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* cpp example run success * add landmarks * fix reviewed problem * add pybind * add readme in examples * fix reviewed problem * new file: tests/models/test_centerface.py * fix reviewed problem 230202
78 lines
3.0 KiB
Markdown
78 lines
3.0 KiB
Markdown
# CenterFace C++部署示例
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本目录下提供`infer.cc`快速完成CenterFace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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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz # x.x.x > 1.0.4
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tar xvf fastdeploy-linux-x64-x.x.x.tgz # x.x.x > 1.0.4
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x # x.x.x > 1.0.4
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make -j
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#下载官方转换好的CenterFace模型文件和测试图片
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wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/CenterFace.onnx
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#使用CenterFace.onnx模型
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# CPU推理
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./infer_demo CenterFace.onnx test_lite_face_detector_3.jpg 0
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# GPU推理
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./infer_demo CenterFace.onnx test_lite_face_detector_3.jpg 1
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# GPU上TensorRT推理
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./infer_demo CenterFace.onnx test_lite_face_detector_3.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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<img width="640" src="https://user-images.githubusercontent.com/44280887/215670067-e14b5205-e303-4c3a-9812-be4a81173dc6.jpg">
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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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## CenterFace C++接口
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### CenterFace类
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```c++
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fastdeploy::vision::facedet::CenterFace(
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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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CenterFace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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> CenterFace::Predict(cv::Mat* im, FaceDetectionResult* 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, FaceDetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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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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