How to Build Your Own Face Filter With OpenCV? - Analytics India Magazine

Face filters are common applications that we use almost every day in our lives. From Snapchat to Instagram there are thousands of filters that allow you to look like an animal, a princess or even another human being. As fun as it is to use these filters, it is also simple to build your own custom face filter. Using basic and efficient OpenCV techniques we will build a custom face filter that replaces your nose with a dog nose.

Read more: https://analyticsindiamag.com/how-to-build-your-own-face-filter-with-opencv/

#machine-learning #data-science #opencv

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How to Build Your Own Face Filter With OpenCV? - Analytics India Magazine

AI & Data Science India Salary Study - 2021

The annual Analytics India Salary report presented by AIM and AnalytixLabs is the only annual study in India that delves into salary trends and provides a comprehensive view of the changing landscape of analytics salaries. The report, now in its seventh year, look at the distribution of average salaries across several categories including years of experience, metropolitan regions, industries, education levels, gender, tools, and skills.

The Data Analytics function is experiencing significant growth and development in terms of skills, capabilities, and funding. Last year, despite the pandemic, the Indian start-up industry witnessed $836.3 million investment, almost a 10% (9.7%) increase than the previous year. Also, more than one in five (21%) analytics teams across firms in India witnessed a growth in the last 12 months and the post-pandemic job market saw an upswing of data science jobs. The development of the data science domain is evidenced by the high salaries drawn by analytics professionals across the organization, with Analytics professionals doing relatively well in spite of the pandemic.

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Python and OpenCV: Apply Filters to Images

I am pretty sure you have tried out various filters available on the social platforms and your camera as well.

Today in this tutorial, we will be applying few of the filters to images. Exciting right?

Let’s begin!

Table of Contents

1. Importing Modules

2. Loading the initial image

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Top 6 Alternatives To Hugging Face

  • With Hugging Face raising $40 million funding, NLPs has the potential to provide us with a smarter world ahead.

In recent news, US-based NLP startup, Hugging Face  has raised a whopping $40 million in funding. The company is building a large open-source community to help the NLP ecosystem grow. Its transformers library is a python-based library that exposes an API for using a variety of well-known transformer architectures such as BERT, RoBERTa, GPT-2, and DistilBERT. Here is a list of the top alternatives to Hugging Face .

Watson Assistant

LUIS:

Lex

Dialogflow

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How to Build Your Own Face Filter With OpenCV? - Analytics India Magazine

Face filters are common applications that we use almost every day in our lives. From Snapchat to Instagram there are thousands of filters that allow you to look like an animal, a princess or even another human being. As fun as it is to use these filters, it is also simple to build your own custom face filter. Using basic and efficient OpenCV techniques we will build a custom face filter that replaces your nose with a dog nose.

Read more: https://analyticsindiamag.com/how-to-build-your-own-face-filter-with-opencv/

#machine-learning #data-science #opencv

How to Build Your Own Face Filter With OpenCV?

Face filters are common applications that we use almost every day in our lives. From Snapchat to Instagram there are thousands of filters that allow you to look like an animal, a princess or even another human being. As fun as it is to use these filters, it is also simple to build your own custom face filter. Using basic and efficient OpenCV techniques we will build a custom face filter that replaces your nose with a dog nose.

In this article, we will learn about the implementation of face filters using a 68 point landmark detector and OpenCV.

Understanding the landmark stabilizer.

For this implementation, we will make use of the 68 point landmark stabilizer. Download the stabilizer using this link. The landmark stabilizer is a file from the dlib package that makes it easy to identify 68 points on the human face. These landmarks are a key factor in building the face filter.

dlibPIN IT

As shown above, these points can help in locating the coordinate points of the nose, eyes and lips. Using these points it is possible to place the filter exactly on the location needed. So let us get started with the implementation.

If you don’t already have dlib installed you can install this package using

pip install dlib  

Once you have downloaded the landmark stabilizer and installed dlib, we can start with the implementation. Select the filter you want to apply for your face. I have selected the animated image of a dog’s nose. If you would like to use the same image you can download it here.


#developers corner #dlib #face filter #landmarks #opencv