Skip to main content

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

Project description

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 build
8 MPlus 5.4 build
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
  1. 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>:~
  1. 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
  1. 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 build -
Label Image Switch File Based QM, MPlus 5.4 TensorFlow Lite 2.1.0 GPU, NPU build -
Object Detection SSD/Camera Based QM, MPlus 5.4 TensorFlow Lite 2.1.0 GPU, NPU build Works with low accuracy. Need better model.
Object Detection OpenCV SSD/Camera Based QM, MPlus 5.4 TensorFlow Lite 2.1.0 GPU, NPU build 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 build -
Fire Detection Camera Based QM, MPlus 5.4 TensorFlow Lite 2.1.0 GPU, NPU build -
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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

eiq-1.0.0.tar.gz (22.5 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page