Marshall-Hand

Code for a hand

https://z.umn.edu/M_H

Manual Installation(Not Recommended, unless you want to learn or your Jetson Nano 2gb is brand new)

Write Image (Jetson Nano 2gb) to SD Card using balenaEtcher

Pull the docker image 'nvcr.io/nvidia/l4t-ml:r32.7.1-py3'

sudo docker pull nvcr.io/nvidia/l4t-ml:r32.7.1-py3

Create a folder to store data

mkdir ~/l4t-data

Plug in Logitech C270 Webcam & list USB devices to verify it is detected

lsusb

Run the docker image 'nvcr.io/nvidia/l4t-ml:r32.7.1-py3'

sudo docker run --runtime nvidia -it --network host --volume ~/l4t-data --device /dev/video0 nvcr.io/nvidia/l4t-ml:r32.7.1-py3

After Jupyter Notebook is running, open a terminal and run the following commands:

Install torch2trt

Clone torch2trt Repo to quick install pre-recs

git clone https://github.com/NVIDIA-AI-IOT/torch2trt

Go into torch2trt folder

cd torch2trt

Install torch2trt Pre-recs (takes a while)

python3 setup.py install --plugins

Install miscellaneous packages

pip3 install tqdm cython pycocotools

Install MathPlotLib

apt-get install python3-matplotlib

go back to root

cd /

Install trt_pose

Clone trt_pose Repo to quick install pre-recs

git clone https://github.com/NVIDIA-AI-IOT/trt_pose

Go into trt_pose folder

cd trt_pose

Install trt_pose Pre-recs (takes a while)

python3 setup.py install

Go back to root

cd /

Install jetcam

Clone Jetcam Repo to quick install pre-recs

git clone https://github.com/NVIDIA-AI-IOT/jetcam

Go into jetcam folder

cd jetcam

Install jetcam Pre-recs (takes a while)

python3 setup.py install

Go back to root

cd /

Install trt_pose_hand

Clone trt_pose_hand Repo to quick install pre-recs

git clone https://github.com/NVIDIA-AI-IOT/trt_pose_hand.git

Go into trt_pose_hand folder

cd trt_pose_hand

Install scikit-learn

pip3 install scikit-learn

Start Docker Container

Normal Launch

sudo docker run --name handV7 --runtime nvidia -it \
 -e DISPLAY=$DISPLAY \
 --network host \
 -v ~/l4t-data:/l4t-data \
 --device /dev/video* \
 --device /dev/ttyUSB* \
 --workdir /trt_hand_pose \
 felipegalind0/trt_hand_pose:v7\
 python3 gesture_classification_print.py

No Serial (remove '--device /dev/ttyUSB0 ')

sudo docker run --name handV7 --runtime nvidia -it \
 -e DISPLAY=$DISPLAY \
 --network host \
 -v ~/l4t-data:/l4t-data \
 --device /dev/video0 \
 --workdir /trt_hand_pose \
 felipegalind0/trt_hand_pose:v7 \
 python3 gesture_classification_print.py

JupyterLab Launch (remove 'python3 gesture_classification_print.py')

sudo docker run --name handV7 --runtime nvidia -it \
 -e DISPLAY=$DISPLAY \
 --network host \
 -v ~/l4t-data:/l4t-data \
 --device /dev/video0 \
 --workdir /trt_hand_pose \
 felipegalind0/trt_hand_pose:v7 \

enable xhost

xhost +local:docker