Suppose I have an STL file which consists of vertex and face data for a given object. How can I calculate the area of the ortho projection of the object given any arbitrary orientation without using Matlab graphics, just simple math with as few built-in Matlab functions as possible. Keep in mind that this is different than just a simple silhouette as there can be gaps in the projection that aren't captured by the object outer outline only.
I originally did this by taking the pixel data from the plot and then counted non-white pixels. That worked great but I need to be able to compile the code for use with C but as far as I can tell any graphics/plotting can't be compiled with Matlab coder.
Image below is a good example (see gap between torso and left arm). A torus is also another good example geometry.
Related
I am trying to upload an STL file to MATLAB and be able to manipulate it but can't find the best way to do it.
What I am trying to do is import an STL a file of a hand tool and be able to rotate the 3D image by giving it roll, pitch and yaw angles. The whole system will involve a live read out from an IMU which calculates these angles (going to use a 9 axis IMU - 9250 and hope to incorporate space movement into this but that's progress for another day) which will feed into a function which alters the orientation of the model made from the STL to show in real time how the body is moving. Its important to note the body is fixed so no points can move relative to each other (simplifying the problem).
Currently I have not got far but have modeled the STL fixed in space:
model = createpde(3);
importGeometry(model,'Test_model.stl');
pdegplot(model);
This will plot the STL file. The model is made up of a certain number of faces and vertices which can be plotted but I cannot see a way of manipulating these. I figure that there should be some way of converting this to a 3D matrix of points in x,y,z which I can mulitply by a rotation vector to give a new position rotated by the three angles.
Rx = rotx(psi);
Ry = roty(theta);
Rz = rotz(phi);
R = Rx*Ry*Rz;
Then multiply the model by this and update the plot.
I will also need a way of offsetting all points by certain values to be able to change the point of rotation (where the IMU is placed). I figure once I get the coordinates in a matrix then I can offset them all by certain values in each direction x, y and z.
Can anyone help with this, I have been looking for similar projects but I have not been able to find anything with good code explanations as of yet. The way I am proposing is only my idea, if there is an easier method then please say. Thanks!
I do not have comment privileges so this may not seem like a complete answer.
I've done exactly this type of thin in MATLAB for other research but had to write my own data parsers as I do not have any tool boxes, or importGeometry() didn't exist at the time. The STL is structured as a list of triangles each with a normal and three vertices. I'd ask you, after importing STL what is the data format? An array of positions, a struct or object? Also, what s/w was used to make it. The gmsh format is easier to work with as it gives you a reduced list of points and lists of connections between them based on what simplex contains the points.
If the output of importGeometry is a struct with the full data set then you will have repeated data and need to (1) parse the struct, (2) delete duplicates, (3) stack the results in a 3-by-N or N-by-3 matrix, then operate on this result with the rotation matrix and update plots.
You haven't really asked a specific question but I hope that my comments were helpful.
How can I create radial images like this (see images below)
My goal is to control the number of radial arms, thinkness, along with the angle they are created. I'm trying to create patterns that will show me different Moiré patterns when overlapped and turned / animated in octave / matlab.
PS: I'm using octave 3.8.1
I've tried the code here but it doesn't give me the fine tuning all of the following parameters, of radial arm amount, angle, and thickness. Also the image package is needed which I'm trying to avoid.
http://www.mathworks.com/matlabcentral/answers/uploaded_files/20287/moire_pattern.m
As I see it the two approaches which would be worth investigating first are equations and patches.
You could for instance generate a generic equation for an arm with parameters to control the rotation angle and the shape of the curve. You could then plot that at each of a given number of rotation angles, with varying linewidths (a plot property not an equation parameter). Your equation would probably not look pretty as you'd be best off specifying it parametrically (in terms of a third variable) or in polar coordinates, and then translating it to cartesian for the plot commands.
With patches you'd be computing the outline of the arm (as opposed to the centreline) and would probably find it convenient to generate the patch for one arm and then transform it for each rotation. This would be a one-liner with the appropriate rotational transform matrix, and the expression you use to generate the arm wouldn't need to be nearly so complex as it wouldn't need to handle the rotation. A quadratic might even do at a push.
Another advantage of patches is that, having generated an arm and rotated it around, you could also flip it and generate the figure with the opposite sense for very little extra code.
I am currently doing some image segmentation on a bone qCT picture, see for instance images below.
I am trying to find the different borders in the picture for instance the outer border separating the bone to the noisy background. In this analysis I am getting a list of points (vec(1,:) containing x values and vex(2,:) containing the y values) in random order.
To get them into order I am using using a block of code which effectively takes the first point vec(1,1),vec(1,2) and then finds the closest point among the rest of the points in the vector. And then repeats.
Now my problem is that I want to smooth the data but how do I do that as the points lie in a circular formation? (I do have the Curve Fitting Toolbox)
Not exactly a smoothing procedure, but a way to simplify your data would be to compute the boundary of the convex hull of the data.
K = convhull(O(1,:), O(2,:));
plot(O(1,K), O(2,K));
You could also consider using alpha shapes if you want more control.
I am trying to detect corners (x/y coordinates) in 2D scatter vectors of data.
The data is from a laser rangefinder and our current platform uses Matlab (though standalone programs/libs are an option, but the Nav/Control code is on Matlab so it must have an interface).
Corner detection is part of a SLAM algorithm and the corners will serve as the landmarks.
I am also looking to achieve something close to 100Hz in terms of speed if possible (I know its Matlab, but my data set is pretty small.)
Sample Data:
[Blue is the raw data, red is what I need to detect. (This view is effectively top down.)]
[Actual vector data from above shots]
Thus far I've tried many different approaches, some more successful than others.
I've never formally studied machine vision of any kind.
My first approach was a homebrew least squares line fitter, that would split lines in half resurivly until they met some r^2 value and then try to merge ones with similar slope/intercepts. It would then calculate the intersections of these lines. It wasn't very good, but did work around 70% of the time with decent accuracy, though it had some bad issues with missing certain features completely.
My current approach uses the clusterdata function to segment my data based on mahalanobis distance, and then does basically the same thing (least squares line fitting / merging). It works ok, but I'm assuming there are better methods.
[Source Code to Current Method] [cnrs, dat, ~, ~] = CornerDetect(data, 4, 1) using the above data will produce the locations I am getting.
I do not need to write this from scratch, it just seemed like most of the higher-class methods are meant for 2D images or 3D point clouds, not 2D scatter data. I've read a lot about Hough transforms and all sorts of data clustering methods (k-Means etc). I also tried a few canned line detectors without much success. I tried to play around with Line Segment Detector but it needs a greyscale image as an input and I figured it would be prohibitivly slow to convert my vector into a full 2D image to feed it into something like LSD.
Any help is greatly appreciated!
I'd approach it as a problem of finding extrema of curvature that are stable at multiple scales - and the split-and-merge method you have tried with lines hints at that.
You could use harris corner detector for detecting corners.
I want to generate a heat map image of a floor. I have the following things:
A black & white .png image of the floor
A three column array stored in Matlab.
-- The first two columns indicate the X & Y coordinates of the floorpan image
-- The third coordinate denotes the "temperature" of that particular coordinate
I want to generate a heat map of the floor that will show the "temperature" strength in those coordinates. However, I want to display the heat map on top of the floor plan so that the viewers can see which rooms lead to which "temperatures".
Is there any software that does this job? Can I use Matlab or Python to do this?
Thanks,
Nazmul
One way to do this would be:
1) Load in the floor plan image with Matlab or NumPy/matplotlib.
2) Use some built-in edge detection to locate the edge pixels in the floor plan.
3) Form a big list of (x,y) locations where an edge is found in the floor plan.
4) Plot your heat map
5) Scatterplot the points of the floor plan as an overlay.
It sounds like you know how to do each of these steps individually, so all you'll need to do is look up some stuff on how to overlay plots onto the same axis, which is pretty easy in both Matlab and matplotlib.
If you're unfamiliar, the right commands look at are things like meshgrid and surf, possibly contour and their Python equivalents. I think Matlab has a built-in for Canny edge detection. I believe this was more difficult in Python, but if you use the PIL library, the Mahotas library, the scikits.image library, and a few others tailored for image manipulation, it's not too bad. SciPy may actually have an edge filter by now though, so check there first.
The only sticking point will be if your (x,y) data for the temperature are not going to line up with the (x,y) pixel locations in the image. In that case, you'll have to play around with some x-scale factor and y-scale factor to transform your heat map's coordinates into pixel coordinates first, and then plot the heat map, and then the overlay should work.
This is a fairly low-tech way to do it; I assume you just need a quick and dirty plot to illustrate how something's working. This method does have the advantage that you can change the style of the floorplan points easily, making them larger, thicker, thinner, different colors, or transparent, depending on how you want it to interact with the heat map. However, to do this for real, use GIMP, Inkscape, or Photoshop and overlay the heatmap onto the image after the fact.
I would take a look at using Python with a module called Polygon
Polygon will allow you to draw up the room using geometric shapes and I believe you can just do the borders of a room as an overlay on your black and white image. While I haven't used to a whole lot at this point, I do know that you only need a single (x,y) coordinate pair to be able to "hit test" against the given shape and then use that "hit test" to know the shape who's color you'd want to change.
Ultimately I think polygon would make your like a heck of a lot easier when it comes to creating the room shapes, especially when they aren't nice rectangles =)
A final little note though. Make sure to read through all of the documentation that Jorg has with his project. I haven't used it in the Python 3.x environment yet, but it was a little painstaking to get it up an running in 2.7.
Just my two cents, enjoy!