OpenMV- Machine Vision for Beginners

MV is the ability of a computer to see using analog to digital conversion and digital signal processing. The key characteristics that make a machine vision module better are sensitivity and resolution. These systems allow machines to see a broader spectrum of wavelengths such as x-rays, infrared or UV light. Nowadays, it is mainly used for object recognition, signature identification, material inspection, medical image analysis etc. Machine vision modules tend to be expensive which make them difficult to access for makers and hobbyists. OpenMV is a python powered machine vision module that aims at making MV accessible to beginners.

OpenMV- Machine Vision for Beginners

OpenMV was created by Hackaday user i.abdalkader and he worked towards making it affordable, small, open source and user friendly. It is programmable in python 3, and includes image processing libraries to make it easier. It is Based on STM32F ARM Cortex-M Digital Signal Controllers (DSCs) running at 168-216MH, andĀ has an ATWINC1500 FCC CertifiedĀ Wi-Fi module which can transmit data at up to 48Mbp. The image sensor used was a OV965x and a OV2640. Additionally, it has 512 KB of RAM and consumes 120 mA.

The libraries included give the MV the ability to detect shapes, faces, QR and barcodes, and it also has ORB key points detector, template matching with normalized cross correlation and more. The OpenMV includes I/O headers to connect shields to extend itĀ“s capabilities. The IDE includes many features for image processing and it is based on QT creator. OpenMV has a micro SD card socket which allows for recording data, and the device measures 45 mm in length, 36 mm in width, 30 mm in height and only weights 16 g.

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About The Author

Ibrar Ayyub

I am an experienced technical writer holding a Master's degree in computer science from BZU Multan, Pakistan University. With a background spanning various industries, particularly in home automation and engineering, I have honed my skills in crafting clear and concise content. Proficient in leveraging infographics and diagrams, I strive to simplify complex concepts for readers. My strength lies in thorough research and presenting information in a structured and logical format.

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