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Telegram With More Functionality Shyna Part 9

Shyna search keyword on Internet with reminders.

Telegram With Functionality: Speech Recognition Part 8

This is just a overview demo of the functionality implemented so far. The code is not shared for this as the functionality is still need lot of improvement and there are lot of feature we need to add.

Telegram Chat-Bot Or Shyna As Telegram User?

Multiprocessing With Speech Control..

Pocket Sphinx with Google Cloud API

Creating Response DB with SQLite

Using NLTK for Speech Response..

Speech Recognition Modules and their uses..

Train Pocket Sphinx Speech Recognition

Speech Recognition and Customise Response!

Installation and Why PocketSphinx?

4) Code your own Face Detection

As shown in the previous post we can code to detect a particular face from a webcam. In this post, I am going to share how to code it for yourself. I'll try to share as many details as needed however, all are welcome to post comments down below. The post will contain multiple videos which are as follows: 1) Imports Needed For FaceDetection: The video will describe the modules we need and why we need them. 2) Commands Needed For FaceDetection: I will go through the code and explain the commands used. Below is the second video where the code is explained. You may download the code from here

3) Introduction of Face Detection

We all know that face recognition is possible and we came across it on daily basis nowadays, for example, your front camera. some phones draw a circle around your face because the code did recognize a human face. But how but it recognize your face in real-time. yes, it is possible many security software use it and I am just going to demonstrate too. In this post, I am going to show you and in the upcoming post, I'll share how can you code it for yourself. trust me it is easy to do. Have fun!

2) Installing Dlib Library

Once the machine setup is done successfully as described in my previous post ("Click me!")  we are good to go to install one more dependency which Dlib. Posting a completely new post just for Dlib because I struggled and like almost I just saved my OS from crashing so it deserves. Installing with the correct command is all that is needed there are many tutorials available for this installation and more I refer more I got confused so after 3 to 4 hour of struggle finally I came up with the necessary command which will be enough to install Dlib on your machine and we can proceed further to install the face_recognition package. Below are the commands I used and work perfectly: sudo apt-get install build-essential cmake sudo apt-get install libgtk-3-dev sudo apt-get install libboost-all-dev wget https://bootstrap.pypa.io/get-pip.py sudo python3 get-pip.py sudo pip3 install numpy sudo pip3 install scipy sudo pip3 install scikit-image sudo pip3 install dlib  sudo pip3 i

1) Setting up Face Recognition Basic requirements

Many are working on the computer's vision power. We are well aware in today's era voice recognition is already there in other words now our computer can hear us. By the help of NLTK the hearing power and understanding the word is much improved. By this, we are now focussing how better we can make our computer or any other machine vision improved. I got curious about this and going to invest my time in this with doing a full-time job so the process will be slow. For start, I select Python language as it is easy to understand and there are a lot of packages available. Preparing a Linux PC with Python 3X with OpenCV and its dependency may take 2 - 3 hour depending upon your machine. Note: Consider having PC with at least 4gb RAM, not because it is Python won't support less RAM but the thing we are going to do may need RAM, plus Python is a scripting language it is slow as compare to other but efficient and easy. Let's start: Requirements: 1) Python3X 2) Ope