We are using tensor flow and Open-CV to detect items in the frame of a web camera. The camera is mounted onto a tilt pan kit to allow us to track the objects in frame as well. Due to the intensive nature of the object detection, we are using a local computation server to process the image and find the objects within it. The computation server returns a processed image and error vector which the Pi coverts to a control vector. It can then display the processed image and adjust its angle to keep the tracked object in the middle of the frame. In order to dramatically decrease the complexity of the project, we would have liked to preform all the processing on the Pi as well however we were unable to get a reasonable response time with either the Pi or the Beagle Bone. The Raspberry Pi takes at least 3 seconds per image to process and the BeagleBone Black atleast 5 seconds.
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Python for Microcontrollers – Adafruit Daily — Python on Microcontrollers Newsletter: A New Arduino MicroPython Package Manager, How-Tos and Much More! #CircuitPython #Python #micropython @ThePSF @Raspberry_Pi
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Each Friday is PiDay here at Adafruit! Be sure to check out our posts, tutorials and new Raspberry Pi related products. Adafruit has the largest and best selection of Raspberry Pi accessories and all the code & tutorials to get you up and running in no time!