This is the online manual for DLTdv8.  At the moment this consists mostly of a series of tutorial videos (below).  To follow along with the tutorial you’ll need the DLTdv8_tutorial_data.zip file. If you want to test out using trained Deep Learning networks with DLTdv8 but can’t or don’t want to train them yourself you can download pre-trained tutorial networks and import them using the Network Manager while following along with the tutorial.

Contents:

List of menu commands with brief explanations

Tips for working with the “compiled” version of DLTdv8

Tutorial videos

2D and 3D tracking

Interactively tracking a point with a deep learning network

Tracking multiple points with a deep learning network

Tracking multiple points in multiple videos with a deep learning network

Re-training a network with data from multiple videos

 

Menu commands

Project

New – active only when DLTdv is first started, creates a new project beginning with video selection

Load – active only when DLTdv is first started, load a previously saved project

Save – save the current project as a MATLAB MAT file that can later be loaded with the “Load” command

Quit – quits DLTdv

Points

Export – save the current 2D and 3D points as text files, either comma-separated spreadsheet format or tab-separated sparse array format

Load – loads 2D and 3D points from text files in either of the Export formats. Useful for pulling data in from other programs

3D

Load new DLT coefficients – Brings up a dialogue to load a new set of DLT coefficients; the DLT coefficients encode the position and orientation of the cameras in 3D space as well as their lens properties, so loading new coefficients means changing the 2D –> 3D calibration

Recompute 3D points – Forces DLTdv to recompute all 3D coordinates from the current 2D coordinates and DLT coefficients. It is possible (although unlikely) for the 3D coordinates to get out of sync with the 2D coordinates, especially if you have re-created a project by loading exported points.

Start calibration tool – Starts the DLTcal8 interface, a reduced form of the DLTdv interface optimized for computing DLT coefficients from manually identified image points and a set of known 3D points, i.e. a calibration frame.

Flip DLT coefficients – “Flips” the DLT coefficients such that coefficients from DLTdv7 and earlier will work with DLTdv8.  DLTdv8 puts the image coordinate system in the upper left corner for better compatibility with computer vision workflows; DLTdv7 and earlier used the lower left corner.

Deep Learning

Manage datastores – brings up the datastore management sub-interface where you can create/import/export and otherwise manage datastores. Datastores are snapshots of all or part of a combined digitized point set and video and are used for training Deep Learning neural networks.

Manage networks – brings up the deep learning network management sub-interface where you can create/import/export/train deep learning networks using datastores created as part of the current project or imported from other projects.

Apply network – brings up the deep learning application sub-interface where you can use deep learning networks to find landmarks in one or more of the videos in this project.

Check graphics card – checks for a CUDA enabled graphics card. If none is found it will report that no GPU is detected, otherwise it will report the specifications for the card.

Help

List of mouse and keyboard commands – brings up a separate window with a formatted list of all the mouse and keyboard commands available in DLTdv

Open online manual – opens this page in a web browser

Version – displays the DLTdv8 version

Check for a new version – checks to see if you’re using the latest version by comparing the internal version number to a file on http://hedricklab.bio.unc.edu

License – displays the license information for DLTdv8 (the BSD 3-clause open source license). Note that “compiled” MATLAB packages are not compatible with the GPL since the binary distribution does not include all the source code necessary, e.g. the source code for MATLAB itself.

 

Tips for working with the “compiled” DLTdv8

The compiled version of DLTdv8 uses the MATLAB runtime, a sort of hidden MATLAB install that does not allow direct user interaction but can run specific MATLAB programs such as DLTdv8.  Unfortunately, on MacOS the MATLAB runtime does not create a display window to show any of the messages or errors that would normally appear on the MATLAB console.  If you find the DLTdv8.app icon and click on it you’ll start DLTdv8, but won’t be able to see some informational messages, especially those related to deep learning network training.  However, if you start DLTdv8 using the MacOS Terminal, the messages will appear there. Here’s how:

start Terminal, input the following command and press enter:

/Applications/DLTdv8a/application/run_DLTdv8a.sh /Applications/MATLAB/MATLAB_Runtime/v97/

 

Video Tutorials

2D and 3D tracking:

 

Interactively tracking a point with a deep learning network:

 

Tracking multiple points with a deep learning network

Tracking multiple points in multiple videos with a deep learning network

 

Re-training a network with data from multiple videos