I need to evenly sample a triangle just using its vertices (which I have in cartesian coordinates). I need to do it recursively (or at least in generations, where I can use the new point before calculating the next). One example would be like this:
take the 3 parent points (in the first execution the vertices)
calculate their midpoint as the child
get all new possible combinations of the child with 2 of its parents
repeat a set amount of times.
Problem: This does not yield an even distribution, but some (nice) pattern.
Using the distance to the parents as an abort criterion enhances the performance by a bit, but the resolution close to the vertices and the midpoint is really bad.
The function currently looks like this:
function points = distMidPoint(parents, distMax)
child = mean(parents);
if any(vecnorm(parents - child,2,2) <= distMax)
% check if distance to the parents falls below a certain threshold
points = [];
return
end
points = child;
for iParents = nchoosek(1:size(parents,1),size(parents,1)-1)'
% concatenate child and any combination of two parents and call function again
newPoint = distMidPoint([parents(iParents,:); child], distMax);
points = [points; newPoint];
end
end
Is there an algorithm I just don't know about that does this or do you have a better idea? As I mentioned it doesn't have to be recursive, it was just my first intuition.
Edit: The algorithm works fine for 2 "vertices" that I connect with a line:
It sounds like you're looking to subdivide your triangle into smaller, equally sized triangles.
One reasonable way to do this would be to recursively divide the triangle into four smaller triangles by "chopping off the corners." This is similar to the Sierpinski triangle, but you would subdivide all four subdivisions in each step.
You can construct a list of your triangles with the following algorithm (this is Matlab-like pseudocode, since I no longer have a valid license):
% takes the vertices of the triangle and the number of times to subdivide.
function points = subdivide(vertices, depth)
if depth <= 0
points = [[v1, v2, v3]] % list of triangles
else
s1 = [v1, midpoint(v1, v2), midpoint(v1, v3)]
s2 = [v2, midpoint(v2, v3), midpoint(v2, v1)]
s3 = [v3, midpoint(v3, v1), midpoint(v3, v2)]
s4 = [midpoint(v1, v2), midpoint(v2, v3), midpoint(v3, v1)]
points = cat(1,
subdivide(s1, depth-1),
subdivide(s2, depth-1),
subdivide(s3, depth-1),
subdivide(s4, depth-1)
)
end
end
Note: here, a triangle is a list of three points. The input is a triangle and the number of times to subdivide. The result is a list of triangles. The midpoint should be easy to write.
Note that the number of triangles in the result is 4^depth (grows exponentially), so don't make it too big. I tried implementing this algorithm in Python, and here's the result with depth=5:
Related
As described, I would like to find the largest empty rectangle in a given 2D point cloud. This information will be then used to get parallel lines shown as green lines in the images, which will then be used to get the angle the LiDAR has rotated.
I've tried convex hull, boundary, and alpha shape functions in matlab with this point cloud data. From the looks of it, I think i have to
(1) break the point cloud into two chunks (gift wrap algo?) and then
(2) apply boundary function; With coordinates extracts from boundary function,
(3) I'm thinking to run RANSAC line fitting.
However, RANSAC needs more points to judge the "fitness" of the line. Currently I'm exploring Hough Transform to see if that line detection would work for this case.
Thus the question here is,
(1) am I in the right track to find the green lines? Or is there a better way?
(2) How to get the angular value/gradient from the line when the line is vertical (tan(90) = inf)
The second image depicts the origin may be in the center of the cloud points or at an offset. One can assume there will be parallel lines with varying distance from the center
You can use kmeans clustering to find two clusters then find two points each from a cluster that distance between them is minimum
%a function for rotation of points
function out = rot(theta,data)
mat = [cos(theta), -sin(theta);
sin(theta), cos(theta)];
out = data * mat.';
end
a= [rand(505,1)*.4 , rand(505,1) ];
b= [rand(500,1)*.4 + .6 , rand(500,1)];
a = [a;b];
rt = rot(.4,a);
%kmeans clustering
km=kmeans(rt,2);
class1 = rt(km==1,:);
class2=rt(km==2,:);
%find min distance
[srch, d] = dsearchn(class1, class2);
[mn, I] = min(d);
plot(rt(:,1), rt(:,2),'.')
hold on
p1 = class1(srch(I),:);
p2 = class2(I,:);
plot(p1(1),p1(2),'*r')
plot(p2(1),p2(2),'*g')
xy = [class1(srch(I),:);class2(I,:)]
plot(xy(:,1),xy(:,2),'-g')
axis equal
I have been reading up quite a bit on Hough circles and how it can be used to detect the center and radius of a circular object. Although I understood how the transform works I am still not able to to understand how to get the center of a circle. The code snippet below is customized purely for coins.png in MATLAB. I tried it with a slightly more complex picture and it fails miserably. I am fixing the radius and then getting the accumulator matrix.
Code:
temp = accum;
temp(find(temp<40))=0; %Thresholding
imshow(temp,[0 max(temp(:))])
temp = imdilate(temp, ones(6,6)); %Dilating
imshow(temp,[0 max(max(temp))]);
temp = imerode(temp,ones(3,3)); %Eroding
imshow(temp,[0 max(max(temp))]);
s = regionprops(im2bw(temp),'centroid'); %Computing centroid
centroids = cat(1, s.Centroid);
I wanted to test the code out on a different picture and found this one on google. The Hough Transform produced a favorable result, but it's more overlapping than the previous case and my method fails.
Can someone suggest what the best method is to compute the centroid of the hough circle?
Original image:
Full Code:
%%Read and find edges using a filter
i = imread('coins4.jpg');
i = rgb2gray(i);
i = imresize(i,0.125);
i_f = edge(i, 'canny',[0.01 0.45]);
imshow(i_f)
%%
[x y] = find(i_f>0); % Finds where the edges are and stores the x and y coordinates
edge_index = size(x);
radius = 30; %Fixed radius value
theta = 0:0.01:2*pi;
accum = zeros(size(i,1), size(i,2)); %Create an accumulator matrix.
r_co = radius*cos(theta);
r_si = radius*sin(theta);
x1 = repmat(x, 1, length(r_co));
y1 = repmat(y, 1, length(r_si));
x_r_co = repmat(r_co, length(x),1);
y_r_si = repmat(r_si, length(y),1);
%% Filling the accumulator
a = x1 - x_r_co;
b = y1 - y_r_si;
for cnt = 1:numel(a)
a_cnt = round(a(cnt));
b_cnt = round(b(cnt));
if(a_cnt>0 && b_cnt>0 && a_cnt<=size(accum,1) && b_cnt<=size(accum,2))
accum(a_cnt,b_cnt) = accum(a_cnt,b_cnt) + 1;
end
end
imshow(accum,[0 max(max(accum))]);
%% Thresholding and get the center of the circle.
close all;
temp = accum;
temp(find(temp<40))=0;
imshow(temp,[0 max(temp(:))])
temp = imdilate(temp, ones(6,6));
imshow(temp,[0 max(max(temp))]);
temp = imerode(temp,ones(3,3));
imshow(temp,[0 max(max(temp))]);
s = regionprops(im2bw(temp),'centroid');
centroids = cat(1, s.Centroid);
imshow(i);
hold on;
plot(centroids(:,1), centroids(:,2),'*b')
If you want to stick with the 2D image you have now, this is a simple algorithm that might work for your first image since all the coins are about the same size and it isn't too cluttered:
find the global maximum of your accumulator array using [~,max_idx] = max(accum(:)); [i,j] = ind2sub(size(accum), max_idx];
Take a small neighbourhood around this pixel (maybe a square with a 10 pixel radius) and calculate its center of mass and get its index (this basically finds the middle of the bright rings you see)
add the center of mass pixel index to a list of circle centers
set all pixels in the small neighbourhood to zero (to prevent double-detection of the same pixel center)
repeat from step 1. until you reach approximately 70% or so of the original global max
If that doesn't work, I'd suggest taking the 3D accumulator approach, which is much better for coins of variable size.
The reason you are getting "donut" shapes for some of the coins rather than circles with intense bright points at the centers is because the radius you are assuming is a little off. If you were to explore the 3rd dimension of the accumulator (the radius dimension), you would find that these "donuts" taper to a point, which you could then detect as a local maximum.
This does require a lot more time and memory but it would lead to the most accurate result and isn't a big adjustment code-wise. You can detect the maxima using a method similar to the one I mentioned above, but in 3D and without the center-of-mass trick in step 2.
So, there is a function in matlab that does exactly what you want, called imfindcircles. You give an image and a range of possible radii, it outputs the computed radii and the centers of the circles. Some other parameters allow to tweak the computation (and in my experience, using them is necessary for good results).
Now, if you want to find the centers and radii yourself (not using matlab) then you want to use a maximum finder on the accumulation matrix. The concept of the Hough transform is that the maxima you find on the accumulation matrix each correspond to a circle (parametrized usually in (radius, x_center, y_center). The tough part here is that you have a gigantic searchspace and many many many many maxima that are not "real" circles but artefacts. I do not know how to reliably differentiate these fake circles of the real ones -- and I expect it's a rather complicated check to do.
Hope this helps!
In MATLAB I'm dropping points into the square [0,1]X[0,1] uniformly at random. I'm then placing an edge between two points if the distance between the two points is less than 0.25. The code to do this (and plot the resulting graph is below).
num_Rx = 50
Rx_positions = rand(num_Rx,2);
%Generate edges between pairs of within norm of 0.25.
adj_matrix = zeros(num_Rx, num_Rx);
for i=1:num_Rx
for j=i+1:num_Rx
dist = norm(Rx_positions(i,:) - Rx_positions(j,:));
if dist < 0.25;
adj_matrix(i,j) = 1;
end
end
end
figure
gplot(adj_matrix, Rx_positions)
i.e. this is an undirected graph, with all edge weights equal to 1.
To find the diameter I want to call graphallshortestpaths() (http://www.mathworks.co.uk/help/bioinfo/ref/graphallshortestpaths.html), and take the maximum value returned (the diameter is the longest shortest path).
However, I'm having difficulty calling this function - I can't figure out how to make a call like the examples, from my adjacency matrix and/or my list of node positions. Specifically I don't understand any of the examples, how the matlab code given corresponds to the data presented.
How would I go about doing this.
It appears that all you're missing is converting your adjacency matrix to a sparse matrix. This should give you what you're looking for:
adj_sparse = sparse(adj_matrix);
distances = graphallshortestpaths(adj_sparse);
% if you don't care which nodes produce the
% longest shortest path, omit long_ind & long_sub
[diameter, long_ind] = max(distances(:));
long_sub = ind2sub(size(distances), long_ind);
I have a problem I've been trying to solve and I cannot come up with the answer. I have written a function in Matlab which, given two lat/lon points and two bearings, will return the two great circle points of intersection.
However, what I really need is the first great circle point of intersection along the two initial headings. I.e. if two airplanes begin at lat/lon points 1 and 2, with initial bearings of bearing1 and bearing2, which of the two great circle intersection points is the first one they encounter? There are many solutions (using haversine) which will give me the closer of the two points, but I don't actually care about which is closer, I care about which I will encounter first given specific start points and headings. There are many cases where the closer of the two intersections is actually the second intersection encountered.
Now, I realize I could do this with lots of conditional statements for handling the different cases, but I figure there's got to be a way to handle it with regard to the order I take all my cross products (function code given below), but I simply can't come up with the right solution! I should also mention that this function is going to be used in a large computationally intensive model, and so I'm thinking the solution to this problem needs to be rather elegant/speedy. Can anyone help me with this problem?
The following is not my function code (I can't list that here), but it is the pseudo code that my function was based off of:
%Given inputs of lat1,lon1,Bearing1,lat2,lon2,Bearing2:
%Calculate arbitrary secondary point along same initial bearing from first
%point
dAngle = 45;
lat3 = asind( sind(lat1)*cosd(dAngle) + cosd(lat1)*sind(dAngle)*cosd(Bearing1));
lon3 = lon1 + atan2( sind(Bearing1)*sind(dAngle)*cosd(lat1), cosd(dAngle)-sind(lat1)*sind(lat3) )*180/pi;
lat4 = asind( sind(lat2)*cosd(dAngle) + cosd(lat2)*sind(dAngle)*cosd(Bearing2));
lon4 = lon2 + atan2( sind(Bearing2)*sind(dAngle)*cosd(lat2), cosd(dAngle)-sind(lat2)*sind(lat4) )*180/pi;
%% Calculate unit vectors
% We now have two points defining each of the two great circles. We need
% to calculate unit vectors from the center of the Earth to each of these
% points
[Uvec1(1),Uvec1(2),Uvec1(3)] = sph2cart(lon1*pi/180,lat1*pi/180,1);
[Uvec2(1),Uvec2(2),Uvec2(3)] = sph2cart(lon2*pi/180,lat2*pi/180,1);
[Uvec3(1),Uvec3(2),Uvec3(3)] = sph2cart(lon3*pi/180,lat3*pi/180,1);
[Uvec4(1),Uvec4(2),Uvec4(3)] = sph2cart(lon4*pi/180,lat4*pi/180,1);
%% Cross product
%Need to calculate the the "plane normals" for each of the two great
%circles
N1 = cross(Uvec1,Uvec3);
N2 = cross(Uvec2,Uvec4);
%% Plane of intersecting line
%With two plane normals, the cross prodcut defines their intersecting line
L = cross(N1,N2);
L = L./norm(L);
L2 = -L;
L2 = L2./norm(L2);
%% Convert normalized intersection line to geodetic coordinates
[lonRad,latRad,~]=cart2sph(L(1),L(2),L(3));
lonDeg = lonRad*180/pi;
latDeg = latRad*180/pi;
[lonRad,latRad,~]=cart2sph(L2(1),L2(2),L2(3));
lonDeg2 = lonRad*180/pi;
latDeg2 = latRad*180/pi;
UPDATE: A user on the Mathworks forums pointed this out:
Actually they might each reach a different point. I might have misunderstood the question, but the way you worded it suggests that both trajectories will converge towards the same point, which is not true. If you image the first point being a little after one intersection and the second point being a little after the other intersection, you have a situation were each "plane" will travel towards the intersection that was the closest to the other plane at the beginning.
I didn't think about this. It is actually very possible that each of the planes intersects a different great circle intersection first. Which just made things much more complicated...
Presumably you have a velocity vector for the direction of the plane (the direction in which you want to look for your first intersection - the "bearing vector on the surface of the earth"). You didn't actually specify this. Let us call it v.
You also have the cartesian coordinates of two points, P1 and P2, as well as the initial position of the plane, P0. Each is assumed to be already on the unit sphere (length = 1).
Now we want to know which is the shorter distance - to P1, or P2 - so we need to know the angle "as seen from the direction of the normal vector". For this we need both the sin and the cos - then we can find the angle using atan2. We obtain sin from the cross product (remember all vectors were normalized), and cos from the dot product. To get the sin "as seen from the right direction", we take dot product with the normal vector.
Here is a piece of code that puts it all together - I am using very simple coordinates for the points and direction vector so I can confirm in my head that this gives the correct answer:
% sample points of P0...P2 and v
P0 = [1 0 0];
P1 = [0 1 0];
P2 = [0 -1 0];
v = [0 1 0];
% compute the "start direction normal":
n0 = cross(P0, v);
n0 = n0 / norm( n0 ); % unit vector
% compute cross and dot products:
cr01 = cross(P0, P1);
cr02 = cross(P0, P2);
cos01 = dot(P0, P1);
cos02 = dot(P0, P2);
% to get sin with correct sign, take dot product with direction normal:
sin01 = dot(cr01, n0);
sin02 = dot(cr02, n0);
% note - since P0 P1 and P2 are all in the same plane
% cr02 and cr02 are either pointing in the same direction as n0
% or the opposite direction. In the latter case we get a sign flip for the sin
% in the former case this does nothing
% Now get the angle, mapped from 0 to 2 pi:
ang1 = mod(atan2(sin01, cos01), 2*pi);
ang2 = mod(atan2(sin02, cos02), 2*pi);
if( ang1 < ang2 )
fprintf(1,'point 1 is reached first\n');
% point 1 is the first one reached
else
fprintf(1,'point 2 is reached first\n');
% point 2 is the first
end
When you change the direction of the velocity vector (pointing towards P2 instead of P1), the program correctly tells you "point 2 is reached first".
Let me know if this works for you!
I have a closed non-self-intersecting polygon. Its vertices are saved in two vectors X, and Y. Finally the values of X and Y are bound between 0 and 22.
I'd like to construct a matrix of size 22x22 and set the value of each bin equal to true if part of the polygon overlaps with that bin, otherwise false.
My initial thought was to generate a grid of points defined with [a, b] = meshgrid(1:22) and then to use inpolygon to determine which points of the grid were in the polygon.
[a b] = meshgrid(1:22);
inPoly1 = inpolygon(a,b,X,Y);
However this only returns true if if the center of the bin is contained in the polygon, ie it returns the red shape in the image below. However what need is more along the lines of the green shape (although its still an incomplete solution).
To get the green blob I performed four calls to inpolygon. For each comparison I shifted the grid of points either NE, NW, SE, or SW by 1/2. This is equivalent to testing if the corners of a bin are in the polygon.
inPoly2 = inpolygon(a-.5,b-.5,X,Y) | inpolygon(a+.5,b-.5,X,Y) | inpolygon(a-.5,b+5,X,Y) | inpolygon(a+.5,b+.5,X,Y);
While this does provide me with a partial solution it fails in the case when a vertex is contain in a bin but none of the bin corners are.
Is there a more direct way of attacking this problem, with preferably a solution that produces more readable code?
This plot was drawn with:
imagesc(inPoly1 + inPoly2); hold on;
line(a, b, 'w.');
line(X, Y, 'y);
One suggestion is to use the polybool function (not available in 2008b or earlier). It finds the intersection of two polygons and returns resulting vertices (or an empty vector if no vertices exist). To use it here, we iterate (using arrayfun) over all of the squares in your grid check to see whether the output argument to polybool is empty (e.g. no overlap).
N=22;
sqX = repmat([1:N]',1,N);
sqX = sqX(:);
sqY = repmat(1:N,N,1);
sqY = sqY(:);
intersects = arrayfun((#(xs,ys) ...
(~isempty(polybool('intersection',X,Y,[xs-1 xs-1 xs xs],[ys-1 ys ys ys-1])))),...
sqX,sqY);
intersects = reshape(intersects,22,22);
Here is the resulting image:
Code for plotting:
imagesc(.5:1:N-.5,.5:1:N-.5,intersects');
hold on;
plot(X,Y,'w');
for x = 1:N
plot([0 N],[x x],'-k');
plot([x x],[0 N],'-k');
end
hold off;
How about this pseudocode algorithm:
For each pair of points p1=p(i), p2=p(i+1), i = 1..n-1
Find the line passing through p1 and p2
Find every tile this line intersects // See note
Add intersecting tiles to the list of contained tiles
Find the red area using the centers of each tile, and add these to the list of contained tiles
Note: This line will take a tiny bit of effort to implement, but I think there is a fairly straightforward, well-known algorithm for it.
Also, if I was using .NET, I would simply define a rectangle corresponding to each grid tile, and then see which ones intersect the polygon. I don't know if checking intersection is easy in Matlab, however.
I would suggest using poly2mask in the Image Processing Toolbox, it does more or less what you want, I think, and also more or less what youself and Salain has suggested.
Slight improvement
Firstly, to simplify your "partial solution" - what you're doing is just looking at the corners. If instead of considering the 22x22 grid of points, you could consider the 23x23 grid of corners (which will be offset from the smaller grid by (-0.5, -0.5). Once you have that, you can mark the points on the 22x22 grid that have at least one corner in the polygon.
Full solution:
However, what you're really looking for is whether the polygon intersects with the 1x1 box surrounding each pixel. This doesn't necessarily include any of the corners, but it does require that the polygon intersects one of the four sides of the box.
One way you could find the pixels where the polygon intersects with the containing box is with the following algorithm:
For each pair of adjacent points in the polygon, calling them pA and pB:
Calculate rounded Y-values: Round(pA.y) and Round(pB.y)
For each horizontal pixel edge between these two values:
* Solve the simple linear equation to find out at what X-coordinate
the line between pA and pB crosses this edge
* Round the X-coordinate
* Use the rounded X-coordinate to mark the pixels above and below
where it crosses the edge
Do a similar thing for the other axis
So, for example, say we're looking at pA = (1, 1) and pB = (2, 3).
First, we calculated the rounded Y-values: 1 and 3.
Then, we look at the pixel edges between these values: y = 1.5 and y = 2.5 (pixel edges are half-offset from pixels
For each of these, we solve the linear equation to find where pA->pB intersects with our edges. This gives us: x = 1.25, y = 1.5, and x = 1.75, y = 2.5.
For each of these intersections, we take the rounded X-value, and use it to mark the pixels either side of the edge.
x = 1.25 is rounded to 1 (for the edge y = 1.5). We therefore can mark the pixels at (1, 1) and (1, 2) as part of our set.
x = 1.75 is rounded to 2 (for the edge y = 2.5). We therefore can mark the pixels at (2, 2) and (2, 3).
So that's the horizontal edges taken care of. Next, let's look at the vertical ones:
First we calculate the rounded X-values: 1 and 2
Then, we look at the pixel edges. Here, there is only one: x = 1.5.
For this edge, we find the where it meets the line pA->pB. This gives us x = 1.5, y = 2.
For this intersection, we take the rounded Y-value, and use it to mark pixels either side of the edge:
y = 2 is rounded to 2. We therefore can mark the pixels at (1, 2) and (2, 2).
Done!
Well, sort of. This will give you the edges, but it won't fill in the body of the polygon. However, you can just combine these with your previous (red) results to get the complete set.
First I define a low resolution circle for this example
X=11+cos(linspace(0,2*pi,10))*5;
Y=11+sin(linspace(0,2.01*pi,10))*5;
Like your example it fits with in a grid of ~22 units. Then, following your lead, we declare a meshgrid and check if points are in the polygon.
stepSize=0.1;
[a b] = meshgrid(1:stepSize:22);
inPoly1 = inpolygon(a,b,X,Y);
Only difference is that where your original solution took steps of one, this grid can take smaller steps. And finally, to include anything within the "edges" of the squares
inPolyFull=unique( round([a(inPoly1) b(inPoly1)]) ,'rows');
The round simply takes our high resolution grid and rounds the points appropriately to their nearest low resolution equivalents. We then remove all of the duplicates in a vector style or pair-wise fashion by calling unique with the 'rows' qualifier. And that's it
To view the result,
[aOrig bOrig] = meshgrid(1:22);
imagesc(1:stepSize:22,1:stepSize:22,inPoly1); hold on;
plot(X,Y,'y');
plot(aOrig,bOrig,'k.');
plot(inPolyFull(:,1),inPolyFull(:,2),'w.'); hold off;
Changing the stepSize has the expected effect of improving the result at the cost of speed and memory.
If you need the result to be in the same format as the inPoly2 in your example, you can use
inPoly2=zeros(22);
inPoly2(inPolyFull(:,1),inPolyFull(:,2))=1
Hope that helps. I can think of some other ways to go about it, but this seems like the most straightforward.
Well, I guess I am late, though strictly speaking the bounty time was till tomorrow ;). But here goes my attempt. First, a function that marks cells that contain/touch a point. Given a structured grid with spacing lx, ly, and a set of points with coordinates (Xp, Yp), set containing cells:
function cells = mark_cells(lx, ly, Xp, Yp, cells)
% Find cell numbers to which points belong.
% Search by subtracting point coordinates from
% grid coordinates and observing the sign of the result.
% Points lying on edges/grid points are assumed
% to belong to all surrounding cells.
sx=sign(bsxfun(#minus, lx, Xp'));
sy=sign(bsxfun(#minus, ly, Yp'));
cx=diff(sx, 1, 2);
cy=diff(sy, 1, 2);
% for every point, mark the surrounding cells
for i=1:size(cy, 1)
cells(find(cx(i,:)), find(cy(i,:)))=1;
end
end
Now, the rest of the code. For every segment in the polygon (you have to walk through the segments one by one), intersect the segment with the grid lines. Intersection is done carefully, for horizontal and vertical lines separately, using the given grid point coordinates to avoid numerical inaccuracies. For the found intersection points I call mark_cells to mark the surrounding cells to 1:
% example grid
nx=21;
ny=51;
lx = linspace(0, 1, nx);
ly = linspace(0, 1, ny);
dx=1/(nx-1);
dy=1/(ny-1);
cells = zeros(nx-1, ny-1);
% for every line in the polygon...
% Xp and Yp contain start-end points of a single segment
Xp = [0.15 0.61];
Yp = [0.1 0.78];
% line equation
slope = diff(Yp)/diff(Xp);
inter = Yp(1) - (slope*Xp(1));
if isinf(slope)
% SPECIAL CASE: vertical polygon segments
% intersect horizontal grid lines
ymax = 1+floor(max(Yp)/dy);
ymin = 1+ceil(min(Yp)/dy);
x=repmat(Xp(1), 1, ymax-ymin+1);
y=ly(ymin:ymax);
cells = mark_cells(lx, ly, x, y, cells);
else
% SPECIAL CASE: not horizontal polygon segments
if slope ~= 0
% intersect horizontal grid lines
ymax = 1+floor(max(Yp)/dy);
ymin = 1+ceil(min(Yp)/dy);
xmax = (ly(ymax)-inter)/slope;
xmin = (ly(ymin)-inter)/slope;
% interpolate in x...
x=linspace(xmin, xmax, ymax-ymin+1);
% use exact grid point y-coordinates!
y=ly(ymin:ymax);
cells = mark_cells(lx, ly, x, y, cells);
end
% intersect vertical grid lines
xmax = 1+floor(max(Xp)/dx);
xmin = 1+ceil(min(Xp)/dx);
% interpolate in y...
ymax = inter+slope*lx(xmax);
ymin = inter+slope*lx(xmin);
% use exact grid point x-coordinates!
x=lx(xmin:xmax);
y=linspace(ymin, ymax, xmax-xmin+1);
cells = mark_cells(lx, ly, x, y, cells);
end
Output for the example mesh/segment:
Walking through all polygon segments gives you the polygon 'halo'. Finally, the interior of the polygon is obtained using standard inpolygon function. Let me know if you need more details about the code.