EasyDL图像专项适配EdgeBoard(FZ)-SDK集成快速开始

$ cmake --version
cmake version 3.13.3

// apt安装cmake
sudo apt update
sudo apt install cmake

// 升级cmake
sudo apt-get install software-properties-common
sudo add-apt-repository ppa:george-edison55/cmake-3.x
sudo apt-get update
sudo apt-get upgrade

$ gcc --version
gcc (Ubuntu/Linaro 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609

// 安装gcc
sudo apt update
sudo apt install build-essential
// 升级gcc
sudo add-apt-repository ppa:ubuntu-toolchain-r/test # 如果找不到add-apt-repository命令，执行：apt-get install software-properties-common
sudo apt-get update
sudo apt-get install -y gcc-5 g++-5

cd /usr/bin # 升级gcc 5之后，还需要替换原来的软链接
sudo rm -r gcc # 移除之前的软连接 
sudo ln -sf gcc-5 gcc # 建立gcc5的软连接 
sudo rm -r g++ # 同gcc 
sudo ln -sf g++-5 g++

// 编译安装OpenCV
cd opencv-3.4.6
mkdir build
cd build

cmake .. -DBUILD_DOCS=OFF -DBUILD_EXAMPLES=OFF -DBUILD_opencv_python2=OFF -DBUILD_opencv_python3=OFF -DBUILD_WITH_DEBUG_INFO=OFF -DBUILD_PACKAGE=OFF -DBUILD_opencv_core=ON -DBUILD_opencv_imgproc=ON -DBUILD_opencv_imgcodecs=ON -DBUILD_opencv_highgui=ON -DBUILD_opencv_video=OFF -DBUILD_opencv_videoio=OFF -DBUILD_opencv_dnn=OFF -DBUILD_opencv_apps=OFF -DBUILD_opencv_flann=OFF -DBUILD_opencv_gpu=OFF -DBUILD_opencv_ml=OFF -DBUILD_opencv_legacy=OFF -DBUILD_opencv_calib3d=OFF -DBUILD_opencv_features2d=OFF -DBUILD_opencv_java=OFF -DBUILD_opencv_objdetect=OFF -DBUILD_opencv_photo=OFF -DBUILD_opencv_nonfree=OFF -DBUILD_opencv_ocl=OFF -DBUILD_opencv_stitching=OFF -DBUILD_opencv_superres=OFF -DBUILD_opencv_ts=OFF -DBUILD_opencv_videostab=OFF -DBUILD_opencv_contrib=OFF -DBUILD_SHARED_LIBS=ON -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=OFF -DBUILD_WITH_CAROTENE=OFF -DCMAKE_BUILD_TYPE:STRING=Release -DWITH_FFMPEG=OFF -DWITH_IPP=OFF -DBUILD_PNG=ON -DBUILD_JPEG=ON -DBUILD_ZLIB=ON -DBUILD_FAT_JAVA_LIB=OFF -DOPENCV_CXX11=OFF -DCMAKE_INSTALL_PREFIX:PATH=/usr/lib/aarch64-linux-gnu/

make # 如果有多个cpu可以用-j加快编译速度,如4个CPU用 make -j4
make install

insmod /home/root/workspace/driver/{zu9|zu5|zu3}/fpgadrv.ko

Failed to to fpga device: -1

date --set "2019-5-18 20:48:00"

libeasyedge.so: file format not recognized; treating as linker script

EasyEdge-Linux-mxxx-bxxx-edgeboard
├── cpp
│   ├── baidu_easyedge_linux_cpp_aarch64_PADDLEMOBILE_FPGA_gcc5.4_vx.x.x_xxxxxxx.tar
├── RES # 模型、标签和配置文件

baidu_easyedge_linux_cpp_aarch64_PADDLEMOBILE_FPGA_gcc5.4_vx.x.x_xxxxxxx
├── demo # 测试demo
│   ├── CMakeLists.txt
│   ├── demo.cpp
│   ├── opencv.cmake
│   └── easyedge_serving
├── include # SDK需要的头文件
│   ├── easyedge
│   │   └── easyedge.h
├── lib # SDK需要的库文件
│   ├── libeasyedge.so -> libeasyedge.so.x.x.x
│   ├── libeasyedge.so.x.x.x
│   ├── libeasyedge_static.a
│   ├── libpaddle-mobile.so -> libpaddle-mobile.so.x.x.x
│   ├── libpaddle-mobile.so.x.x.x
│   └── libverify.so
└── ReadMe.txt # 文档等其他说明

cd demo # 进入demo文件夹
vi demo.cpp # 若vi未找到命令，执行  sudo apt install vim

    global_controller()->set_licence_key("set your license here");

    log_config.enable_debug = false;

mkdir build # 创建build目录
cd build 
cmake .. # 如果系统中安装了opencv3.4以上
# 或者
cmake .. -DEDGE_BUILD_OPENCV=ON # 自动编译安装opencv

make # 如果有多个cpu可以用-j加快编译速度,如4个CPU用 make -j4

./easyedge_demo {模型RES文件夹}  {测试图片路径}

./easyedge_demo ../../../RES  /xxx/test.jpg

ethaddr=00:0a:35:00:00:09
uenvcmd=fatload mmc 1 0x3000000 image.ub && bootm 0x3000000

bootargs=earlycon console=ttyPS0,115200 clk_ignore_unused cpuidle.off=1 root=/dev/mmcblk1p2 rw rootwait cma=128M

./easyedge_serving {模型RES文件夹} {序列号} {主机ip, 默认 0.0.0.0} {端口, 默认 24401}

 ./easyedge_serving ../../../RES "1111-1111-1111-1111" 0.0.0.0  24401

ifconfig # 如果没有ifconfig命令，执行 sudo apt install net-tools

find_package(OpenCV REQUIRED)

// 导入头文件
include_directories(
		${OpenCV_INCLUDE_DIRS}
        ${CMAKE_SOURCE_DIR}/../include/
)
// 导入库文件
link_directories(
        ${CMAKE_SOURCE_DIR}/../lib/
)
// 链接库文件
target_link_libraries({your_executable_file} ${OpenCV_LIBS} easyedge paddle-mobile)

// 引入SDK头文件
#include "easyedge/easyedge.h" 
// step 0: 设置序列号
 global_controller()->set_licence_key("set your license here");
 // step 1: 配置模型资源目录
 PaddleFluidConfig config;
 config.model_dir = {模型文件目录};
 // step 2: 创建并初始化Predictor
 auto predictor = global_controller()->CreateEdgePredictor(config);
 auto img = cv::imread({测试图片路径});
 // step 3: 预测图像
 std::vector<EdgeResultData> result;
 predictor->infer(img, result);