我希望使用OpenCv中的单应性矩阵拼接两张全景图像。我已经找到了3x3单应性矩阵，但是我无法使用内置函数拼接两张图片。我必须手动拼接这两张图片。以下是我的代码：
import cv2
import numpy as np
MIN_MATCH_COUNT = 10
img1 = cv2.imread("pano1/cyl_image00.png")
img2 = cv2.imread("pano1/cyl_image01.png")
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.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)
des1 = np.float32(des1)
des2 = np.float32(des2)
matches = flann.knnMatch(des1, des2, k=2)
goodMatches = []
for m, n in matches:
if m.distance < 0.7 * n.distance:
goodMatches.append(m)
src_pts = 0
dst_pts = 0
if len(goodMatches) > MIN_MATCH_COUNT:
dst_pts = np.float32([kp1[m.queryIdx].pt for m in goodMatches]).reshape(-1, 2)
src_pts = np.float32([kp2[m.trainIdx].pt for m in goodMatches]).reshape(-1, 2)
def generateRandom(src_Pts, dest_Pts, N):
r = np.random.choice(len(src_Pts), N)
src = [src_Pts[i] for i in r]
dest = [dest_Pts[i] for i in r]
return np.asarray(src, dtype=np.float32), np.asarray(dest, dtype=np.float32)
def findH(src, dest, N):
A = []
for i in range(N):
x, y = src[i][0], src[i][1]
xp, yp = dest[i][0], dest[i][1]
A.append([x, y, 1, 0, 0, 0, -x * xp, -xp * y, -xp])
A.append([0, 0, 0, x, y, 1, -yp * x, -yp * y, -yp])
A = np.asarray(A)
U, S, Vh = np.linalg.svd(A)
L = Vh[-1, :] / Vh[-1, -1]
H = L.reshape(3, 3)
return H
def ransacHomography(src_Pts, dst_Pts):
maxI = 0
maxLSrc = []
maxLDest = []
for i in range(70):
srcP, destP = generateRandom(src_Pts, dst_Pts, 4)
H = findH(srcP, destP, 4)
inlines = 0
linesSrc = []
lineDest = []
for p1, p2 in zip(src_Pts, dst_Pts):
p1U = (np.append(p1, 1)).reshape(3, 1)
p2e = H.dot(p1U)
p2e = (p2e / p2e[2])[:2].reshape(1, 2)[0]
if cv2.norm(p2 - p2e) < 10:
inlines += 1
linesSrc.append(p1)
lineDest.append(p2)
if inlines > maxI:
maxI = inlines
maxLSrc = linesSrc.copy()
maxLSrc = np.asarray(maxLSrc, dtype=np.float32)
maxLDest = lineDest.copy()
maxLDest = np.asarray(maxLDest, dtype=np.float32)
Hf = findH(maxLSrc, maxLDest, maxI)
return Hf
H = ransacHomography(src_pts, dst_pts)
目前为止，一切都很好。我找到了单应性矩阵(H)。
接下来，我尝试拼接两张全景图像。 首先，我创建一个大数组来拼接图像(img3)。 我将img1复制到img3的前半部分。 我尝试通过单应性矩阵找到img2的新坐标，并将新的img2坐标复制到img3中。
以下是我的代码:
height1, width1, rgb1 = img1.shape
height2, width2, rgb2 = img2.shape
img3 = np.empty((height1, width1+width2, 3))
img3[:, 0:width1] = img1/255.0
for i in range(len(img2)):
for j in range(len(img2[0])):
pp = H.dot(np.array([[i], [j], [1]]))
pp = (pp / pp[2]).reshape(1, 3)[0]
img3[int(round(pp[0])), int(round(pp[1]))] = img2[i, j]/255.0
但是这部分不起作用。我该怎么解决这个问题？