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* fit yolov7face file path * TODO：添加yolov7facePython接口Predict * resolve yolov7face.py * resolve yolov7face.py * resolve yolov7face.py * add yolov7face example readme file * [Doc] fix yolov7face example readme file * [Doc]fix yolov7face example readme file * support BlazeFace * add blazeface readme file * fix review problem * fix code style error * fix review problem * fix review problem * fix head file problem * fix review problem * fix review problem * fix readme file problem * add English readme file * fix English readme file
78 lines
3.1 KiB
Markdown
78 lines
3.1 KiB
Markdown
[English](README.md) | 简体中文
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# BlazeFace C++部署示例
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本目录下提供`infer.cc`快速完成BlazeFace在CPU/GPU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BlazeFace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/blzeface-1000e.tgz
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#使用blazeface-1000e模型
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# CPU推理
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./infer_demo blazeface-1000e/ test_lite_face_detector_3.jpg 0
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# GPU推理
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./infer_demo blazeface-1000e/ test_lite_face_detector_3.jpg 1
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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/49013063/206170111-843febb6-67d6-4c46-a121-d87d003bba21.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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## BlazeFace C++接口
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### BlazeFace类
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```c++
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fastdeploy::vision::facedet::BlazeFace(
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const string& model_file,
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const string& params_file = "",
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const string& config_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE)
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```
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BlazeFace模型加载和初始化，其中model_file为导出的PADDLE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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> * **config_file**(str): 配置文件路径，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PADDLE格式
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#### Predict函数
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> ```c++
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> BlazeFace::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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