My take on image preprocessing and augmentation for machine learning applications!

ririgi (46)in #steemiteducation • 7 years ago (edited)

Right now, machine learning is a big thing (from Google's self driving car to disease prediction!) And in this field, DATA is everything. If you dont have a good quality data, then your accuracy is gonna be a mess. So it's customary to preprocess your data. But there are so many preprocessing techniques??? Which one/s should I use? There are already a lot of tutorials online, but I'd like to share what I've been doing in my current project.

Preprocessing Techniques

_{Original and Downsized(Top) vs
Cropped and Downsized(Bottom)}

It all depends on your data. The data that I am currently working on are images from multiple databases. Imagine the heterogeneity of the data! So here are some preprocesing methods that I can do:

Normalization > I adjust the pixel values to a range of 0 to 1. I do this to avoid the values from blowing up.
Downsizing the image > Large images took too long to load, so downsizing the images can speed up the computation.
Cropping the image > Sometimes there are unnecessary portions in the images, I can just crop on the area that I want to focus on

Data Augmentation

Another thing in machine learning, you must have a BIG DATA! The bigger the better. This is because the data is used to learn. A technique that I can do to increase the amount of data is data augmentation. For images I do the following:

Random adjustment of the brightness
Random adjustment of the contrastt
Random rotation
Random flipping

And there are many data augmentation techniques that I can do! Like distortion of the images, perspective skewing and many more. However, I should still consider the images that I am working on. Ask yourself. Will your data still make sense if you skew or distort the images? or if you flip it? As of now, I am content with what I listed above.

Here are sample augmentations featuring a sleeping Loki.

_{a) Original Image, b) Brightness, c) Contrast, d) Rotated Image, and e) Flipped Image}

Python Libraries

I wrote the scripts that I used in the images, but here are some libraries that you can use:

OpenCV
Pillow
Scikit-image

Or you can use libraries such as ImgAug and Augmentor which are built on top of image processing libraries. Personally, I like Augmentor since it uses a pipeline where you just have to provide the operations you want and the number of output images. It is easy to use! Here is a sample code:

_{This pipeline produces a total of 1000 images which are cropped at the center,
rotated and zoomed according to a certain probability.}

I know preprocessing is tedious, but using such libraries help us speed up the process. If the data is all set, I can now start feeding it to my machine learning algorithm(which can be a topic for another blogpost)! I hope this post helps you on your future projects. :-)

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