Using Inference Output

The Raspberry Pi writes all detection information to NetworkTables, which can be used by your robot code. Below is a Java example for parsing and using this data.

NetworkTables Format

  • ML – The table containing all inference data.

    • nb_objects – the number (double) of detected objects in the current frame.

    • object_classes – a string array of the class names of each object. These are in the same order as the coordinates.

    • boxes – a double array containg the coordinates of every detected object. The coordinates are in the following format: [top_left__x1, top_left_y1, bottom_right_x1, bottom_right_y1, top_left_x2, top_left_y2, … ]. There are four coordinates per box. A way to parse this array in Java is shown below.

The below VisionSubsystem Java class parses the data from NetworkTables and stores it in a usable way.

Example code coming soon!

Using the arrays created by the VisionSubsystem, one can make a simple command to turn to face a game piece. In this example, a hatch is used. One thing to note is the ~15fps of inference attained by a Google Coral is not fast enough for PID input directly, however calculating the relative heading of a game piece and then turning to that heading works accurately.

Example code coming soon!