technical-recipes.blogspot.com

This is a very similar post to that posted previously, which used a OpenCV and cvBlobsLib to identify contours in video footage and display them on the screen. Please refer to this for detail on how to convert the Red, Green, Blue (RGB) format into Hue, Saturation, Value (HSV) format, and threshold the HSV into the black and white format needed for finding contours in OpenCV: https://www.technical-recipes.com/2011/track-colored-objects-in-video-using-opencv-and-cvblobslib/ This example achieves the same but without using the cvBlobsLib to identify contours / strongly connected components, just 100% OpenCV calls.

A quick guide to setting up and installing OpenCV for using in the Netbeans integrated development environment in Linux. Step 1: Download and extract OpenCV for Linux Versions of OpenCV can be downloaded from here: http://opencv.org/downloads.html Save it to the location of your choice. Open a command prompt, navigate to the download location and unzip:

A quick guide to setting up and installing OpenCV for using in the Code::Blocks integrated development environment in Linux. The version of Linux I am currently using is Ubuntu 14.04. At the time of writing the version of OpenCV for Linux used is 2.4.9. (I had originally tried version 2.4.10 but had problems compiling it with the version of gcc I had (4.8.2), so I reverted to 2.4.9 instead.)

As a newcomer to image processing, I have attempted to achieve the tracking of coloured objects in some sample video footage . In my case, my little one’s blue gloves moving in a snow-covered landscape (a bitterly cold Musselburgh allotments , December 2010).

Introduction A recent stab at grabbing images from the Flea2 camera using APIs from the FlyCapture2 SDK by Point Gray Research (PGR).  Additionally, the camera was to be used in  "Format 7 mode", so that we may grab partial regions of the complete image.

The results of some experimentation with and comparison between raw OpenCV functions and the cvBlobsLib library to detect darker coloured spots against lighter backgrounds.

OpenCV is a free, open source library that enables your computer application to "see" and make decisions from the image data it acquires.  Here are some guides for setting up OpenCV for use in Microsoft Visual Studio Environments:

A short example of how to utilize various open source library functions that can be used to identify and analyse strongly connected components for a given input image. In the example I have given here, the image represents microarray sample spots printed to a slide using a Xaar inket printer.  Using our robotic equipment, a camera is mounted to the printhead, so that images are taken of the spots, as they are being printed on-the-fly, usually in linear groups of 12 or 32 at a time: