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# PP-Tracking Python部署示例
English | [简体中文](README_CN.md)
# PP-Tracking Python Deployment Example
在部署前，需确认以下两个步骤
Before deployment, two steps require confirmation
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
本目录下提供`infer.py`快速完成PP-Tracking在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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
```bash
#下载部署示例代码
# Download deployment example code
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/tracking/pptracking/python
# 下载PP-Tracking模型文件和测试视频
# 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推理
# CPU inference
python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device cpu
# GPU推理
# GPU inference
python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device gpu
# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
# 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.）
python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device gpu --use_trt True
```
## PP-Tracking Python接口
## PP-Tracking Python Interface
```python
fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PP-Tracking模型加载和初始化，其中model_file, params_file以及config_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/deploy/pptracking/cpp/README.md)
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
**参数**
**Parameter**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **config_file**(str): 推理部署配置文件
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
> * **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函数
### predict function
> ```python
> PPTracking.predict(frame)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
> Model prediction interface. Input images and output detection results.
>
> **参数**
> **Parameter**
>
> > * **frame**(np.ndarray): 输入数据，注意需为HWC，BGR格式,frame为视频帧如：_,frame=cap.read()得到
> > * **frame**(np.ndarray): Input data in HWC or BGR format. The video frame is obtained through: _,frame=cap.read()
> **返回**
> **Return**
>
> > 返回`fastdeploy.vision.MOTResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > Return `fastdeploy.vision.MOTResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure
### 类成员属性
#### 预处理参数
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
### Class Member Variable
#### Pre-processing Parameter
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
## 其它文档
## Other Documents
- [PP-Tracking 模型介绍](..)
- [PP-Tracking C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
- [PP-Tracking Model Description](..)
- [PP-Tracking C++ Deployment](../cpp)
- [Model Prediction Results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)

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[English](README.md) | 简体中文
# PP-Tracking Python部署示例
在部署前，需确认以下两个步骤
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
本目录下提供`infer.py`快速完成PP-Tracking在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
```bash
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/tracking/pptracking/python
# 下载PP-Tracking模型文件和测试视频
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推理
python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device cpu
# GPU推理
python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device gpu
# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
python infer.py --model fairmot_hrnetv2_w18_dlafpn_30e_576x320 --video person.mp4 --device gpu --use_trt True
```
## PP-Tracking Python接口
```python
fd.vision.tracking.PPTracking(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PP-Tracking模型加载和初始化，其中model_file, params_file以及config_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/deploy/pptracking/cpp/README.md)
**参数**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径
> * **config_file**(str): 推理部署配置文件
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
### predict函数
> ```python
> PPTracking.predict(frame)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
>
> **参数**
>
> > * **frame**(np.ndarray): 输入数据，注意需为HWC，BGR格式,frame为视频帧如：_,frame=cap.read()得到
> **返回**
>
> > 返回`fastdeploy.vision.MOTResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
### 类成员属性
#### 预处理参数
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
## 其它文档
- [PP-Tracking 模型介绍](..)
- [PP-Tracking C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)