Fire Extinguisher Detector Server

This is an example of a Network Compute Bridge server. It is similar to tensorflow_server.py, but uses Keras Retinanet instead of TensorFlow. The provided model specifically detects fire extinguishers.

Build and Export

This example can be run on a local machine directly, but is easier to use with Docker.

The Docker image can be built and exported with the following commands:

# builds the image
sudo docker build -t fire_ext_detector .

# exports the image, uses pigz
sudo docker save fire_ext_detector | pigz > fire_ext_detector.tar.gz

To build the image for CORE I/O:

# Prerequisites
# Install the pigz and qemu packages
sudo apt-get install qemu binfmt-support qemu-user-static pigz
# This step will execute the registering scripts
docker run --rm --privileged multiarch/qemu-user-static --reset -p yes

# Builds the image
sudo docker build -t fire_ext_detector:l4t -f Dockerfile.l4t .

# Exports the image, uses pigz
sudo docker save fire_ext_detector:l4t | pigz > fire_ext_detector.tar.gz

Execution

To run this example on a Spot CORE, run:

./start_server.sh

Otherwise, run:

sudo docker run -d --name retinanet_server --network host --env BOSDYN_CLIENT_USERNAME --env BOSDYN_CLIENT_PASSWORD --restart unless-stopped fire_ext_detector -d . --port $PORT $ROBOT_IP

$PORT is the port to use for the server on the machine the server is running on
$ROBOT_IP is the IP address or hostname of your Spot.
BOSDYN_CLIENT_USERNAME is the environment variable for the username for the robot
BOSDYN_CLIENT_PASSWORD is the environment variable for the password for the robot

To run this example on a CORE I/O, run:

# Load the image
sudo docker load -i fire_ext_detector.tar.gz

# Run the container
sudo docker run -d \
    --gpus all \
    --name retinanet_server \
    --network host \
    --restart unless-stopped \
    -v /opt/payload_credentials/payload_guid_and_secret:/opt/payload_credentials/payload_guid_and_secret \
    fire_ext_detector:l4t \
    -d . 192.168.50.3 --payload-credentials-file /opt/payload_credentials/payload_guid_and_secret

Spot Extension for CORE I/O

This example can also be built into a Spot Extension using a provided convenience script

cd {/path/to/python/examples/network_compute_bridge/fire_extinguisher_server/}

python3 ../../extensions/build_extension.py \
    --dockerfile-paths Dockerfile.l4t \
    --build-image-tags fire_ext_detector:l4t \
    -i fire_ext_detector_image.tar.gz \
    --package-dir . \
    --spx fire_extinguisher_detector.spx

The output file will be called fire_ext_detector.spx and can be uploaded to a CORE I/O.

Action Configuration

To configure the Fire Extinguisher Detector as an Inspection on Spot’s control tablet, go to Hamburger menu –> SETTINGS –> ACTIONS.

Scroll down to CUSTOM INSPECTIONS and select “Create New Action.”

Choose the “Empty Inspection” template action and click the CREATE button.

Set the ROBOT BODY CONTROL to “Body Pose” and the ACTION to “Daq.”

Click “Add Network Compute Bridge Workers” and select “Fire Extinguisher Server - Fire Ext”

Add the Spot CAM - Ptz as an input image to the Fire Extinguisher Server - Fire Ext Network Compute Bridge Worker. Note that this input image is what will be used as the input to the Worker. It is not a standalone image. So, make sure to add an input image and not a standalone image.

Click save to finish configuring the action.

The configuration can be verified by teleoperating Spot to a fire extinguisher and setting up a clear shot with the SPOT CAM PTZ.

Click the red action button, select the configured action and fine tune the shot.

Click CONFIGURE to observe the input image.

After a short period of time, you can see the output of the worker on the second page by clicking the arrows below the input image.

If you see a green box around the fire extinguisher gauge, then the action is configured and working correctly. If you do not see any green boxes, then reduce the “MIN_CONFIDENCE” or exit the action and set up a clearer shot of the fire extinguisher (e.g., consider zooming out further than indicated in the above screenshots).