python - 如何找到数据集的基本矩阵？

Question

我想比较 openCV 中用于计算基本矩阵的不同算法。我已经使用下面的代码来找到对应图像对的基本矩阵。有没有办法在不使用 for 循环的情况下对数据集做同样的事情？

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
img1 = cv.imread('myleft.jpg',0)  #queryimage # left image
img2 = cv.imread('myright.jpg',0) #trainimage # right image
sift = cv.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
# FLANN parameters
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)
flann = cv.FlannBasedMatcher(index_params,search_params)
matches = flann.knnMatch(des1,des2,k=2)
pts1 = []
pts2 = []
# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
    if m.distance < 0.8*n.distance:
        pts2.append(kp2[m.trainIdx].pt)
        pts1.append(kp1[m.queryIdx].pt)
pts1 = np.int32(pts1)
pts2 = np.int32(pts2)
F, mask = cv.findFundamentalMat(pts1,pts2,cv.FM_LMEDS)
# We select only inlier points
pts1 = pts1[mask.ravel()==1]
pts2 = pts2[mask.ravel()==1]

标签： pythonopencvcomputer-visionfundamental-matrix