MATLAB 中有没有办法计算哪些离散图像区域包围或被另一个区域包围? [英] Is there a way in MATLAB to compute which discrete image regions enclose or are enclosed by another region?

问题描述

给定下图:

我想确定哪些颜色区域被其他颜色区域包围或包围.这如何计算?有没有办法创建一种显示这些信息的树或表?

I'd like to identify which colored regions are enclosed by or enclose which other colored regions. How might this be computed? Is there a way to create a sort of tree or table that shows this information?

示例:所有红色像素都在黄色区域内.

Example: All the red pixels are within the yellow region.

推荐答案

据我所知，没有内置函数可以执行此计算，但这里有一个关于如何获取所需信息的想法...

There's no built-in function I know of that can perform this calculation, but here's an idea for how you might get at the information you want...

首先，您需要从上方获取 RGB 图像并将其转换为索引图像和颜色图.这是一种方法:

First, you'll want to take your RGB image from above and turn it into an indexed image and a color map. Here's one way to do it:

img = double(imread('nested_regions.png'))./255;  % Load the RGB image
map = unique(reshape(img, [], 3), 'rows');        % Find the unique colors
labelImage = rgb2ind(img, map);                   % Get a labeled (i.e. indexed) image
nColors = size(map, 1);

接下来，您需要遍历每个标记区域，创建一个遮罩，然后使用 imfill.如果填充区域包含图像其余部分不包含的标签值，则这些区域完全包含在您填充的区域中.下面的代码使用 setdiff 功能:

Next, you'll want to loop over each labeled region, create a mask, then fill any "holes" in that mask using imfill. If the filled regions contain label values that the rest of the image doesn't, then those regions are completely contained by the region you filled. The code below does this using the setdiff function:

contains = cell(nColors, 1);              % Storage for the contained region labels
str=' # | contains
---+------------
';  % String for displaying output

for iColor = 1:nColors

  maskImage = (labelImage == iColor-1);      % Mask of the current region
  filledImage = imfill(maskImage, 'holes');  % Mask with holes filled
  holeImage = (filledImage & ~maskImage);    % Mask of the filled holes
  contains{iColor} = setdiff(unique(labelImage(holeImage)), ...
                             unique(labelImage(~holeImage))).';  %.'
  str = [str ' ' num2str(iColor-1) ' | ' num2str(contains{iColor}) '
'];

end

imshow(labelImage, map, 'InitialMagnification', 60);  % Display image
colorbar();                                           %   with a colorbar
fprintf(str);  % Create some formatted text output

上面运行后，你会得到如下:

After running the above, you will get the following:

 # | contains
---+------------
 0 | 1  2  3  4  5  6  7  8  9
 1 | 3  4  5  7  9
 2 | 3  4  5  7  9
 3 | 
 4 | 3
 5 | 3  4
 6 | 
 7 | 3  4  5
 8 | 
 9 | 3  4  5  7

例如，红色像素(标记为区域 7)包围了标记区域 3、4 和 5(分别为灰蓝色、紫色和石灰)中的所有像素.某些区域不形成闭合轮廓，例如 6(浅紫色)和 8(橙色).区域 1(绿色)实际上并未完全包含在区域 2(蓝色)中，因为一个或两个绿色的虚假像素位于蓝色区域之外.

For example, the red pixels (labeled as region 7) surround all the pixels in labeled regions 3, 4, and 5 (gray-blue, purple, and lime, respectively). Some regions don't form closed contours, like 6 (light purple) and 8 (orange). Region 1 (green) actually isn't fully contained by region 2 (blue) since a spurious pixel or two of green is outside the blue region.

希望这能给你一些想法！

Hope this gives you some ideas!

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