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huangjianhui b565c15bf7 [Model] Add tinypose single && pipeline model (#177 )

* Add tinypose model

* Add PPTinypose python API

* Fix picodet preprocess bug && Add Tinypose examples

* Update tinypose example code

* Update ppseg preprocess if condition

* Update ppseg backend support type

* Update permute.h

* Update README.md

* Update code with comments

* Move files dir

* Delete premute.cc

* Add single model pptinypose

* Delete pptinypose old code in ppdet

* Code format

* Add ppdet + pptinypose pipeline model

* Fix bug for posedetpipeline

* Change Frontend to ModelFormat

* Change Frontend to ModelFormat in __init__.py

* Add python posedetpipeline/

* Update pptinypose example dir name

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Create keypointdetection_result.md

* Create README.md

* Create README.md

* Create README.md

* Update README.md

* Update README.md

* Create README.md

* Fix det_keypoint_unite_infer.py bug

* Create README.md

* Update PP-Tinypose by comment

* Update by comment

* Add pipeline directory

* Add pptinypose dir

* Update pptinypose to align accuracy

* Addd warpAffine processor

* Update GetCpuMat to  GetOpenCVMat

* Add comment for pptinypose && pipline

* Update docs/main_page.md

* Add README.md for pptinypose

* Add README for det_keypoint_unite

* Remove ENABLE_PIPELINE option

* Remove ENABLE_PIPELINE option

* Change pptinypose default backend

* PP-TinyPose Pipeline support multi PP-Detection models

* Update pp-tinypose comment

* Update by comments

* Add single test example

Co-authored-by: Jason <jiangjiajun@baidu.com>

2022-10-21 09:28:23 +08:00

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PP-PicoDet + PP-TinyPose (Pipeline) 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
1. 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

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

注意: PP-TinyPose单模型独立部署，请参考PP-TinyPose 单模型

#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/keypointdetection/det_keypoint_unite/python

# 下载PP-TinyPose模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_TinyPose_256x192_infer.tgz
tar -xvf PP_TinyPose_256x192_infer.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
tar -xvf PP_PicoDet_V2_S_Pedestrian_320x320_infer.tgz
wget https://bj.bcebos.com/paddlehub/fastdeploy/000000018491.jpg
# CPU推理
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
# GPU推理
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
# 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 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

运行完成可视化结果如下图所示

PPTinyPosePipeline Python接口

fd.pipeline.PPTinyPose(det_model=None, pptinypose_model=None)

PPTinyPosePipeline模型加载和初始化，其中det_model是使用fd.vision.detection.PicoDet参考Detection文档初始化的检测模型，pptinypose_model是使用fd.vision.keypointdetection.PPTinyPose参考PP-TinyPose文档初始化的检测模型

参数

det_model(str): 初始化后的检测模型

pptinypose_model(str): 初始化后的PP-TinyPose模型

predict函数

PPTinyPosePipeline.predict(input_image)
模型预测结口，输入图像直接输出检测结果。

参数

input_image(np.ndarray): 输入数据，注意需为HWC，BGR格式

返回

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

类成员属性

后处理参数

detection_model_score_threshold(bool): 输入PP-TinyPose模型前，Detectin模型过滤检测框的分数阈值

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