Snapchat like Image Overlays with Dlib, OpenCV, and Python

You're probably familiar with Snapchat, and it's filters feature where you can put some cool and funny image overlays on your face images. As computer vision enthusiasts, we typically look at applications like these, and try to understand how it's done, and whether we can build something similar.

It turns out, we can make our own application with Snapchat like image overlays using Python, OpenCV, and Dlib.

Snapchat like Image Overlays with Dlib, OpenCV, and Python

So, how do we build it?

1. We'll first load the Webcam feed using OpenCV.
2. We'll load an image (in our example, and image for the 'eye') to be used as the overlay.
3. Use Dlib's face detection to localize the faces, and then use facial landmarks to find where the eyes are.
4. Calculate the size and the position of the overlay for each eye.
5. Finally, place the overlay image over each eye, resized to the correct size.

Let's start.

Wink Detection using Dlib and OpenCV

A couple of weeks ago, I was going through a tutorial for eye blink detection by Adrian at PyImageSearch. It was an excellent tutorial, which explained the use of Eye Aspect Ratio (EAR) in order to detect when an eye gets closed. Then, few weeks back, I was having a chat with Shirish Ranade, a reader of this blog and a fellow computer vision and machine learning enthusiast, on whether we can perform an action by winking at the computer. So, I decided to try out a code to detect winking.

Wink Detection Running with Dlib and OpenCV

It's an interesting idea to perform an action or a task just by winking at your computer. It can be thought as a form of gesture detection or facial expression detection as well. So, here's how you can build your own 'wink' detector for it.

We start by importing all the necessary packages,

 import numpy as np  
 import cv2  
 import dlib  
 from scipy.spatial import distance as dist  

Extracting individual Facial Features from Dlib Face Landmarks

If you remember, in my last post on Dlib, I showed how to get the Face Landmark Detection feature of Dlib working with OpenCV. We saw how to use the pre-trained 68 facial landmark model that comes with Dlib with the shape predictor functionality of Dlib, and then to convert the output of into a numpy array to use it in an OpenCV context. We were able to get all 68 feature points on to our face image.

Dlib detecting the 68 Face Landmarks

The 68 feature points which the Dlib model detects include the Jawline of the face, left and right eyes, left and right eyebrows, the nose, and the mouth. So, what if you only want to detect few of those features on a face? E.g. you may only want to detect the positions of the eyes and the nose. Is there a way to extract only few of the features from the Dlib shape predictor?

There is actually a very simple way to do that. Here’s how.

Getting Dlib Face Landmark Detection working with OpenCV

Dlib has excellent Face Detection and Face Landmark Detection algorithms built-in. Its face detection is based on Histogram of Oriented Gradients (HOG) feature combined with a linear classifier, on a sliding window detection scheme (Ref. http://dlib.net/) and it provides pre-trained models for face landmark detection. It also provides handy utility functions like dlib.get_frontal_face_detector() to make our lives easier.

Dlib Face Landmark Detection in action

Note: Image used for testing is in the Public Domain - https://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg

To check out Dlib with it's native functions, you can try out the Dlib example from the official site: http://dlib.net/face_landmark_detection.py.html.It works well, but we can do better.

Although Dlib offers all the simplicity in implementing face landmark detection, it's still no match for the flexibility of OpenCV. (Simply put, Dlib is a library for Machine Learning, while OpenCV is for Computer Vision and Image Processing)

So, can we use Dlib face landmark detection functionality in an OpenCV context? Yes, here's how.