删除无用代码，更新python脚本

examples/vision/keypointdetection/tiny_pose/rknpu2/README.md

@@ -51,5 +51,5 @@ python tools/rknpu2/export.py --config_path tools/rknpu2/config/PP_TinyPose_256x
 ## 详细部署文档
 
 - [模型详细介绍](../README_CN.md)
-- [Python部署](python)
+- [Python部署](./python)
-- [C++部署](cpp)
+- [C++部署](./cpp)

examples/vision/keypointdetection/tiny_pose/rknpu2/python/README_CN.md

@@ -0,0 +1,70 @@
+[English](README.md) | 简体中文
+# PP-TinyPose 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`pptinypose_infer.py`快速完成PP-TinyPose在NPU加速部署的`单图单人关键点检测`示例。执行如下脚本即可完成
+
+>> **注意**: PP-Tinypose单模型目前只支持单图单人关键点检测，因此输入的图片应只包含一个人或者进行过裁剪的图像。多人关键点检测请参考[PP-TinyPose Pipeline](../../../det_keypoint_unite/python/README.md)
+
+```bash
+# 下载PP-TinyPose模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/hrnet_demo.jpg
+
+# CPU推理
+python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg
+```
+
+运行完成可视化结果如下图所示
+<div  align="center">  
+<img src="https://user-images.githubusercontent.com/16222477/196386764-dd51ad56-c410-4c54-9580-643f282f5a83.jpeg", width=359px, height=423px />
+</div>
+
+## PP-TinyPose Python接口
+
+```python
+fd.vision.keypointdetection.PPTinyPose(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+```
+
+PP-TinyPose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/EXPORT_MODEL.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
+> PPTinyPose.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
+用户可按照自己的实际需求，修改下列后处理参数，从而影响最终的推理和部署效果
+
+> > * **use_dark**(bool): 是否使用DARK进行后处理[参考论文](https://arxiv.org/abs/1910.06278)
+
+
+## 其它文档
+
+- [PP-TinyPose 模型介绍](..)
+- [PP-TinyPose 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)

examples/vision/keypointdetection/tiny_pose/rknpu2/python/pptinypose_infer.py

@@ -0,0 +1,50 @@
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--tinypose_model_dir",
+        required=True,
+        help="path of paddletinypose model directory")
+    parser.add_argument(
+        "--image", required=True, help="path of test image file.")
+    return parser.parse_args()
+
+
+def build_tinypose_option(args):
+    option = fd.RuntimeOption()
+    option.use_rknpu()
+    return option
+
+
+args = parse_arguments()
+
+tinypose_model_file = os.path.join(args.tinypose_model_dir, "PP_TinyPose_256x192_infer_rk3588_unquantized.rknn")
+tinypose_params_file = os.path.join(args.tinypose_model_dir, "")
+tinypose_config_file = os.path.join(args.tinypose_model_dir, "infer_cfg.yml")
+# 配置runtime，加载模型
+runtime_option = build_tinypose_option(args)
+tinypose_model = fd.vision.keypointdetection.PPTinyPose(
+    tinypose_model_file,
+    tinypose_params_file,
+    tinypose_config_file,
+    runtime_option=runtime_option,
+    model_format=fd.ModelFormat.RKNN)
+tinypose_model.disable_normalize()
+tinypose_model.disable_permute()
+
+# 预测图片检测结果
+im = cv2.imread(args.image)
+tinypose_result = tinypose_model.predict(im)
+print("Paddle TinyPose Result:\n", tinypose_result)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_keypoint_detection(
+    im, tinypose_result, conf_threshold=0.5)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("TinyPose visualized result save in ./visualized_result.jpg")

fastdeploy/vision/keypointdet/pptinypose/pptinypose.cc

@@ -139,18 +139,12 @@ bool PPTinyPose::Postprocess(std::vector<FDTensor>& infer_result,
            "Only support batch = 1 in FastDeploy now.");
   result->Clear();
 
-  std::cout << "Postprocess" << std::endl;
-  std::cout << "infer_result.size() is " << infer_result.size() << std::endl;
   if (infer_result.size() == 1) {
     FDTensor result_copy = infer_result[0];
-    std::cout << "Reshape result_copy!" << std::endl;
     result_copy.Reshape({result_copy.shape[0], result_copy.shape[1],
                          result_copy.shape[2] * result_copy.shape[3]});
-    std::cout << "Resize infer_result!" << std::endl;
     infer_result.resize(2);
-    std::cout << "Do ArgMax!" << std::endl;
     function::ArgMax(result_copy,&infer_result[1],-1);
-    std::cout << "Done!" << std::endl;
   }
 
   // Calculate output length