libcoral

This repository contains sources for the libcoral C++ API, which provides convenient functions to perform inferencing and on-device transfer learning with TensorFlow Lite models on Coral devices.

For developer documentation, see our guide to Run inference on the Edge TPU with C++ and check out the libcoral API reference.

Compilation

Be sure to clone this repo with submodules:

git clone --recurse-submodules https://github.com/google-coral/libcoral

If you already cloned without the submodules. You can add them with this:

cd libcoral

git submodule init && git submodule update

Then you can build everything using make command which invokes Bazel internally.

For example, run make tests to build all C++ unit tests or make benchmarks to build all C++ benchmarks. To get the list of all available make targets run make help. All output goes to out directory.

Linux

On Linux you can compile natively or cross-compile for 32-bit and 64-bit ARM CPUs.

To compile natively you need to install at least the following packages:

sudo apt-get install -y build-essential \
                        libpython3-dev \
                        libusb-1.0-0-dev \

and to cross-compile:

sudo dpkg --add-architecture armhf
sudo apt-get install -y crossbuild-essential-armhf \
                        libpython3-dev:armhf \
                        libusb-1.0-0-dev:armhf

sudo dpkg --add-architecture arm64
sudo apt-get install -y crossbuild-essential-arm64 \
                        libpython3-dev:arm64 \
                        libusb-1.0-0-dev:arm64

Compilation or cross-compilation is done by setting CPU variable for make command:

make CPU=k8      tests  # Builds for x86_64 (default CPU value)
make CPU=armv7a  tests  # Builds for ARMv7-A, e.g. Pi 3 or Pi 4
make CPU=aarch64 tests  # Builds for ARMv8, e.g. Coral Dev Board

macOS

You need to install the following software:

Xcode from https://developer.apple.com/xcode/
Xcode Command Line Tools: xcode-select --install
Bazel for macOS from https://github.com/bazelbuild/bazel/releases
MacPorts from https://www.macports.org/install.php
Ports of python interpreter and numpy library: sudo port install python35 python36 python37 py35-numpy py36-numpy py37-numpy
Port of libusb library: sudo port install libusb

Right after that all normal make commands should work as usual. You can run make tests to compile all C++ unit tests natively on macOS.

Docker

Docker allows to avoid complicated environment setup and build binaries for Linux on other operating systems without complicated setup, e.g.,

make DOCKER_IMAGE=debian:buster DOCKER_CPUS="k8 armv7a aarch64" DOCKER_TARGETS=tests docker-build
make DOCKER_IMAGE=ubuntu:18.04 DOCKER_CPUS="k8 armv7a aarch64" DOCKER_TARGETS=tests docker-build

libclassify.so

build libclassify using

make DOCKER_IMAGE=debian:buster DOCKER_CPUS="aarch64" DOCKER_TARGETS=examples docker-build

Use with C

include the header file: https://github.com/ilagomatis/libcoral/blob/master/coral/examples/classify.h

/*main.c*/

#include<stdio.h>
#include<stdlib.h>
#include<string.h>
#include"classify.h"
int main(){
	
	char *model_path = (char*)malloc(100*sizeof(char));
	strcpy(model_path, "mobilenet_v1_1.0_224_quant_edgetpu.tflite\0");
	printf("model_path: %s\n", model_path);
	
	char *image_path = (char*)malloc(100*sizeof(char));
	strcpy(image_path, "cat.rgb\0");
	printf("image_path: %s\n", image_path);

	char *labels_path = (char*)malloc(100*sizeof(char));
	strcpy(labels_path, "imagenet_labels.txt\0");
	printf("labels_path: %s\n", labels_path);

	float input_mean = 128;
	float input_std = 128;

	char*  out = classify_image(model_path,
			       	          image_path, 
					  labels_path, 
					  input_mean, 
					  input_std);
	
	printf("%s", out);
	free(model_path);
	free(image_path);
	free(labels_path);
	free(out);
	return 0;
}

Compile & Run

place libclassify.so on the same directory as main.c

gcc main.c -L. -libclassify -o classify
export LD_LIBRARY_PATH=.
./classify