how to extract (x) coordinates and (y) coordinates of the top boundary from bwimage

[ssyang gau]

% 读取图像

bw=im2bw( imread('test.jpg'),0.4)

% 反转图像，使曲线为黑色，背景为白色（可选，视具体图像而定）
% bw = ~bw;

% 获取图像尺寸
[height, width] = size(bw);

% 初始化坐标数组
x_coords = [];
y_coords = [];

% 对每一列，找到最高的白色像素点的 Y 坐标
for x = 1:width
    column = bw(:, x);
    y = find(column, 1, 'first'); % 找到第一个白色像素的行索引
    if ~isempty(y)
        x_coords(end+1) = x;
        y_coords(end+1) = y;
    end
end

% 绘制提取的曲线
figure;
imshow(img);
hold on;
plot(x_coords, y_coords, 'r-', 'LineWidth', 2);
title('提取的顶部曲线');
hold off;

% 保存坐标到变量或文件
% 例如，将坐标保存到变量中
top_curve_coords = [x_coords', y_coords'];

% 或者保存到 CSV 文件
% csvwrite('top_curve_coords.csv', top_curve_coords);

% 显示坐标（可选）
disp('顶部曲线的 X 坐标:');
disp(x_coords');
disp('顶部曲线的 Y 坐标:');
disp(y_coords');

[ssyang gau]

the result is not fit the original boundary.

Answer 1

[Mathieu NOE]

hello

this is a solution that does not even require the Image processing toolbox

of course I had to do a bit of gym to convert from RGB to BW, but you can reintroduce im2bw as you do in your code.

and also I strongly reduces the size of your image to something a bit more manageable (factor 8 reduction in both directions) and much faster at the end.

maybe you could also avoid saving bw image in color format with jpeg , use directly png with 2 colors indexing (BW). This would save time and memory.

so here we go

as additionnal bonus you also get the bottom boundary , but if you have only interest in the top boundary , use xtop & ytop data as shown below :

A = imread('image2.jpg'); % image 2 is reduced by factor 8 in each direction

% a bit of gym to convert to grayscale , that we will "binarize" with
% logical operation 
A = double(A);
% rgb2gray converts RGB values to grayscale values by forming a weighted sum of the R, G, and B components:
% 0.2989 * R + 0.5870 * G + 0.1140 * B 
A = 0.299 * A(:,:,1) + 0.587 * A(:,:,2) + 0.114 * A(:,:,3);

A = flipud(A); % to have image displayed with correct y direction
[y,x] = find(A<0.5*256); % find black dots coordinates : threshold is set at 50% of 256

[xtop,ytop,xbottom,ybottom,] = top_bottom_boundary(x,y);

figure(1)
imagesc(A);
set(gca,'YDir','normal');
colormap('gray');
hold on
plot(x,y, 'k.', xtop, ytop, '-r', xbottom, ybottom, '-g','linewidth',2)
legend('data','top boundary','bottom boundary');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


function [x3,y3,x4,y4] = top_bottom_boundary(x,y)

% based on FEX : https://fr.mathworks.com/matlabcentral/answers/299796-tight-boundary-around-a-set-of-points


    %split data into classes and find max/min for each class
    class_label = unique(x);
    upper_boundary = zeros(size(class_label));
    lower_boundary = zeros(size(class_label));
    for idx = 1:numel(class_label)
        class = y(x == class_label(idx));
        upper_boundary(idx) = max(class);
        lower_boundary(idx) = min(class);
    end

%     left_boundary = y(x == class_label(1));
%     right_boundary = y(x == class_label(end));
% 
%     % left_boundary
%     x1 = class_label(1)*ones(size(left_boundary));
%     y1 = left_boundary;
% 
%     % right_boundary
%     x2 = class_label(end)*ones(size(right_boundary));
%     y2 = right_boundary;

    % top boundary
    x3 = class_label;
    y3 = upper_boundary;

    % bottom boundary
    x4 = class_label;
    y4 = lower_boundary;


end

[Mathieu NOE]

seems that the rendering on my PC shows a better matching boundary line vs image.

we could add a bit of smoothing if needed ? (also remember I use the reduced image for faster processing).

[Mathieu NOE]

hello again

problem solved ?