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72 lines
3.3 KiB
Markdown
72 lines
3.3 KiB
Markdown
English | [简体中文](README_CN.md)
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# PP-Tracking Python Deployment Example
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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. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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This directory provides examples that `infer.py` fast finishesshes the deployment of PP-Tracking on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
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```bash
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# Download deployment example code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/tracking/pptracking/python
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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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python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device cpu
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# GPU inference
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python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device gpu
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# TensorRT inference on GPU （Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
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python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device gpu --use_trt True
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```
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## PP-Tracking Python Interface
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```python
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fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PP-Tracking model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/deploy/pptracking/cpp/README.md) for more information
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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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> ```python
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> PPTracking.predict(frame)
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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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> > * **frame**(np.ndarray): Input data in HWC or BGR format. The video frame is obtained through: _,frame=cap.read()
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> **Return**
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>
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> > Return `fastdeploy.vision.MOTResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure
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### Class Member Variable
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#### Pre-processing Parameter
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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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## Other Documents
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- [PP-Tracking Model Description](..)
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- [PP-Tracking C++ Deployment](../cpp)
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- [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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