使用Python实现SIFT-RANSAC算法

SIFT-RANSAC算法是一种图像配准算法，可以用于在两幅图像中找到相同区域。下面是使用Python实现SIFT-RANSAC算法的步骤：

1.导入必要的库

import cv2
import numpy as np
from scipy.spatial import distance
from collections import Counter

2.加载图像

img1 = cv2.imread('image1.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('image2.jpg', cv2.IMREAD_GRAYSCALE)

3.提取SIFT特征

sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

4.特征匹配

bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)

5.筛选好的匹配点

good = []
for m, n in matches:
    if m.distance < 0.75 * n.distance:
        good.append(m)

6.使用RANSAC算法进行配准

MIN_MATCH_COUNT = 10
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)
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    matchesMask = mask.ravel().tolist()
else:
    print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))
    matchesMask = None

7.计算匹配点的正确率

if matchesMask is not None:
    distances = []
    for i in range(len(good)):
        if matchesMask[i] == 1:
            pt1 = src_pts[i][0]
            pt2 = dst_pts[i][0]
            distances.append(distance.euclidean(pt1, pt2))
    correct_ratio = Counter(distances).most_common()[0][1] / len(distances)
    print("Correct Ratio: {:.2f}%".format(correct_ratio * 100))

以上是使用Python实现SIFT-RANSAC算法的步骤，其中RANSAC算法可以有效地筛选出正确的匹配点，提高匹配的准确率。