Introduction to OpenCV

Difficulty Level : Basic
Last Updated : 09 Sep, 2019

OpenCV is one of the most popular computer vision libraries. If you want to start your journey in the field of computer vision, then a thorough understanding of the concepts of OpenCV is of paramount importance.
In this article, I will try to introduce the most basic and important concepts of OpenCV in an intuitive manner.
This article will cover the following topics:

Reading an image
Extracting the RGB values of a pixel
Extracting the Region of Interest (ROI)
Resizing the Image
Rotating the Image
Drawing a Rectangle
Displaying text

This is the original image that we will manipulate throughout the course of this article.

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Original Image

Let’s start with the simple task of reading an image using OpenCV.

Reading an image

# Importing the OpenCV library
import cv2
# Reading the image using imread() function
image = cv2.imread('image.png')
  
# Extracting the height and width of an image
h, w = image.shape[:2]
# Displaying the height and width
print("Height = {},  Width = {}".format(h, w))

Now we will focus on extracting the RGB values of an individual pixel.
Note – OpenCV arranges the channels in BGR order. So the 0th value will correspond to Blue pixel and not Red.

Extracting the RGB values of a pixel

# Extracting RGB values. 
# Here we have randomly chosen a pixel
# by passing in 100, 100 for height and width.
(B, G, R) = image[100, 100]
  
# Displaying the pixel values
print("R = {}, G = {}, B = {}".format(R, G, B))
  
# We can also pass the channel to extract 
# the value for a specific channel
B = image[100, 100, 0]
print("B = {}".format(B))

Extracting the Region of Interest (ROI)

# We will calculate the region of interest 
# by slicing the pixels of the image
roi = image[100 : 500, 200 : 700]

Region Of Interest

Resizing the Image

# resize() function takes 2 parameters, 
# the image and the dimensions
resize = cv2.resize(image, (800, 800))

Resized Image

The problem with this approach is that the aspect ratio of the image is not maintained. So we need to do some extra work in order to maintain a proper aspect ratio.

# Calculating the ratio
ratio = 800 / w
  
# Creating a tuple containing width and height
dim = (800, int(h * ratio))
  
# Resizing the image
resize_aspect = cv2.resize(image, dim)

Resizing with proper aspect ratio

Rotating the Image

# Calculating the center of the image
center = (w // 2, h // 2)
  
# Generating a rotation matrix
matrix = cv2.getRotationMatrix2D(center, -45, 1.0) 
  
# Performing the affine transformation
rotated = cv2.warpAffine(image, matrix, (w, h))

Rotated Image

There are a lot of steps involved in rotating an image. So, let me explain each of them in detail.

The 2 main functions used here are –

getRotationMatrix2D()
warpAffine()

getRotationMatrix2D()
It takes 3 arguments –

center – The center coordinates of the image
Angle – The angle (in degrees) by which the image should be rotated

Scale – The scaling factor

It returns a 2*3 matrix consisting of values derived from alpha and beta
alpha = scale * cos(angle)
beta = scale * sine(angle)

Rotation Matrix

warpAffine()

The function warpAffine transforms the source image using the rotation matrix:

dst(x, y) = src(M11X + M12Y + M13, M21X + M22Y + M23)

Here M is the rotation matrix, described above.
It calculates new x, y co-ordinates of the image and transforms it.

Drawing a Rectangle
It is an in-place operation.

# We are copying the original image, 
# as it is an in-place operation.
output = image.copy()
  
# Using the rectangle() function to create a rectangle.
rectangle = cv2.rectangle(output, (1500, 900), 
                          (600, 400), (255, 0, 0), 2)

Rectangle

It takes in 5 arguments –

Image
Top-left corner co-ordinates
Bottom-right corner co-ordinates
Color (in BGR format)
Line width

Displaying text
It is also an in-place operation

# Copying the original image
output = image.copy()
  
# Adding the text using putText() function
text = cv2.putText(output, 'OpenCV Demo', (500, 550), 
                   cv2.FONT_HERSHEY_SIMPLEX, 4, (255, 0, 0), 2)