# trt_pose

**Repository Path**: codepool_admin/trt_pose

## Basic Information

- **Project Name**: trt_pose
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-01-21
- **Last Updated**: 2025-01-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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._