Is it possible to speed up finding points inside a 3D shape (ie inShape)?
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Hi-
I am hoping to figure out a way (and it may not be possible) to speed up finding points inside a 3D shape, ie by inShape as below. This is for a research lab analysis of some microscopy images. A snippet of the code from the larger analysis block is below, along with an image of the graphs that are output from this snippet. The general idea is
--Start with I1, which is an m x n x p array of the points shown in the leftmost graph, pixellated (so values = number of points in that "pixel").
--Goal is to make mask1 that is an evenly spaced m x n x p array of points with values = 1 that are in the shp, and values = 0 that are outside the shp
--The slow step is finding which points inside the mask are within the alphaShape (ie, inShape). It doesn't take so long on a single run (1-3 seconds depending on size of the original image), but this bit of code gets run frequently in the overall analysis workflow. I haven't really found any solutions to speed it up beyond cropping the set of test points down to the limits of the image like I have already. inShape doesn't run on the GPU and it's possible the overhead of transfer wouldn't help anything anyway. I recognize that this just might be the limit of working with relatively large data.
--I can upload a sample I1 if needed, but I'm wondering first if there is an obvious conceptual coding change I can make.
Thanks-
So :
% Example code
% I1 is set previously, it's an m x n x p array of 3d space where the values are pixel intensity at each point
% x1 y1 and z1 are m x 1 arrays that contain all the xy or z points of the original image acquisition after pixellation
%
% Copy the image array and set points that are outside the max and min edges of the real image to nan. the +/-1 is a buffer to include all points
mask1 = I1;
mask1(1:min(x1)-1,:,:) = nan;
mask1(:,1:min(y1)-1,:) = nan;
mask1(:,:,1:min(z1)-1) = nan;
mask1(max(x1)+1:end,:,:) = nan;
mask1(:,max(y1)+1:end,:) = nan;
mask1(:,:,max(z1)+1:end) = nan;
mask1(~isnan(mask1)) = 0; % set those points that are inside the image to 0 (in image = 0, out image = nan)
% convert the mask points that are in the image to a grid, which is a rectangular prism at this point
[cx, cy, cz] = ind2sub(size(mask1), find(mask1 == 0));
marr = [cx cy cz];
% Create 3D alpha shape for the image data and find points in marr that are within the shape
shp = alphaShape(x1,y1,z1,48);
tf = inShape(shp,marr(:,1),marr(:,2),marr(:,3)); % THIS IS THE SLOW STEP
% Crop marr to the positions that are inShape and populate mask1 with 1s. Yield is mask1, and m x n x p array with 1s where the image is.
marr = marr(tf == 1,:);
for i=1:sum(tf)
mask1(marr(i,1),marr(i,2),marr(i,3)) = 1;
end
mask1(isnan(mask1)) = 0;
figure
hold on
scatter3(x1,y1,z1,'.');
plot(shp,'FaceAlpha',0.2)
hold off
title('Original points and shp')
figure
hold on
scatter3(cx,cy,cz,1,'.');
plot(shp,'FaceAlpha',0.2)
hold off
title('Cropped rectangular mask')
figure
hold on
scatter3(marr(:,1),marr(:,2),marr(:,3),'.')
plot(shp,'FaceAlpha',0.2)
hold off
title('End product after inshape')
Here's the graph outputs (this is the xy view)
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回答(1 个)
raymeout
2020-9-10
编辑:raymeout
2020-9-10
Have you looked at the function pointLocation? But to use this function, you'll need to create a tetrahedral triangulation first (e.g. tri = alphaTriangulation(shp)).
pointLocation will find the tetrahedron, which encloses the specific points → if a point is in none (→ outside), it will get NaN...
I did not do any speed benchmark, but in the cases I used the function, it seemed to be pretty fast, even for bigger point clouds and tetrahedral meshes.
3 个评论
raymeout
2020-9-11
alphaTriangulation returns the full triangulation of the alphaShape... You can create the required object by e.g.:
tri_object = triangulation(tri, 3DPoints);
But either way: if it isn't faster, it won't be of any use in this case...
(A possible reason, why your code with inShape might be a bit slower compared to the pointLocation-version could also be, that inShape might use pointLocation and alphaTriangulation internally anyways (since pointLocation was introduced earlier). But I didn'd check this statement any thurther...)
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