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Source:fedoramagazine.org/announcing-fedora-30   Hey guys. Let me be very straight 😀. I used Ubuntu 16.04 for nearly 3 years and before that I had Windows 10 on my system. But Ubuntu's programming environment enticed me😋. In the beginning, I had to face lots of challenges and sharing here is beyond the scope of this article. But thanks to its large community and groups which got me to solve the problems easily.   Just a few days before, my device showed problems like, shutting down all of a sudden even during the power supply, freezing suddenly amidst a session. This happened quite frequently, sometimes thrice at a time and all I had to do was a force shutdown. Gosh! I can't tell you how that felt experiencing the same problem again and again. It was a total headache.😓 That is what urged me to install Fedora 30 LTS, for having an experience of working in a new environment. So I thought of writing an article including the necessary things to do for getting used to...

In this post, you'll able to compress an image of higher size relatively to a smaller size. Here size I mean the image's memory consumption, not the aspect ratio (though it is also somewhat related to the size). Before we begin, let's be familiar with what Image Compression, Color Quantization and K-Means Clustering is. Basically  K-Means Clustering  is used to find the central value (centroid) for k  clusters of data. Then each data point is assigned to the cluster whose center is nearest to k . Then, a new centroid is calculated for each of the k  clusters based upon the data points that are assigned in that cluster. In our case, the data points will be Image pixels. Assuming that you know what pixels are, these pixels actually comprises of 3 channels, Red, Green and Blue . Each of these channels' have intensity ranging from 0 to 255, i.e., altogether 256. So as a whole, total number of colors in each pixel is, 256 x 256 x 256.  Each pixel(color) has 2^...

Okay, not from completely scratch though, in this Article you are going to learn to build a simple face detection and recognition console based application using Opencv python and Deeplearning Before Starting: If you don't have enough time to read the whole article or you are too lazy to read articles  Scroll all the way down and there is source code at last heading Resources. If you really love to learn step by step, there are lots of comments inside the code. I highly recommand you to read and go through it And at last, Don't panic :D Lets start: Installing Libraries: dlib (by davis king) Face_recognition (by adam geitgey ) (wraps around dlib’s facial recognition functionality making it to work easily with dlib) we also need to install imutils. Actually imutils is used to make basic image processing functions such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV but we will be using it to maintain di...