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找到具有特征匹配的关键点之间的距离。有什么办法吗？我正在使用SIFT / BFMatcher，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()

Answer 1

以下算法查找img1的关键点与img2中的特色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是对称矩阵，img1关键点之间的距离KP_dist2与img2相同，FM_dist是一个列表两个图像的特色匹配关键点之间的距离为len(FM_dist) == len(good)。

希望这有帮助！

Python - 使用OpenCV的要素匹配关键点之间的距离

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