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

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How to Build Your Own Face Filter With OpenCV?

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

#python programming examples #python and opencv: apply filters to images #apply filters to images #python and opencv #opencv #filters to images

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

#opinions #alternatives to hugging face #chatbot #hugging face #hugging face ai #hugging face chatbot #hugging face gpt-2 #hugging face nlp #hugging face transformer #ibm watson #nlp ai #nlp models #transformers

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

Face Recognition with Python [source code included]

Python can detect and recognize your face from an image or video

Face Detection and Recognition is one of the areas of computer vision where the research actively happens.

The applications of Face Recognition include Face Unlock, Security and Defense, etc. Doctors and healthcare officials use face recognition to access the medical records and history of patients and better diagnose diseases.

About Python Face Recognition

In this python project, we are going to build a machine learning model that recognizes the persons from an image. We use the face_recognition API and OpenCV in our project.

Tools and Libraries

  • Python – 3.x
  • cv2 – 4.5.2
  • numpy – 1.20.3
  • face_recognition – 1.3.0

To install the above packages, use the following command.

pip install numpy opencv-python

To install the face_recognition, install the dlib package first.

pip install dlib

Now, install face_recognition module using the below command

pip install face_recognition

#machine learning tutorials #face recognition #face recognition opencv #ml project #python face recognition #face recognition with python

Face Liveness Detection through Blinking Eyes

Detect the Presence of Live Human Face with Open Source Tools

#face-liveness-detection #opencv-python #face-recognition #keras #progaming #opencv