View source for Hailo/Hailo-8/AI Software and Tools/Hailo TAPPAS Installation

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TAPPAS is Hailo's set of full application examples, implementing pipeline elements and pre-trained AI tasks. Demonstrating Hailo's system integration
<br>

First, after installing the 'Hailo AI Software Suite' with [[Hailo/Hailo-8/AI Software and Tools/Hailo AI Software Suite Installation|Hailo AI Software Suite Installation]], you will find a TAPPAS folder in the <code>/artifacts</code> directory. But if you want to install TAPPAS isolated, you must need to follow this wiki.

== TAPPAS Installation == 
Here are the steps to install TAPPAS:

=== 1. Download TAPPAS ===
First, download the file ''tappas_VERSION_linux_installer.zip'' from the Hailo Developer Zone, as shown in the image below:
[[File:TAPPAS1.png|640x640px|thumb|center|How to download TAPPAS]]

=== 2. Unzip TAPPAS file ===

Next, move the .zip file to the folder where the Hailo AI Software Suite is located. Then, unzip the downloaded file using the following command:
<pre>
unzip tappas_VERSION_linux_installer.zip
</pre>

=== 3. Install the dependencies === 
After unzipping the file, you need to install the required dependencies with the following commands:
<pre>
sudo apt-get install -y rsync ffmpeg x11-utils python3-dev python3-pip python3-setuptools python3-virtualenv python-gi-dev libgirepository1.0-dev gcc-12 g++-12 cmake git libzmq3-dev
</pre>
<pre>
 sudo apt-get install -y libopencv-dev python3-opencv
</pre>
<pre>
sudo apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 gstreamer1.0-pulseaudio
</pre>
<pre>
sudo apt install python3-gi python3-gi-cairo gir1.2-gtk-3.0
</pre>

<pre>
sudo apt-get install libcairo2-dev
</pre>

== 2. Install TAPPAS ==

First, activate your virtual environment using the following command:
<pre>
source hailo_ai_sw_suite/hailo_venv/bin/activate
</pre>
Next, define the TAPPAS_WORKSPACE by running the following command:
<pre>
export TAPPAS_WORKSPACE=[directory]/hailo_ai_sw_suite/artifacts/tappas/
</pre>

:'''Note:''' 

:Replace [directory] with the path to the directory where the hailo_ai_sw_suite directory is located.

Then, execute the following command:
<pre>
 ./install.sh --skip-hailort
</pre>

:'''Note:''' 

:The install.sh script is located in the TAPPAS[version] folder.

== Run TAPPAS examples ==

=== TAPPAS examples ===
TAPPAS includes several examples. To access them, navigate to the following path:
<pre>
$TAPPAS_WORKSPACE/hailo_ai_sw_suite/artifacts/tappas/apps/h8/gstreamer/general
</pre>

In this directory, you will find the following examples:
<pre>
cascading_networks  depth_estimation  face_recognition       license_plate_recognition           multistream_detection    python      sanity_pipeline century             detection         instance_segmentation  multi_person_multi_camera_tracking  multistream_multidevice  README.rst  tiling
</pre>

When accessing each example, a <code>.sh</code> file with the same name as the folder will be found. The following demonstrates how to execute the 'detection' example.

First, navigate to the detection folder by running:
<pre>
cd detection
</pre>

Then, execute the example with the following command:
</pre>
 ./detection.sh
</pre>

This script will execute a GStreamer pipeline that plays a video and displays the predictions made by the model as shown bellow:

[[File:TAPPAS2.png|600x600px|thumb|center|Detection TAPPAS example]]


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