Person Sensor Micro Bit

Example of accessing the Person Sensor from MicroPython on a BBC Micro:bit.

The Person Sensor from Useful Sensors is a small, low-cost hardware module that detects nearby peoples’ faces, and returns information about how many there are, where they are relative to the device, and performs facial recognition. It is designed to be used as an input to a larger system, and this example shows how to read it from a BBC micro:bit board using MicroPython. It can also be used as the starting point for other MicroPython-based boards, but since the exact syntax used to access the I2C bus varies across platforms, you'll probably need to modify the lines that mention i2c in the main Python script. For a full developer's guide, see usfl.ink/ps_dev.

BoM

To build this project you'll need:

• Micro:bit board.
• Person Sensor from Useful Sensors.
• Qwiic connector cable.
• SparkFun Qwiic micro:bit breakout.

I suggest the SparkFun breakout because it converts the micro:bit's small I2C pins into a standard Qwiic socket, but if you're skilled at soldering you could attach the wires directly to the edge pins on the board instead.

Assembling

Slot the micro:bit into the breakout slot, with the two I2C sockets on the breakout facing in the same direction as the front of the board (the side with the buttons and LED display on it). Plug one end of the Qwiic cable into either of the breakout's I2C sockets, and the other into the socket at the top of the person sensor. If you power the micro:bit board, you should see the green LED on the person sensor light up when you point it at yourself.

Running

Create a new project in the Python micro:bit editor. Click on the Open button, and choose the main.py file in this folder. Make sure you have your micro:bit plugged in and click Send to micro:bit to upload the program to the board. If you click on Show serial on the right hand pane you should see information about any found faces shown as text logs. If no faces are seen, there should be output like this:

0 []

The first number is how many face have been detected, and the second part shows information about any faces that were found. Here's an abbreviated example of the output with a face present:

1 [{'box_height': 158, 'box_width': 201, 'id': -1, 'box_confidence': 99, 'box_left': ]