通过l * a * b进行颜色分割 [英] Colour Segmentation by l*a*b
问题描述
我正在使用MatLab网站上的代码，使用L a b *颜色空间进行基于颜色的分割：
http://www.mathworks.com/help/images/ examples / color-based-segmentation-using-the-lab-color-space.html
I'm using the code on the MatLab website, "Color-Based Segmentation Using the Lab* Color Space": http://www.mathworks.com/help/images/examples/color-based-segmentation-using-the-l-a-b-color-space.html
所以我试图自己选择一些区域而不是使用load region_coordinates，使用roipoly（fabric），但我卡住了。如何保存刚绘制的多边形的坐标？我实际上是在解决方案II页面底部遵循lennon310的建议：
关于使用L * a * b * 进行颜色分割的几个问题
So I'm trying to select some areas myself instead of using the "load region_coordinates", using roipoly(fabric), but i get stuck. How do I save coordinates of the polygon I just drew? I'm actually following advice from lennon310 at Solution II, bottom of page: A few questions about color segmentation with L*a*b*
我不确定何时保存 region_coordinates 并执行尺寸（region_coordinates，1）
I'm not sure when to save region_coordinates and do size(region_coordinates,1)
我制作以下更改（第1步）：
1）已移除load region_coordinates
1) Removed "load region_coordinates"
2）添加了region_coordinates = roipoly（fabric）;
2) Added "region_coordinates = roipoly(fabric);"
以下是代码：
`
%%第1步
` %% Step 1
fabric = imread(file);
figure(1); %Create figure window. "If h is not the handle and is not the Number property value of an existing figure, but is an integer, then figure(h) creates a figure object and assigns its Number property the value h."
imshow(fabric)
title('fabric')
%load regioncoordinates; % 6 marices(?) labelled val(:,:,1-6), 5x2 (row x column)
region_coordinates = roipoly(fabric);
nColors = 6;
sample_regions = false([size(fabric,1) size(fabric,2) nColors]); %Initializing an Image Dimension, 3x3 (:,:,:) to zero? Zeros() for arrays only I guess.
%Size one is column, size two is row?
for count = 1:nColors
sample_regions(:,:,count) = roipoly(fabric,region_coordinates(:,1,count),region_coordinates(:,2,count));
end
figure, imshow(sample_regions(:,:,2)),title('sample region for red');
%%第2步
% Convert your fabric RGB image into an L*a*b* image using rgb2lab .
lab_fabric = rgb2lab(fabric);
%Calculate the mean a* and b* value for each area that you extracted with roipoly. These values serve as your color markers in a*b* space.
a = lab_fabric(:,:,2);
b = lab_fabric(:,:,3);
color_markers = zeros([nColors, 2]);%... I think this is initializing a 6x2 blank(0) array for colour storage. 6 for colours, 2 for a&b colourspace.
for count = 1:nColors
color_markers(count,1) = mean2(a(sample_regions(:,:,count))); %Label for repmat, Marker for
color_markers(count,2) = mean2(b(sample_regions(:,:,count)));
end
%For example, the average color of the red sample region in a*b* space is
fprintf('[%0.3f,%0.3f] \n',color_markers(2,1),color_markers(2,2));
%%步骤3：使用最近邻规则对每个像素进行分类
％
%% Step 3: Classify Each Pixel Using the Nearest Neighbor Rule %
color_labels = 0:nColors-1;
% Initialize matrices to be used in the nearest neighbor classification.
a = double(a);
b = double(b);
distance = zeros([size(a), nColors]);
%Perform classification, Elucidean Distance.
for count = 1:nColors
distance(:,:,count) = ( (a - color_markers(count,1)).^2 + (b - color_markers(count,2)).^2 ).^0.5;
end
[~, label] = min(distance,[],3);
label = color_labels(label);
clear distance;
%%步骤4：显示最近邻分类的结果
％
％标签矩阵包含织物图像中每个像素的颜色标签。
％使用标签矩阵按颜色分隔原始织物图像中的对象。
%% Step 4: Display Results of Nearest Neighbor Classification % % The label matrix contains a color label for each pixel in the fabric image. % Use the label matrix to separate objects in the original fabric image by color.
rgb_label = repmat(label,[1 1 3]);
segmented_images = zeros([size(fabric), nColors],'uint8');
for count = 1:nColors
color = fabric;
color(rgb_label ~= color_labels(count)) = 0;
segmented_images(:,:,:,count) = color;
end
%figure, imshow(segmented_images(:,:,:,1)), title('Background of Fabric');
%Looks different somehow.
figure, imshow(segmented_images(:,:,:,2)), title('red objects');
figure, imshow(segmented_images(:,:,:,3)), title('green objects');
figure, imshow(segmented_images(:,:,:,4)), title('purple objects');
figure, imshow(segmented_images(:,:,:,5)), title('magenta objects');
figure, imshow(segmented_images(:,:,:,6)), title('yellow objects');
`
推荐答案
您可以在 roipoly 的调用中使用输出参数检索多边形的坐标。然后，您可以获得多边形的二进制蒙版，以及顶点坐标（如果需要）。
You can retrieve the coordinates of the polygon using output arguments in the call to roipoly. You can then get a binary mask of the polygon, as well as vertices coordinates if you want.
演示的简单示例：
clear
clc
close all
A = imread('cameraman.tif');
figure;
imshow(A)
%// The vertices of the polygon are stored in xi and yi;
%// PolyMask is a binary image where pixels == 1 are white.
[polyMask, xi, yi] = roipoly(A);
这看起来像这样：
< img src =https://i.stack.imgur.com/LQa1d.jpgalt =在此输入图像说明>
如果你想看到带有二元掩码的顶点：
And if you wish to see the vertices with the binary mask:
%// display polymask
imshow(polyMask)
hold on
%// Highlight vertices in red
scatter(xi,yi,60,'r')
hold off
其中包含以下内容：
总结一下：
1）多边形的顶点存储在xi和yi中。
1) The polygon's vertices are stored in xi and yi.
2）您可以使用 imshow（polyMask）绘制多边形的binaryMask 。
3）如果您需要白色像素的坐标，可以使用以下内容：
3) If you need the coordinates of the white pixels, you can use something like this:
[row_white,col_white] = find(polyMask == 1);
你很高兴。希望有所帮助！
You're then good to go. Hope that helps!
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