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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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pico
A minimalistic framework for real-time object detection (with a pre-trained face detector) (by nenadmarkus)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
I guess, but it’s also pretty open code these days now that the S3’s LX7 has some nice vector extensions and other additions for ML applications :)
https://github.com/espressif/esp-who
It’s not out of reach for hobbyists, I think, though getting it to run very well might be. And I would certainly tell anyone interested to start with something more powerful first!
Probably not, unless you're willing to stretch the definition of 'real time' or 'image'. The classic Arduino Uno processor aka the ATMega328 does about 1 million 8-bit instructions per second per MHz of clock[0]; the Uno iirc has a 8mhz crystal. Even for the contrived example of a 1MP webcam that outputs uint8 grayscale values, you'd only be able to read it at about 8 frames per second, max, much less do anything with it. If you limited yourself to 64x64 boolean images, maybe; you could probably run a simple NN to categorize MNIST examples or do blob detection.
If you want to mess around with image processing within the realm of the Arduino framework, you could pick up a super cheap ESP32-CAM dev board [1], which has a small camera and a dual core microprocessor with much less anemic specs. You can of course program it using the Arduino IDE or, as I prefer doing it, using PlatformIO [2] which is a CLI tool and VSCode extension that allows you to use the [Arduino, ZephyrRTOS, mbed] framework with a zillion different dev boards and architectures.
[0]: https://en.m.wikipedia.org/wiki/ATmega328
[1]: https://www.amazon.com/HiLetgo-ESP32-CAM-Development-Bluetoo...
[2]: https://platformio.org/
The oscillator bit is, but I don't appear to have uploaded the one with the filter which I will need to find.
https://github.com/ErroneousBosh/slttblep
The filter is just an SVF with, with a precomputed expo scale "bent over" at the top to correct for quantisation. From that the two SVF coefficients ω/Q and ω*Q are calculated every time there's a control update and of course because you can't divide on an Arduino it uses a lookup table of reciprocals. I could probably use a "wider" table of 16-bit values for better precision.
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