FastDeploy/examples/vision/matting/modnet/cpp/README.md

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# MODNet C++ Deployment Example

This directory provides examples that `infer.cc` fast finishes the deployment of ArcFace 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. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.

```bash
mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
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 MODNet model files and test images 

wget https://bj.bcebos.com/paddlehub/fastdeploy/modnet_photographic_portrait_matting.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg


# CPU inference
./infer_demo modnet_photographic_portrait_matting.onnx matting_input.jpg matting_bgr.jpg 0
# GPU inference
./infer_demo modnet_photographic_portrait_matting.onnx matting_input.jpg matting_bgr.jpg 1
# TensorRT inference on GPU
./infer_demo modnet_photographic_portrait_matting.onnx matting_input.jpg matting_bgr.jpg 2
```

The visualized result after running is as follows

<div width="840">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186851995-fe9f509f-97d4-4967-a3b0-ce2b3c2f5dca.jpg">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186851964-4c9086b9-3490-4fcb-82f9-2106c63aa4f3.jpg">
</div>

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)

## MODNet C++ Interface 

### MODNet Class

```c++
fastdeploy::vision::matting::MODNet(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)
```

MODNet 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++
> MODNet::Predict(cv::Mat* im, MattingResult* result,
>                 float conf_threshold = 0.25,
>                 float nms_iou_threshold = 0.5)
> ```
>
> 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 Result](../../../../../docs/api/vision_results/) for MattingResult
> > * **conf_threshold**: Filtering threshold of detection box confidence
> > * **nms_iou_threshold**: iou threshold during NMS processing

### Class Member Variable
#### Pre-processing Parameter
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results


> > * **size**(vector&lt;int&gt;): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [256, 256]
> > * **alpha**(vector&lt;float&gt;): Preprocess normalized alpha, and calculated as `x'=x*alpha+beta`，alpha  defaults to [1. / 127.5, 1.f / 127.5, 1. / 127.5]
> > * **beta**(vector&lt;float&gt;): Preprocess normalized beta, and calculated as `x'=x*alpha+beta`，beta defaults to [-1.f, -1.f, -1.f]
> > * **swap_rb**(bool): Whether to convert BGR to RGB in pre-processing. Default True

- [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)