Image inpainting concept in Computer Vision

Introduction

Image inpainting helps fill missing or damaged parts of pictures so they look complete and natural again.

Restoring old photos with scratches or tears.

Removing unwanted objects from pictures smoothly.

Fixing missing areas in images caused by errors.

Creating artistic effects by filling blank spaces.

Improving images for better analysis when parts are corrupted.

Syntax

Computer Vision

inpainted_image = inpainting_model.predict(damaged_image, mask)

The 'damaged_image' is the input picture with missing or damaged parts.

The 'mask' shows which parts of the image need to be filled or fixed.

Examples

Fill a hole in the image where the mask marks the missing area.

Computer Vision

inpainted = model.inpaint(image_with_hole, hole_mask)

Use the model to predict and fill missing parts in the damaged image.

Computer Vision

restored_img = inpainting_model.predict(damaged_img, mask)

Sample Model

This code loads a damaged image and a mask showing missing parts. It uses OpenCV's inpainting to fill those parts and saves the fixed image.

Computer Vision

import cv2
import numpy as np

# Load image with missing part
image = cv2.imread('damaged_photo.jpg')

# Create mask where missing parts are white (255), rest is black (0)
mask = cv2.imread('mask.png', cv2.IMREAD_GRAYSCALE)

# Use OpenCV's Telea inpainting method
inpainted_image = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)

# Save and show result
cv2.imwrite('restored_photo.jpg', inpainted_image)
print('Inpainting done, saved as restored_photo.jpg')

Important Notes

Good masks are important; they tell the model exactly where to fix.

Different inpainting methods exist; some are faster, some give better quality.

Inpainting works best when missing areas are small or surrounded by clear context.

Summary

Image inpainting fills missing or damaged parts of pictures.

It uses a mask to know where to repair.

Commonly used for photo restoration and object removal.