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Add RKYOLOv5 RKYOLOX RKYOLOV7 (#709)
* 更正代码格式 * 更正代码格式 * 修复语法错误 * fix rk error * update * update * update * update * update * update * update Co-authored-by: Jason <jiangjiajun@baidu.com>
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examples/vision/detection/rkyolo/cpp/README.md
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examples/vision/detection/rkyolo/cpp/README.md
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# RKYOLO C++部署示例
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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。
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在部署前,需确认以下两个步骤:
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1. 软硬件环境满足要求
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2. 根据开发环境,下载预编译部署库或者从头编译FastDeploy仓库
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以上步骤请参考[RK2代NPU部署库编译](../../../../../docs/cn/build_and_install/rknpu2.md)实现
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## 生成基本目录文件
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该例程由以下几个部分组成
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```text
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.
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├── CMakeLists.txt
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├── build # 编译文件夹
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├── image # 存放图片的文件夹
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├── infer_rkyolo.cc
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├── model # 存放模型文件的文件夹
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└── thirdpartys # 存放sdk的文件夹
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```
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首先需要先生成目录结构
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```bash
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mkdir build
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mkdir images
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mkdir model
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mkdir thirdpartys
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```
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## 编译
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### 编译并拷贝SDK到thirdpartys文件夹
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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
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fastdeploy-0.0.3目录,请移动它至thirdpartys目录下.
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### 拷贝模型文件,以及配置文件至model文件夹
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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。
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转换为RKNN后的模型文件也需要拷贝至model。
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### 准备测试图片至image文件夹
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```bash
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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cp 000000014439.jpg ./images
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```
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### 编译example
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```bash
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cd build
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cmake ..
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make -j8
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make install
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```
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## 运行例程
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```bash
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cd ./build/install
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./infer_picodet model/ images/000000014439.jpg
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```
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../../docs/api/vision_results/)
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