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.
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.
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):
What I have is a plot showing the area of connected components. What I want to do is to further work on the plot figure such as clean it up a bit or imcomplement it etc. and then be able to apply the axes from the original plot to this image and be able to extract the ylabel.
Let me explain the above issue with my code and some examples.
This is the plot I have, the y-axis represents the object areas. This is the important axis that I want to transfer to the new image.
Since I am interested in the axes only I copy that using
h = findobj(gcf,'type','axes');
So that I can work with the figure without the axes and borders interfering I save it without these attributes
set(gca, 'visible', 'off'); % Hide the axis and borders
hgexport(gcf, 'plot1.jpg', hgexport('factorystyle'), 'Format', 'jpeg');
This is what I get:
So far so good.
Now comes the processing or in other words changing the plot to my needs.
plot_img = rgb2gray(imread('plot1.jpg'));
img_bw_plot = im2bw(plot_img, graythresh(plot_img));
[rows cols] = size(plot_img);
new = zeros(size(plot_img));
for i = 1: rows
for j = 1: cols
if (img_bw_plot(i,j) == 0)
new(i, 1:10) = 255;
end
end
end
f = figure;
imshow(new);
copyobj(h,f)
This produces a weird overlapped image where instead of copying only the axes, the entire image is copied on top of the new. The datacursormode also fails to work beyond the over lapping image.
First of all I'm a little bit confused that if you have the figure in the first place why aren't you extracting your data from it using something like:
lines=findobj(gca,'type','line');
y=zeros(1,length(lines));
for i=1:length(lines)
y(i)=get(lines(i),'ydata');
end
and there you'll have all the data.
But let's say the original figure isn't like a figure figure where you'd have access to the children of the axes object (though all of them being copied together kind of suggests that this is not the case). What you need to realize is that an "axes" object in MATLAB isn't just the axes of the graph, but the whole graph. For example when you have 5 subplots, each of those smaller plots is an axes object and the graph itself is one of its children which is a "line" object (refer to my example above).
So after this long lecture :), one solution is that you could manually create those axes around your newly drawn image instead of copying the axes object as such:
set(gca,'visible','on');
s=size(new);
set(gca,'ytick',linspace(1,s(1),7),'yticklabel',linspace(6000,0,7));
This should do the trick of placing 7 ticks on the y-axis in the same manner as you have on your original figure. The same method would apply to manually creating the labels for the x-axis.
(I tried putting the resulting image here but I don't have the enough reputations to do so. That's on stackoverflow bro!)
Mind you, though, that this creates the labels on the graph giving you the illusion of the same axis while the actual coordinates of the points are actually determined by the size of the image you're saving. So if you want to make sure the image is the same size, you need to work on resizing your original figure to end up being the same size, which given then 0-6000, would be a really big image.
I have an image that I want to divide in three parts and find the centroid of the parts separately and display them on original image, I used blkproc for dividing the image in [1 3] grids, but can't display the centroids. Here is the code I wrote,
i=imread('F:\line3.jpg');
i2=rgb2gray(i);
bw=im2bw(i2);
imshow(bw)
fun=#(x) regionprops(x,'centroid');
b=blkproc(bw,[1 3],fun);
But I can't get to display the centroids, as well as get their values. Any help will be much appreciated.
You can just use the plot command to plot over the top of the image.
Whatever you [X,Y] centroid coordinates are, say cx(1:3) and cy(1:3)
numCentroids is the number of centroids you are plotting.
hold on;
for ii = 1:length(numCentroids)
plot(cx(ii),cy(ii),'Marker','s','MarkerSize',10,'MarkerFaceColor','r','MarkerEdgeColor','k')
end
If you wanted to write more elegant code, you could run the plot command once across all your centroids and then make the line style type invisible. The answer I supplied should work though.
Here's an example image with made up centroids.
Strong recommendation - use blockproc instead of blkproc. It is better designed and easier to use.
Now, first of all, the second input to blockproc is the blocksize and not the grid size. So if you want to divide your image into [1 3] grid, which I understand as a single row of three blocks, then you should set your blocksize as:
blocksize = [size(i,1) ceil(size(i,2)/3)];
The second thing is to turn off the 'TrimBorder' parameter in blockproc. The code would look something like:
fun=#(x) regionprops(x,'centroid');
blocksize = [size(i,1) ceil(size(i,2)/3)];
b=blockproc(bw,blocksize,fun,'TrimBorder',false);
One minor thing - I would recommend not using the variable name 'i'. By default it represents the imaginary number i = sqrt(-1); in Matlab.
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.
=======================
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;