Need More Fonts on OpenCV?

OpenCV has a built-in simple function to add text on your images - the cv2.putText() function. With just one line of code, you can add text anywhere on the image. You just need to specify the position, colour, scale (font size), and which the font to use as the minimum parameters.

 cv2.putText(image,  
           text_to_show,  
           (20, 40),  
           fontFace=cv2.FONT_HERSHEY_SIMPLEX,  
           fontScale=1,  
           color=(255, 255, 255))  

OpenCV also gives you a choice from a handful of fonts - all variants of the "Hershey" font.

But, there may come a point where you want more fonts. Have you wished that you could use a specific True Type or Open Type font on OpenCV?

The good news is, it's possible.

True Type Fonts working on OpenCV

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.

Codes of Interest Facebook Community is now Live!

We are on Facebook!

The Codes of Interest Page is now live on Facebook. I created the page so that our community can come together to share ideas, discuss about questions, quickly address issues you face with your Deep Learning / Machine Learning and Computer Vision experiments, and talk about what you would like to see from the Codes of Interest site.

Join Now

... and start discussing.

Build Deeper: The Path to Deep Learning

Learn the bleeding edge of AI in the most practical way: By getting hands-on with Python, TensorFlow, Keras, and OpenCV. Go a little deeper...

Get your copy now!

Encog-Node: Simple Machine Learning on Node.js

Before I got into serious Machine Learning and Computer Vision coding (which I mostly use Python for), I did a lot of my development on Node.js. Few years back (around 2012), I was trying to add a simple neural network to one of my Node.js applications. I looked around, but couldn't find a satisfactory node module which was lightweight and flexible. Around that time, I came across the Encog Machine Learning framework, which was created by Jeff Heaton, and was one of the most popular Machine Learning libraries for Java at the time. I noticed that there was a Javascript version of the Encog library, which worked surprisingly well, and set myself on to porting that to Node.js.

I released the first version of Encog-Node in early 2012, and the latest version v0.3.0 is now available from NPM - https://www.npmjs.com/package/encog-node, and is recommended for anyone who wants to add lightweight, simple machine learning capabilities to their Node.js applications.

GitHub user Rui Cardoso contributed a lot for the latest release, with restructuring and cleaning up the codebase, and adding more examples.

You can install it by simply running,

 npm install encog-node  

in your node application.

Milestones of Deep Learning

Deep Learning has been around for about a decade now. We talked about how Deep Learning evolved through Artificial Intelligence, and Machine Learning (See "What is Deep Learning?"). Since its inception, Deep Learning has taken the world by storm due to its success. Here are some of the more significant achievements of Deep Learning throughout the years,

AlexNet - 2012

The AlexNet Architecture (Image from the research paper)

• Proved that Convolutional Neural Networks actually works. AlexNet - and its research paper "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton - is commonly considered as what brought Deep Learning in to the mainstream.
• Won 2012 ILSVRC (ImageNet Large-Scale Visual Recognition Challenge) with 15.4% error rate. (For reference, the 2nd best entry at ILSVRC had 26.2% error rate).
• 8 layers: 5 convolutional, 3 fully connected.
• Used ReLU for the non-linearity function rather than the conventional tanh function used until then.
• Introduced the use of Dropout Layers, and Data Augmentation to overcome overfitting.

Research Paper: ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton