I am trying to get a subset of an image from 4 points. Following the solution given in here I located the points and sorted them in terms of maximum and minimum x,y values. After that I did:
subim = image(x_min:x_max,y_min:y_max,:);
in order to obtain a sub-image formed by the rectangle contained in x_min:x_max,y_min:y_max, but this didn't work.
What am I doing wrong?
Like #Divakar answer you,
matlab image works with row and columns attribute as you can see in figure 1 on this help.
And in figure 2, they show how ( x , y ) axis are define. As you can see, y is the rows axis and x the columns.
So when you are using imshow or imtool to get coordinate on a figure, it's show in (x,y) instead of (row,col). Just by inverting your coordinate, you'll get what you need.
Be aware not all library or langague use the same order, like Numpy in python is same as Matlab but OpenCV is the inverse.
Related
I have a set of data points, x, y, and z contained in a matrix, record.
In record, each row is a data-point where the first value is the x-coordinate, the second is the y-coordinate, and the third is the z-coordinate. I would like to represent this as a surface plot. I tried:
surf([record(:,1), record(:,2)], record(:,3))
But the results were not what I expected. Any advice?
Try this code for instance.
[x,y,z]=sphere(n);
surf(x,y,z);
axis equal
This code plots with 3 parameters surf the surface of a sphere. As far as I understood from your code you want to utilize the 2 parameters surf for your application.
According to surf help when utilizing 2 parameters surf:
surf(Z) and surf(Z,C) use x = 1:n and y = 1:m. In this case,
the height, Z, is a single-valued function, defined over a
geometrically rectangular grid.
where:
The color scaling is determined
by the range of C
It just doesn't look like you want to utilize C as the color scaling parameter. For better understanding, can you send the contents of record for reference?
G'day
I'm trying to program a smart way to find the closest grid points to the points along a contour.
The grid is a 2-dimensional grid, stored in x and y (which contain the x and y kilometre positions of the grid cells).
The contour is a line, made up of x and y locations, not necessarily regularly spaced.
This is shown below - the red dots are the grid, and the blue dots are the points on the contour. How do I find the indices of the red dot closest to each blue dot?
Edit - I should mention that the grid is a latitude/longitude grid, of an area fairly close to the south pole. So, the points (the red dots) are the position in metres from the south pole (using a polar stereographic representation). Since the grid is a geographic grid there is unequal grid spacing - with slightly different shaped cells (where the red dots define the vertices of the cells) due to the distortion at high latitudes.
The result is that I can't just find which row/column of the x and y matrix corresponds closest to the input point coordinates - unlike a regular grid from meshgrid, the values in the rows and columns vary...
Cheers
Dave
The usual method is to go:
for every blue point {
for every red point {
is this the closest so far
}
}
But a better way is to put the red data into a kd tree. This is a tree that splits the data along its mean, then splits the two data sets along their means etc until you have them separated into a tree structure.
This will change your searching effeciancy from O(n*m) to O(log(n)*m)
Here is a library:
http://www.mathworks.com.au/matlabcentral/fileexchange/4586-k-d-tree
This library will provide you the means to easily make a kd tree out of the data and to search for the closest point in it.
Alternatively you can use a quadtree, not as simple but the same idea. (you may have to write your own library for that)
Make sure the largest data set (in this case your red points) go into the tree as this will provide the greatest time reduction.
I think I've found a way to do it using the nearest flag of griddata.
I make a matrix that is the same size as the grid x and y matrices, but is filled with the linear indices of the corresponding matrix element. This is formed by reshaping a vector (which is 1:size(x,1)*size(x,2)) to the same dimensions as x.
I then use griddata and the nearest flag to find the linear index of the point closest to each point on my contour (blue dots). Then, simply converting back to subscript notation with ind2sub leaves me with a 2 row vectors describing the matrix subscripts for the points closest to each point on the blue-dotted contour.
This plot below shows the contour (blue dots), the grid (red dots) and the closest grid points (green dots).
This is the code snippet I used:
index_matrix1 = 1:size(x,1)*size(x,2);
index_matrix1 = reshape(index_matrix1,size(x));
lin_ind = griddata(x,y,index_matrix1,CX,CY,'nearest'); % where CX and CY are the coords of the contour
[sub_ind(1,:),sub_ind(2,:)] = ind2sub(size(x),lin_ind);
I suppose that in the stereographic representation, your points form a neat grid in r-theta coordinates. (I'm not too familiar with this, so correct me if I'm wrong. My suggestion may still apply).
For plotting you convert from the stereographic to latitude-longitude, which distorts the grid. However, for finding the nearest point, consider converting the latitude-longitude of the blue contour points into stereographic coordinates, where it is easy to determine the cell for each point using its r and theta values.
If you can index the cell in the stereographic representation, the index will be the same when you transform to another representation.
The main requirement is that under some transformation, the grid points are defined by two vectors, X and Y, so that for any x in X and y in Y, (x, y) is a grid point. Next transform both the grid and the contour points by that transformation. Then given an arbitrary point (x1, y1), we can find the appropriate grid cell by finding the closest x to x1 and the closest y to y1. Transform back to get the points in the desired coordinate system.
dsearchn: N-D nearest point search.
[k, d] = dsearchn(A,B) : returns the distances, d, to the closest points. d is a column vector of length p.
http://au.mathworks.com/help/matlab/ref/dsearchn.html?s_tid=gn_loc_drop
I am trying to plot a 3d view of a very large CT dataset. My data is in a 3d matrix of 2000x2000x1000 dimension. The object is surrounded by air, which is set to NaN in my matrix.
I would like to be able to see the greyscale value of the surface of the object (no isosurface) but I cannot quite work out how to do that in Matlab. Can anyone help me please?
Given that I a dealing with a huge matrix and I am only interested in the surface of the object, does anyone know a good trick how to reduce the size of my dataset?
The function surf(X,Y,Z) allows you to plot 3d data, where (X,Y) gives the coordinates in the x-y-plane while Z gives the z-coordinate and the surface color.
By default the function does not plot anything for the NaN entries, so you should be good to go with the surf function.
To set the surf-function to use a grayscale plotting use:
surf(matrix3d);
colormap(gray);
This plots the matrix in a surface plot and sets the colormap to grayscale.
In addition, as I understand your data, you might be able to eliminate entire plane-segments in your matrix. If for instance the plane A(1,1:2000,1:1000) is NaN in all entries you could eliminate all those entries (thus the entire Y,Z-plane in entry X=1). This will however require some heavy for loops, which might be over the top. This depends on how many data matrices you have compared to how many different plot you want for each matrix.
I will try to give you some ideas. I assume lack of a direct 3D "surface detector".
Since you have a 3D matrix where XY-planes are CT scan slices and each slice is an image, I would try to find edges of each slice say with edge. This would require some preprocessing like first thresholding each slice image. Then I can either use scatter3 to display the edge data as a 3D point cloud or delaunay3 to display the edge data as a surface.
I hope this will help you achieve what you are asking for.
I managed to get it working:
function [X,Y,Z,C] = extract_surface(file_name,slice_number,voxel_size)
LT = imread(file_name);%..READ THE 2D MAP
BW = im2bw(LT,1);%..THRESHOLD TO BINARY
B = bwboundaries(BW,8,'noholes');%..FIND THE OUTLINE OF THE IMAGE
X = B{1}(:,1);%..EXTRACT X AND Y COORDINATES
Y = B{1}(:,2);
indices = sub2ind(size(LT),X,Y);%..FIND THE CORRESPONDING LINEAR INDICES
C = LT(indices);%..NOW READ THE VALUES AT THE OUTLINE POSITION
Z = ones(size(X))*slice_number;
I can then plot this with
figure
scatter3(X,Y,Z,2,C)
Now the only thing I could improve is to have all these points in the scatter plot connected with a surface. #upperBound you suggested delaunay3 for this purpose - I cannot quite figure out how to do this. Do you have a tip?
If I explain why, this might make more sense
I have a logical matrix (103x3488) output of a photo of a measuring staff having been run through edge detect (1=edge, 0=noedge). Aim- to calculate the distance in pixels between the graduations on the staff. Problem, staff sags in the middle.
Idea: User inputs co-ordinates (using ginput or something) of each end of staff and the midpoint of the sag, then if the edges between these points can be extracted into arrays I can easily find the locations of the edges.
Any way of extracting an array from a matrix in this manner?
Also open to other ideas, only been using matlab for a month, so most functions are unknown to me.
edit:
Link to image
It shows a small area of the matrix, so in this example 1 and 2 are the points I want to sample between, and I'd want to return the points that occur along the red line.
Cheers
Try this
dat=imread('83zlP.png');
figure(1)
pcolor(double(dat))
shading flat
axis equal
% get the line ends
gi=floor(ginput(2))
x=gi(:,1);
y=gi(:,2);
xl=min(x):max(x); % line pixel x coords
yl=floor(interp1(x,y,xl)); % line pixel y coords
pdat=nan(length(xl),1);
for i=1:length(xl)
pdat(i)=dat(yl(i),xl(i));
end
figure(2)
plot(1:length(xl),pdat)
peaks=find(pdat>40); % threshhold for peak detection
bigpeak=peaks(diff(peaks)>10); % threshold for selecting only edge of peak
hold all
plot(xl(bigpeak),pdat(bigpeak),'x')
meanspacex=mean(diff(xl(bigpeak)));
meanspacey=mean(diff(yl(bigpeak)));
meanspace=sqrt(meanspacex^2+meanspacey^2);
The matrix pdat gives the pixels along the line you have selected. The meanspace is edge spacing in pixel units. The thresholds might need fiddling with, depending on the image.
After seeing the image, I'm not sure where the "sagging" you're referring to is taking place. The image is rotated, but you can fix that using imrotate. The degree to which it needs to be rotated should be easy enough; just input the coordinates A and B and use the inverse tangent to find the angle offset from 0 degrees.
Regarding the points, once it's aligned straight, all you need to do is specify a row in the image matrix (it would be a 1 x 3448 vector) and use find to get non-zero vector indexes. As the rotate function may have interpolated the pixels somewhat, you may get more than one index per "line", but they'll be identifiable as being consecutive numbers, and you can just average them to get an approximate value.
When trying to plot a normal PDF with mean=0 and standard deviation=20 using the MATLAB command normpdf() I get weird results, see picture.
The code used to plot the figure is as follows:
plot(normpdf((-100:0.1:100),0,20))
What is the correct way of using this function?
When you call plot with ONE argument, it plots those numbers on the y axis, using the index numbers of those values for the x axis. If you wanted the x axis scaled properly, you had to provide them in the first place. Thus...
x = -100:0.1:100;
plot(x,normpdf(x,0,20),'-')
I assume you expected the x-axis to be centered at 0? You need to specify an x-vector for plot. Try plot([-100:0.1:100], normpdf((-100:0.1:100),0,20));.