NXP eIQ

NXP i.MX series

The i.MX 8M Plus family focuses on neural processing unit (NPU) and vision system, advance multimedia, andindustrial automation with high reliability.

• The Neural Processing Unit (NPU) of i.MX 8M Plus operating at up to 2.3 TOPS

NXP Demo Experience (Yocto 3.3 ~ )

• Preinstalled on NXP-provided demo Linux images
• imx-image-full image must be used
• Yocto 3.3 (5.10.52_2.1.0 ) ~ Yocto 4.2 (6.1.1_1.0.0)
• Need to connect the internet

Start the demo launcher by clicking NXP Logo is displayed on the top left-hand corner of the screen

Machine Learning Demos

• NNStreamer demos
  • Object classification
  • Object detection
  • Pose detection
  • Brand detection
  • ML gateway

   

• OpenCV demos
  • Face recognition
  • Selfie segmentation

 

NNStreamer Demo: Object Detection

Click the "Object Detection " and Launch Demo

 

Set some parameters:

 

• 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)

$ demoexperience tui

 

eIQ - 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:

• File:pyeiq-3.0.0.tar.gz
• File:pyeiq-3.1.0.tar.gz

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>