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[English](README.md) | 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](../../../../../docs/cn/build_and_install/rknpu2.md)实现
## 生成基本目录文件
该例程由以下几个部分组成
```text
.
├── 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
```bash
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](../../../../../../docs/cn/build_and_install/rknpu2.md)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
```bash
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
cp 000000014439.jpg ./images
```
### 编译example
```bash
cd build
cmake ..
make -j8
make install
```
## 运行例程
```bash
cd ./build/install
./infer_picodet model/ images/000000014439.jpg
```
- [模型介绍](../../)
- [Python部署](../python)
- [视觉模型预测结果](../../../../../../docs/api/vision_results/)