eiq 1.0.0

eIQ package provides classes and scripts to manage the eIQ Samples Apps.

Welcome to PyeIQ

PyeIQ provide high level classes to allow the user execute eIQ applications and demos.

i.MX Board	BSP Release	Building Status
8 QM	5.4
8 MPlus	5.4
8 M Mini	5.4	-

Getting Started with PyeIQ

Before installing PyeIQ, ensure all dependencies are installed. Most of them are common dependencies found in any GNU/Linux Distribution; package names will be different, but it shouldn't be difficult to search using whatever package management tool that's used by your distribution.

The procedures described in this document target a GNU/Linux Distribution Ubuntu 18.04.

Software Requirements

1. Install the following packages in the GNU/Linux system:

~# apt install python3 python3-pip

2. Then, use pip3 tool to install the Virtualenv tool:

~$ pip3 install virtualenv

Building the PyeIQ Package

1. Clone the PyeIQ repository from CAF.

2. Use Virtualenv tool to create an isolated Python environment:

~/pyeiq$ virtualenv env
~/pyeiq$ source env/bin/activate

• Generate the PyeIQ package:

(env) ~/pyeiq# python3 setup.py sdist bdist_wheel

• Copy the package to the board:

(env) ~/pyeiq$ scp dist/eiq-<version>.tar.gz root@<boards_IP>:~

3. To deactivate the virtual environment:

(env) ~/pyeiq$ deactivate
~/pyeiq$

Deploy the PyeIQ Package

1. Install the PyeIQ Wheel file in the board:

root@imx8qmmek:~# pip3 install eiq-<version>.tar.gz

2. Check the installation:

   • Start an interactive shell mode with Python3:

   root@imx8qmmek:~# python3
   

   • Check the PyeIQ latest version:

   >>> import eiq
   >>> eiq.__version__
   

   • The output is the PyeIQ latest version installed in the system.

Running the Demos

All the demos are installed in the /opt/eiq/demos folder. Follow a list of the available demos in PyeIQ:

Demo/App Name	Demo/App Type	i.MX Board	BSP Release	BSP Framework	Inference	Status	Notes
Label Image	File Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU		-
Label Image Switch	File Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU		-
Object Detection	SSD/Camera Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU		Works with low accuracy. Need better model.
Object Detection OpenCV	SSD/Camera Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU		Higher accuracy than above one.
Object Detection Native GStreamer	SSD/Camera Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU	-	Fixing undetermined GStreamer hangs.
Object Detection Yolov3	SSD/File Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU	-	Pending issues.
Object Detection Yolov3	SSD/Camera Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU	-	Pending issues.
Fire Detection	File Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU		-
Fire Detection	Camera Based	QM, MPlus	5.4	TensorFlow Lite 2.1.0	GPU, NPU		-
Fire Detection	Camera Based	-	5.4	PyArmNN 19.08	-	-	Requires 19.11
Coral Posenet	Camera Based	-	-	-	-	-	Ongoing
NEO DLR	Camera Based	-	-	-	-	-	Ongoing

1. To run the demos:
   • Choose the demo and execute it:

   root@imx8qmmek:~# cd /opt/eiq/demos/
   root@imx8qmmek:~/opt/eiq/demos/# python3 <demo>.py
   

   • Use help if needed:

   root@imx8qmmek:~/opt/eiq/demos/# python3 <demo>.py --help
   

Copyright and License

© 2020 NXP Semiconductors.

Free use of this software is granted under the terms of the BSD 3-Clause License.