mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 00:57:33 +08:00
02eab973ce
* 第一次提交 * 补充一处漏翻译 * deleted: docs/en/quantize.md * Update one translation * Update en version * Update one translation in code * Standardize one writing * Standardize one writing * Update some en version * Fix a grammer problem * Update en version for api/vision result * Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop * Checkout the link in README in vision_results/ to the en documents * Modify a title * Add link to serving/docs/ * Finish translation of demo.md
132 lines
4.4 KiB
Markdown
132 lines
4.4 KiB
Markdown
[English](../../../en/quick_start/models/cpp.md) | 中文
|
||
|
||
# C++部署
|
||
|
||
确认开发环境已准备FastDeploy C++部署库，参考[FastDeploy安装](../../build_and_install/)安装预编译的FastDeploy，或根据自己需求进行编译安装。
|
||
|
||
本文档以PaddleDetection目标检测模型PPYOLOE为例展示CPU上的推理示例
|
||
|
||
## 1. 获取模型和测试图像
|
||
|
||
```bash
|
||
wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
|
||
wget https://bj.bcebos.com/fastdeploy/tests/test_det.jpg
|
||
tar xvf ppyoloe_crn_l_300e_coco.tgz
|
||
```
|
||
|
||
## 2. 准备C++推理代码
|
||
|
||
如下C++代码保存为`infer_demo.cc`
|
||
|
||
```c++
|
||
#include "fastdeploy/vision.h"
|
||
int main() {
|
||
std::string model_file = "ppyoloe_crn_l_300e_coco/model.pdmodel";
|
||
std::string params_file = "ppyoloe_crn_l_300e_coco/model.pdiparams";
|
||
std::string infer_cfg_file = "ppyoloe_crn_l_300e_coco/infer_cfg.yml";
|
||
// 模型推理的配置信息
|
||
fastdeploy::RuntimeOption option;
|
||
auto model = fastdeploy::vision::detection::PPYOLOE(model_file, params_file, infer_cfg_file, option);
|
||
|
||
assert(model.Initialized()); // 判断模型是否初始化成功
|
||
|
||
cv::Mat im = cv::imread("test_det.jpg");
|
||
fastdeploy::vision::DetectionResult result;
|
||
|
||
assert(model.Predict(&im, &result)); // 判断是否预测成功
|
||
|
||
std::cout << result.Str() << std::endl;
|
||
|
||
cv::Mat vis_im = fastdeploy::vision::Visualize::VisDetection(im, result, 0.5);
|
||
// 可视化结果保存到本地
|
||
cv::imwrite("vis_result.jpg", vis_im);
|
||
std::cout << "Visualized result save in vis_result.jpg" << std::endl;
|
||
return 0;
|
||
}
|
||
```
|
||
|
||
## 3. 准备CMakeList.txt
|
||
|
||
FastDeploy中包含多个依赖库，直接采用`g++`或编译器编译较为繁杂，推荐使用cmake进行编译配置。示例配置如下，
|
||
|
||
假设下载或准备的FastDeploy C++ SDK在`/Paddle/Download`目录下，且目录名为`fastdeploy_cpp_sdk`，在开发者的项目中只需添加如下代码，即可引入`FASTDEPLOY_INCS`和`FASTDEPLOY_LIBS`两个变量，分别表示依赖的头文件和库文件
|
||
|
||
```cmake
|
||
include(/Paddle/Download/fastdeploy_cpp_sdk/FastDeploy.cmake)
|
||
```
|
||
|
||
```cmake
|
||
PROJECT(infer_demo C CXX)
|
||
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
|
||
|
||
include(/Path/to/fastdeploy_cpp_sdk/FastDeploy.cmake)
|
||
|
||
# 添加FastDeploy依赖头文件
|
||
include_directories(${FASTDEPLOY_INCS})
|
||
|
||
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer_demo.cc)
|
||
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
|
||
```
|
||
|
||
## 4. 编译可执行程序
|
||
|
||
假设当前目录已经准备好`infer_demo.cc`和`CMakeLists.txt`两个文件，即可进行编译
|
||
|
||
### Linux & Mac
|
||
|
||
打开命令行终端，进入`infer_demo.cc`和`CmakeLists.txt`所在的目录，执行如下命令
|
||
|
||
```bash
|
||
mkdir build & cd build
|
||
cmake ..
|
||
make -j
|
||
```
|
||
|
||
编译完成后，使用如下命令执行可得到预测结果
|
||
```bash
|
||
./infer_demo
|
||
```
|
||
执行时如提示`error while loading shared libraries: libxxx.so: cannot open shared object file: No such file...`，说明程序执行时没有找到FastDeploy的库路径，可通过执行如下命令，将FastDeploy的库路径添加到环境变量之后，重新执行二进制程序。
|
||
```bash
|
||
source /Path/to/fastdeploy_cpp_sdk/fastdeploy_init.sh
|
||
```
|
||
|
||
执行完屏幕会输出如下日志
|
||
```bash
|
||
DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
|
||
415.047180,89.311569, 506.009613, 283.863098, 0.950423, 0
|
||
163.665710,81.914932, 198.585342, 166.760895, 0.896433, 0
|
||
581.788635,113.027618, 612.623474, 198.521713, 0.842596, 0
|
||
267.217224,89.777306, 298.796051, 169.361526, 0.837951, 0
|
||
...
|
||
...
|
||
```
|
||
|
||
同时可视化的检测结果图片保存在本地`vis_result.jpg`，查看效果如下
|
||
<div align="center">
|
||
<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
|
||
</div>
|
||
|
||
### Windows
|
||
|
||
上面的编译过程适用于Linux/Mac，Windows上编译流程如下
|
||
|
||
在Windows菜单中，找到`x64 Native Tools Command Prompt for VS 2019`打开，进入`infer_demo.cc`和`CMakeLists.txt`所在目录，执行如下命令
|
||
```bat
|
||
mkdir build
|
||
cd build
|
||
cmake .. -G "Visual Studio 16 2019" -A x64
|
||
msbuild infer_demo.sln /m /p:Configuration=Release /p:Platform=x64
|
||
```
|
||
|
||
执行完后，即会在`build/Release`目录下生成`infer_demo.exe`程序，使用如下命令执行可得到预测结果
|
||
```bat
|
||
Release\infer_demo.exe
|
||
```
|
||
|
||
Windows上更多SDK使用方式参阅[Windows平台使用FastDeploy C++ SDK](../../faq/use_sdk_on_windows.md)
|
||
|
||
## 其它文档
|
||
|
||
- [切换模型推理的硬件和后端](../../faq/how_to_change_backend.md)
|