git clone https://github.com/JutsFunFor/yolo.git && cd yolo

sudo docker build .

sudo docker tag "YOUR_IMAGE_ID YOUR_DOCKER_HUB/IMAGE_NAME:TAG"

modelPath - change for best_cm/best_right/best_left models

rtspAddress - change according camera IP. Default CM IP 192.168.1.22

threshold - thresh for detection confidence level. Depends on light conditions in complex and should be tuned

natsUrl - default nsta url on port 4222

connectTimeout - timeout in seconds to connect to nats server

sendResultsTopic - sepcified topic for results

actionCompletedTopic - specified topic for receiving signals (actions)

tensorSize - default image size for model input

actions - list of actions to perform inference (add actions if neccessary)

You have to mount volume that contains inference scripts into docker container.

Do not forget to change modelPath prefix in config to /yolo_cm/best_cm.pt or /yolo_left/best_left.pt or /yolo_right/best_right.pt in order to mount this path:

export YOLO_CM="/yolo_cm/yolov8_service.py /yolo_cm/config.json"

export YOLO_LEFT="/yolo_left/yolov8_service.py /yolo_left/config.json"

export YOLO_RIGHT="/yolo_right/yolov8_service.py /yolo_right/config.json"

Kiosk inference cm model example:

sudo docker run -it -v /home/foodtronics/yolo:/yolo_cm --privileged alekseyml/yolov8:nuke python3 $YOLO_CM

Kiosk left model example (do not forget to change modelPath from /yolo_cm/best_cm.pt to /yolo_left/best_left.pt and camera IP):

sudo docker run -it -v /home/foodtronics/yolo:/yolo_left --privileged alekseyml/yolov8:nuke python3 $YOLO_LEFT

Kiosk right model example:

sudo docker run -it -v /home/foodtronics/yolo:/yolo_right --privileged alekseyml/yolov8:nuke python3 $YOLO_RIGHT

Multiclass detection assumes that there are several objects on image that can be refered to the same class. So, result line (results after detection that are sent to sendResultsTopic in json format) contains class name + _idx postfix. This postfix refers to object index on image. For example result class name can be represented as Warning_0, Warning_1, ... Warnign_N.

Nozzle0, Nozzle1, Warning, HasCup, NoCup, Flood

Buffer0 - Buffer6, Flood, LiftCup2, LiftCup3, LiftEmpty2, LiftEmpty3, NoCup1, NoCup2, Warning, hasCup1, hasCup2

Buffer0 - Buffer6, Flood, HasCup1, hasCup2, LiftCup1, LiftCup2, LiftEmpty1, LiftEmpty2, NoCup1, NoCup2, NozzleCup0, NozzleCup1, NozzleEmpty0, NozzleEmpty1, Warning

For each class name there are Xmin, Ymin, Xmax, Ymax and Conf parameters (parse as string).

There are also OrderId, OrderNumber, MenuItemId parameters wich are parsed from topic message. You can manually disable this behaviour commenting 124-126 line yolov8_client.py

Dataset available at roboflow https://app.roboflow.com/coffee200all

For new cases of detector usage you have to update weights.

1. Upload new cases (video or images) to roboflow
2. Label that samples according existing dataset (check for all already labeled images)
3. Export dataset with yolov8 format from roboflow
4. Traing model on new dataset
5. Choose best weights and load it into git repo

Workflow described at https://blog.roboflow.com/how-to-train-yolov8-on-a-custom-dataset/.