Saturday, September 9, 2017

What is the image_data_format parameter in Keras, and why is it important

We've talked about the image_dim_ordering parameter in Keras and why is it important. But since from Keras v2 changed the name of the parameter, I thought of bringing this up again.

As you know, Keras  is a higher-level neural networks library for Python, which is capable of running on top of TensorFlow, CNTK (Microsoft Cognitive Toolkit), or Theano, (and with limited support for MXNet and Deeplearning4j), which Keras refers to as 'Backends'.

Which backend Keras should use is defined in the keras.json file, which is located at ~/.keras/keras.json in Linux and Mac OS, and at %USERPROFILE%\.keras\keras.json on Windows.

The default keras.json file (default set to TensorFlow) would look like this,
   "epsilon": 1e-07,  
   "floatx": "float32",  
   "image_data_format": "channels_last",  
   "backend": "tensorflow"  
The "backend" parameter should either be "tensorflow", "cntk", or "theano". When switching the backend, make sure to switch the "image_data_format" parameter too. For "tensorflow "or "cntk" backends, it should be “channels_last”. For “theano”, it should be “channels_first”.

Wednesday, August 30, 2017

Build Deeper: Deep Learning Beginners' Guide

I've been away from writing a post for about three weeks. That's because I've been preparing something exciting.

Today, I'm happy to announce the first book release from Codes of Interest - Build Deeper: Deep Learning Beginners' Guide.

Build Deeper: Deep Learning Beginners' Guide
Build Deeper: Deep Learning Beginners' Guide

Deep Learning has become a household name. It’s the bleeding edge in AI, and already achieving some phenomenal feats. Breakthroughs are happening daily, and the tech giants are not only pursuing it, they’re leading it.

Build Deeper: Deep Learning Beginners' Guide is the ultimate guide for anyone taking their first step into Deep Learning. Learn what Deep Learning is, and how it came to be. See what it's capable of, and its milestones. And get hands-on with building your first Deep Learning model.

All you need to get started is a bit of enthusiasm, and some basic programming skills.

Build Deeper: Deep Learning Beginners' Guide is now available from Amazon.

Tuesday, August 8, 2017

Using Bottleneck Features for Multi-Class Classification in Keras and TensorFlow

Training an Image Classification model - even with Deep Learning - is not an easy task. In order to get sufficient accuracy, without overfitting requires a lot of training data. If you try to train a deep learning model from scratch, and hope build a classification system with similar level of capability of an ImageNet-level model, then you'll need a dataset of about a million training examples (plus, validation examples also). Needless to say, it's not easy to acquire, or build such a dataset practically.

So, is there any hope for us to build a good image classification system ourselves?

Yes, there is!

Luckily, Deep Learning supports an immensely useful feature called 'Transfer Learning'. Basically, you are able to take a pre-trained deep learning model - which is trained on a large-scale dataset such as ImageNet - and re-purpose it to handle an entirely different problem. The idea is that since the model has already learned certain features from a large dataset, it may be able to use those features as a base to learn the particular classification problem we present it with.

This task is further simplified since popular deep learning models such as VGG16 and their pre-trained ImageNet weights are readily available. The Keras framework even has them built-in in the keras.applications package.

An image classification system built with transfer learning
An image classification system built with transfer learning

The basic technique to get transfer learning working is to get a pre-trained model (with the weights loaded) and remove final fully-connected layers from that model. We then use the remaining portion of the model as a feature extractor for our smaller dataset. These extracted features are called "Bottleneck Features" (i.e. the last activation maps before the fully-connected layers in the original model). We then train a small fully-connected network on those extracted bottleneck features in order to get the classes we need as outputs for our problem.

Sunday, July 30, 2017

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.

           (20, 40),  
           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
True Type Fonts working on OpenCV

Friday, July 21, 2017

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
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.

Tuesday, July 11, 2017

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.

The Codes of Interest Facebook Page

... and start discussing.

Build Deeper: Deep Learning Beginners' Guide is the ultimate guide for anyone taking their first step into Deep Learning.

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 -, 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.