I am trying to map my image point by point to 3 dimensional space.
For example, if my original image has intensity of 100 at location X, I want to plot this point in 3D location Y with intensity of 100. I want to repeat this steps for every point/pixel, and get a final image. My biggest problem is that I want to do it point by point.
I appreciate any comments or advice. Thank you.
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p.s.
As I was writing this question, I just came up with an idea. I know how to print 'whole' image into certain location/shape in 3D by using warp() function. Instead of using my whole image as an argument to warp function, if I give one point intensity value and one 3D point as arguments for warp function, and repeat this steps for every image point, will I get a descent looking final image in 3D? If there is a better function to use, please let me know.
Sounds like you are looking for scatter3:
I = imread('cameraman.tif');
[x y]=meshgrid(1:size(I,1), 1:size(I,2));
scatter3(x(:),y(:),I(:),15,I(:),'filled');
axis tight; colormap gray
And this is what you get (after some changes to view point):
PS,
I used a single scatter3 command to plot all the points at once. You may (I have no idea why you would like to do so) do it one by one
figure;
for ii=1:numel(x)
scatter( x(ii), y(ii), I(ii), 15, I(ii), 'filled');
hold on; % need this!
end
axis tight; colormap gray;
Related
I have a segmented image as shown here
i want to fit a curve along the top pixels of the segmented image(show as red curve) and i want to find the top point along the curve show in blue. I have already worked on basic idea like traversing through the top to bottom and collecting the top point along each column. i want to know is there any easy solution for this problem like directly taking out the boundary pixels and find the top point.I am using MATLAB for this problem
%download the image
img = logical(imread('http://i.stack.imgur.com/or2iX.png'));
%for some reason it appeared RGB with big solid borders.
%to monochrome
img = img(:,:,1);
%remove borders
img = img(~all(img,2), ~all(img,1));
%split into columns
cimg = num2cell(img,1);
%find first nonzero element per column
ridx = cellfun(#(x) find(x,1,'first'), cimg);
figure, imshow(img)
hold on
%image dim1 is Y, dim2 is X
plot(1:size(img,2),ridx-1,'r','linewidth',2)
%find top point
[yval, xval] = min(ridx);
If you want a smoother curve, try polyfit/polyval
#EDIT
If we want the line to have break at gaps between connected components, we should change the code to something like
bord_idx = sub2ind(size(img), ridx, 1:size(img,2));
regs=regionprops(bwlabel(img),'pixelidxlist');
regs_idx = struct2cell(regs);
split_step = cellfun(#(x) sum(ismember(bord_idx,x)), regs_idx);
split_step = split_step(split_step>0);
split_yvals = mat2cell(ridx',split_val);
split_xvals = mat2cell([1:size(img,2)]',split_val);
figure, imshow(img)
hold on
for k = 1:length(split_step),
plot(split_xvals{k}, split_yvals{k}, 'r', 'linewidth', 2),
end
However, the result is not ideal if one region is positioned over the other. If the "shadowed" points are needed, you should try bwtraceboundary or convexhull and find where the border turns down
As far as "simplest matlab solution" by which I think you mean built in matlab functions: imclose()->edge()->bwboundaries()->findpeaks()'on each boundary'->'filter results based on width and magnitude of peaks'. *you will need to tune all the parameters in these functions, I am just listing what would get you there if appropriately applied.
As far as processing speed is concerned, I think I would have done exactly what you did, basically collecting the top edge from a top down column search and then looking for the point of highest inflection. As soon as you start doing processing of any type, you start doing several operations per pixel which will quickly become more expensive than your initial search (just requires that your image and target are simple enough)
That being said, here are some ideas that may help:
1:If you run a sufficiently heavy closing (dilate->erode), that should fill in all that garbage at the bottom.
2: If you know that your point of interest is not at left or right of picture (boundaries), you could take the right and left edge points and calculate a slope to be applied as an offset to flatten the whole image.
3: If your image always has the large dark linear region below the peak as seen here, you could locate those edges with houghlines looking for verticals and then search only the columns between them.
4: If speed is a concern, you could do a more sophisticated search pattern than left to right, as your peak has a pretty good distribution around it which could help with faster localization of maxima.
All of this is done in Matlab. I have a 2D RGB image, with some depth data for key vertexes. That is, I have three vectors (m x 1): X, Y, Z. Together, [X(i), Y(i)] specifies the point in the image with depth Z(i).
The crux of my problem is this:
I would like to plot the image "warped" with the depth information. But, each time I keep calling functions like 'mesh(X,Y,Z,RGBImage)' and 'surf', I get weird errors like "Z need to be matrix not vector". Also, I haven't even been able to implement the 'warp' function, as I am not sure how to translate my data into a usable format.
Any help would be really appreciated.
EDIT: I finally got it to work the way I would like. The only thing that needed to be changed, that was not answered, was to include the line
s = surf(Xmat,Ymat,Zmat, T1, 'edgecolor', 'none', 'FaceColor', ...
'texturemap');
Where
T1 = rgb2gray(OrignialRGBImage);. Much thanks!
I don't have access to MATLAB right now, so I am giving the outline of the solution.
First, create two matrices using meshgrid: [Xmat,Ymat] = meshgrid(1:imgCols,1:imgRows);. Now, you can find every row of [X,Y] at [Xmat(i,j),Ymat(i,j)] for some (i,j). In the next step, create Zmat such that Zmat(i,j) is the correct depth value for [Ymat(i,j),Xmat(i,j)]. You should be able to do this as follows:
Zmat = zeros(imgRows,imgCols);
for i=1:size(X,1)
Zmat(Y(i),X(i))=Z(i);
end
Now you can do surf with Xmat,Ymat,Zmat as follows. Then overlay image as texture.
s = surf(Xmat,Ymat,Zmat);
set(s, 'faceColor', 'texture','edgecolor', 'none','cdata', subimage); %not tested,
%see if it works
Overlaying part taken from here.
If the above part doesn't work, then you can overlay Zmat in the heatmap style, see this question.
I have the follow code in matlab which is supposed to draw a polygon on a image (has to be a 2d image, be just a patch).
numCorners=8;
dotPos=[];
for rr=1:numCorners
dotPos(end+1)=(cos(rr/numCorners*2*pi))*100;
dotPos(end+1)=(sin(rr/numCorners*2*pi))*100;
end
BaseIm=zeros(1000,1000);
dotpos=[500,500];
imageMatrix =drawpolygon(BaseIm, dotPos, 1); or how else do draw a white polygon here?
imshow(imageMatrix);
This doesn't work as drawpolygon does not appear to exist in this way any idea how to do this?
Note that the resulting data must be an image of equal size of baseIM and must be an array of doubles (ints can be converted) as this is test data for another algorithm.
I have since found the inpolygon(xi,yi,xv,yv); function which I could combine with a for loop if I knew how to properly call it.
If you just need to plot two polygons, you can use the fill function.
t=0:2*pi;
x=cos(t)*2;
y=sin(t)*2
fill(x,y,'r')
hold on
fill(x/2,y/2,'g')
As an alternative, you can use the patch function:
figure
t=0:2*pi;
x=cos(t)*2;
y=sin(t)*2
patch(x,y,'c')
hold on
patch(x/2,y/2,'k')
Edit
The fill and patch functions allow to add polygons also over an actual image too.
% Load an image on the axes
imshow('Jupiter_New_Horizons.jpg')
hold on
% Get the axis limits (just to center the polygons
x_lim=get(gca,'xlim')
y_lim=get(gca,'ylim')
% Create the polygon's coords
t=0:2*pi;
x=cos(t)*50+x_lim(2)/2;
y=sin(t)*50+y_lim(2)/2
% Add the two polygons to the image
f1_h=fill(x,y,'r')
hold on
f1_h=fill(x/2,y/2,'g')
Hope this helps.
On a shape from a logical image, I am trying to extract the field of view from any point inside the shape on matlab :
I tried something involving to test each line going through the point but it is really really long.(I hope to do it for each points of the shape or at least each point of it's contour wich is quite a few times)
I think a faster method would be working iteratively by the expansion of a disk from the considered point but I am not sure how to do it.
How can I find this field of view in an efficient way?
Any ideas or solution would be appreciated, thanks.
Here is a possible approach (the principle behind the function I wrote, available on Matlab Central):
I created this test image and an arbitrary point of view:
testscene=zeros(500);
testscene(80:120,80:120)=1;
testscene(200:250,400:450)=1;
testscene(380:450,200:270)=1;
viewpoint=[250, 300];
imsize=size(testscene); % checks the size of the image
It looks like this (the circle marks the view point I chose):
The next line computes the longest distance to the edge of the image from the viewpoint:
maxdist=max([norm(viewpoint), norm(viewpoint-[1 imsize(2)]), norm(viewpoint-[imsize(1) 1]), norm(viewpoint-imsize)]);
angles=1:360; % use smaller increment to increase resolution
Then generate a set of points uniformly distributed around the viewpoint.:
endpoints=bsxfun(#plus, maxdist*[cosd(angles)' sind(angles)'], viewpoint);
for k=1:numel(angles)
[CX,CY,C] = improfile(testscene,[viewpoint(1), endpoints(k,1)],[viewpoint(2), endpoints(k,2)]);
idx=find(C);
intersec(k,:)=[CX(idx(1)), CY(idx(1))];
end
What this does is drawing lines from the view point to each directions specified in the array angles and look for the position of the intersection with an obstacle or the edge of the image.
This should help visualizing the process:
Finally, let's use the built-in roipoly function to create a binary mask from a set of coordinates:
FieldofView = roipoly(testscene,intersec(:,1),intersec(:,2));
Here is how it looks like (obstacles in white, visible field in gray, viewpoint in red):
I have a grid of wells in an image and I'm trying to analyze this in Matlab. I want to create a box around each well to use as a mask. The way I am trying to go about this is to find the offset vectors from the X and Y normal and then use that to make a grid since I know the size of the wells.
I can mask out some of the wells but not all of them---but this doesn't matter since I know that there is a well in every position (see here). I can use regionprops to get the centers but I can't figure out how to move to the next step.
Here is an image with the centers I can extract
Some people have suggested that I do an FFT of the image but I can't get it to work. Any thoughts or suggestions would be greatly appreciated. Thanks in advance!
Edit: Here is the mask with the centers from the centroid feature of regionprops.
here's a quick and dirty 2 cents:
First blur and invert the image so that the well lines will have high intensity values vs the rest, and further analysis will be less sensitive to noise:
im=double(imread('im.jpg'));
im=conv2(im,fspecial('Gaussian',10,1),'same');
im2=abs(im-max(im(:)));
Then, take a local threshold using the average intensity around a neighborhood of (more or less) a well size (~200 pixels)
im3=imfilter(im2,fspecial('average',200),'replicate');
im4=im2-im3;
bw=im2bw(im4,0);
Fill holes (or wells):
[bw2,locations] = imfill(bw,'holes');
Remove objects smaller than some size:
bw3 = bwareaopen(bw2, 2000, 8);
imagesc(bw3);
You can take it from there...