图像的对极线校正的程序matlab
时间: 2023-12-14 07:05:57 浏览: 50
下面是一个简单的MATLAB程序示例,用于实现图像对极线校正:
```matlab
% 读取图像
img1 = imread('img1.jpg');
img2 = imread('img2.jpg');
% 提取SIFT特征点
sift = cv.SIFT();
[kp1, des1] = sift.detectAndCompute(img1);
[kp2, des2] = sift.detectAndCompute(img2);
% 匹配特征点
bf = cv.BFMatcher('normType', 'Hamming');
matches = bf.match(des1, des2);
% 筛选匹配点
good_matches = [];
for i = 1:length(matches)
m = matches(i);
if m.distance < 0.75 * n.distance
good_matches = [good_matches, m];
end
end
% 计算本质矩阵
pts1 = [];
pts2 = [];
for i = 1:length(good_matches)
m = good_matches(i);
pts1 = [pts1; kp1(m.queryIdx+1).pt];
pts2 = [pts2; kp2(m.trainIdx+1).pt];
end
F = cv.findFundamentalMat(pts1, pts2, 'Method', 'RANSAC', 'RansacReprojThreshold', 3, 'Confidence', 0.99);
% 计算对极线
lines1 = cv.computeCorrespondEpilines(pts2, 2, F);
lines2 = cv.computeCorrespondEpilines(pts1, 1, F);
% 对极线校正
img1_rectified = img1;
img2_rectified = img2;
for i = 1:length(good_matches)
r = pts1(i, :);
l = lines1(i, :);
x0 = 0;
y0 = -l(3) / l(2);
x1 = size(img1, 2);
y1 = -(l(3) + l(1) * x1) / l(2);
img1_rectified = cv.line(img1_rectified, [x0, y0], [x1, y1], 'Color', [0, 255, 0], 'Thickness', 1);
end
for i = 1:length(good_matches)
r = pts2(i, :);
l = lines2(i, :);
x0 = 0;
y0 = -l(3) / l(2);
x1 = size(img2, 2);
y1 = -(l(3) + l(1) * x1) / l(2);
img2_rectified = cv.line(img2_rectified, [x0, y0], [x1, y1], 'Color', [0, 255, 0], 'Thickness', 1);
end
% 显示结果
imshow(img1_rectified);
imshow(img2_rectified);
```
以上程序中使用了OpenCV库中的SIFT特征点提取、双向暴力匹配、RANSAC算法估计本质矩阵、以及对极线校正等步骤。需要注意的是,MATLAB中的OpenCV库与Python中的OpenCV库略有不同,但基本思路和函数调用方式类似。如果需要更多的参数调整和优化,可以参考Python程序进行修改。