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79 lines
3.4 KiB
Markdown
79 lines
3.4 KiB
Markdown
English | [简体中文](README_CN.md)
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# PP-Tracking C++ Deployment Example
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This directory provides examples that `infer.cc` fast finishes the deployment of PP-Tracking 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 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.
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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-Tracking model files and test videos
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wget https://bj.bcebos.com/paddlehub/fastdeploy/fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
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tar -xvf fairmot_hrnetv2_w18_dlafpn_30e_576x320.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/person.mp4
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# CPU inference
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 0
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# GPU inference
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 1
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# TensorRT Inference on GPU
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./infer_demo fairmot_hrnetv2_w18_dlafpn_30e_576x320 person.mp4 2
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```
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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-Tracking C++ Interface
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### PPTracking Class
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```c++
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fastdeploy::vision::tracking::PPTracking(
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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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PP-Tracking 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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> PPTracking::Predict(cv::Mat* im, MOTResult* result)
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> ```
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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, tracking id, confidence of each box, and object class id. Refer to [visual model prediction results](../../../../../docs/api/vision_results/) for the description of MOTResult
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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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