Intro to Image Manipulation

What are Images? What form do they exist in?

Note: This series assumes that you have a intermediate level understanding of Python, and a basic level understanding Linear Algebra (esp. Matrices and Matrix Operations) and Calculus.

Images are everywhere, starting from X-Rays used to save people’s physical lives to memes that are used to save their mental lives.

Being blind in this beautiful world is a very depressing thing indeed, so why let the computers miss out on the fun ?

One might argue that, that’s why cameras exist, but the thing about it is, cameras only help us capture the image data and put it into a frame or a computer. You can view it, but it means nothing to the computer. To a computer, all images are simply matrices.

If you keep zoom in to an image, you might start seeing squares all over your image. These squares are pixels. Each Pixel has 3 channels.

In a R.G.B image, the 3 channels are [Red, Blue, Green]. The colour of the pixel is a mixture of the red, blue and green channels.

If the Red Channel has a value of 1 and the other 2 channels have a value of 0. Then the pixel will be Red in colour. Most computers have pixel colour values ranging from 0 to 255(uint8). Some might have them range from 0 to 65535(uint16).

White — [255,255,255]

Black — [0,0,0]

Red — [255,0,0]

Green — [0,255,0]

Blue — [0,0,255]

This is the image we’ll work on! — PC Suraj Nukala

We start by importing the required packages…

import matplotlib.pyplot as plt
import numpy as np

We read the image using the ‘pyplot’ inbuilt function,

img = plt.imread(‘asuma.jpg’)

Now, the img variable has the data of the image, ‘asuma.jpg’

This is a numpy array, of the first 5 rows and columns in the image.

Numpy is a Python Library that helps one work with Linear Algebra, Matrices and Fourier Transforms. It was created in 2005 as an open-source project by Travis Oliphant. It remains one of the standard libraries in the field of Data Science.

The Image size is 1080 x 1920, but what is the 3?, it is the number of channels.

Let us look at the pixel on the 1000th row and 500th column, and check if it is grey in colour.

We were right! [rows, columns, channels]

Let’s now cover the dude’s face with a big blue square. The face region spans from 0th to 500th rows and 1150th to 1650th columns.

Lets, create our own image, and give colours to it,

Let’s extract each channel of the image we created,

Look at how the Red channel has only the Red part brightened and similarly for the other 2 channels, manipulating these 3 channels is crucial for efficient image processing.

There are many computer vision libraries, some of the best of skimage and opencv-python. SciPy is also a good alternative.

SK-Image does not allow video-processing in realtime, but opencv does. OpenCV is also faster than skimage, but has lesser pre-implemented algorithms. Pillow is the standard library for image processing in python.

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