finding edge/ plot from image/ maze

Then scan your image column-by-column finding the first row where each index occurs. For any missing columns (like where one color went underneath a different color, use interp1 to interpolate the value. Something like

[rows, columns, numberOfColorChannels] = size(rgbImage);
brownCurve = nan(1, columns);
redCurve = nan(1, columns);
purpleCurve = nan(1, columns);
greenCurve = nan(1, columns);
orangeCurve = nan(1, columns);
blueCurve = nan(1, columns);
for col = 1 : columns
    thisColumn = indexedImage(:, col);
    % Find if there is a red curve in this column.
    row = find(thisColumn == redIndex, 1, 'first');
    if ~isempty(row)
        redCurve(col) = row;
    end
    % Find if there is a brown curve in this column.
    row = find(thisColumn == brownIndex, 1, 'first');
    if ~isempty(row)
        brownCurve(col) = row;
    end
end
% Interpolate missing data points
missingIndexes = isnan(redCurve);
x = 1 : columns;
redCurve = interp1(x(~missingIndexes), y(~missingIndexes), x);
% Etc.

Of course there is a lot missing there but I'm confident you'll be able to finish it, right?

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Answer 3

DGM 2023-3-28

0
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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1936539-finding-edge-plot-from-image-maze#answer_1202984

编辑：DGM 2023-3-28

drawing.svg.zip

I'm assuming that the goal is to take the second image and reduce it to plottable data such as would generate a plot like the first one. I'm just going to do this the same way I always do it, so see the links for explanations, examples, and more links to the same. The same commentary applies here, with the additional caveat that I have no idea what this plot actually means.

See the attached SVG file.

% using the following FEX tools:

% https://www.mathworks.com/matlabcentral/fileexchange/72225-load-svg-into-your-matlab-code

% filename of manually-fit svg file

fname = 'drawing.svg';

% data range from original image axis labels

% this is where the rectangle is drawn in the SVG

xrange = [0 1]; % this is arbitrary

yrange = [0 60];

% spline discretization parameter [0 1]

coarseness = 0.01;

% get plot box geometry

str = fileread(fname);

str = regexp(str,'((?<=<rect)(.*?)(?=\/>))','match');

pbx = regexp(str,'((?<=x=")(.*?)(?="))','match');

pby = regexp(str,'((?<=y=")(.*?)(?="))','match');

pbw = regexp(str,'((?<=width=")(.*?)(?="))','match');

pbh = regexp(str,'((?<=height=")(.*?)(?="))','match');

pbrect = [str2double(pbx{1}{1}) str2double(pby{1}{1}) ...

str2double(pbw{1}{1}) str2double(pbh{1}{1})];

% get coordinates representing the curve

S = loadsvg(fname,coarseness,false);

% if there are multiple paths you want to extract

% you'll need to do do the rescaling, etc for each element of S

for k = 1:numel(S) % there are multiple curves

x = S{k}(:,1);

y = S{k}(:,2);

% rescale to fit data range

x = xrange(1) + diff(xrange)*(x-pbrect(1))/pbrect(3);

y = yrange(1) + diff(yrange)*(pbrect(4) - (y-pbrect(2)))/pbrect(4);

% get rid of nonunique points

[x,idx,~] = unique(x,'stable');

y = y(idx);

% shove the prepared data back into S for later

S{k} = [x y];

end

% the last two curves are actually just marker lines used to locate M,K

% in order for these to be the "last two", they must have the highest

% z-order of the curves. Use "bring to front", "raise selection", or

% simply draw them last.

xM = mean(S{k-1}(:,1));

xK = mean(S{k}(:,1));

S = S(1:end-2); % get rid of markers

% plot the curves

for k = 1:numel(S)

x = S{k}(:,1);

y = S{k}(:,2);

plot(x,y); hold on

end

% plot the marker lines

xline(xM);

xline(xK);

grid on;

xlim(xrange)

ylim(yrange)

% set xticks and labels

xticks([0 xM xK 1])

xticklabels({'\Gamma' 'M' 'K','\Gamma'})

It's difficult to tell which curves are which when they intersect, but I am assuming that the final curve ordering is simply based on height. If the curve trajectories need to be done differently, then simply edit the attached SVG.

%% split and order curves based on height

% interpolate each curve on to a common abcissa

npoints = 1000;

xnew = linspace(xrange(1),xrange(2),npoints);

ynew = zeros(numel(S),npoints);

for k = 1:numel(S)

x = S{k}(:,1);

y = S{k}(:,2);

ynew(k,:) = max(interp1(x,y,xnew),yrange(1));

end

ynew = sort(ynew,1,'descend');

% plot the curves

clf

plot(xnew,ynew)

% plot the marker lines

xline(xM);

xline(xK);

grid on;

xlim(xrange)

ylim(yrange)

% set xticks and labels

xticks([0 xM xK 1])

xticklabels({'\Gamma' 'M' 'K','\Gamma'})

If instead, the goal is to extract the curves from both images, then simply perform the same transcription on the first image using a vector graphics editor.