To estimate the homography in OpenCV is a simple task. Other scripts were either too old to works on python3 or the functions are out of data. I tried very hard to find a script which was able to stitch images. As we described before, the homography matrix will be used with the best matching points to estimate a relative orientation transformation within the two images. Stich two or more images to one with opencv. Now, let’s see how we can use Python and OpenCV to create a panorama image. The final step is to stitch these two images together and to create a panorama image. So, once we have obtained the best matches between the images, our next step is to calculate the homography matrix. Once the algorithm matches identical keypoints in both images we can easily overlap them as you can see in the following image. What is left is just several lines of code. We finished three first steps in our previous tutorial, so the last 3 steps are left to do. In our first tutorial, we did the most job. Select the top best matches for each descriptor of an image 4. Compute distances between every descriptor in one image and every descriptor in the other image 3. Compute the sift-key points and descriptors for left and right images 2. So here is the list of steps from our first tutorial on what we should do to get our final stitched result:ġ.
#Panorama stitcher for four images python open cv code
(Depending on the way their code defines H, you might need to reverse the above multiplication order.) So you would multiply to obtain H_0i and then use it to transform image i to line up with image 0.įor background on why you multiply the transformations, see: Transformations and Matrix Multiplication specifically the "Composition of tranformations" part.Welcome to our second image stitching tutorial part, where we’ll finish our first tutorial part, and we’ll receive our stitched image. Where H_01 is the H that transforms image 1 to line up with image 0. The result is the Homography from that image to image 0.įor example: the H that transforms image 3 to line up with image 0 is H_03 = H_01 * H_12 * H_23. The other option, if you know the order that you want to stitch, is to find the Homography from one image to the next, and then multiply them. It should work decently well, assuming each new image is somewhere on the border of the current panorama, and there isn't too much perspective distortion.
This method basically attempts to match the next image to any part of the current panorama.
Something like this pseudocode: panorama = images
The easiest way (though not super efficient) given the functions you've written, is to just grow the panorama image by stitching it with each successive image. See section 4 of this seminal paper Automatic Panoramic Image Stitching using Invariant Features for an in depth explanation.