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OpenCV Image Manipulation

OpenCV Image Manipulation

Manipulating Images with OpenCV

Introduction

In the world of image processing, it’s essential to grasp the basics. This article explores key operations in OpenCV, guiding you through crafting digital images, handling image reading and display, and understanding colour space conversions. Let’s equip ourselves with the knowledge needed to navigate the intricacies of image manipulation.

Video Source: Rob Mulla
Image Manipulation

Understanding Digital Images

A digital image is like a mosaic made up of pixels, each with specific spatial coordinates (x, y) and intensity values. Grayscale images, using values from 0 to 255, differ from RGB images, which blend Red, Green, and Blue channels to create a spectrum of colours. Knowing these basics sets the stage for a deeper exploration.

How to Read and Display Images in OpenCV

Time Needed : 00 hours 30 minutes

Carefully follow the next series of instructions to get the best results from this tutorial. This is going to be interesting, so, grab a cup of coffee, and let's get started.

  1. Step 1: Importing OpenCV and Reading Images

    To begin, we import the imread method from the OpenCV library in Python. We then proceed to import the “car” image from the image folder within the project.

    For this tutorial, I used an image of a car, which you can get together with my source file by clicking here or using the download button at the bottom of this page. Nevertheless, you can use any image you want.

    The above operations can be done as shown in the following image:

    OpenCV Image Manipulation
    The output provides insights into the image structure: an 8-bit unsigned integer array with dimensions (1024, 1024, 3). This array allows for flexible manipulation.

  2. Step 2: Accessing Pixel Values

    Exploring the array, we look into the values of the first pixel at coordinates (0, 0). Pixels, represented by three values, unveil intensity information specific to each channel.

  3. Step 3: Displaying Images

    Both Matplotlib and OpenCV offer methods for image display. OpenCV's imshow method operates in BGR order, requiring careful consideration when transitioning to Matplotlib. A conversion from BGR to RGB ensures accurate representation.

    The above comparison is demonstrated with the following images:

    OpenCV Image ManipulationOpenCV Image Manipulation
    OpenCV Image Manipulation

    Understanding the differences between BGR and RGB representations facilitates seamless integration.

  4. Step 4: Converting Between Colour Spaces

    Colour space conversion, a crucial skill, uses OpenCV's cvtColor method. Transitioning between BGR and RGB is demonstrated, emphasizing the importance of channel order.

    This is shown in the following image:

    OpenCV Image Manipulation

  5. Step 5: Grayscale Conversion

    Taking it a step further, the conversion from RGB to grayscale produces a single-channel image.

    This can be seen in the following image:

    OpenCV Image Manipulation
    A nuanced approach to grayscale conversion is presented, ensuring a clear understanding of image transformations.

In case you prefer copying parts of the code, or reading the full content of the downloaded source file before extracting and running it, check out the raw source code below.

Also, the code was written in a Jupyter Notebook, not with the default Python file, you might have to take note of that, as it determines how the code should be compiled to avoid any worries. (within Jupyter Notebook or Jupyter Lab) and not using the default Python compiler.

Jupyter | Cell 1
from cv2 import imread  
img = imread('Images/car.jpg')  # Read an RGB image
print('Datatype:', img.dtype, '\nDimensions:', img.shape) # Check datatype and dimensions
Visit My GitHub At https://github.com/N-Elmer/
Jupyter | Cell 2
import matplotlib.pyplot as plt  # Using Matplotlib
plt.imshow(img)
plt.title('Displaying image using Matplotlib')
plt.show()
Visit My GitHub At https://github.com/N-Elmer/
Jupyter | Cell 3
from cv2 import imshow, waitKey  # Using OpenCV
imshow('Displaying image using OpenCV', img)
waitKey(0)
Visit My GitHub At https://github.com/N-Elmer/
Jupyter | Cell 4
from cv2 import cvtColor, COLOR_BGR2RGB  # Convert BGR to RGB
img_rgb = cvtColor(img, COLOR_BGR2RGB)  # Display the converted image
plt.imshow(img_rgb)
plt.show()
Visit My GitHub At https://github.com/N-Elmer/
Jupyter | Cell 5
from cv2 import COLOR_RGB2GRAY  # Convert RGB to grayscale
img_gray = cvtColor(img_rgb, COLOR_RGB2GRAY)  # Display the grayscale image
imshow('Grayscale Image', img_gray)
waitKey(0)
Visit My GitHub At https://github.com/N-Elmer/

Conclusion on OpenCV Image Manipulation

This tutorial explores fundamental OpenCV operations, empowering you to understand image formulation, access pixel values, display images, and navigate colour space conversions.

With this knowledge, you’re ready to tackle intricate image-processing tasks. If you have any questions, feel free to ask in the comments below. Dive into the world of image processing with confidence!

Some Frequently Asked Questions and Their Answers

  1. What is OpenCV and how does it relate to image manipulation?

    OpenCV is a powerful computer vision library widely used for image processing.

  2. Can you provide practical examples of image processing using OpenCV?

    Yes, there are practical examples demonstrating image preprocessing with OpenCV.

  3. How can OpenCV be used for real-time image manipulation in Python?

    OpenCV allows real-time image manipulation by capturing frames and applying edits in Python.

  4. What are the essential concepts in computer vision and image processing for beginners?

    A beginner's guide to computer vision and image processing with OpenCV simplifies essential concepts in these cutting-edge fields.

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