Image stitching Python

Question

I have to stitch two or more images together using python and openCV. I found this code for finding keypoints and matches, but I don't know how to continue. Help me please!

import numpy as np
import cv2

MIN_MATCH_COUNT = 10

img1 = cv2.imread('a.jpg',0)          # queryImage
img2 = cv2.imread('b.jpg',0) # trainImage

# Initiate SIFT detector
sift = cv2.SIFT()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)

FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)

flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1,des2,k=2)

# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)

Answer 1

Your question is not very clear, but I assume what you mean is that you have a bunch of images and you want to have opencv find the corresponding landmarks and then warp/scale each picture so that they can form one big image.

A method without using the stitcher class, basically looping over pictures and determining the best fitting one each iteration, is documented in this github code

Answer 2

One approach to image stitching consists of the following steps.

Firstly, as you've already figured out, you need a feature point detector and the some way to find correspondences between feature points on both images. It's typically a good idea to eliminate a lot of correspondences because they will likely contain a lot of noise. A super simple way to eliminate a lot of noise is to look for symmetry in the matches.

This is roughly what your code does up to this point.

Next, to stitch images together, you need to warp one of the images to match the perspective of the other image. This is done by estimating the homography using the correspondences. Because your correspondences will still likely contain a lot of noise, we typically use RANSAC to robustly estimate the homography.

A quick google search provides many examples of this being implemented.