img_stripes = imread7bmp; img_stripes = imbinarizeimg_stripes; 将图像转为二维 img_stripes = img_stripes1; 计算每个条纹的方向 stats = regionpropsimg_stripes Orientation; 计算条纹之间的距离 distances = ; for i = 1lengthstats

如下所示：

img_stripes = imread('7.bmp'); img_stripes = imbinarize(img_stripes); %将图像转为二维 img_stripes = img_stripes(:,:,1); %计算每个条纹的方向 stats = regionprops(img_stripes, 'Orientation'); %计算条纹之间的距离 distances = []; for i = 1:length(stats) for j = i+1:length(stats) %如果方向相似，则进一步分析 if abs(stats(i).Orientation - stats(j).Orientation) < 30 %计算相邻条纹的像素 pixels1 = stats(i).PixelList; pixels2 = stats(j).PixelList; %将像素按照方向排序 if abs(stats(i).Orientation) > 45 pixels1 = fliplr(pixels1); end if abs(stats(j).Orientation) > 45 pixels2 = fliplr(pixels2); end %选择相同级别的像素 level1 = unique(pixels1(:,2)); level2 = unique(pixels2(:,2)); levels = intersect(level1, level2); pixels1 = pixels1(ismember(pixels1(:,2), levels), :); pixels2 = pixels2(ismember(pixels2(:,2), levels), :); %计算最小距离 d = pdist2(pixels1, pixels2); distances = [distances; min(d(:))]; end end end %选择有效的距离 idx = find(distances > 0.02 & distances < 0.6); distances = distances(idx); %显示条纹和距离 figure; imshow(img_stripes); hold on; for i = 1:length(stats) plot(stats(i).BoundingBox(1), stats(i).BoundingBox(2), 'r*'); end for i = 1:length(idx) [m, n] = ind2sub([length(stats), length(stats)], idx(i)); x1 = stats(m).BoundingBox(1) + stats(m).BoundingBox(3)/2; y1 = stats(m).BoundingBox(2) + stats(m).BoundingBox(4)/2; x2 = stats(n).BoundingBox(1) + stats(n).BoundingBox(3)/2; y2 = stats(n).BoundingBox(2) + stats(n).BoundingBox(4)/2; plot([x1, x2], [y1, y2], 'b-'); end hold off; title('Stripes and Distances');