Take better photos with a Raspberry Pi Pico light meter
Armed with a Raspberry Pi Pico and a couple of achingly cool vintage cameras, Team VEEB created a must-have photography tool to help them on their mission to take better photos.
This open source incident light meter is named Photon, and Team VEEB built it for a fraction of what a “proper” camera company would charge you for one. They tend to run from a couple of hundred to a couple of thousand pounds.
How do incident light meters work?
Light meters make photos taken on any camera better. They tell you how much or how little light you’re letting in so you can get the camera settings perfect before you hit the button to capture a photo. The act of measuring is called “incident metering”.
You don’t need a pricey modern digital camera to use with a light meter, and to prove it, Team VEEB tested Photon on some refurbished old film cameras. If you’re into that kind of thing, they tested on a medium format Hasselblad 503cx and a 35mm Pentax Asahi. These may be things of beauty, but neither features a built-in light meter, so the Pico-based DIY version will be doing all the heavy lifting.
What can the Pico light meter do?
The tiny and simple creation can be used to choose the aperture or shutter speed and to measure ambient light brightness as well as the red, green, and blue components of the light. It can also set your camera’s ISO (sensitivity to light). The DIY Pico version was tested alongside a shop-bought light meter from Sekonic and passed with flying colours. And all of the beautiful photos below came back from the lab correctly exposed.
Tucked away inside the stopwatch-sized case are the following affordable parts:
- Raspberry Pi Pico
- Waveshare OLED Screen
- Pimoroni LiPo Power SHIM for Pico (soldered directly to the Pico)
- Rotary encoder (to adjust settings)
- Pimoroni Light Sensor
- LiPo/LiIon battery (connected to the SHIM)
It was all built with a bit of maths, some bespoke code, and components costing less than $50. If you’d like to make your own, all the code, calculations, and instructions you’ll need are here: github.com/veebch.
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