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# YOLOv5 Golang Deployment Example

This directory provides examples that `infer.go` uses CGO to call FastDeploy C API and finish the deployment of YOLOv5 model on CPU/GPU.

Before deployment, two steps require confirmation

- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
- 2.  Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)

Taking inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.4 above (x.x.x>1.0.4) or develop version (x.x.x=0.0.0) is required to support this model.

### Use Golang and CGO to deploy YOLOv5 model

Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above.
```bash
wget https://fastdeploy.bj.bcebos.com/dev/cpp/fastdeploy-linux-x64-0.0.0.tgz
tar xvf fastdeploy-linux-x64-0.0.0.tgz
```

Copy FastDeploy C APIs from precompiled library to the current directory.
```bash
cp -r fastdeploy-linux-x64-0.0.0/include/fastdeploy_capi .
```

Download the YOLOv5 ONNX model file and test images
```bash
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
```

Configure the `cgo CFLAGS: -I` to FastDeploy C API directory path and  the `cgo LDFLAGS: -L` to FastDeploy dynamic library path. The FastDeploy dynamic library is located in the `/lib` directory.
```bash
cgo CFLAGS: -I./fastdeploy_capi
cgo LDFLAGS: -L./fastdeploy-linux-x64-0.0.0/lib -lfastdeploy
```

Use the following command to add Fastdeploy library path to the environment variable.
```bash
source /Path/to/fastdeploy-linux-x64-0.0.0/fastdeploy_init.sh 
```

Compile the Go file `infer.go`.
```bash
go build infer.go
```

After compiling, use the following command to obtain the predicted results.
```bash
# CPU inference
./infer -model yolov5s.onnx -image 000000014439.jpg -device 0
# GPU inference
./infer -model yolov5s.onnx -image 000000014439.jpg -device 1
```

Then visualized inspection result is saved in the local image `vis_result.jpg`.