How to build an example using libcoral API

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Applicable for STM32MP13x lines, STM32MP15x lines

This article explains how to build an example using the libcoral API[1]. The libcoral is a C/C++ API, which is used for three main purposes:

  • Inferencing: facilitates the implementation of neural network inference on Coral Edge TPU™
  • Pipelining: provides functions to pipeline a model on multiple Coral Edge TPUs
  • Transfer learning: enables on board transfer learning

Several examples can be found on the libcoral GitHub[2]. This article only shows how to build a simple image classification example from scratch. The method used here can easily be applied to other examples.

1 Description[edit]

This simple example is based on two image classification models, which allow the identification of the subject represented by an image.

libcoral API image classification example

The purpose of this example is beyond the image classification aspect to demonstrate how to use the libcoral API to realize inferences of two models alternatively on only one Google Coral Edge TPU™[3].

This example depends only on the TensorFlow™ Lite[4] interpreter and the libraries associated to the Edge TPU™.

The models used with this example are two variants, Inat_bird and Inat_plant respectively for bird and plant classification of the MobileNet v2 downloaded from the Coral GitHub testing models[5].

2 Installation[edit]

2.1 Install from the OpenSTLinux AI package repository[edit]

Warning white.png Warning
The software package is provided AS IS, and by downloading it, you agree to be bound to the terms of the software license agreement (SLA). The detailed content licenses can be found here.

After having configured the AI OpenSTLinux package, you can install the X-LINUX-AI components for this example:

 apt-get install libcoral

2.2 Source code location[edit]

The source code and the header files of this example are located in the libcoral API examples GitHub [6].

3 How to use the example[edit]

3.1 Installation of the X-LINUX-AI SDK[edit]

First of all, the installation of the X-LINUX-SDK is required to be able to cross-compile AI applications for STM32 boards.

3.2 Start the SDK[edit]

Info white.png Information
The SDK environment setup script must be run once on each new working terminal on which you cross-compile.

Once the OpenSTLinux SDK is installed, go to the installation directory and source the environment:

 cd $HOME/STM32MPU_workspace/STM32MP1X-Ecosystem-v4.0.0/Developer-Package/SDK
 source environment-setup-cortexa7t2hf-neon-vfpv4-ostl-linux-gnueabi
Warning white.png Warning
The path to the SDK must be adapted depending of your working configuration (board used).

3.3 Set up the file tree[edit]

This example requires the following file tree to operate out of the box:

 mkdir -p sources/coral/examples
 cd sources/coral/examples

3.4 Download the example[edit]

As mentioned before the source and header files must be downloaded from the libcoral API examples GitHub [6]:


3.5 Create the Makefile[edit]

Create the following Makefile in the sources/coral/examples directory:

# Copyright (c) 2022 STMicroelectronics

TARGET_BIN = two_models_one_tpu

CXXFLAGS += -Wall $(shell pkg-config --cflags absl_base libglog)
CXXFLAGS += -I../../
# Fix undefined reference during the link
CXXFLAGS += -std=c++11

LDFLAGS  = $(shell pkg-config --libs absl_base libglog)
LDFLAGS += -lcoral -ledgetpu -ltensorflow-lite -lglog

		-labsl_flags_internal \
		-labsl_flags_marshalling \
		-labsl_flags_reflection \
		-labsl_statusor \
		-labsl_flags_parse \
		-labsl_strings \

OBJS = $(

all: $(TARGET_BIN)

$(OBJS): $(SRCS)
	$(CXX) $(CXXFLAGS) -c $^

	$(CXX) $(LDFLAGS) $^ -o $@
	rm -rf $(OBJS) $(TARGET_BIN)

Minimal required libraries like Abseil, Glog, Tflite, Edgetpu, and Coral are linked during the cross-compilation.

3.6 Download and prepare test data[edit]

An additional package is necessary on the host PC to preprocess data for this example: imagemagick

 apt-get install imagemagick

First create the directory to store test data:

 mkdir edgetpu_cpp_example

Next download the models and the test pictures:

 wget -O edgetpu_cpp_example/inat_plant_edgetpu.tflite
 wget -O edgetpu_cpp_example/inat_bird_edgetpu.tflite
 wget -O edgetpu_cpp_example/inat_plant_labels.txt
 wget -O edgetpu_cpp_example/inat_bird_labels.txt
 wget -O edgetpu_cpp_example/bird.jpg
 wget -O edgetpu_cpp_example/plant.jpg

Finally preprocess the pictures to make them fit with the inputs shape of the models:

 cd edgetpu_cpp_example && convert bird.jpg -resize 224x224! bird.rgb && convert plant.jpg -resize 224x224! plant.rgb

3.7 Cross-compilation and launch[edit]

Run the cross-compilation:

 cd ..

Once the compilation is finished, a binary file named two_models_one_tpu has been created.
Copy the binary file and the test data directory onto the board:

 scp -r edgetpu_cpp_example/ root@<board_ip>:/path/
 scp two_models_one_tpu    root@<board_ip>:/path/
Info white.png Information
The Coral Edge TPU™ must be plugged on the board before launching the script

Connect to the board and launch the example:


After 2000 inferences the result is:

Running model: edgetpu_cpp_example/inat_bird_edgetpu.tflite and model: edgetpu_cpp_example/inat_plant_edgetpu.tflite for 2000 inferences
[Bird image analysis] max value index: 659 value: 0.652344
[Plant image analysis] max value index: 1680 value: 0.964844
Using one Edge TPU, # inferences: 2000 costs: 106.278 seconds.

Where the max value index represents the index of the class detected and the value represents the confidence. On these particular pictures, the bird detected is a poecile atricapillus (black-capped chickadee) and the plant is a helianthus annuus (sunflower). The index and the name of each class are available in the inat_bird_labels.txt and inat_plant_labels.txt stored in the edgetpu_cpp_example directory.

4 References[edit]