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How can I compute a contour bounded by the combination of multiple contour lines?

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Antoine Bridet
Antoine Bridet on 12 Apr 2018
Commented: Adam Danz on 1 May 2020
I wrote a Matlab script that computes about a hundred of contour lines which all have the same general shape but are very slightly different in dimensions (and they can sometimes intersect). You can imagine concentric circles, except that the contours don't have a regular geometric shape and can often overlap each other. I would like to extract the contour of the 'blank space' that remains inside of this chaos:
The contour lines come from Matlab's "ContourMatrix" with this code:
[~,hcontour] = contour(x,y,z,'LevelList',k);
c = hcontour.ContourMatrix;
I then wrote a loop that selects the relevant contour lines and stores them in a matrix where the first three lines contain the X,Y and Z coordinates for the first contour line, then the next three lines contain the coordinates for the second line etc...
When I plot the outcome, the problem is that no single line is entirely contained within all the others, so I can't just compute the area of all the lines and see which is the smallest. I need to compute the boundary that only contains blank space and doesn't cross any of the contour lines.
Thanks in advance!


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Antoine Bridet
Antoine Bridet on 3 May 2018
Dear Chad,
I've attached a sample data set stored in a .txt file. Its dimensions are 75x1025, with the following structure:
  • Row 1 -> X Data 1
  • Row 2 -> Y Data 1
  • Row 3 -> Z Data 1
  • Row 4 -> X Data 2
  • Row 5 -> Y Data 2
  • etc...
Even though I've stored the Z values, they are irrelevant to our problem so I think that you can ignore them. My best attempt so far is to use Matlab's 'polyshape' function to transform the contour lines into polygons, and then the 'intersect' function which returns the common area between two polygons. It works, but it doesn't look like a very efficient solution.
Wick on 3 May 2018
Yes, if you do that one contour at a time it should work. Is computational time a concern? I doubt my solution is going to be quick.
Edit: Just took a look at some of those contours. The data for X < 50 is quite noisy and doesn't lend itself to the analysis you suggest. And the data from 200 < x < 300 is too sparse to use kde2d effectively.
Working on something else. It won't be fast either.
Antoine Bridet
Antoine Bridet on 3 May 2018
It turns out that the computation time is quite reasonnable even in a "real life" situation. The data set I sent you has 25 lines, but even ith 120 lines the program needs about 2.5 seconds to run this section which is quite acceptable (considering that I'm using a relatively modest laptop).
Edit: To add a little bit of context, the data I provided represents lenghts in meters. The X-axis data goes from 0m to about 400m and the Y-axis data goes from about -60m to +60m. The noise present in the X-Axis between 0m and say 10m isn't much of a concern and is mostly a result of the way that the measurement is done.

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Accepted Answer

Wick on 3 May 2018
Just saw that you had posted a response that the noise in the small x isn't a concern. Well, I've got an overkill of overkill solutions for you then. But it's quite robust.
I've interpolated the contours to add points between the dots. I then made a 2D histogram on a fine resolution grid to make a binary decision as to where there are points. I smoothed that binary function because contour won't work on constant values in Z. I extract the contours at the edges of the binary values and determine which contour is the inside one.
For the data you sent, it takes about 3 seconds to run when the 'interp_scale' variable is set to 1000 (not including plotting time). That's a resolution of 0.11 m or so. When I cranked 'interp_scale' to 10,000 it took 90 seconds to run but that's 0.011 m resolution so probably overkill. (about 60% worse resolution in 'y').
With that said, I'm comfortable that this is pretty bullet-proof. If you find that the contour on the inside isn't smooth you can decrease the multiplier on x_bins and y_bins to generate more interpolated points per 2D grid.
I'm sure there are ways to speed this up by only considering a small section of the overall region at a time but this brute-force solution works.
Good luck!
W = load('testset.txt');
XX = W(1:3:end,:);
YY = W(2:3:end,:);
ZZ = W(3:3:end,:);
interp_scale = 300; % additional points for every point in the original contours
x_bins = 4 * interp_scale; % about 4x interp_scale is as high as you can go without having breaks in the histogram.
y_bins = 1 * interp_scale;
XX = [XX NaN*ones(size(XX,1),1)]; % breaks the segments apart to interpolate in a minute
YY = [YY NaN*ones(size(YY,1),1)];
ZZ = [ZZ NaN*ones(size(ZZ,1),1)];
X = reshape(XX',numel(XX),1);
Y = reshape(YY',numel(YY),1);
% this would be much more elegant if you only performed it for X > 50 on a contour-by-contour basis
X = interp1(X,linspace(1,numel(X),interp_scale*numel(X)),'pchip')';
Y = interp1(Y,linspace(1,numel(Y),interp_scale*numel(Y)),'pchip')';
X = X(~isnan(X));
Y = Y(~isnan(Y));
% axis([105 126 -27 -16])
x_edges = linspace(0,450,x_bins + 1);
y_edges = linspace(-80,80,y_bins + 1);
x_centers = 0.5*(x_edges(1:end-1) + x_edges(2:end));
y_centers = 0.5*(y_edges(1:end-1) + y_edges(2:end));
N = histcounts2(X,Y,x_edges,y_edges);
N(N>0) = 1;
% there's probably a built-in smoothing algortihm that's prettier than
% this, but this works
NB = zeros(size(N,1)+2,size(N,2)+2);
NB(2:end-1,2:end-1) = N;
NB(2:end-1,2:end-1) = N + 0.25*NB(1:end-2,2:end-1) + 0.25*NB(3:end,2:end-1) + ...
0.25*NB(2:end-1,1:end-2) + 0.25*NB(2:end-1,3:end);
N = NB(2:end-1,2:end-1);
if interp_scale <= 1000 % if there are few enough elements we'll plot the intermediate stages
h = pcolor(x_centers,y_centers',N');
axis([105 126 -27 -16])
[C, Ch] = contour(x_centers, y_centers, N',[0.5,0.5]);
C3 = Ch.ContourMatrix;
C3 = contourc(x_centers, y_centers, N',[0.75,0.75]); % use contourc if you're not plotting
% break out the contours into x,y curves
jj = 1;
kk = 1;
while jj < size(C3,2)
Z = C3(1,jj);
L = C3(2,jj);
P(kk).X = C3(1,jj+1:jj+L); %#ok<*SAGROW>
P(kk).Y = C3(2,jj+1:jj+L); % one P for each contour curve
jj = jj + 1 + L;
kk = kk + 1;
% we have many contours so we have to find which one is the closest.
distance_to_profile = zeros(length(P));
for jj = 1:length(P)
% (200,0) is in the middle of the opening. Shortest distance of any
% profile to it will be the one we want.
distance_to_profile(jj) = min((P(jj).X-200).^2 + P(jj).Y.^2);
[~,index] = min(distance_to_profile);
profile_x = P(index).X;
profile_y = P(index).Y;

  1 Comment

Antoine Bridet
Antoine Bridet on 4 May 2018
Dear Chad,
Thanks a lot for your dedication! This really solves my problem and I do agree that it seems to be more robust. I am going to test this with different data sets and sizes and see how it behaves.

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More Answers (2)

Wick on 1 May 2018
Edited: Wick on 2 May 2018
The solution is to find a convex hull that encompasses all the points from all the contours. An example using the 'peaks' function is shown.
W = peaks(25);
[xx,yy] = meshgrid(linspace(-1,1,25));
% this draws the contours we're going to encompass
[~,hcontour] = contour(xx,yy,W,-3:2:5);
C3 = hcontour.ContourMatrix;
hold on
% this while loop allows me to build a matrix of all the X,Y pairs form all
% the contours. There is not (to my knowledge) a built-in way to do this.
jj = 1;
X = [];
Y = [];
while jj < size(C3,2)
Z = C3(1,jj);
L = C3(2,jj);
X = [X C3(1,jj+1:jj+L)];
Y = [Y C3(2,jj+1:jj+L)];
jj = jj + 1 + L;
hold off
dt = delaunayTriangulation(X',Y');
[Outside_Index, Av] = convexHull(dt);
Outside_X = dt.Points(Outside_Index,1);
Outside_Y = dt.Points(Outside_Index,2);
Edited to remove the 'column' function calls. They're not necessary in this demo.


Antoine Bridet
Antoine Bridet on 2 May 2018
Thank you for your answer. A few remarks:
  • I use a loop similar to yours in order to build a matrix of the X,Y pairs. I haven't found a built-in solution either.
  • What is this ' column ' function? When I try to run your code, i get the following error:
Undefined function or variable 'column'.
  • I've found a ' columns ' function, is this the one you were refering to? If so, I'll install the required Database Toolbox.
EDIT: I tried to run this code simply by removing the 'column' function and I think it still works. So the result is the smallest surface that contains all the points, which is interesting but not exactly what I'm after. I'm trying to find the largest surface that doesn't contain any point. I'll try to adapt your code and I'll get back to you but if you have any suggestion I'm also interested!
Wick on 2 May 2018
sorry, my column function is my own utility that I didn't realize was in there. It's just:
function out = column(x)
out = reshape(x,numel(x),1);
I misread the original request. I thought you were looking for the outer envelope, not the inner one.
I don't know of a built-in way to do that. However, I have a quick hack that would work better for your results than my demo.
Pick a point that is "inside" the final result and convert the (x,y) pairs into polar coordinates about that point as the origin. invert the radius for each point (r = 1./r) and calculate new Cartesian coordinates. The exterior convex hull of that set of points should be the same as the interior of the original set once you convert them back to the original coordinate system.
Edit: The idea of using inverse radius doesn't work. The interior envelope curvature relative to the origin may change such that you'd need a concave section of the voronoi diagram which convexHull can't find.
Adam Danz
Adam Danz on 1 May 2020
FYI, there are several function on the file exchange the extract the controur line coordinates.

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Antoine Bridet
Antoine Bridet on 3 May 2018
Even though it might not be the definitive answer to the problem, here is my attempt at a working solution:
j_storage = dlmread('testset.txt');
j_param1 = 1;
j_param2 = 1;
testpgon = figure('Name','Test Polygons',... % Parameters for the figure
'rend','opengl',... % WARNING : do NOT use painters as renderer!
'OuterPosition',[0 0 1920 1200]);
hold off
while j_param1<=size(j_storage,1)
j_Pgon{j_param2} = polyshape(j_storage(j_param1,:),j_storage(j_param1+1,:),'Simplify',true);
if j_param2 == 2
j_commonArea = intersect(j_Pgon{j_param2-1},j_Pgon{j_param2});
elseif j_param2 > 2
j_commonArea = intersect(j_Pgon{j_param2},j_commonArea);
elseif j_param2 == 1
j_param1 = j_param1 + 3;
j_param2 = j_param2 + 1;
Note that my program uses a dataset in an array called 'j_storage' so to run it on your machine, you'll want to run this code with the attached file in the same folder (it is the same as the one already provided in the discussion above).


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