Image Inpainting using OpenCV Python

The process of image inpainting involves restoring or replacing missing parts in an image, generally by estimating the missing area based on surrounding pixels. OpenCV, with its powerful computer vision library, has cv2.inpaint() available for image inpainting employing different algorithms.

Here’s a basic guide for performing image inpainting with OpenCV in Python:

Requirements:

1. Python 3.x
2. OpenCV (cv2)

Installation:

To install OpenCV use

pip install opencv-python

Steps for Image Inpainting in OpenCV:

• Import Libraries:

First, import OpenCV and other necessary libraries.

import cv2 import
numpy as np

 

 

• Load the Image: Load the image with missing parts and the mask that indicates the regions to be inpainted.

 

 img = cv2.imread('image.jpg')  # Load the original image
 mask = cv2.imread('mask.jpg', 0)  # Load the mask, grayscale

• Inpainting: OpenCV’s inpaint() function allows you to perform inpainting using two main methods:

1. INPAINT_TELEA (based on the Telea algorithm)
2. INPAINT_NS (based on the Navier-Stokes algorithm)

The third argument (3) is the inpainting radius. It controls the extent of influence that surrounding pixels have on the inpainted area. A higher value will consider a larger neighborhood of pixels.

result = cv2.inpaint(img, mask, 3, cv2.INPAINT_TELEA)

The second argument (mask) is a binary image where white pixels are 255 and indicate areas to inpaint, and black pixels are 0 and indicate regions to leave unchanged.

 

• Save/Display the Result: After inpainting, display or save the resulting image.  

cv2.imshow('Inpainted Image', result)

cv2.waitKey(0)

cv2.destroyAllWindows()

 

Explanation of Parameters:

1. Image (img): The input image containing the parts that are either missing or damaged.
2. Mask (mask): Binary image where white areas represent 255 and indicate the areas to be inpainted, while black areas are represented by 0 and remain untouched.
3. Inpainting Radius (3): It represents the radius of how far surrounding pixels would affect the inpainting. Higher values will make use of a broader neighborhood of pixels.
4. Inpainting Algorithm: You can use either cv2.INPAINT_TELEA (faster, smoother output) or cv2.INPAINT_NS(slower, but more accurate).

Conclusion

OpenCV’s cv2.inpaint()provides an efficient and straightforward method for restoring missing parts of an image. By specifying a mask and choosing an inpainting algorithm, you can repair images, remove objects, or fill in any missing areas seamlessly.