I am planning to make this story as simple and easy. You are probably reading this because you wasted some time in configuring GPU for you laptop or PC to work with tensorflow.
I will try to cover more generally. But the implementation and the configuration that I am focusing on is with Ubuntu-18.04.

First of all what all things we need?

1. We need a system with GPU. Of course you have that, that is why you are here.
2. We need Nvidia GPU drivers. For the latest use cases we require 450.0 + versions.
3. We need to install CUDA toolkit.
4. …

This short story will give you a walkthrough on how to do masked face recognition. I tried various methods for doing masked face recongition but nothing worked well. Even this methodology is also not giving us performance similar to an ordinary masked face recognition but still better than others.

With the pandemic out break, AI powered smart survellience camera face struggling times to detect the face of people who always wear mask. So there is a necessity to track people no matter which mask they use.

How can we solve this? The only way is to do just as the…

I was facing some difficulty in installing face-recognition module. The solution was simple. Go the original repos and see how it works. I hope this will work for you too.

I installed everything on an Ubuntu docker container. So I had to configure many more stuffs which I am not sharing. Don’t worry. This includes pretty basic things like installing python3, pip, dev packages even git too.

For installing face-recognition module for Ubuntu 18.04: (Try for other OS, I used this for 18.04 and it worked)

1. Install cmake:

pip install cmake

After cmake is successfully installed

2. Optional: Install git…

The accounted global facility management market size in 2019 was 1.24 trillion USD and is predicted to reach 1.62 trillion USD by 2027. This accounts for a 4.0% increase in the Compound Annual Growth Rate (CAGR).

Companies outsourcing Facilities Management

Several economic factors have made outsourcing more relevant for facilities management. Facilities management is ripe for disruption: it lags behind other functions such as production equipment maintenance by both digital maturity and penetration of technology. Although technology is available for facilities management, several obstacles have inhibited adoption, such as a lack of digital skills within the function, other priorities for leadership, and a focus…

The gadget for the growing fields of computer vision and 3D perception which packs a lot of capabilities into a small unit.

I started to explore OpenCV AI Kit D (OAK-D) because of the OpenCV AI Competition. This is an international Artificial Intelligence competition celebrating OpenCV’s 20th anniversary. A total prize worth of $400K will be awarded with global and regional winners. This international competition is sponsored by Azure and Intel. In this challenge teams has to work on real life problems using OAK-D.

What is OAK-D?

OAK is a modular, open-source ecosystem composed of MIT-licensed hardware, software, and AI training — that…

We are going to see how to identify contours in openCV. Contours are the line or points that join the continuous points of the boundary of an object. Contours are very essential tools when you get into object detection, shape analysis, object recognition etc.

Let’s start by importing an image with openCV and also changing it to gray scale. We already discussed how to convert an image to gray scale in the previous stories.

1. Translation

Translation is the process of shifting an image along the x and y axis. These translation can be used as data augmentation steps if you are working with deep learning projects.

In order to do this we are going to create a translation function , In this function we are going to take inputs of image, x and y to translate. We need to create a translation matrix to create translation. We do this by the help of numpy . We do the translation with the help of cv.warpAffine() function provided by openCV. …

Probably you will be directed to this story from the part-1 of OpenCV. If not I would recommend reading it will give you a better idea. But it is not necessary though.

These are some of the very basic function in openCV which you have to use on whatever computer vision project you are doing.

1. Converting an image to grayscale image

For converting an RGB image to gray scale image we should use a function called cv.cvtColor() with the argument <img, color_feature(for converting to grayscale it is cv.COLOR_BGR2GRAY)>