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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
79 lines
3.4 KiB
Markdown
79 lines
3.4 KiB
Markdown
English | [简体中文](README_CN.md)
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# CenterFace C++ Deployment Example
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This directory provides examples that `infer.cc` fast finishes the deployment of CenterFace on CPU/GPU and GPU accelerated by TensorRT.
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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 CenterFace 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/CenterFace.onnx
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# Use CenterFace.onnx model
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# CPU inference
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./infer_demo CenterFace.onnx test_lite_face_detector_3.jpg 0
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# GPU inference
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./infer_demo CenterFace.onnx test_lite_face_detector_3.jpg 1
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# TensorRT inference on GPU
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./infer_demo CenterFace.onnx test_lite_face_detector_3.jpg 2
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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/44280887/215670067-e14b5205-e303-4c3a-9812-be4a81173dc6.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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## CenterFace C++ Interface
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### CenterFace Class
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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 model loading and initialization, among which model_file is the exported ONNX 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 ONNX 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. ONNX format by default
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#### Predict Function
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> ```c++
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> CenterFace::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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