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[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>
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huangjianhui
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examples/vision/keypointdetection/tiny_pose/python/README.md
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examples/vision/keypointdetection/tiny_pose/python/README.md
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# PP-TinyPose 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`pptinypose_infer.py`快速完成PP-TinyPose在CPU/GPU，以及GPU上通过TensorRT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
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>> **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)
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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/tiny_pose/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/hrnet_demo.jpg
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# CPU推理
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python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device cpu
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# GPU推理
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python pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.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 pptinypose_infer.py --tinypose_model_dir PP_TinyPose_256x192_infer --image hrnet_demo.jpg --device gpu --use_trt True
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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/16222477/196386764-dd51ad56-c410-4c54-9580-643f282f5a83.jpeg", width=359px, height=423px />
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</div>
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## PP-TinyPose Python接口
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```python
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fd.vision.keypointdetection.PPTinyPose(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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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)
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径
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> * **config_file**(str): 推理部署配置文件
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
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### predict函数
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> ```python
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> PPTinyPose.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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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
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> > * **use_dark**(bool): 是否使用DARK进行后处理[参考论文](https://arxiv.org/abs/1910.06278)
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## 其它文档
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- [PP-TinyPose 模型介绍](..)
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- [PP-TinyPose 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/runtime/how_to_change_backend.md)
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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--tinypose_model_dir",
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required=True,
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help="path of paddletinypose model directory")
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parser.add_argument(
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"--image", required=True, help="path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="type of inference device, support 'cpu' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="wether to use tensorrt.")
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return parser.parse_args()
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def build_tinypose_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("image", [1, 3, 256, 192])
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return option
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args = parse_arguments()
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tinypose_model_file = os.path.join(args.tinypose_model_dir, "model.pdmodel")
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tinypose_params_file = os.path.join(args.tinypose_model_dir, "model.pdiparams")
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tinypose_config_file = os.path.join(args.tinypose_model_dir, "infer_cfg.yml")
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# 配置runtime，加载模型
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runtime_option = build_tinypose_option(args)
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tinypose_model = fd.vision.keypointdetection.PPTinyPose(
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tinypose_model_file,
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tinypose_params_file,
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tinypose_config_file,
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runtime_option=runtime_option)
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# 预测图片检测结果
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im = cv2.imread(args.image)
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tinypose_result = tinypose_model.predict(im)
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print("Paddle TinyPose Result:\n", tinypose_result)
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# 预测结果可视化
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vis_im = fd.vision.vis_keypoint_detection(
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im, tinypose_result, conf_threshold=0.5)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("TinyPose visualized result save in ./visualized_result.jpg")
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