git clone https://github.com/sofwerx/assault-rifle-detection.git $HOME/Documents/pistol-detection
cd $HOME/Documents/pistol-detection
docker build -t gpu_tf .
xhost +local:docker
nvidia-docker run --rm --network host --privileged -it -v ~/.Xauthority:/root/.Xauthority -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY --env="QT_X11_NO_MITSHM=1" -v /dev/video0:/dev/video0 -v $HOME/Documents/pistol-detection/tf_files:/tf_files --device /dev/snd gpu_tf bash
cd object_detection
As it's unsafe for Slack API tokens to be shared publically, get the API token from https://api.slack.com/custom-integrations/legacy-tokens and export as an environment variable via
export SLACK_API_TOKEN=[75-char-token]
For optimization, the object detection code has been split into two seperate scripts that can be run simulatenously, but should be run in seperate instances. Depending on which instance one is running, do the following:
cp /detect_pistol/person-camera-session-one.py .
or
cp /detect_pistol/person-camera-session-two.py .
then, for both:
wget http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_coco_2017_11_08.tar.gz
tar -xvf faster_rcnn_resnet101_coco_2017_11_08.tar.gz
Select Camera
RECEPTION_EAST RECEPTION_WEST DIRTYWERX_NORTH DIRTYWERX_SOUTH THUNDERDRONE_INDOOR_EAST THUNDERDRONE_INDOOR_WEST OUTSIDE_WEST OUTSIDE_NORTH_WEST OUTSIDE_NORTH OUTSIDE_NORTH_EAST DIRTYWERX_RAMP
Select Gun Type PISTOL LONGGUN
Run session one in its own instance, selecting which camera to use
python person-camera-session-one.py RECEPTION_EAST
Session two can be ran simultaneously with session one in a seperate instance. Choose which camera is being used and what object is being detected.
python person-camera-session-two.py RECEPTION_EAST PISTOL