How to Set Up and Use VATIC: Your Guide to Video Annotation

Video annotation is essential for many computer vision projects, but it can be a daunting task if you don’t have the right tools. Thankfully, VATIC (Video Annotation Tool for Interactive Crowdsourced annotation) is an excellent option. Let’s explore how to install and use VATIC effectively!

Installation of VATIC

VATIC has been optimized for Ubuntu with an Apache 2.2 HTTP server and a MySQL server. Here’s a step-by-step process to get you started:

1. Download VATIC

Begin by downloading the installation script:

$ wget http://mit.edu/vondrick/vatic/vatic-install.sh

Next, make it executable and run it:

$ chmod +x vatic-install.sh

$ ./vatic-install.sh

• Change to the vatic directory:

$ cd vatic

2. Configure the HTTP Server

Next, open the Apache configuration file located at:

/etc/apache2/sites-enabled/000-default

Replace its contents with the code below, customizing as necessary:

WSGIDaemonProcess www-data
WSGIProcessGroup www-data

    ServerName vatic.domain.edu
    DocumentRoot /path/to/vatic/public
    WSGIScriptAlias /server /path/to/vatic/server.py
    CustomLog /var/log/apache2/access.log combined

Don’t forget to enable the headers module and restart Apache:

$ sudo cp /etc/apache2/mods-available/headers.load /etc/apache2/mods-enabled
$ sudo apache2ctl graceful

3. SQL Server Configuration

For better performance, create a dedicated database:

$ mysql -u root
mysql> create database vatic;

4. Setup Configuration

In the vatic directory, copy and edit the configuration file:

$ cp config.py-example config.py

Edit config.py and input your Amazon Mechanical Turk credentials and other settings.

Using VATIC for Video Annotation

Once you’ve installed VATIC, you can start annotating videos. However, there’s a process. Imagine you are a chef preparing a meal. First, you gather your ingredients (video frames), then you cook (annotate them), and finally, you plate (export the annotations) to serve to your guests (your project!). Now, let’s dig deeper into how to use it:

Frame Extraction

Before using VATIC for annotation, you need to extract frames from your video file. Use the following command:

$ mkdir /path/to/output_directory
$ turkic extract /path/to/video.mp4 /path/to/output_directory

This will create JPEG frames that VATIC can process.

Importing a Video

To import the extracted frames for annotation, use:

$ turkic load identifier /path/to/output_directory Label1 Label2...

Here, customize the identifier and Label fields to match your annotation categories (e.g., Person, Car).

Troubleshooting Common Issues

If you encounter problems during installation or while using the tool, consider the following troubleshooting steps:

• Ensure that all dependencies are correctly installed.
• Double-check the paths specified in the Apache configuration.
• Make sure your database is correctly set up and the connection string in config.py is accurate.
• If you receive errors related to Mechanical Turk, and you are not using it, you may ignore them.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Setting up VATIC is a structured process, similar to establishing a well-organized kitchen before cooking. Through proper installation and configuration, you can harness its power to create extensive video datasets for your projects.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.