To compute the homography, we first select correspondant points between the two images. Then we use these coordinates to find a projective transformation between the two by solving using least squares. The method used is described in this paper: Homography Estimation. I used 7-8 correspondants to pass into the function for every pair of images.

Using the homography from the previous part we first find the corner points of the image we want to warp. Then we can use inverse warping to interpolate the pixels to fill into the resulting image. The positions where there are no pixels from the original image are kept black.

To check that our homography and warping is working as we want it to, we can take an image of something that has a square shape then try to warp the points to a set of square coordinate we define. For example, in the image of the door below I chose the 4 correspondants which forms a square in the warped image.

Now that we have the images of our final mosaic, we can stitch them together by aligning them correctly in one final image.