Image-alignment--PYTHON-EDITION

Alignment of an unaligned image with a base/reference image using feature detection, feature matching, and homography in OpenCV.

Features

• Detects keypoints and descriptors using ORB (can be swapped with AKAZE or SIFT for higher accuracy).
• Matches features with Brute Force Hamming matcher.
• Filters matches to keep only the best ones.
• Computes a homography matrix to warp the unaligned image into alignment with the base image.
• Optionally saves the aligned result to a file.
• Displays side-by-side comparison of the unaligned, base, and aligned images.

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Example Output

How It Works

1. Read the images — Load both base and unaligned images.
2. Resize — Ensure both images have the same dimensions.
3. Feature Detection — Use ORB to detect keypoints and compute descriptors.
4. Feature Matching — Match descriptors between the two images.
5. Filter Matches — Keep the top matches (default: best 10%).
6. Compute Homography — Estimate the perspective transformation between matched points.
7. Warp Image — Apply the transformation to align the unaligned image with the base.

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Developed by Mohammed Yasser Mohammed call me ;)

email : es-mohamed.yasser2027@alexu.edu.eg