How to find the homography that best warps the images into the same perspective

Question

I have used RANSAC algorithm to find the homography and wrap perspective operation to apply it to an image. here is the code

MIN_MATCH_COUNT = 10
img1 = cv2.imread('bus1.jpg',0)
img2 = cv2.imread('bus2.jpg',0)
sift = cv2.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)

good = []
for m,n in matches:
   if m.distance < 0.7*n.distance:
      good.append(m)

if len(good)>MIN_MATCH_COUNT:

  src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
  dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)

  M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
  h,w = img1.shape

  result=cv2.warpPerspective(img2,M,(w,h))

cv2.imshow('result',result)
cv2.waitKey(0)
cv2.destroyAllWindows()

output is not showing the whole image .what is wrong? how to wrap the image?

Answer 1

You are computing the homography from img1 to img2 but you are applying it to img2 and not img1.

Change result = cv2.warpPerspective(img2, M, (w,h)) to result = cv2.warpPerspective(img1, M, (2 * w, h)) (the 2 * w is so that there is a bigger part of the warped image included in result)