[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>

examples/vision/keypointdetection/det_keypoint_unite/python/README.md

@@ -0,0 +1,74 @@
+# 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](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 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)
+
+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](../../tiny_pose//python/README.md)
+
+```bash
+#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
+<div  align="center">  
+<img src="https://user-images.githubusercontent.com/16222477/196393343-eeb6b68f-0bc6-4927-871f-5ac610da7293.jpeg", width=640px, height=427px />
+</div>
+
+## PPTinyPosePipeline Python接口
+
+```python
+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](../../../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
+
+**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
+
+> ```python
+> 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](../../../../../docs/api/vision_results/)
+
+### 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
+
+## 其它文档
+
+- [Pipeline 模型介绍](..)
+- [Pipeline 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/runtime/how_to_change_backend.md)