bbai64

Small projects and experiments with the BeagleBone AI-64 platform written in Go and Python.
Includes custom Image Classification and Object Detection train scripts with Tensorflow as well as scripts for further models compilation for TI TFlite Delegate.
Based on bbai64-emmc-flasher-debian-11.8-xfce-edgeai-arm64-2023-10-07-10gb.img.xz image from arm64-debian-11-x-bullseye-monthly-snapshots-2023-10-07.
setup_script.sh from /opt/edge_ai_apps/ must be run to install edgeai-gst-plugins

cd /opt/edge_ai_apps/ && sudo ./setup_script.sh

Overlays

To add support of various periphery as well as IMX219 CSI cameras fdtoverlays property of /boot/firmware/extlinux/extlinux.conf should be modified with /overlay/THE_OVERLAY_NAME.dtbo. For example:

fdtoverlays /overlays/BONE-PWM0.dtbo /overlays/BONE-PWM1.dtbo /overlays/BONE-I2C1.dtbo /overlays/BBAI64-CSI0-imx219.dtbo /overlays/BBAI64-CSI1-imx219.dtbo

Checkout arm64 overlays list

P8 P9 headers periphery mapping

https://elinux.org/Beagleboard:BeagleBone_cape_interface_spec

• I2C
• PWM
• SPI
• UART

Docs

edgeai_dataflows
gstreamer plugins

Go installation

Latest Go toolchain builds

wget https://go.dev/dl/go1.21.6.linux-arm64.tar.gz
sudo rm -rf /usr/local/go
sudo rm -rf /usr/bin/go
sudo tar -C /usr/local -xzf go1.21.6.linux-arm64.tar.gz

Update ~/.bashrc with

export PATH=$PATH:/usr/local/go/bin

Apply changes and check go version

source ~/.bashrc
go version

IMX219 Dynamic Camera Configuration files for Image Signal Processor

Taken from TI's PROCESSOR-SDK-J721E
Required by tiovxisp gstreamer plugin to work with IMX219 SCI camera.

wget https://github.com/Hypnotriod/bbai64/raw/master/imaging.zip
sudo unzip imaging.zip -d /opt/

libtensorflowlite_c.so 2.9.0 for linux arm64

wget https://github.com/Hypnotriod/bbai64/raw/master/libtensorflowlite_c-2.9.0-linux-arm64.tar.gz
sudo tar -C /usr/local -xvf libtensorflowlite_c-2.9.0-linux-arm64.tar.gz
sudo ldconfig

libtensorflow.2.4.1.so for linux arm64

wget https://github.com/kesuskim/libtensorflow-2.4.1-linux-arm64/raw/master/libtensorflow.tar.gz
sudo tar -C /usr/local -xvf libtensorflow.tar.gz
sudo ldconfig

Prepare edgeai-tidl-tools on Ubuntu PC

sudo apt-get install libyaml-cpp-dev
sudo apt-get install cmake
conda create --name tensorflow_tidl
conda activate tensorflow_tidl
git clone --depth 1 --branch 08_02_00_05 https://github.com/TexasInstruments/edgeai-tidl-tools
cd edgeai-tidl-tools
export SOC=am68pa
./setup.sh

Compile tflite model artifacts for tidl delegate on Ubuntu PC

TI Deep Learning Library User Guide
User options for TIDL Acceleration

make compile-image-classification TIDL_TOOLS_PATH=/path_to_tidl_tools/edgeai-tidl-tools/tidl_tools/
make compile-object-detection TIDL_TOOLS_PATH=/path_to_tidl_tools/edgeai-tidl-tools/tidl_tools/

Compile tflite model artifacts for tidl delegate using Docker container

make build-edgeai-tidl-tools-docker-container
make compile-object-detection-docker
make compile-image-classification-docker

CUDA, cuDNN

tensorflow cuDNN CUDA configuration list
cuda-11.2.0 for tensorflow 2.10.1
cuda-10.0 for tensorflow 1.15
cudnn-archive

Image classification custom model training on Ubuntu / Windows PC

• Prepare conda environment

cd python/image_classification
conda create --name tensorflow_ic
conda activate tensorflow_ic
conda install python=3.7
pip install -r requirements.txt

• Add your train (to train and validate) and test (to test the final result) images to train_data and test_data folders respectively. Each image related to specific class should be in its own subfolder named by the class name.
• config.json - training configuration file.
  • Fill classes field with your class names in desired order.
  • You may tweak the epochs, validation_split, batch_size e.t.c
• train.py - script to train the model.
  • At the end of successful training should generate labels/labels.txt and saved_model_tflite/saved_model.tflite files.

Object detection custom model training on Ubuntu / Windows PC

protocolbuffers_v3.20

• Prepare conda environment

cd python/object_detection
conda create --name tensorflow_od
conda activate tensorflow_od
conda install python=3.7
pip install -r requirements_tf1.txt
# pip install -r requirements_tf2.txt
git clone --depth 1 https://github.com/tensorflow/models.git
# git clone https://github.com/tensorflow/models.git && git reset --hard a0d092533701cbbf4cde97337b1e4aac51943c4d
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf1/setup.py .
# cp object_detection/packages/tf2/setup.py .
python3 -m pip install .
pip install protobuf==3.20.3

• Prepare you images annotation with labelImg graphical image annotation tool. Should generate annotation xml file for each image file.
• Add your train (for training) and test (for evaluation) images and xmls files to train_data and test_data folders respectively.
• Download and extract the content of your model of choise from link below and put into python/object_detection/base_model folder, for example: ssd_mobilenet_v2_coco
  • tf1_detection_zoo
  • tf2_detection_zoo
• Update in python/object_detection/base_model/pipeline.config the input_path: "PATH_TO_BE_CONFIGURED" fields of train_input_reader and eval_input_reader with "PATH_TO_BE_CONFIGURED/train" and "PATH_TO_BE_CONFIGURED/eval" respectively
• Also delete if exists line batch_norm_trainable: true from python/object_detection/base_model/pipeline.config
• config.json - training configuration file.
  • Check the base_config_path and fine_tune_checkpoint paths to your model.
  • Update the input_shapes with the input shape of your model. Check fixed_shape_resizer field in pipeline.config
  • Fill classes field with your class names in desired order.
  • You may tweak the num_steps, batch_size e.t.c
• train.py - script to train the model.
  • --skip - to skip phases prepare train export
  • At the end of successful training should generate labels/labels.txt and saved_model_tflite/saved_model.tflite files.

wifi vehicle hardware

• 2 channels RC car platform with steering servo and ESC (Electronic Speed Control)
• 3.3v to 5-6v PWM signal conversion circuit
• Arducam IMX219 sensor based Camera Module with 15-pin to 22-pin FPC (Flexible Printed Circuit) cable
• Waveshare UPS Module 3S for BBAI64 powering and power monitoring
• Gamepad for use as the car controller on the web page

Build and run go apps with make example

make build-wifi-vehicle
make run-wifi-vehicle

imx219-stereo-camera-mjpeg-stream.py

BeagleBone AI-64 MJPEG stream of Waveshare IMX219-83 Stereo Camera with GStreamer example