How to apply Perspective Transformations on an image using OpenCV Python?

Perspective transformation refers to a process in which an image is distorted or transformed in such a manner that it appears to have been viewed from a different angle or direction. One of the common uses of perspective transformation is to correct distortion that occurs when capturing an image from an off-axis angle. In this article, we will discuss how to apply perspective transformations on an image using OpenCV in Python.

What is Perspective Transformation?

Perspective transformation is a type of transformation where an image is transformed in such a manner that it appears to have been viewed from a different angle or direction. This type of transformation is commonly used in computer vision.

The perspective transformation can be obtained from a matrix which contains the parameters required to perform the transformation. This matrix is called the perspective transformation matrix.

Applying Perspective Transformations using OpenCV Python

OpenCV is a popular library for computer vision applications. One of the features of OpenCV is the ability to perform perspective transformations on images. We can easily apply perspective transformations on images using OpenCV in Python.

To apply a perspective transformation on an image using OpenCV in Python, we need to perform the following steps:

1. Read the original image
2. Define the perspective transformation matrix
3. Apply the perspective transformation on the image
4. Display the transformed image

In the following code snippet, we will apply perspective transformation on an image using OpenCV in Python:

import cv2
import numpy as np

# Read the original image
img = cv2.imread('original_image.jpg')
cv2.imshow('Original Image', img)

# Define the four corners of the original image
pts1 = np.float32([[56, 65], [368, 52], [28, 387], [389, 390]])

# Define the four corners of the desired output
pts2 = np.float32([[0, 0], [300, 0], [0, 300], [300, 300]])

# Compute the perspective transformation matrix
M = cv2.getPerspectiveTransform(pts1, pts2)

# Apply the perspective transformation on the image
dst = cv2.warpPerspective(img, M, (300, 300))

# Display the transformed image
cv2.imshow('Transformed Image', dst)
cv2.waitKey(0)
cv2.destroyAllWindows()

In the above code snippet, we first read the original image and display it. We then define the four corners of the original image, and the four corners of the desired output. We use these two sets of points to compute the perspective transformation matrix using the cv2.getPerspectiveTransform function.

Once we have the perspective transformation matrix, we can apply the perspective transformation on the image using the cv2.warpPerspective function. Finally, we display the transformed image using cv2.imshow.

Conclusion

In this article, we discussed how to apply perspective transformations on an image using OpenCV in Python. We saw that by defining the four corners of the original image and the desired output, we can easily compute the perspective transformation matrix and apply it on the image using the cv2.warpPerspective function. With this knowledge, you can now apply perspective transformations on images to correct distortions and achieve other interesting effects.