SharkSight

SharkSight: Dive into the Depths of Computer Vision with Hammerhead Precision

Introduction

SharkSight is a computer vision application designed to detect and track objects in real-time using the Jetson Nano platform. This application is specifically designed to detect and track cubes and cones for the 2023 FIRST Robotics Competition Game: Charged Up.

Installation

Prerequisites

The following components are needed to run SharkSight:

• NVIDIA Jetson module (Xavier, Nano, TX2, etc.)
• Camera (or cameras) compatible with the Jetson module
• Python 3.6 or later installed
• OpenCV 4.1.1 or later installed
• numpy 1.16.2 or later installed

Installing Jetson Inference

SharkSight uses the Jetson Inference library to perform object detection and tracking. To install Jetson Inference, follow the instructions listed in the Jetson-Inference GitHub repository

Note: When installing the pretrained models, make sure to install the ssd-mobilenet-v2 model.

Custom Model Training

SharkSight uses a modified version of the ssd-mobilenet-v2 model to detect and track cubes and cones. To train a custom model, follow the instructions listed in the Jetson-Inference GitHub repository

Ensure that the ssd-mobilenet-v2 model is installed before training a custom model. Additionally, make sure the dataset is in the Pascal VOC format. The dataset used to train the custom model can be found here.

The following commands were used to train the custom model:

python3 train_ssd.py --dataset-type=voc --data=data/cubes_cones --model-dir=/models/cubes_cones --batch-size=4 --workers=2

And Converting to ONNX format:

python3 onnx_export.py --model-dir=models/cubes_cones

Creating A Swap File (Optional if using pre-trained model)

If you are training a custom model, you will need to create a swap file to prevent the Jetson Nano from running out of memory. To create a swap file, follow the instructions:

1. Check if you have any existing swap space by running the command sudo swapon --show. If you see any output, you already have some swap space configured.
2. Run the command sudo fallocate -l 4G /swapfile to create a 4GB swap file.
3. Run the command sudo chmod 600 /swapfile to set the correct permissions for the swap file.
4. Set up the swap file by running the command sudo mkswap /swapfile.
5. Enable the swap file by running the command sudo swapon /swapfile.
6. To make the swap file permanent, run the command sudo nano /etc/fstab and add the following line to the end of the file: /swapfile swap swap defaults 0 0
7. To check if the swap file was created successfully, run the command sudo swapon --show.

Installing PyNetworkTables

SharkSight uses PyNetworkTables to send data to the robot. WPILib 2023 requires Ubuntu 22.04 which is not supported by Jetson, so NT4 is not supported (pyntcore)

• Run python3 -m pip install pynetworktables

Installing CScore

SharkSight uses CScore to stream video to the dashboard. To install CScore, run the following commands:

export CPPFLAGS=-I/usr/include/opencv4
python3 -m pip install robotpy-cscore===2022.0.3

Creating Persistent USB Camera Connections

SharkSight uses USB cameras to detect and track objects. To ensure that the cameras are always connected to the same Jetson port, follow the instructions:

1. Run the command lsusb to list all USB devices connected to the Jetson.
2. Create a udew rule by running the command sudo nano /etc/udev/rules.d/99-usb-serial.rules and add the following line to the file: SUBSYSTEM=="video4linux", KERNELS=="2-2.4:1.0", NAME="video0" and SUBSYSTEM=="video4linux", KERNELS=="2-2.4:1.1", NAME="video1" (replace 2-2.4:1.0 and 2-2.4:1.1 with the kernel names of your cameras)
3. Run the command sudo udevadm control --reload-rules to reload the rules.

Setting a Static IP Address

SharkSight uses a static IP address to communicate with the robot. To set a static IP address, follow the instructions:

1. Open the /etc/default/networking file by running the command sudo nano /etc/default/networking.
2. Change CONFIGURE_INTERFACES to no.
3. Open the /etc/network/interfaces file by running the command sudo nano /etc/network/interfaces.

source-directory /etc/network/interfaces.d
source interfaces.d/eth0 

4. Create a new file in the /etc/network/interfaces.d directory by running the command sudo nano /etc/network/interfaces.d/eth0.

    auth eth0
    iface eth0 inet static
    address 10.TE.AM.11
    netmask 255.255.255.0
    gateway 10.TE.AM.4

1. sudo reboot

Note: You must also set a static ip address to the RoboRio, navigate to the RoboRio Dashboard 172.22.11.2 over USB or roborio-TEAM-frc.local

Running SharkSight

To run SharkSight, run the following command:

python3 shark_sight.py [--threshold THRESHOLD] [--capture-height CAPTURE_HEIGHT]
                      [--capture-width CAPTURE_WIDTH] [--stream-height STREAM_HEIGHT]
                      [--stream-width STREAM_WIDTH] [--stream-compression STREAM_COMPRESSION]
                      [--display]

Make sure to ensure that the cameras are connected to the Jetson before running SharkSight. Additionally, make sure that the robot is connected to the same network as the Jetson. In the code, ensure the team number is correct and that net variable is set to the correct model and directory.

The script will begin capturing and processing images from the connected camera(s), detecting and tracking objects in real-time. The application will output a live video stream to the CameraServer, which can be viewed using the SmartDashboard application

Command Line Arguments

The following command line arguments can be used to configure SharkSight:

• --threshold: Sets the minimum confidence level for object detection (default is 0.5).
• --capture-height: Sets the resolution height to capture images from the camera (default is 720).
• --capture-width: Sets the resolution width to capture images from the camera (default is 1280).
• --stream-height: Sets the resolution to stream to the CameraServer (default is 270).
• --stream-width: Sets the resolution to stream to the CameraServer (default is 480).
• --stream-compression: Sets the compression to stream for clients that do not specify it (default is 30).
• --display: Enables the display output (default is false).

Starting SharkSight on Boot

To start SharkSight on boot, follow the instructions:

1. Create a new service by running the command sudo nano /etc/systemd/system/shark_sight.service.
2. Add the following lines to the file:

    [Unit]
    Description=SharkSight
    After=network.target
    Wants=network-online.target systemd-networkd-wait-online.service

    [Service]
    Type=simple
    User=NAME
    ExecStart=/usr/bin/python3 /home/NAME/shark_sight/shark_sight.py
    Restart=on-failure
    RestartSec=1

    [Install]
    WantedBy=multi-user.target

1. Test the service by running the command sudo systemctl start shark_sight.service and journalctl -u shark_sight.service.
2. If the service starts successfully, enable it by running the command sudo systemctl enable shark_sight.service.

Headless Mode

SharkSight can be run in headless mode, which means that it will not display any output to the screen. To run SharkSight in headless mode, follow the instructions:

1. Run the command sudo systemctl set-default multi-user.target to disable the graphical user interface.

Using SSH

In order to change files without having to connect a monitor and keyboard to the Jetson, you can use SSH. To use SSH, follow the instructions:

$ ssh -Y [email protected] in the Command Prompt