I have a series of Lat/Long points in a SQL Server database. I would like to be able to find shapes. By that I mean if in the mess of coordinates there are 8 coordinates making a perfect circle, or 7 coordinates making a triangle I would like to know.
I'd be surprised if there is already something out there which does this already, especially in C# (the language I'm using). But My question is really, how should I approach this?
I probably have 200k, but their timestamped, so I should only be working with maybe 1k at a time...
What you're trying to do is called least squares fitting.
Basically, you pick a shape. Let's pick a straight line for now.
You calculate the sum of the squares of the offsets ("the residuals") of the points from the line. You do this with different lines until you've minimized the sum of the squares.
I have no idea how you would automate this for several types of shapes.
You need to find a library, or develop your self, a way to calculate Least Squares over shapes.
If the error margin is over a threshold level of R2 then you do not have that "Shape". You will need to define a formula for the shape you test against (For example a circle: x2+y2=r2).
For things that do not have curves (triangle,square, ect.) it will be harder to do as they do not have a "Formula". You can use the least square for finding each side of the shape for a line (y=mX+b) and then building those lines together to make shapes.
Related
I want to slice a 3D model relative to an infinite plane(In WPF). I'm checking if edges intersect with the infinite plane. If true, I'll create a new point at the intersection position, so I'm getting a couple of points that I want to generate a cap on so that the model is closed after slicing. For example, if this is the cross section, the result would be as follows:
Note: The triangulation ain't important. I just need triangles.
I also need to detect the holes as follows(holes are marked in red):
If it is impossible to do it the way I think(It seems to be so), the how should I do it? How do developers cap an object after being sliced?
There is also too much confusion. For example, The first picture's result may be:
What am I missing??
EDIT:
After some research, I knew one thing that I am missing:
The input is now robust, and I need the exact same output. How do I accomplish that??
In the past, I have done this kind of thing using a BSP.
Sorry to be so vague, but its not a a trivial problem!
Basically you convert your triangle mesh into the BSP representation, add your clipping plane to the BSP, and then convert it back into triangles.
As code11 said already you have too few data to solve this, the points are not enough.
Instead of clipping edges to produce new points you should clip entire triangles, which would give you new edges. This way, instead of a bunch of points you'd have a bunch of connected edges.
In your example with holes, with this single modification you'd get a 3 polygons - which is almost what you need. Then you will need to compute only the correct triangulation.
Look for CSG term or Constructive Solid Geometry.
EDIT:
If the generic CSG is too slow for you and you have clipped edges already then I'd suggest to try an 'Ear Clipping' algorithm.
Here's some description with support for holes:
https://www.geometrictools.com/Documentation/TriangulationByEarClipping.pdf
You may try also a 'Sweep Line' approach:
http://sites-final.uclouvain.be/mema/Poly2Tri/
And similar question on SO, with many ideas:
Polygon Triangulation with Holes
I hope it helps.
Building off of what zwcloud said, your point representation is ambiguous. You simply don't have enough points to determine where any concavities/notches actually are.
However, if you can solve that by obtaining additional points (you need midpoints of segments I think), you just need to throw the points into a shrinkwrap algorithm. Then at least you will have a cap.
The holes are a bit more tricky. Perhaps you can get away with just looking at the excluded points from the output of the shrinkwrap calculation and trying to find additional shapes in that, heuristically favoring points located near the centroid of your newly created polygon.
Additional thought: If you can limit yourself to convex polygons with only one similarly convex hole, the problem will be much easier to solve.
So I need a write method to create a curve between two points, with each point having a normalized vector pointing in an arbitrary direction. I have been trying to devise such a method but haven't been able to wrap my head around the math.
Here, since a picture is worth a thousand words this is what I need:
In the picture, the vectors are perpendicular to the red lines. I believe the vectors need to be weighted the same with a weight equivalent to the distance between the points. It needs to be so that when two points are on top of each other pointing in opposite directions it still all looks like one smooth curve (top curve in the picture). Also, I need to integrate the curves to find their lengths. I don't know why I haven't been able to think of how to calculate all of this but I haven't.
Also I'm using csharp the language doesn't really matter.
Cubic Bezier will indeed achieve the requested effect. You need four control points per curve segment. Two define the endpoints and two others the directions of the tangents at the endpoints. There are two degrees of freedom left, telling how far the control points can be along the tangents.
The arc length cannot be computed analytically and you will need numerical methods. This other question gives you useful information.
I have a List of 2D points. What's an efficient way of iterating through the points in order to determine whether the list of points are in a straight line, or curved (and to what degree). I'd like to avoid simply getting slopes between smaller subsets. How would I go about doing this?
Thanks for any help
Edit: Thanks for the response. To clarify, I don't need it to be numerically accurate, but I'd like to determine if the user has created a curved shape with their mouse and, if so, how sharp the curve is. The values are not too important, as long as it's possible to determine the difference between a sharp curve and a slightly softer one.
If you simply want to know if all your points fit more or less on a curve of degree d, simply apply Lagrange interpolation on the endpoints and d-2 equally spaced points from inside your array. This will give you a polynomial of degree d.
Once you have your curve, simply iterate over the array and see how far away from the curve each point is. If they're farther than a threshold, your data doesn't fit your degree d polynomial.
Edit: I should mention that iterating through values of d is a finite process. Once d reaches the number of points you have, you'll get a perfect fit because of how Lagrange interpolation works.
To test if it's a straight line, compute the correlation coefficient. I'm sure that's covered on wikipedia.
To test if it's curved is more involved. You need to know what kind of curves you expect, and fit against those.
Here is a method to calculate angle: Calculate Angle between 2 points using C#
Simply calculate angle between each and every point in your list and create list of angles, then compare if angles list values are different. If they are not different then it means it's straight line, otherwise it's curve...
If it's a straight line then angle between all points has to be a same.
The question is really hazy here: "I'd like to avoid simply getting slopes between smaller substes"
You probably want interpolation a-la B-splines. They use two points and two extra control points if memory serves me. Implementations are ubiquitous since way back (at least 1980's). This should get you underway
Remember that you'll probably need to add control points to make the curve meet the endpoints. One trick to make sure those are reached is to simply duplicate the endpoints as extra controlpoints.
Cheers
Update Added link to codeproject
it would appear that what I remember from back in the 80's could have been Bezier curves - a predecessor of sorts.
Given an elevation map consisting of lat/lon/elevation pairs, what is the fastest way to find all points above a given elevation level (or better yet, just the the 2D concave hull)?
I'm working on a GIS app where I need to render an overlay on top of a map to visually indicate regions that are of higher elevation; it's determining this polygon/region that has me stumped (for now). I have a simple array of lat/lon/elevation pairs (more specifically, the GTOPO30 DEM files), but I'm free to transform that into any data structure that you would suggest.
We've been pointed toward Triangulated Irregular Networks (TINs), but I'm not sure how to efficiently query that data once we've generated the TIN. I wouldn't be surprised if our problem could be solved similarly to how one would generate a contour map, but I don't have any experience with it. Any suggestions would be awesome.
It sounds like you're attempting to create a polygonal representation of the boundary of the high land.
If you're working with raster data (sampled on a rectangular grid), try this.
Think of your grid as an assembly of right triangles.
Let's say you have a 3x3 grid of points
a b c
d e f
g h k
Your triangles are:
abd part of the rectangle abed
bde the other part of the rectangle abed
bef part of the rectangle bcfe
cef the other part of the rectangle bcfe
dge ... and so on
Your algorithm has these steps.
Build a list of triangles that are above the elevation threshold.
Take the union of these triangles to make a polygonal area.
Determine the boundary of the polygon.
If necessary, smooth the polygon boundary to make your layer look ok when displayed.
If you're trying to generate good looking contour lines, step 4 is very hard to to right.
Step 1 is the key to this problem.
For each triangle, if all three vertices are above the threshold, include the whole triangle in your list. If all are below, forget about the triangle. If some vertices are above and others below, split your triangle into three by adding new vertices that lie precisely on the elevation line (by interpolating elevation). Include the one or two of those new triangles in your highland list.
For the rest of the steps you'll need a decent 2d geometry processing library.
If your points are not on a regular grid, start by using the Delaunay algorithm (which you can look up) to organize your pointss in into triangles. Then follow the same algorith I mentioned above. Warning. This is going to look kind of sketchy if you don't have many points.
Assuming you have the lat/lon/elevation data stored in an array (or three separate arrays) you should be able to use array querying techniques to select all of the points where the elevation is above a certain threshold. For example, in python with numpy you can do:
indices = where(array > value)
And the indices variable will contain the indices of all elements of array greater than the threshold value. Similar commands are available in various other languages (for example IDL has the WHERE() command, and similar things can be done in Matlab).
Once you've got this list of indices you could create a new binary array where each place where the threshold was satisfied is set to 1:
binary_array[indices] = 1
(Assuming you've created a blank array of the same size as your original lat/long/elevation and called it binary_array.
If you're working with raster data (which I would recommend for this type of work), you may find that you can simply overlay this array on a map and get a nice set of regions appearing. However, if you need to convert the areas above the elevation threshold to vector polygons then you could use one of many inbuilt GIS methods to convert raster->vector.
I would use a nested C-squares arrangement, with each square having a pre-calculated maximum ground height. This would allow me to scan at a high level, discarding any squares where the max height is not above the search height, and drilling further into those squares where parts of the ground were above the search height.
If you're working to various set levels of search height, you could precalculate the convex hull for the various predefined levels for the smallest squares that you decide to use (or all the squares, for that matter.)
I'm not sure whether your lat/lon/alt points are on a regular grid or not, but if not, perhaps they could be interpolated to represent even 100' ft altitude increments, and uniform
lat/lon divisions (bearing in mind that that does not give uniform distance divisions). But if that would work, why not precompute a three dimensional array, where the indices represent altitude, latitude, and longitude respectively. Then when the aircraft needs data about points at or above an altitude, for a specific piece of terrain, the code only needs to read out a small part of the data in this array, which is indexed to make contiguous "voxels" contiguous in the indexing scheme.
Of course, the increments in longitude would not have to be uniform: if uniform distances are required, the same scheme would work, but the indexes for longitude would point to a nonuniformly spaced set of longitudes.
I don't think there would be any faster way of searching this data.
It's not clear from your question if the set of points is static and you need to find what points are above a given elevation many times, or if you only need to do the query once.
The easiest solution is to just store the points in an array, sorted by elevation. Finding all points in a certain elevation range is just binary search, and you only need to sort once.
If you only need to do the query once, just do a linear search through the array in the order you got it. Building a fancier data structure from the array is going to be O(n) anyway, so you won't get better results by complicating things.
If you have some other requirements, like say you need to efficiently list all points inside some rectangle the user is viewing, or that points can be added or deleted at runtime, then a different data structure might be better. Presumably some sort of tree or grid.
If all you care about is rendering, you can perform this very efficiently using graphics hardware, and there is no need to use a fancy data structure at all, you can just send triangles to the GPU and have it kill fragments above or below a certain elevation.
Is there an algorithm ( preferably in C# implementation) that allows me to compare how similar two lines are? In my case I have one reference line, and I have a lot of secondary lines, I need to choose, out of so many secondary lines, which is the closest to the reference line.
Edit: It is a 2D line, with start and stop points. When you compare the similarities, you to take into account of the full blown line. The direction of the line ( i.e., whether it's from left to right or vice versa) is not important. And yes, it has to do with how close it is from one another
I know this is kind of subjective ( the similarity, not the question), but still, I am sure there are people who have done work on this.
Obvious metrics include slope, length, and distance between midpoints. You could calculate those and then find weightings that you like.
If you want to kind of wrap them all up into one thing, try the sum of the distances between the endpoints.
You're going to have to try a few things and see which cases irritate you and then figure out why.
lines (and in general hyperplanes) sit on an object call Grassmanian; e.g. lines in the plane sit in Gr(1,3), which is isomorphic to the 2-dimensional projective space, and yours is the simplest non trivial one: Gr(2,4). It is a compact metric space, which comes with a standard metric (arising from the plucker embedding - see the link above). However, this metric is a little expensive to compute, so you may want to consider an approximation (just as you'd consider using dot product instead of angle in 2 dimensions - it works find for small angles)
A more detailed explantion (based in the metric defined in the linked wikipedia article):
For each line l take two points (x1,y1,z1) and (x2,y2,z2) on it. Let A be the 4 by 2 matrix whose columns are (1,x1,y1,z1)^t and (1,x2,y2,z2)^t. Define P to be the 4 by 4 matrix
A(A^tA)^(-1)A^t. Then P is dependent only on l and not of the choice of the two points.
The metric you want is the absolute value of the top eigen value of the difference between the matrices corresponding to the two lines.
If you are talking about lines in the graphical sense, then I would look at a combination of things like line length and angle.
Depending on your situation, you may be able to make optimizations such as using the square of the length (saves a square root) and dy/dx for angle (saves a trig function, but watch for the divide-by-zero case).