我试图在Python中使用opencv找到两个图像rgb
和rotated
的单应矩阵:
print(rgb.shape, rotated.shape)
H = cv2.findHomography(rgb, rotated)
print(H)
而我得到的错误是
(1080, 1920, 3) (1080, 1920, 3)
---------------------------------------------------------------------------
error Traceback (most recent call last)
<ipython-input-37-26874dc47f1f> in <module>()
1 print(rgb.shape, rotated.shape)
----> 2 H = cv2.findHomography(rgb, rotated)
3 print(H)
error: OpenCV(3.4.1) C:\projects\opencv-python\opencv\modules\calib3d\src\fundam.cpp:372: error: (-5) The input arrays should be 2D or 3D point sets in function cv::findHomography
我还尝试使用cv2.findHomography(rgb[:,:,0], rotated[:,:,0])
查看通道或通道排序是否导致任何问题,但它甚至不适用于2D矩阵。
输入应该如何?
cv2.findHomography()
不接收两张图片并返回H
。
如果你需要为两个RGB图像找到H
作为np.arrays:
import numpy as np
import cv2
def findHomography(img1, img2):
# define constants
MIN_MATCH_COUNT = 10
MIN_DIST_THRESHOLD = 0.7
RANSAC_REPROJ_THRESHOLD = 5.0
# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
# find matches
FLANN_INDEX_KDTREE = 1
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 < MIN_DIST_THRESHOLD * 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)
H, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, RANSAC_REPROJ_THRESHOLD)
return H
else: raise Exception("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
注意:
Python 3
和OpenCV 3.4
上测试过opencv-contrib-python
包,因为SIFT
有专利问题,已从opencv-python
中删除img1
的H矩阵并将其重叠在img2
上。如果你想知道如何做到这一点,那就是here