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Unity-Robotics-Hub/tutorials/pose_estimation/Documentation/quick_demo_train.md

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Data Collection: Quick Demo

If you just want to run the completed project in order to collect your training and validation data this section can help do it.

To learn how to build something like this from scratch, see Part 1 and Part 2 of our tutorial.

Table of Contents

Prerequisites

To follow this tutorial you need to clone this repository even if you want to create your Unity project from scratch.

Note For Windows Users: You need to have a software enabling you to run bash files. One option is to download GIT. During installation of GIT, add GIT Bash to windows context menu by selecting its option. After installation right click in your folder select GIT Bash Here.

  1. Open a terminal and put yourself where you want to host the repository.
git clone https://github.com/Unity-Technologies/Unity-Robotics-Hub.git

  1. Install Unity 2020.2.*.

  2. Open the completed project. To do so, open Unity Hub, click the Add button, and select PoseEstimationDemoProject from the Unity-Robotics-Hub/tutorials/pose_estimation/ folder.

Setup

  1. Open the scene. Go to Assets > Scenes and double click on TutorialPoseEstimation.

  2. The size of the images that will be used for pose estimation depends on a setting in the Game view. Select the Game view and select Free Aspect. Then select the +, with the message Add new item on it if you put your mouse over the + sign. For the Width select 650 and for the Height select 400. A gif below shows you how to do it.

Switching to Data Collection Mode

The completed project is set up for inference mode by default, so we must switch it to data collection mode.

  1. Uncheck the ROSObjects GameObject in the Hierarchy tab to disable it.

  2. On the Simulation Scenario GameObject, check the Fixed Length Scenario component to enable it.

  3. On the Main Camera GameObject, check the Perception Camera (Script) component to enable it.

Data Collection

To get strarted with the data collection, follow the instructions in Part 3: Collect the Training and Validation Data of the tutorial. This section will explain how to set the random seed of the environment, choose how many training data examples you'd like to collect, and get it running.

If you'd like to then move on to training a pose estimation model on the data you've collected, move on to Part 3: Train the Deep Learning Model.

Have fun!