FastDeploy/examples/vision/tracking/pptracking/cpp

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PP-Tracking C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of PP-Tracking on CPU/GPU and GPU accelerated by TensorRT. 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 PP-Tracking 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-Tracking model files and test videos
wget https://bj.bcebos.com/paddlehub/fastdeploy/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
tar -xvf fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4


# CPU inference
./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 0
# GPU inference
./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 1
# TensorRT Inference on GPU
./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 2

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-Tracking C++ Interface

PPTracking Class

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

PP-Tracking 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

PPTracking::Predict(cv::Mat* im, MOTResult* result)

Model prediction interface. Input images and output detection results.

Parameter

• im: Input images in HWC or BGR format
• result: Detection results, including detection box, tracking id, confidence of each box, and object class id. Refer to visual model prediction results for the description of MOTResult

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