydra_analysis_plot_timeseries_2d_3d ${DATAFILE} -k ${DATAFILE_RETRACKED} --disable-kalman-smoothing

Align your new calibration using a GUI

Now we will take our existing "unaligned" calibration, and despite the scaling and alignment issue, track some points so that we have 3D coordinates. We will use these 3D coordinates to "align" our calibration -- to adjust its scaling, rotation, and translation to arrive at a final calibration which outputs coordinates in the desired frame.

Now, using the unaligned calibration from above, collect a dataset which outlines the geometry of your arena or other key 3D points which we serve as reference 3D points used to discover the correct alignment. We will use these unaligned 3D points output from the unaligned calibration to determine an alignment and scaling used to transform these 3D points to the correct, "aligned" 3D points. These alignment parameters will be used to update the original unaligned calibration into the final aligned calibration.

The easiest way to acquire unaligned 3D points is to acquire a new dataset directly by running Braid with the new unaligned calibration. Alternatively, one can use a pre-existing 2D dataset and re-track it with flydra_kalmanize as above. We will call this dataset for alignment ${NEW_TRACKED_DATA}.

With this new dataset for alignment, we render the 3D tracks with a 3D model of some pre-specified geometry in the correct coordinate frame. We then adjust the alignment parameters by hand in a GUI. Here we align our newly tracked data in file ${NEW_TRACKED_DATA} against the sample_bowl.xml file from here. In this example, the sample_bowl.xml file outlines a circle with diameter 0.33 meters on the Z=0 plane. In this example, we tracked an LED outlining a circle of diameter 0.33 meters and on the plane which will be Z=0 in our final coordinate frame. Here is how to run the GUI program for running the alignment:

flydra_analysis_calibration_align_gui --stim-xml ~/src/flydra/flydra_analysis/flydra_analysis/a2/sample_bowl.xml ${NEW_TRACKED_DATA}

Automatic alignment of calibrations

As an alternative to using the GUI to perform alignment, it is possible to find the correct alignment automatically. The flydra program flydra_analysis_align_calibration does this. The major mathematical operations are performed in the estsimt function in flydra_core.align.

(TODO: Write an example doing this.)

Calibration with water

As described here, Braid can track objects in water. To enable this, place the string <water>1.333</water> in the XML camera calibration file.

User's Guide for Braid and Strand Camera