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* [PaddlePaddle Hackathon4 No.186] Add PaddleDetection Models Deployment Go Examples Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> * Fix YOLOv8 Deployment Go Example Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> --------- Signed-off-by: wanziyu <ziyuwan@zju.edu.cn> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
56 lines
2.1 KiB
Markdown
56 lines
2.1 KiB
Markdown
[English](README.md) | 简体中文
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# YOLOv8 Golang 部署示例
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本目录下提供`infer.go`, 使用CGO调用FastDeploy C API快速完成YOLOv8模型在CPU/GPU上部署的示例
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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，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.4以上(x.x.x>1.0.4)或FastDeploy的Develop版本(x.x.x=0.0.0)
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### 使用Golang和CGO工具进行YOLOv8模型推理部署
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在当前目录下，下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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```bash
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wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
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tar xvf fastdeploy-linux-x64-0.0.0.tgz
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```
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将FastDeploy C API文件拷贝至当前目录
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```bash
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cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
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```
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下载官方转换好的 YOLOv8 ONNX 模型文件和测试图片
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```bash
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov8s.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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```
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配置`infer.go`中的`cgo CFLAGS: -I`参数配置为C API文件路径，`cgo LDFLAGS: -L`参数配置为FastDeploy的动态库路径，动态库位于预编译库的`/lib`目录中
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```bash
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cgo CFLAGS: -I./fastdeploy_capi
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cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
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```
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将FastDeploy的库路径添加到环境变量
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```bash
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source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh
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```
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编译Go文件`infer.go`
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```bash
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go build infer.go
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```
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编译完成后，使用如下命令执行可得到预测结果
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```bash
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# CPU推理
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./infer -model yolov8s.onnx -image 000000014439.jpg -device 0
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# GPU推理
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./infer -model yolov8s.onnx -image 000000014439.jpg -device 1
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```
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可视化的检测结果图片保存在本地`vis_result.jpg`
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