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* 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>
PP-PicoDet + PP-TinyPose (Pipeline) Python部署示例
在部署前，需确认以下两个步骤
-
- 软硬件环境满足要求，参考FastDeploy环境要求
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- 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