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Face detection is a computer technology that is being applied for many different applications that require the identification of human faces in digital images or video. It can be regarded as a specific case of object-class detection, where the task is to find the locations and sizes of all objects in an image that belongs to a given class. The technology is able to detect frontal or near-frontal faces in a photo, regardless of orientation, lighting conditions or skin color . Not Face Recognition! It’s about Detection. Face recognition describes a biometric technology that goes way beyond recognizing when a human face is present. It actually attempts to establish whose face it is. In this article, I’m not going deep into recognizing. I’ll keep that for a future blog article and for the time being, I’m going to explain how to run a simple Face  Detection program using your WebCam with Python Or we can run simple program on image as well . We are dealing with below Cascade models for fac

This is first tutorial of the series beginning with installation instruction of opencv2 / opencv3 in python anaconda virtual Environment . We will come with lot of exciting blog like face detection and recognition in video/image/livestream , object or people tracking etc , So stay tuned and subscribe for more updates . We are installing it on MAC OS , you need below tools to setup OpenCV: Xcode Homebrew Anaconda OS X Step 1: Install Xcode Go to App Store , Search for Xcode Install it . Step 2: Install HomeBrew Open Terminal(Application->Utilites->terminal) Write this following in terminal: ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install )" brew install python Step 3: Install Anaconda Python Package Follow the Installation instructions, should be pretty standard, however Continuum has a guide here . Type Conda Info and check Installation . click here for Anaconda cheat sheet . Step 4: Create Conda Virtual Env and setup OpenCV You

From last few years ,microservices are an accelerating trend . Indeed, microservices approach offers tangible benefits including an increase in scalability, flexibility, agility, and other significant advantages. Netflix, Google, Amazon, and other tech leaders have successfully switched from monolithic architecture to microservices. Meanwhile, many companies consider following this example as the most efficient way for business growth. On the contrary, the monolithic approach is a default model for creating a software application. Still, its trend is going down because building a monolithic application poses a number of challenges associated with handling a huge code base, adopting a new technology, scaling, deployment, implementing new changes and others. So is the monolithic approach outdated and should be left in the past? And is it worth shifting the whole application from a monolith to microservices ? Will developing a microservices application help you reach you

In this tutorial, we are going to learn how HashMap works internally.This is a very popular java interview question from the collection framework and been asked so many times to check candidates understanding on Map collection. If you want video explanation then go through this : Let’s suppose A hashmap is a storage place where we want to keep all of the data. So building upon this, we might want to add data, retrieve data or manipulate data, right? We’ll go through all of this by understanding the structure of the HashMap Java Class. `public class java.util.HashMap<K, V> {}` What does this mean? It says, that a HashMap (A storage place) will store the data in the form of a key-value pair where K is key and V is value. Why key-value, you’d ask? So this is because it makes the retrieval of data easier. What a hashmap does is, it takes the data and store the data in a bucket and then label that bucket with the hash of the key. So when we want that data back, w

Problem of creating knowledge graph from unstructured data is a well known machine learning problem. Not even a single org has achieved 100% accuracy for completely enriched knowledge graph . I have few findings that will help to kick-start for a person who is new in to this . Before move to findings , i will let you to walk through the problem of building knowledge graph from unstructured corpus . Lets consider this scenario . Suppose we have very small corpus : "Apple was founded by Steve jobs and current CEO is Tim Cook. Apple launched several products like Ipad, iphone , MAC etc. " Corpus may be very complex sentences also . Problem is how can we build a knowledge graph out of this unstructured corpses . If we create generic knowledge graph , then our system should be able to provide answers like "who founded Apple ?" , " What are products launched by Apple ?" etc . Few techniques to create knowledge graph : 1.) Supervised Technique : Supervised mod

Below are the steps to setup your development environment  for Deep learning : 1.) Download and Install Anaconda from here : https://www.continuum.io/downloads 2.) Create a conda environment for data science development so that it doesn't affect the other install components .     conda create -n tensor_keras_py3.5 python=3.5 pandas scikit-learn jupyter matplotlib 3.) Activate the created environment     source activate tensor_keras_py3.5 4.) Install tensorflow inside activated env.     pip install tensorflow 5.) Install keras inside activated env.    pip install keras 6.) Install opencv inside activated env.    pip install opencv-python 7.) Install IMUTILS  pip install imutils Test your environment 1.) Type ipython in the shell  , which should open ipython console . 2.) Type import tensorflow,keras  , it should reply using tensorflow backend  Switching keras backend from Tensorflow to theano keras backend is set in a hidden file stored