DeepLabCut-Utils - Community contributions#
The DeepLabCut-Utils repository contains various scripts as well as links to other packages related to DeepLabCut. Feel free to contribute your own analysis methods, and perhaps some short notebook of how to use it. Thanks!
DLC-Utils
Caution
Please direct inquiries to the contributors/code maintainers of that code. Note that the software(s) are provided “as is”, without warranty of any kind, express or implied. The DeepLabCut team is not responsible for the maintenance of these packages, and cannot guarantee that they will work with present & future versions of DeepLabCut.
Example scripts for scaling up your DLC analysis & training#
These two scripts illustrate how to train, test, and analyze videos for multiple projects automatically (scale_training_and_evaluation.py) and how to analyze videos that are organized in subfolders automatically (scale_analysis_oversubfolders.py). Feel free to adjust them for your needs!
Contributed by Alexander Mathis
Using DLC + XROMM_DLCTools + xmalab#
Contributed by @homfunc
The DeepLabCut repository can also be used as a sibling checkout together with:
../XROMM_DLCTools../xmalab
In that local layout, XROMM_DLCTools uses the optional dlc dependency group to import this checkout directly, and its baseline harness runs both a DeepLabCut smoke scenario and a broader end-to-end local workflow integration scenario.
See XROMM + DeepLabCut local integration for the local integration notes and exact commands.
Using your DLC outputs, loading, simple ROI analysis, visualization examples#
Time spent of a body part in a particular region of interest (ROI)#
You can compute time spent in particular ROIs in frames. This demo Jupyter Notebook shows you how to load the outputs of DLC and perform the analysis (plus other plotting functions):
Contributed by Federico Claudi and Jupyter Notebook from Alexander Mathis
DeepLabCut-Display GUI#
Open and view data to understand pose estimation errors and trends. Filter data by likelihood threshold.
Contributed by Jacob Shirey
A GUI-based ROI tool for time spent of a body part in a defined region of interest#
Contributed by Harry Carey
Linear transformation and scaling of DLC output data (transform_and_scale)#
This package is designed for anyone who wants to know where a tracked marker is within a reference frame (i.e. behavioral context). DeepLabCut outputs coordinates in relation to the field of view of the recorded video. With this tool, these coordinates can be linearly transformed and scaled to the reference frame of the behavioral context, meaning that the output coordinates are distances [cm] to a corner of the behavioral context, instead of distances [px] to a corner of the video field of view.
Contributed by Michael Schellenberger
Clustering tools (using the output of DLC)#
Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion#
Behavior clustering with MotionMapper#
Adapted from MotionMapper
Contributed by Mackenzie Mathis
Behavior clustering with B-SOiD#
B-SOiD is an open source unsupervised algorithm for discovery of spontaneous behaviors, and you can use the outputs of DLC to feed directly into B-SOiD in MATLAB.
Machine-learning helper packages (using the output of DLC)#
Behavior analysis with machine learning in R (ETH-DLCAnalyzer)#
Deep learning based behavioral analysis enables high precision rodent tracking and is capable of outperforming commercial solutions. Oliver Sturman, Lukas von Ziegler, Christa Schläppi, Furkan Akyol, Benjamin Grewe, Johannes Bohacek
Behavior analysis with machine learning classifiers (SimBA)#
A pipeline for using pose estimation (i.e. DeepLabCut) then behavioral annotation and generation of supervised machine-learning-based classifiers. <– you can use the outputs of DLC to feed directly into SimBA (in Python).
Code written by: Simon Nilsson (please direct use questions to Simon).
3D DeepLabCut helper packages#
A wrapper package for DeepLabCut 2.0 for 3D videos (anipose)#
Maintainer: Pierre Karashchuk
3D reconstruction with EasyWand/Argus DLT system with DeepLabCut data#
Written by Brandon Jackson, post our DLC workshop in Jan 2020:
A small set of utilities that allow conversion between the data storage formats of DeepLabCut (DLC) and one of the DLT-based 3D tracking systems: either Ty Hedrick’s DigitizingTools in MATLAB, or the Python-based Argus. These functions should allow you to use data previously digitized in a DLT system to create the files needed to train a DLC model, and to import DLC-tracked points back into a DLT 3D calibration to reconstruct 3D points.
Pupil Tracking#
From Tom Vaissie - tvaissie@scripps.edu
Please see the README.txt file for details; this code makes the video in case study 7 http://www.mousemotorlab.org/deeplabcut/.
Using DeepLabCut for USB-CGPIO feedback#
Maintainer: Brandon Forys
Legacy utility functions (no longer required in DLC 2+)#
Warning
These utilities are marked as legacy and are no longer required in DLC 2+.
DLC 1 to DLC 2 conversion code#
This code allows you to import the labeled data from DLC 1 to DLC 2 projects. Note, it is not streamlined and should be used with care.
Contributed by Alexander Mathis
Running project created on Windows on Colaboratory#
Note
UPDATE: as of DeepLabCut 2.0.4 onwards you no longer need to use this code! You can simply create the training set on the cloud and it will automatically convert your project for you.
This solves a path problem when creating a project and annotating data on Windows (see issue #172). This functionality will be included in a later version of DLC 2 (DONE!)
Usage: change in lines 70 and 71 of convertWin2Unix.py
basepath='/content/drive/My Drive/DeepLabCut/examples/'
projectname='Reaching-Mackenzie-2018-08-30'
then run this script on Colaboratory after uploading your labeled data to the drive. Thereby it will be converted to Unix format, then create a training set (with DeepLabCut) and proceed as usual…
Contributed by Alexander Mathis