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130 lines
5.5 KiB
Markdown
130 lines
5.5 KiB
Markdown
# InsightFace C++部署示例
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本目录下提供infer_xxx.cc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。
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以ArcFace为例提供`infer_arcface.cc`快速完成ArcFace在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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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CPU推理为例，在本目录执行如下命令即可完成编译测试
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```bash
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mkdir build
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cd build
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-0.4.0.tgz
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tar xvf fastdeploy-linux-x64-0.4.0.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.4.0
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make -j
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#下载官方转换好的ArcFace模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r100.onnx
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wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_0.JPG
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wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_1.JPG
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wget https://bj.bcebos.com/paddlehub/test_samples/test_lite_focal_arcface_2.JPG
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# CPU推理
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./infer_arcface_demo ms1mv3_arcface_r100.onnx test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG test_lite_focal_arcface_2.JPG 0
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# GPU推理
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./infer_arcface_demo ms1mv3_arcface_r100.onnx test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG test_lite_focal_arcface_2.JPG 1
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# GPU上TensorRT推理
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./infer_arcface_demo ms1mv3_arcface_r100.onnx test_lite_focal_arcface_0.JPG test_lite_focal_arcface_1.JPG test_lite_focal_arcface_2.JPG 2
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```
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运行完成可视化结果如下图所示
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<div width="700">
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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321537-860bf857-0101-4e92-a74c-48e8658d838c.JPG">
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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184322004-a551e6e4-6f47-454e-95d6-f8ba2f47b516.JPG">
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<img width="220" float="left" src="https://user-images.githubusercontent.com/67993288/184321622-d9a494c3-72f3-47f1-97c5-8a2372de491f.JPG">
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</div>
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## InsightFace C++接口
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### ArcFace类
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```c++
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fastdeploy::vision::faceid::ArcFace(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX)
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```
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ArcFace模型加载和初始化，其中model_file为导出的ONNX模型格式。
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### CosFace类
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```c++
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fastdeploy::vision::faceid::CosFace(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX)
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```
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CosFace模型加载和初始化，其中model_file为导出的ONNX模型格式。
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### PartialFC类
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```c++
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fastdeploy::vision::faceid::PartialFC(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX)
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```
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PartialFC模型加载和初始化，其中model_file为导出的ONNX模型格式。
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### VPL类
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```c++
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fastdeploy::vision::faceid::VPL(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX)
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```
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VPL模型加载和初始化，其中model_file为导出的ONNX模型格式。
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为ONNX格式
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#### Predict函数
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> ```c++
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> ArcFace::Predict(cv::Mat* im, FaceRecognitionResult* result)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 检测结果，包括检测框，各个框的置信度, FaceRecognitionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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### 类成员变量
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#### 预处理参数
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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
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> > * **size**(vector<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]
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> > * **alpha**(vector<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]
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> > * **beta**(vector<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]
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> > * **swap_rb**(bool): 预处理是否将BGR转换成RGB，默认true
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> > * **l2_normalize**(bool): 输出人脸向量之前是否执行l2归一化，默认false
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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