I have a matrix (vector of vectors) with several points (measurements from sensors) that are supposed to represent walls. All the walls are parallel/perpendicular.
I want to fit these points to the respective walls. I thought of using ransac but I can't find a easy way to implement this on the matrix in cpp, without having to do visualization code, like point cloud library.
Do I have to write my own RANSAC or does this exist?
You may try RANSAC in OpenCV library. If it is not enough, take it's code (it is open source) and modify it according to your problem details.
Or you may add some pictures here for better understanding of your issue details.
In PointCloudLibrary there's a Ransac implementation for 3D. You can use it for your own application. It can identify planes too.
Related
I have a set of 2D points of a known density I want to mesh by taking the holes in account. Basically, given the following input:
I want something link this:
I tried PCL ConcaveHull, but it doens't handle the holes and splitted mesh very well.
I looked at CGAL Alpha shapes, which seems to go in the right direction (creating a polygon from a point cloud), but I don't know how to get triangles after that.
I though of passing the resulting polygons to a constrained triangulation algorithm and mark domains, but I didn't find how to get a list of polygons.
The resulting triangulated polygon is about a two step process at the least. First you need to triangulate your 2D points (using something like a Delaunay2D algorithm). There you can set the maximum length for the triangles and get the the desired shape. Then you can decimate the point cloud and re-triangulate. Another option is to use the convex hull to get the outside polygon, then extract the inside polygon through a TriangulationCDT algorithm, the apply some PolygonBooleanOperations, obtain the desired polygon, and finaly re-triangulate.
I suggest you look into the Geometric Tools library and specifically the Geometric Samples. I think everything you need is in there, and is much less library and path heavy than CGAL (the algorithms are not free for this type of work unless is a school project) or the PCL (I really like the library for segmentation, but their triangulation breaks often and is slow).
If this solves your problem, please mark it as your answer. Thank you!
Doing some research, I found that in Matlab they have this function to create linear structuring elements with a certain angle:
strel('line',len,deg)
The documentations says creates a linear structuring element that is symmetric with respect to the neighborhood center, with approximate length len and angle deg., which basically is a Mat like this with different sizes and angles:
I'm trying to create a similar structuring element in different angles but I couldnt find an equivalent function in Opencv for C++. Is there a way of doing that?
I appreciate any help. Thanks in advance
The closest function OpenCV has is getStructuringElement. Unfortunately, the only shapes it supports are rectangle, cross and ellipse.
Therefore, it is probably the easiest to create/estimate it yourself.
I can't find anything similar to Savitzky Golay Polynomial Fit on opencv. This is a standard smoothing operation though, so it seems like something they should have. Does anybody know of anything they have? Using C++ for what its worth.
Thanks!
-Tim
It is not clear what you need to do: fit or smooth, you mentioned both. But if you need to smooth using OpenCV you can try Kalman filter (fit in its way and smooth), smooth-2D (using your 1D-data) or your own convolution smooth kernel 1D+1D using 1D-data for kernelX only convolution (the fastest way to smooth).
OpenCV is near to real time image and video processing library, and it contains most common task solvers for this, where is no polynomial fitting among them yet. But if you really need fitting (not just smoothing) you can use polynomial fitting matrix equation and calculate your answer by yourself in a simple way thanks to Mat objects in OpenCV which has inv()(inverse) and t()(transpose) functions.
I have various point clouds defining RT-STRUCTs called ROI from DICOM files. DICOM files are formed by tomographic scanners. Each ROI is formed by point cloud and it represents some 3D object.
The goal is to get 2D curve which is formed by plane, cutting ROI's cloud point. The problem is that I can't just use points which were intersected by plane. What I probably need is to intersect 3D concave hull with some plane and get resulting intersection contour.
Is there any libraries which have already implemented these operations? I've found PCL library and probably it should be able to solve my problem, but I can't figure out how to achieve it with PCL. In addition I can use Matlab as well - we use it through its runtime from C++.
Has anyone stumbled with this problem already?
P.S. As I've mentioned above, I need to use a solution from my C++ code - so it should be some library or matlab solution which I'll use through Matlab Runtime.
P.P.S. Accuracy in such kind of calculations is really important - it will be used in a medical software intended for work with brain tumors, so you can imagine consequences of an error (:
You first need to form a surface from the point set.
If it's possible to pick a 2d direction for the points (ie they form a convexhull in one view) you can use a simple 2D Delaunay triangluation in those 2 coordinates.
otherwise you need a full 3D surfacing function (marching cubes or Poisson)
Then once you have the triangles it's simple to calculate the contour line that a plane cuts them.
See links in Mesh generation from points with x, y and z coordinates
Perhaps you could just discard the points that are far from the plane and project the remaining ones onto the plane. You'll still need to reconstruct the curve in the plane but there are several good methods for that. See for instance http://www.cse.ohio-state.edu/~tamaldey/curverecon.htm and http://valis.cs.uiuc.edu/~sariel/research/CG/applets/Crust/Crust.html.
I am just starting to use OpenCV to detect specific curves in an image. First, I want to verify if there is a curve, and next, I would like to identify the type of curve according to vertical or horizontal convex or concave curve. Is there an available function in OpenCV? If not, can you give me some ideas about how can I possibly write such a function? Thanks! By the way, I'm using C++.
Template matching is not a robust way to solve this problem (its like looking at an object from a small pinhole) and edge detectors don't necessarily return you the true edges in the image; false edges such as those due to shadows are returned too. Further, you have to deal with the problem of incomplete edges and other problems that scales up with the complexity of the scene in your image.
The problem you posed, in general, is a very challenging one and, except for toy examples, there are no good solutions.
A rough attempt could be to first try to detect plausible edges using an edge detector (e.g. the canny edge detector suggested). Next, use RANSAC to try to fit a subset of the points in the detected edges to your curve model.
For e.g. let's say you are trying to detect a curve of the following form f(x) = ax^2 + bx + c. RANSAC will basically try to find from among the points in the detected edges, a subset of them that would best fit this curve model. To detect different curves, change f(x) accordingly and run RANSAC for each of them. You can then try to determine if the curve represented by f(x) really exists in your image using some heuristic applied to from the points that were assigned to it by RANSAC (e.g. if too few points were fitted to the model it is likely that the curve is not there. But how to determine a good threshold for the number of points?). You model will get more complex when you have to account for allowable transformation such as rotation etc.
The problem with this approach is you are basically trying fit what you think should be in the image to the points and sometimes, even if what you are looking for is not there, it will return you the "best possible" fit. For e.g. you have a whole bunch of points detected from a concentric circle. If you try to detect straight lines from these points, RANSAC will return you the best fit line! In fact, it could give you many different lines from different runs depending on which points it selected during its random initialization stage.
For more details on how to use RANSAC on this sort of problem, have a look at RANSAC for Dummies by Marco Zuliani. He also has a nice MATLAB toolbox to accompany this tech report, which you can probably port to the language of your choice.
Unless you know what you background looks like, or if you are in control of it e.g. by forcing a clean background, this is a very difficult problem to solve.