English | [简体中文](README_CN.md)
# InsightFace C++ Deployment Example
This direct ory provides examples that `infer_xxx.cc` fast finishes the deployment of InsighFace, including ArcFace\CosFace\VPL\Partial_FC on CPU/GPU and GPU accelerated by TensorRT.
Taking ArcFace as an example, we demonstrate how `infer_arcface.cc` fast finishes the deployment of InsighFace on CPU/GPU and GPU accelerated by TensorRT.
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 the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory.
```bash
mkdir build
cd build
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j
# Download the official converted ArcFace model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/face_demo.zip
unzip face_demo.zip
# CPU inference
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 0
# GPU inference
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 1
# TensorRT inference on GPU
./infer_arcface_demo ms1mv3_arcface_r100.onnx face_0.jpg face_1.jpg face_2.jpg 2
```
The visualized result after running is as follows
The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md)
## InsightFace C++ Interface
### ArcFace Class
```c++
fastdeploy::vision::faceid::ArcFace(
const string& model_file,
const string& params_file = "",
const RuntimeOption& runtime_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX)
```
ArcFace model loading and initialization, among which model_file is the exported ONNX model format.
### CosFace Class
```c++
fastdeploy::vision::faceid::CosFace(
const string& model_file,
const string& params_file = "",
const RuntimeOption& runtime_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX)
```
CosFace model loading and initialization, among which model_file is the exported ONNX model format.
### PartialFC Class
```c++
fastdeploy::vision::faceid::PartialFC(
const string& model_file,
const string& params_file = "",
const RuntimeOption& runtime_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX)
```
PartialFC model loading and initialization, among which model_file is the exported ONNX model format.
### VPL Class
```c++
fastdeploy::vision::faceid::VPL(
const string& model_file,
const string& params_file = "",
const RuntimeOption& runtime_option = RuntimeOption(),
const ModelFormat& model_format = ModelFormat::ONNX)
```
VPL model loading and initialization, among which model_file is the exported ONNX model format.
**Parameter**
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path. Only passing an empty string when the model is in ONNX format
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(ModelFormat): Model format. ONNX format by default
#### Predict function
> ```c++
> ArcFace::Predict(const cv::Mat& im, FaceRecognitionResult* result)
> ```
>
> Model prediction interface. Input images and output detection results.
>
> **Parameter**
>
> > * **im**: Input images in HWC or BGR format
> > * **result**: Detection results, including detection box and confidence of each box. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for FaceRecognitionResult
### Change pre-processing and post-processing parameters
Pre-processing and post-processing parameters can be changed by modifying the member variables of InsightFaceRecognitionPostprocessor and InsightFaceRecognitionPreprocessor
#### InsightFaceRecognitionPreprocessor member variables (preprocessing parameters)
> > * **size**(vector<int>): This parameter changes the size of the resize during preprocessing, containing two integer elements for [width, height] with default value [112, 112].
Revise through InsightFaceRecognitionPreprocessor::SetSize(std::vector& size)
> > * **alpha**(vector<float>): Preprocess normalized alpha, and calculated as `x'=x*alpha+beta`. alpha defaults to [1. / 127.5, 1.f / 127.5, 1. / 127.5].
Revise through InsightFaceRecognitionPreprocessor::SetAlpha(std::vector& alpha)
> > * **beta**(vector<float>): Preprocess normalized beta, and calculated as `x'=x*alpha+beta`. beta defaults to [-1.f, -1.f, -1.f].
Revise through InsightFaceRecognitionPreprocessor::SetBeta(std::vector& beta)
#### InsightFaceRecognitionPostprocessor member variables (post-processing parameters)
> > * **l2_normalize**(bool): Whether to perform l2 normalization before outputting the face vector. Default false.
Revise through InsightFaceRecognitionPostprocessor::SetL2Normalize(bool& l2_normalize)
- [Model Description](../../)
- [Python Deployment](../python)
- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
