FastDeploy/examples/vision/keypointdetection/tiny_pose/cpp

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PP-TinyPose C++ Deployment Example

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

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

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library

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.

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# Download PP-TinyPose model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
tar -xvf PP_TinyPose_256x192_infer.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/hrnet_demo.jpg


# CPU inference
./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 0
# GPU inference
./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 1
# TensorRT inference on GPU
./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 2
# KunlunXin XPU inference
./infer_tinypose_demo PP_TinyPose_256x192_infer hrnet_demo.jpg 3

The visualized result after running is as follows

The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

PP-TinyPose C++ Interface

PP-TinyPose Class

fastdeploy::vision::keypointdetection::PPTinyPose(
        const string& model_file,
        const string& params_file = "",
        const string& config_file,
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::PADDLE)

PPTinyPose model loading and initialization, among which model_file is the exported Paddle model format.

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path
• config_file(str): Inference deployment configuration file
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. Paddle format by default

Predict function

PPTinyPose::Predict(cv::Mat* im, KeyPointDetectionResult* result)

Model prediction interface. Input images and output keypoint detection results.

Parameter

• im: Input images in HWC or BGR format
• result: Keypoint detection results, including coordinates and the corresponding probability value. Refer to Vision Model Prediction Results for the description of KeyPointDetectionResult

Class Member Property

Post-processing Parameter

• use_dark(bool): Whether to use DARK for post-processing. Refer to Reference Paper

• Model Description
• Python Deployment
• Vision Model Prediction Results
• How to switch the model inference backend engine