3. Edge AI sample apps

There are various ways you can explore running a typical Edge AI usecase on AM68A EVM,

  • Trying the out-of-box Edge AI gallery

  • Develop Edge AI applications using Python and C++ reference applications

  • Run optimized end-to-end GStreamer pipelines using OpTIFlow

  • Run optimized end-to-end OpenVX pipelines using TIOVX Apps

The SDK is packaged with networks which does 3 DL tasks as below,

  • Image Classification: Detects top N most approximate classes in the dataset for the given input frame

  • Object Detection: Detects and draws bounding boxes around the objects, also classifies the objects to one of the classes in dataset

  • Semantic Segmentation: Classifies each pixel into class in dataset

3.1. Out-of-box GUI app

When the AM68A EVM is powered on with SD card in place, the Edge AI Gallery comes up on boot as shown.

One can connect a USB 2.0 mouse and click on the buttons in the left panel which starts the Edge AI application running the selected DL task. In the background, a GStremer pipeline is launched which reads a compressed video file and runs a DL network on the decoded content. The output of DL inference is overlayed on the image and sent to display. Users can select different DL tasks to execute on the compressed video.

There is also a “Custom” button, which when pressed allows users to select a custom input (Camera/VideoFile/Image) and a custom model available in the filesystem. This will automatically construct a GStreamer pipeline with required elements and launch the application.

  • For a model to pop up on GUI, it needs to be present under /opt/model_zoo/

  • For a videofile to pop up on GUI, the videos needs to be present under /opt/edgeai-test-data/videos/

  • For an image to pop up on GUI, the images needs to be present under /opt/edgeai-test-data/iamges/


Only Raw .H264/.H265 video will loop if selected as Input source. Other formats like .MP4/.MOV/.AVI/.MKV will not loop.


3.2. Python/C++ apps

Python based demos are simple executable scripts written for image classification, object detection and semantic segmentation. Demos are configured using a YAML file. Details on configuration file parameters can be found in Configuring applications

Sample configuration files for out of the box demos can be found in edgeai-gst-apps/configs this folder also contains a template config file which has brief info on each configurable parameter edgeai-gst-apps/configs/app_config_template.yaml

Here is how a Python based image classification demo can be run,

/opt/edgeai-gst-apps/apps_python# ./app_edgeai.py ../configs/image_classification.yaml

The demo captures the input frames from connected USB camera and passes through pre-processing, inference and post-processing before sent to display. Sample output for image classification and object detection demos are as below,




To exit the demo press Ctrl+C.

C++ apps are cross compiled while packaging, they can be directly tested as given below

/opt/edgeai-gst-apps/apps_cpp# ./bin/Release/app_edgeai ../configs/image_classification.yaml

To exit the demo press Ctrl+C.

C++ apps can be modified and built on the target as well using below steps

/opt/edgeai-gst-apps/apps_cpp# rm -rf build bin lib
/opt/edgeai-gst-apps/apps_cpp# mkdir build
/opt/edgeai-gst-apps/apps_cpp# cd build
/opt/edgeai-gst-apps/apps_cpp/build# cmake ..
/opt/edgeai-gst-apps/apps_cpp/build# make -j2

3.3. OpTIFlow

In Edge AI Python and C++ applications, post processing and DL inference are done between appsink and appsrc application boundaries. This makes the data flow sub-optimal because of unnecessary data format conversions to work with open source components.

This is solved by providing DL-inferer plugin which calls one of the supported DL runtime and a post-process plugin which works natively on NV12 format, avoiding unnecessary color formats conversions.

Users can write their own pipeline or use optiflow application to generate and run the end-to-end pipeline. Optiflow application shares the same config file as used by Python/C++ apps.

To directly run the end-to-end pipeline use the following command.

/opt/edgeai-gst-apps/optiflow# ./optiflow.py ../configs/object_detection.yaml

To exit the demo press Ctrl+C.

To just dump the end-to-end pipeline use the following command.

/opt/edgeai-gst-apps/optiflow# ./optiflow.py ../configs/object_detection.yaml -t


Python, C++ and OpTIFlow applications are similar by construction and can accept the same config file

3.4. EdgeAI Tiovx Apps

EdgeAI Tiovx Apps creates and runs optimized end-to-end OpenVx analytics pipelines based on the user defined configuration.

Please visit EdgeAI Tiovx Apps wiki for in-depth documentation.

To run a sample demo

/opt/edgeai-tiovx-apps/# ./bin/Release/edgeai-tiovx-apps-main configs/linux/object_detection.yaml

To exit the demo press Ctrl+C.