如何找到最能将图像扭曲成相同视角的单应性

Question

我已经使用 RANSAC 算法找到单应性并包裹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。有什么问题吗？如何包装图片？

Answer 1

您正在计算从 img1 到 img2 的单应性，但您将其应用于 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 是为了在结果中包含更大部分的扭曲图像）

如何找到最能将图像扭曲成相同视角的单应性

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

opencv

image-processing

homography

ransac