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[English](README.md) | 简体中文
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# PP-PicoDet + PP-TinyPose (Pipeline) Python部署示例
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在部署前,需确认以下两个步骤
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- 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)
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- 2. 根据开发环境,下载预编译部署库和samples代码,参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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本目录下提供`det_keypoint_unite_infer.py`快速完成多人模型配置 PP-PicoDet + PP-TinyPose 在CPU/GPU,以及GPU上通过TensorRT加速部署的`单图多人关键点检测`示例。执行如下脚本即可完成
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>> **注意**: PP-TinyPose单模型独立部署,请参考[PP-TinyPose 单模型](../../tiny_pose//python/README.md)
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/keypointdetection/det_keypoint_unite/python
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# 下载PP-TinyPose模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
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tar -xvf PP_TinyPose_256x192_infer.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
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tar -xvf PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/000000018491.jpg
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# CPU推理
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python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device cpu
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# GPU推理
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python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device gpu
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# 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)
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python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device gpu --use_trt True
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# 昆仑芯XPU推理
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python det_keypoint_unite_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --det_model_dir PP_PicoDet_V2_S_Pedestrian_320x320_infer --image 000000018491.jpg --device kunlunxin
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```
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运行完成可视化结果如下图所示
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<div align="center">
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<img src="https://user-images.githubusercontent.com/16222477/196393343-eeb6b68f-0bc6-4927-871f-5ac610da7293.jpeg", width=640px, height=427px />
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</div>
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## PPTinyPosePipeline Python接口
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```python
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fd.pipeline.PPTinyPose(det_model=None, pptinypose_model=None)
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```
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PPTinyPosePipeline模型加载和初始化,其中det_model是使用`fd.vision.detection.PicoDet`[参考Detection文档](../../../detection/paddledetection/python/)初始化的检测模型,pptinypose_model是使用`fd.vision.keypointdetection.PPTinyPose`[参考PP-TinyPose文档](../../tiny_pose/python/)初始化的检测模型
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**参数**
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> * **det_model**(str): 初始化后的检测模型
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> * **pptinypose_model**(str): 初始化后的PP-TinyPose模型
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### predict函数
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> ```python
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> PPTinyPosePipeline.predict(input_image)
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> ```
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>
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> 模型预测结口,输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **input_image**(np.ndarray): 输入数据,注意需为HWC,BGR格式
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> **返回**
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>
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> > 返回`fastdeploy.vision.KeyPointDetectionResult`结构体,结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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### 类成员属性
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#### 后处理参数
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> > * **detection_model_score_threshold**(bool):
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输入PP-TinyPose模型前,Detectin模型过滤检测框的分数阈值
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## 其它文档
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- [Pipeline 模型介绍](..)
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- [Pipeline C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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