Linux / amd64
The gaze demo container contains a demo of running gaze detection model on Jetson. The container supports running gaze detection on a video file input.
The container has 3 models:
MTCNN model for face detection with input image resolution of 260X135. The model was converted from Caffe to TensorRT.
NVIDIA Facial landmarks model with input resolution of 80X80 per face. The model was converted from TensorFlow to TensorRT.
NVIDIA Gaze model with input resolution of 224X224 per left eye, right eye and whole face. The model was converted from TensorFlow to TensorRT.
Note that the gaze demo currently has TensorRT engine files built for Jetson AGX Xavier and Jetson Xavier NX and hence this demo can be run on Jetson AGX Xavier or Jetson Xavier NX only.
The container requires JetPack 4.4 Developer Preview (L4T R32.4.2)
Ensure these prerequisites are available on your system:
Jetson device running L4T r32.4.2
JetPack 4.4 Developer Preview (DP)
First, pull the container image:
sudo docker pull nvcr.io/nvidia/jetson-gaze:r32.4.2
To run gaze detection on a built-in video, run the following commands:
sudo xhost +si:localuser:root sudo docker run --runtime nvidia -it --rm --network host -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix nvcr.io/nvidia/jetson-gaze:r32.4.2 python3 run_gaze_sequential.py /videos/gaze_video.mp4 --loop --codec=h264
To run gaze detection on a your own video (.h264 format), run the following commands (you would need -v option to mount your video directory)
sudo xhost +si:localuser:root sudo docker run --runtime nvidia -it --rm --network host -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix -v /my_video_directory/:/userVideos nvcr.io/nvidia/jetson-gaze:r32.4.2 python3 run_gaze_sequential.py /userVideos/my_video_name --loop --codec=h264
my_video_directory with the full path to the directory where you have saved your video and replace
my_video_name with the name of your video.
Cloud native demo on Jetson showcases how Jetson is bringing cloud native methodolgoies like containarizaton to the edge. The demo is built around the example use case of AI applications for service robots and show cases people detection, pose detection, gaze detection and natural language processing all running simultaneously as containers on Jetson.
Please follow for instructions in https://github.com/NVIDIA-AI-IOT/jetson-cloudnative-demo gitlab on running People detection demo container as part of the cloud native demo.
gaze demo container includes various software packages with their respective licenses included within the container.
If you have any questions or need help, please visit the Jetson Developer Forums.