TensorRT-使用TensorRT加速实现实时人体姿态估计算法-算法加速-优质项目部署实战.zip

# trt_pose > Want to detect hand poses? Check out the new [trt_pose_hand](http://github.com/NVIDIA-AI-IOT/trt_pose_hand) project for real-time hand pose and gesture recognition! <img src="https://user-images.githubusercontent.com/4212806/67125332-71a64580-f1a9-11e9-8ee1-e759a38de215.gif" height=256/> trt_pose is aimed at enabling real-time pose estimation on NVIDIA Jetson. You may find it useful for other NVIDIA platforms as well. Currently the project includes - Pre-trained models for human pose estimation capable of running in real time on Jetson Nano. This makes it easy to detect features like ``left_eye``, ``left_elbow``, ``right_ankle``, etc. - Training scripts to train on any keypoint task data in [MSCOCO](https://cocodataset.org/#home) format. This means you can experiment with training trt_pose for keypoint detection tasks other than human pose. To get started, follow the instructions below. If you run into any issues please [let us know](../../issues). ## Getting Started To get started with trt_pose, follow these steps. ### Step 1 - Install Dependencies 1. Install PyTorch and Torchvision. To do this on NVIDIA Jetson, we recommend following [this guide](https://forums.developer.nvidia.com/t/72048) 2. Install [torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt) ```python git clone https://github.com/NVIDIA-AI-IOT/torch2trt cd torch2trt sudo python3 setup.py install --plugins ``` 3. Install other miscellaneous packages ```python sudo pip3 install tqdm cython pycocotools sudo apt-get install python3-matplotlib ``` ### Step 2 - Install trt_pose ```python git clone https://github.com/NVIDIA-AI-IOT/trt_pose cd trt_pose sudo python3 setup.py install ``` ### Step 3 - Run the example notebook We provide a couple of human pose estimation models pre-trained on the MSCOCO dataset. The throughput in FPS is shown for each platform | Model | Jetson Nano | Jetson Xavier | Weights | |-------|-------------|---------------|---------| | resnet18_baseline_att_224x224_A | 22 | 251 | [download (81MB)](https://drive.google.com/open?id=1XYDdCUdiF2xxx4rznmLb62SdOUZuoNbd) | | densenet121_baseline_att_256x256_B | 12 | 101 | [download (84MB)](https://drive.google.com/open?id=13FkJkx7evQ1WwP54UmdiDXWyFMY1OxDU) | To run the live Jupyter Notebook demo on real-time camera input, follow these steps 1. Download the model weights using the link in the above table. 2. Place the downloaded weights in the [tasks/human_pose](tasks/human_pose) directory 3. Open and follow the [live_demo.ipynb](tasks/human_pose/live_demo.ipynb) notebook > You may need to modify the notebook, depending on which model you use ## See also - [trt_pose_hand](http://github.com/NVIDIA-AI-IOT/trt_pose_hand) - Real-time hand pose estimation based on trt_pose - [torch2trt](http://github.com/NVIDIA-AI-IOT/torch2trt) - An easy to use PyTorch to TensorRT converter - [JetBot](http://github.com/NVIDIA-AI-IOT/jetbot) - An educational AI robot based on NVIDIA Jetson Nano - [JetRacer](http://github.com/NVIDIA-AI-IOT/jetracer) - An educational AI racecar using NVIDIA Jetson Nano - [JetCam](http://github.com/NVIDIA-AI-IOT/jetcam) - An easy to use Python camera interface for NVIDIA Jetson ## References The trt_pose model architectures listed above are inspired by the following works, but are not a direct replica. Please review the open-source code and configuration files in this repository for architecture details. If you have any questions feel free to reach out. * _Cao, Zhe, et al. "Realtime multi-person 2d pose estimation using part affinity fields." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017._ * _Xiao, Bin, Haiping Wu, and Yichen Wei. "Simple baselines for human pose estimation and tracking." Proceedings of the European Conference on Computer Vision (ECCV). 2018._