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Building Machine Vision Applications Using OpenMV web-new-cam-v2-angle_grande.openmv.io_.jpg
Machine vision is quickly finding its way into a wide variety of applications. Developers don’t need to be experts in machine learning or machine vision in order to get started.

Visual awareness is an indispensable sense to human beings, and it is becoming indispensable to smart machines as well. Machine vision is finding its way into a myriad of applications such as smart doorbells, robotics, drones, and many other applications. The problem with machine vision though is that it can be complicated and seem to require specialized knowledge to get an application up and running.

According to the OpenMV website, the OpenMV project is about creating low-cost, extensible, Python powered, machine vision modules and aims at becoming the “Arduino of Machine Vision”. There are five components to the OpenMV that make it interesting to developers interested in getting started with machine vision.

First, all development is done in a custom IDE that is also used to deploy the machine vision scripts to a camera module. The IDE provides a mechanism for developers to update their scripts, update the onboard firmware, and explore numerous examples on how to perform specific functions. There are examples on how to use machine learning for image classification, color and blog tracking, eye tracking, shape detection, and much more. These example snippets can help a developer get up and running very quickly.