System requirements

Base requirements

Before installing Pose-Trigger, make sure you have set up the following hardware:

  1. A linux computer (we tested on Ubuntu 18.04 LTS)

  2. a 16-bit monochrome video camera from ImagingSource (e.g. refer to the Reference setup specifications).

    Note

    Other Video4Linux2-compliant cameras should also work with a few adjustments in the code, but will require some efforts.

  3. For a faster working of DeepLabCut, NVIDIA graphics board with a large amount of RAM is required.

    Note

    For example, running DeepLabCut on ResNet-50 requires ~10.6 GB of RAM, so we use GeForce RTX 2080 Ti that has 11 GB on-board RAM (refer to the Reference setup specifications).

Requirements for trigger-output generation

In addition to the pose-estimation feature, the trigger-output feature requires the followings:

  1. The trigger-output server (“FastEventServer”).

  2. An output board based on Arduino or its clone.

For Intel 64-bit CPUs, Pose-Trigger comes with the working FastEventServer program; you don’t need to install it manually. For other architectures (e.g. AMD and ARM CPUs), refer to Appendix: Compiling FastEventServer.

Preparation of the Arduino-based output board may be non-trivial. Please refer to Appendix: Preparing an Output Board.

Reference setup specifications

We develop and test Pose-Trigger in the following environment:

Hardware

Reference setup hardware specifications

Part name

Model type

CPU

3.7 GHz Core i7-9700K

RAM

64 GB DDR4-3200

GPU

NVIDIA GeForce RTX 2080 Ti (11 GB RAM)

Camera

ImagingSource DMK 37BUX287

Output board

Arduino UNO, rev. 2 (clone), with arduino-fasteventtrigger

Software

Reference setup software environment

Software

Specification

Operating system

Ubuntu 18.04 LTS

Python environment

Anaconda3, Python 3.7.7

CUDA Toolkit

version 10.1 (through conda)

Tensorflow

version 1.13.1 (tensorflow-gpu package of conda)

DeepLabCut

version 2.1.3

NumPy

version 1.19.1 (through conda)