How to capture images and video on the beaglebone – I do this using Open Source Computer Vision (OpenCV) image processing. OpenCV image processing captures image and video data on the BBB (Beaglebone Black) with the help of USB webcams by using python script programming. It is simple tutorial where you can also add effects in the form of black and white image, motion detection, sharp picture etc. I have used opencv library which programs functions of real time computer and machine learning. In this project required tools are Webcam and Beaglebone Black.
How to use the Raspberry Pi camera with OpenCV
OpenCV is a popular cross-platform framework for image processing and real-time computer vision. The software framework is useful for efficiently developing real-time computer vision applications that would be extremely time-consuming to create from scratch. The power of OpenCV’s framework is that it runs seamlessly on embedded platforms like microcomputers. This means developing and running embedded…
The top computer vision tools for embedded systems
Computer vision is reaching new levels, far beyond basic image processing. This is thanks to the integration of artificial intelligence. AI now enables computers and systems to derive meaningful information from digital images that can be used in advanced industries. Currently, one of the most common applications is in security and surveillance. A computer vision…
Controlling Arduino using OpenCV Interface
Opencv generally used for projects like face recognition bot or a bot that tacks certain objects. Control systems with augmented reality can be achieved like this.There can be a lot of future work and applications for this project. Instead of blue object we can use Haar cascade training and track hand movements. Then we can use gesture recognition so as to control home appliances according to specific hand movements Arduino has redefined cheap home automation. However the way of communicating with Arduino means entering commands from computer or via Bluetooth/WiFi . This project explores the possibility of controlling Arduino by using image processing through the open source platform ‘opencv’.