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87 lines
3.9 KiB
Markdown
87 lines
3.9 KiB
Markdown
English | [简体中文](README_CN.md)
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# PFLD C++ Deployment Example
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This directory provides examples that `infer.cc` fast finishes the deployment of PFLD 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. FastDeploy version 1.0.2 or above (x.x.x>=1.0.2), or the nightly built version is required to support this model.
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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
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# Download the official converted PFLD model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/pfld-106-lite.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
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# CPU inference
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./infer_demo --model pfld-106-lite.onnx --image facealign_input.png --device cpu
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# GPU inference
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./infer_demo --model pfld-106-lite.onnx --image facealign_input.png --device gpu
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# TensorRT Inference on GPU
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./infer_demo --model pfld-106-lite.onnx --image facealign_input.png --device gpu --backend trt
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```
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The visualized result after running is as follows
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<div width="500">
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<img width="470" height="384" float="left" src="https://user-images.githubusercontent.com/19977378/197931737-c2d8e760-a76d-478a-a6c9-4574fb5c70eb.png">
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</div>
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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/en/faq/use_sdk_on_windows.md)
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## PFLD C++ Interface
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### PFLD Class
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```c++
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fastdeploy::vision::facealign::PFLD(
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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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PFLD 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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> PFLD::Predict(cv::Mat* im, FaceAlignmentResult* result)
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> ```
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>
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> Model prediction interface. Input images and output landmarks results directly.
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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**: landmarks result. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of FaceAlignmentResult
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### Class Member Variable
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
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> > * **size**(vector<int>): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [112, 112]
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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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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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