Question

我使用RANSAC算法查找单应性和包装透视操作以将其应用于图像。这是代码

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()

输出没有显示整个图像。有什么不对？如何包装图像？

Answer 1

你正在计算从img1到img2的单应性，但是你将它应用于img2而不是img1。

将result = cv2.warpPerspective(img2, M, (w,h))更改为result = cv2.warpPerspective(img1, M, (2 * w, h))（2 * w是因为结果中包含较大部分的扭曲图像）

如何找到最能将图像扭曲到相同视角的单应性

1 个答案: