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92 lines
4.3 KiB
Markdown
Executable File
92 lines
4.3 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# PP-TinyPose C++ Deployment Example
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This directory provides the `Multi-person keypoint detection in a single image` example that `pptinypose_infer.cc` fast finishes the deployment of PP-TinyPose on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
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>> **Attention**: PP-Tinypose single model currently supports single-person keypoint detection in a single image. Therefore, the input image should contain one person only or should be cropped. For multi-person keypoint detection, refer to [PP-TinyPose Pipeline](../../det_keypoint_unite/cpp/README.md)
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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 inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) 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 PP-TinyPose model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
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tar -xvf PP_TinyPose_256x192_infer.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/hrnet_demo.jpg
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# CPU inference
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./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 0
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# GPU inference
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./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 1
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# TensorRT inference on GPU
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./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 2
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# KunlunXin XPU inference
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./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 3
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```
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The visualized result after running is as follows
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/196386764-dd51ad56-c410-4c54-9580-643f282f5a83.jpeg", width=359px, height=423px />
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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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## PP-TinyPose C++ Interface
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### PP-TinyPose Class
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```c++
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fastdeploy::vision::keypointdetection::PPTinyPose(
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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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PPTinyPose 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
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> * **config_file**(str): Inference deployment configuration file
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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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> PPTinyPose::Predict(cv::Mat* im, KeyPointDetectionResult* result)
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> ```
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>
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> Model prediction interface. Input images and output keypoint 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**: Keypoint detection results, including coordinates and the corresponding probability value. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of KeyPointDetectionResult
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### Class Member Property
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#### Post-processing Parameter
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> > * **use_dark**(bool): Whether to use DARK for post-processing. Refer to [Reference Paper](https://arxiv.org/abs/1910.06278)
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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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