# InsightFace Python部署示例
本目录下提供infer_xxx.py快速完成InsighFace模型包括ArcFace\CosFace\VPL\Partial_FC在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。

在部署前，需确认以下两个步骤

- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/environment.md)  
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start)

以ArcFace为例子, 提供`infer_arcface.py`快速完成ArcFace在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成

```bash
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/faceid/insightface/python/

#下载ArcFace模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_0.JPG
wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_1.JPG
wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_2.JPG

# CPU推理
python infer_arcface.py --model ms1mv3_arcface_r100.onnx --face test_lite_focal_arcface_0.JPG --face_positive test_lite_focal_arcface_1.JPG --face_negative test_lite_focal_arcface_2.JPG --device cpu
# GPU推理
python infer_arcface.py --model ms1mv3_arcface_r100.onnx --face test_lite_focal_arcface_0.JPG --face_positive test_lite_focal_arcface_1.JPG --face_negative test_lite_focal_arcface_2.JPG --device gpu
# GPU上使用TensorRT推理
python infer_arcface.py --model ms1mv3_arcface_r100.onnx --face test_lite_focal_arcface_0.JPG --face_positive test_lite_focal_arcface_1.JPG --face_negative test_lite_focal_arcface_2.JPG --device gpu --use_trt True
```

运行完成可视化结果如下图所示

<div width="700">
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321537-860bf857-0101-4e92-a74c-48e8658d838c.JPG">
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184322004-a551e6e4-6f47-454e-95d6-f8ba2f47b516.JPG">
<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321622-d9a494c3-72f3-47f1-97c5-8a2372de491f.JPG">
</div>

```bash
Prediction Done!
--- [Face 0]:FaceRecognitionResult: [Dim(512), Min(-2.309220), Max(2.372197), Mean(0.016987)]
--- [Face 1]:FaceRecognitionResult: [Dim(512), Min(-2.288258), Max(1.995104), Mean(-0.003400)]
--- [Face 2]:FaceRecognitionResult: [Dim(512), Min(-3.243411), Max(3.875866), Mean(-0.030682)]
Detect Done! Cosine 01: 0.814385, Cosine 02:-0.059388

```

## InsightFace Python接口

```python
fastdeploy.vision.faceid.ArcFace(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
fastdeploy.vision.faceid.CosFace(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
fastdeploy.vision.faceid.PartialFC(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
fastdeploy.vision.faceid.VPL(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
```

ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式

**参数**

> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
> * **model_format**(Frontend): 模型格式，默认为ONNX

### predict函数

> ```python
> ArcFace.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
>
> **参数**
>
> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> > * **conf_threshold**(float): 检测框置信度过滤阈值
> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值

> **返回**
>
> > 返回`fastdeploy.vision.FaceRecognitionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)

### 类成员属性
#### 预处理参数
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果


> > * **size**(list[int]): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[112, 112]
> > * **alpha**(list[float]): 预处理归一化的alpha值，计算公式为`x'=x*alpha+beta`，alpha默认为[1. / 127.5, 1.f / 127.5, 1. / 127.5]
> > * **beta**(list[float]): 预处理归一化的beta值，计算公式为`x'=x*alpha+beta`，beta默认为[-1.f, -1.f, -1.f]
> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认True
> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认False


## 其它文档

- [InsightFace 模型介绍](..)
- [InsightFace C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)