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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
79 lines
3.4 KiB
Markdown
79 lines
3.4 KiB
Markdown
English | [简体中文](README_CN.md)
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# BlazeFace C++ Deployment Example
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This directory provides examples that `infer.cc` fast finishes the deployment of BlazeFace on CPU/GPU。
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 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)
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Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory.
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```bash
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mkdir build
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cd build
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# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
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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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#Download the official converted YOLOv7Face model files and test images
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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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#Use blazeface-1000e model
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# CPU inference
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./infer_demo blazeface-1000e/ test_lite_face_detector_3.jpg 0
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# GPU Inference
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./infer_demo blazeface-1000e/ test_lite_face_detector_3.jpg 1
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```
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The visualized result after running is as follows
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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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The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
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- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## BlazeFace C++ Interface
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### BlazeFace Class
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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 model loading and initialization, among which model_file is the exported PADDLE model format
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. Only passing an empty string when the model is in PADDLE format
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> * **config_file**(str): Config file path. Only passing an empty string when the model is in PADDLE format
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. PADDLE format by default
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#### Predict Function
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> ```c++
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> BlazeFace::Predict(cv::Mat& im, FaceDetectionResult* result)
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> ```
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> Model prediction interface. Input images and output detection results.
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>
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> **Parameter**
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>
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> > * **im**: Input images in HWC or BGR format
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> > * **result**: Detection results, including detection box and confidence of each box. Refer to [Vision Model Prediction Result](../../../../../docs/api/vision_results/) for FaceDetectionResult
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- [Model Description](../../)
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- [Python Deployment](../python)
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- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
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