This project of mine is the implementation of some computer vision and linear algebra methods to stich images. These images were taken by a person who was standing at one point and rotating his camera from left to right to take several overlapping images.(FYI, these images show the beautiful view from the main-gate of the EC department of NIT Rourkela, my Alma Mater)
| Input 1 | Input 2 | Stiched Output |
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| Input 1 | Input 2 | Input 3 | Stiched Output |
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- Find key points in both the images
- Keypoints between two images are matched by identifying their nearest neighbours. But in some cases, the second closest-match may be very near to the first. It may happen due to noise or some other reasons. In that case, ratio of closest-distance to second-closest distance is taken.
- Theoretically, we need 9 matchings to estimate the homography matrix but the libraries select a bunch of them, detect outliers if any using the RANSAC algorithm, and then solve equations to estimate the homography matrix.
- Then that homography matrix is used to generate a perspective for stitching the images together.
Use the package manager pip
- OpenCV
- Numpy
- matplotlib (for visualization)
python panorama.py path/to/img/ path/to/img/
This program can stich more than 2 images as well. Refer to the below given example.
python panorama.py images/ec1.jpeg images/ec2.jpeg images/ec3.jpeg