Python: OpenCV findHomography输入
Python: OpenCV findHomography输入
提问于 2018-06-20 09:56:28
我试图用Python中的opencv找到两个图像rgb和rotated的同形矩阵：
print(rgb.shape, rotated.shape)
H = cv2.findHomography(rgb, rotated)
print(H)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])来查看通道或频道排序是否会导致任何问题，但它甚至不能用于2D矩阵。
输入应该如何？
回答 1
Stack Overflow用户
回答已采纳
发布于 2018-06-20 13:36:16
cv2.findHomography()不接收两个图像并返回H。
如果您需要为两个RGB映像找到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：
- 在
Python 3和OpenCV 3.4上测试 - 您需要
opencv-contrib-python包，因为SIFT存在专利问题，并且已从opencv-python中删除 - 这给出了转换
img1的H矩阵，并将其与img2重叠。如果你想知道怎么做，那就是here
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/50945385
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