NXP eIQ

Source: Select the camera to use or to use the example video
Backend: Select whether to use the NPU (if available) or CPU for inferences.
Height: Select the input height of the video if using a camera.
Width: Select the input width of the video if using a camera.
Label Color: Select the color of the overlay labels.

The result of NPU object detection:

NXP Demo Experience - Text User Interface(TUI)

Demos can also be launched from the command line through log-in into the board remotely or using the onboard serial debug console. Keep in mind that most demos still require a display to run successfully.

To start the text user interface, type the following command into the command line.

$ demoexperience tui

The interface can be navigated using the following keyboard inputs:

Up and down arrow keys: Select a demo from the list on the left
Enter key: Runs the selected demo
Q key or Ctrl+C keys: Quit the interface
H key: Opens the help menu

Demos can be closed by closing the demo onscreen or pressing the "Ctrl" and "C" keys at the same time.

PyeIQ - A Python Framework for eIQ on i.MX Processors (Yocto 3.0)

PyeIQ is written on top of eIQ™ ML Software Development Environment and provides a set of Python classes

allowing the user to run Machine Learning applications in a simplified and efficiently way without spending time on

cross-compilations, deployments or reading extensive guides.

Installation

Method 1: Use pip3 tool to install the package located at PyPI repository:

$ pip3 install pyeiq

Method 2: Get the latest tarball Download files and copy it to the board:

$ pip3 install <tarball>

pyeiq tarball:

For the 5.4.70_2.3.0 BSP:

Install the v3.0.0 version and it run on NPU

For the 5.10.72_2.2.0 ~ 6.1.22_2.0.0 BSP (Suggest to use the demo experience)

Install the v3.1.0 version, but it run on CPU

Download PyeIQ Cache Data

Download link: https://github.com/ADVANTECH-Corp/pyeiq-data
Decompress the files to /home/root/.cache/

$ tar -zxvf eiq-cache-data_3.0.0.tar.gz

How to Run Samples

Start the manager tool:

$ pyeiq

The above command returns the PyeIQ manager tool options:

Manager Tool Command	Description	Example
pyeiq --list-apps	List the available applications.
pyeiq --list-demos	List the available demos.
pyeiq --run <app_name/demo_name>	Run the application or demo.	# pyeiq --run object_detection_tflite
pyeiq --info <app_name/demo_name>	Application or demo short description and usage.
pyeiq --clear-cache	Clear cached media generated by demos.	# pyeiq --info object_detection_tflite

PyeIQ Demos

covid19_detection
object_classification_tflite
object_detection_tflite

Demos Example - Running Object Detection

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include face detection and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

Run the Object Detection Default Image demo using the following line:

$ pyeiq --run object_detection_tflite

* This runs inference on a default image:

Run the Object Detection Custom Image demo using the following line:

$ pyeiq --run object_detection_tflite --image=/path_to_the_image

Run the Object Detection Video File using the following line:

$ pyeiq --run object_detection_tflite --video_src=/path_to_the_video

Run the Object Detection Video Camera or Webcam using the following line:

$ pyeiq --run object_detection_tflite --video_src=/dev/video<index>

eIQ Toolkit

The eIQ Toolkit is a machine-learning software development environment that enables the use of ML algorithms on
NXP microcontrollers, microprocessors, and SoCs.
The eIQ Toolkit is for those interested in building machine learning solutions on embedded devices. A background in
machine learning, especially in supervised classification, is helpful to understand the entire pipeline. However, if the
user does not have any background in the areas mentioned above, the eIQ Toolkit is designed to assist the user.
The eIQ Toolkit consists of the following three key components:

eIQ Portal
eIQ Model Tool
eIQ Command-line Tools

Download link: https://www.nxp.com/design/software/eiq-ml-development-environment/eiq-toolkit-for-end-to-end-model-development-and-deployment:EIQ-TOOLKIT\

There are two approaches available with the eIQ Toolkit, based on what the user provides and what the expectations are.

Bring Your Own Data (BYOD) – the users bring image data, use the eIQ Toolkit to develop their own model, and deploy it on the target.
Bring Your Own Model (BYOM) – the users bring a pretrained model and use the eIQ Toolkit for optimization, deployment, or profiling.