Python - 使用OpenCV计算特征匹配关键点之间的距离

Question

我正在尝试实现一个程序，该程序将输入两个立体图像并查找具有特征匹配的关键点之间的距离。是否有任何方法可以做到这一点？我正在使用SIFT/BFMatcher，并且我的代码如下：

import numpy as np
import cv2
from matplotlib import pyplot as plt

img1 = dst1
img2 = dst2

# Initiate SIFT detector
sift = cv2.SIFT()

# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)

# Apply ratio test
good = []
for m, n in matches:
    if m.distance < 0.3 * n.distance:
        good.append([m])

# cv2.drawMatchesKnn expects list of lists as matches.
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, flags=2, outImg=img2)

plt.imshow(img3), plt.show()

- user3601754

Answer 1

以下算法找到了img1的关键点与其在img2中特征匹配关键点之间的距离（省略第一行）：

# Apply ratio test
good = []
for m,n in matches:
    if m.distance < 0.3 * n.distance:
        good.append(m)

# Featured matched keypoints from images 1 and 2
pts1 = np.float32([kp1[m.queryIdx].pt for m in good])
pts2 = np.float32([kp2[m.trainIdx].pt for m in good])

# Convert x, y coordinates into complex numbers
# so that the distances are much easier to compute
z1 = np.array([[complex(c[0],c[1]) for c in pts1]])
z2 = np.array([[complex(c[0],c[1]) for c in pts2]])

# Computes the intradistances between keypoints for each image
KP_dist1 = abs(z1.T - z1)
KP_dist2 = abs(z2.T - z2)

# Distance between featured matched keypoints
FM_dist = abs(z2 - z1)

因此，KP_dist1是一个对称矩阵，其中包含img1关键点之间的距离，KP_dist2也是同样的，但是针对img2。而FM_dist是一个列表，其中包含两个图像中特征匹配的关键点之间的距离，且len(FM_dist) == len(good)。

希望这可以帮助你！

- ebeneditos