我正在尝试创建一个程序，该程序可以使用opencv从python中的两个图像计算平面的旋转。为此，我找到了表示翻译的单应性矩阵，然后使用openCv中的decomposeHomographyMat函数使用内在相机矩阵将其分解。
我使用混合器测试了准确性，方法是创建一个带有QR码的平面，然后将其旋转已知值as seen here where the plane has been rotated by 15,30,15 in XYZ Euler coordinates，尽管我希望最终程序为现实中正在转换的平面拍照
使用this technique在Blender中找到了固有的相机矩阵。而且还发现了在搅拌机中使用摄像机校准功能，方法是放置一个棋盘格并从多个角度和平移位置获取渲染。
但是，当我运行代码时，我得到的ZYX Euler输出为[27.9 ，-25.4，-26.31]，而不是[15，-30，-15]，这是不准确的。下面是将代码输出到期望值的其他一些示例，以使您了解代码的准确性：
预期-[0 -30 0]
被歪曲-[0.82 -34.51 -1.91]
预期-[0 0 15]
计算得出-[0 0 -15.02]
预期-[15 0 15]
计算得出-[16.23 3.76 -13.76]
我想知道是否有任何方法可以提高计算的旋转矩阵的精度，或者这是否是我可以获得的最佳精度，以及如果这是我可以获得的其他最佳精度，我可以做些什么通过图像计算平面在3轴上的旋转（也可以添加额外的相机）。
任何帮助将不胜感激！
我正在使用的代码如下所示：
#Import modules
import cv2
import numpy as np
from matplotlib import pyplot as plt
import glob
import math
########################################################################
#Import pictures
img1 = cv2.imread("top.png", cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread("150015.png", cv2.IMREAD_GRAYSCALE)
#Feature Extraction
MIN_MATCH_COUNT = 10
sift = cv2.xfeatures2d.SIFT_create()
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)
# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
if m.distance < 0.80*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)
#Finds homography matrix
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,1)
matchesMask = mask.ravel().tolist()
h,w = img1.shape
pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
dst = cv2.perspectiveTransform(pts,M)
img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
else:
print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
matchesMask = None
draw_params = dict(matchColor = (0,255,0), # draw matches in green color
singlePointColor = None,
matchesMask = matchesMask, # draw only inliers
flags = 2)
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.imshow(img3, 'gray'),plt.show()
#Camera calibration matrix
K = ((3,3))
K = np.zeros(K)
#Camera calibration matrix from blender python script
#K = np.matrix('1181.2500 0 540; 0 2100 540; 0 0 1')
#Camera calibration matrix from importing checkboard into blender
K = np.matrix('1307.68697 0 600.618354; 0 1309.66779 605.481488; 0 0 1')
#Homography matrix is decomposed
num, Rs, Ts, Ns = cv2.decomposeHomographyMat(M, K)
# Checks if a matrix is a valid rotation matrix.
def isRotationMatrix(R) :
Rt = np.transpose(R)
shouldBeIdentity = np.dot(Rt, R)
I = np.identity(3, dtype = R.dtype)
n = np.linalg.norm(I - shouldBeIdentity)
return n < 1e-6
# Calculates rotation matrix to euler angles
# The result is the same as MATLAB except the order
# of the euler angles ( x and z are swapped ).
def rotationMatrixToEulerAngles(R) :
assert(isRotationMatrix(R))
sy = math.sqrt(R[0,0] * R[0,0] + R[1,0] * R[1,0])
singular = sy < 1e-6
if not singular :
x = math.atan2(R[2,1] , R[2,2])
y = math.atan2(-R[2,0], sy)
z = math.atan2(R[1,0], R[0,0])
else :
x = math.atan2(-R[1,2], R[1,1])
y = math.atan2(-R[2,0], sy)
z = 0
return np.array([x, y, z])
#Conver the 4 rotation matrix solutions into XYZ Euler angles
i=0
for i in range(0,4):
R = Rs[i]
angles = rotationMatrixToEulerAngles(R)
x = np.degrees(angles[0])
y = np.degrees(angles[1])
z = np.degrees(angles[2])
anglesDeg = np.array([x,y,z])
print(anglesDeg)
我从Blender生成的图像如下：
top.png（Ox，0y，0z）
003000.png（0x，30y，0z）
150015.png（15x，0y，15z）
153000.png（15x，30y，0z）
153015.png（15x，30y，15z）
And here is an image with keypoints matching for the 153015.png comparison
