l*a*b 颜色分割

Question

我正在使用 MatLab 网站上的代码，"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

所以我尝试自己 select 一些区域，而不是使用 "load region_coordinates"，使用 roipoly(fabric)，但我被卡住了。如何保存刚绘制的多边形的坐标？实际上，我在页面底部的解决方案 II 中遵循了 lennon310 的建议： A few questions about color segmentation with L*a*b*

我不确定何时保存 region_coordinates 并执行 size(region_coordinates,1)

我做了以下更改（第 1 步）：

1) 已删除 "load region_coordinates"

2) 添加了 "region_coordinates = roipoly(fabric);"

代码如下：

` %% 步骤 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));

%% 第 3 步：使用最近邻规则对每个像素进行分类 %

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;

%% 第四步：显示最近邻分类结果 % % 标签矩阵包含织物图像中每个像素的颜色标签。 % 使用标签矩阵将原始织物图像中的对象按颜色分开。

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');



`

Answer 1

您可以在调用 roipoly 时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掩码，以及顶点坐标（如果需要）。

简单示例演示：

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);

看起来像这样：

如果您想查看带有二进制掩码的顶点：

%// display polymask
imshow(polyMask)
hold on

%// Highlight vertices in red
scatter(xi,yi,60,'r')
hold off

给出以下内容：

总结一下：

1) 多边形的顶点存储在 xi 和 yi 中。

2) 您可以使用 imshow(polyMask).

绘制多边形的 binaryMask

3) 如果你需要白色像素的坐标，你可以这样使用：

[row_white,col_white] = find(polyMask == 1);

然后就可以开始了。希望对您有所帮助！