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.
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.
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
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 andnumpy
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 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
build libclassify using
make DOCKER_IMAGE=debian:buster DOCKER_CPUS="aarch64" DOCKER_TARGETS=examples docker-build
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;
}
place libclassify.so on the same directory as main.c
gcc main.c -L. -libclassify -o classify
export LD_LIBRARY_PATH=.
./classify