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FastDeploy/examples/vision/detection/rkyolo/cpp/README_CN.md
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English | 简体中文

RKYOLO C++部署示例

本目录下提供infer_xxxxx.cc快速完成RKYOLO模型在Rockchip板子上上通过二代NPU加速部署的示例。

在部署前，需确认以下两个步骤:

  1. 软硬件环境满足要求
  2. 根据开发环境，下载预编译部署库或者从头编译FastDeploy仓库

以上步骤请参考RK2代NPU部署库编译实现

生成基本目录文件

该例程由以下几个部分组成

.
├── CMakeLists.txt
├── build  # 编译文件夹
├── image  # 存放图片的文件夹
├── infer_rkyolo.cc
├── model  # 存放模型文件的文件夹
└── thirdpartys  # 存放sdk的文件夹

首先需要先生成目录结构

mkdir build
mkdir images
mkdir model
mkdir thirdpartys

编译

编译并拷贝SDK到thirdpartys文件夹

请参考RK2代NPU部署库编译仓库编译SDK，编译完成后，将在build目录下生成 fastdeploy-0.0.3目录，请移动它至thirdpartys目录下.

拷贝模型文件，以及配置文件至model文件夹

在Paddle动态图模型 -> Paddle静态图模型 -> ONNX模型的过程中，将生成ONNX文件以及对应的yaml配置文件，请将配置文件存放到model文件夹内。 转换为RKNN后的模型文件也需要拷贝至model。

准备测试图片至image文件夹

wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
cp 000000014439.jpg ./images

编译example

cd build
cmake ..
make -j8
make install

运行例程

cd ./build/install
./infer_picodet model/ images/000000014439.jpg