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111 lines
3.5 KiB
Markdown
111 lines
3.5 KiB
Markdown
# C++部署
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## 准备预测库
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参考编译文档[FastDeploy编译](../compile/README.md)进行编译，或直接使用如下预编译库
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| 编译库 | 平台 | 支持设备 | 说明 |
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|:------ | :---- | :------- | :----- |
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|[fastdeploy-linux-x64-0.0.3.tgz](https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-0.0.3.tgz) | Linux | CPU | 集成ONNXRuntime |
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|[fastdeploy-linux-x64-gpu-0.0.3.tgz](https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-gpu-0.0.3.tgz) | Linux | CPU/GPU | 集成ONNXRuntime, TensorRT |
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|[fastdeploy-osx-x86_64-0.0.3.tgz](https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-osx-x86_64-0.0.3.tgz) | Mac OSX Intel CPU | CPU | 集成ONNXRuntime |
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|[fastdeploy-osx-arm64-0.0.3.tgz](https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-osx-arm64-0.0.3.tgz) | Mac OSX M1 CPU | CPU | 集成ONNXRuntime |
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## 使用
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FastDeploy提供了多种领域内的模型，可快速完成模型的部署，本文档以YOLOv5在Linux上的部署为例
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```
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# 下载库并解压
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wget https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-0.0.3.tgz
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tar xvf fastdeploy-linux-x64-0.0.3.tgz
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# 下载模型和测试图片
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wget https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.onnx
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wget https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/bus.jpg
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```
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### YOLOv5预测代码
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准备如下`yolov5.cc`代码
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```
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#include "fastdeploy/vision.h"
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int main() {
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typedef vis = fastdeploy::vision;
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auto model = vis::ultralytics::YOLOv5("yolov5s.onnx"); // 加载模型
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if (!model.Initialized()) { // 判断模型是否初始化成功
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std::cerr << "Initialize failed." << std::endl;
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return -1;
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}
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cv::Mat im = cv::imread("bus.jpg"); // 读入图片
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vis::DetectionResult res;
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if (!model.Predict(&im, &res)) { // 预测图片
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std::cerr << "Prediction failed." << std::endl;
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return -1;
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}
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std::cout << res.Str() << std::endl; // 输出检测结果
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return 0;
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}
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```
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### 编译代码
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编译前先完成CMakeLists.txt的开发，在`yolov5.cc`同级目录创建`CMakeLists.txt`文件，内容如下
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```
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PROJECT(yolov5_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.16)
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# 在低版本ABI环境中，可通过如下代码进行兼容性编译
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# add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
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# 在下面指定下载解压后的fastdeploy库路径
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set(FASTDEPLOY_INSTALL_DIR /ssd1/download/fastdeploy-linux-x64-0.0.3/)
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(yolov5_demo ${PROJECT_SOURCE_DIR}/yolov5.cc)
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message(${FASTDEPLOY_LIBS})
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# 添加FastDeploy库依赖
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target_link_libraries(yolov5_demo ${FASTDEPLOY_LIBS})
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~
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```
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此时当前目录结构如下所示
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```
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- demo_directory
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|___fastdeploy-linux-x64-0.0.3/ # 预测库解压
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|___yolov5.cc # 示例代码
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|___CMakeLists.txt # cmake文件
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|___yolov5s.onnx # 模型文件
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|___bus.jpeg # 测试图片
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```
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执行如下命令进行编译
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```
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cmake .
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make -j
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```
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编译后可执行二进制即为当前目录下的`yolov5_demo`，使用如下命令执行
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```
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./yolov5_demo
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```
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即会加载模型进行推理，得到结果如下
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```
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DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
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223.395126,403.948669, 345.337189, 867.339050, 0.856906, 0
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668.301758,400.781372, 808.441772, 882.534973, 0.829716, 0
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50.210758,398.571289, 243.123383, 905.016846, 0.805375, 0
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23.768217,214.979355, 802.627869, 778.840820, 0.756311, 5
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0.737200,552.281006, 78.617218, 890.945007, 0.363471, 0
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```
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