BEHIND THE SCENES OF IMAGE PROCESSING (8 OF 9)
Image Processing using Python — Homography Matrix
In this article, we embark on a nostalgic journey into image processing, specifically focusing on the implementation of homography techniques. Our aim is to demonstrate how homography can be utilized to transform and analyze an image, as we delve into the captivating world of Monopoly — a game that often evokes memories of childhood rivalry and endless quarrels with siblings and cousins.
Understanding the Power of Homography in Image Processing
Homography is a vital concept in image analysis, enabling us to map the perspective of a scene onto a different plane or view. By applying homography transformations, we can rectify distortions, align images, and even overlay virtual objects seamlessly. This technique plays a crucial role in enhancing visual content and extracting valuable information from images.
To begin, let us import all necessary libraries.
import numpy as np
import matplotlib.pyplot as plt
from skimage import transform
from skimage.io import imread, imshow
import cv2Now, let us see the image!
monopoly = imread('monopoly.jpg')
imshow(monopoly)
Implementing Homography: Reliving the Monopoly Memories
To showcase the capabilities of homography, we will apply this technique to an image of a Monopoly game — a cherished childhood pastime. By leveraging homography transformations, we can rectify perspective distortions that may occur due to the camera angle or viewing position. This will allow us to analyze the game board, identify specific elements, and relive the memories of our youthful quarrels and camaraderie around the Monopoly board.
# Convert the image from BGR to RGB for proper visualization with Matplotlib
monopoly = cv2.cvtColor(monopoly, cv2.COLOR_BGR2RGB)
# Define the source points
src = np.array([170, 90,
1560, 1080,
0, 1100,
1360, 70,
]).reshape((4, 2))
# Plot the image
plt.imshow(monopoly)
# Plot the points on the image with red color
plt.scatter(src[:, 0], src[:, 1], color='red', marker='o')
# Show the image with the plotted points
plt.show()
Now that I have identified the source points for the transformation, I will now identify its destination points.
dst = np.array([0, 100,
1500, 1000,
0, 1000,
1500, 100,
]).reshape((4, 2))
tform = transform.estimate_transform('projective', src, dst)Since we both have the source and destination points. Let us now implement its projective transformation.
tf_img = transform.warp(monopoly, tform.inverse)
fig, ax = plt.subplots()
ax.imshow(tf_img)
_ = ax.set_title('projective transformation')
We have successfully applied the homography matrix to the Monopoly image, achieving a successful transformation. Well done!
Significance and Applications of Homography in Image Processing
Homography finds significance in various image processing applications. Beyond rectifying distortions, it enables us to augment reality, perform 3D reconstructions, and enhance visual effects. In the context of gaming, homography plays a vital role in analyzing game boards, facilitating virtual overlays, and extracting valuable information for player interactions.
Closing Thoughts
Through the implementation of homography in image processing, we have witnessed the transformative capabilities of this technique. By rectifying perspective distortions and analyzing the Monopoly game image, we can relive the memories of childhood experiences and the spirited rivalries that Monopoly often brings. Homography’s power extends beyond nostalgic reflections, finding applications in augmented reality, gaming, and various other domains. So, let’s embrace the magic of homography in image processing, as we journey back in time to the Monopoly board and immerse ourselves in the joyous and sometimes tumultuous memories of childhood gameplay.
References
Benjur Emmanuel L. Borja. 2023. Image Processing(MSDS2023). Asian Institute of Management.
