I have a fairly simple object with shape defined by 12 vertices. When doing hidden lines calculation on this object(I am using Cad Control to do this) it returns collection of lines making up the shape which is usually much more than minimum count of lines to draw such a shape, please see attached picture:
Each segment between points is a line. I would like to remove points that are marked in red color leaving only minimum count (yellow cross) necessary to draw shape.
One approach would be to sort them clockwise and then loop through them checking if a cross product of three adjacent points in the list is zero and then deleting the middle one. Unfortunately, it is impossible to predict, how points will be sorted, therefore this is not an option.
Second approach would be to loop through the collection of lines offered by cad control and to find all points that are on the same line, sort them (pointsLineA, pointsLineB, pointsLineC, etc) From there it would be much easier.
So far I have accomplished that I loop through line collection (get each lines points) and in nested loop I loop through the same collection(copy of it) to check if the points of any random line in the collection lie on the same line as points from line from first loop. This involves two loops and modifying collections on the run. To make it short, it is a MESS. If you would like to see code sample, please let me know.
To make sure everything is clear - my first objective is to group points so that in each group would appear points only belonging to one line. Any suggestions?
With 12 vertices (or hundreds of vertices) I wouldn't do space partitioning (I'm thinking about adaptive quadtree, 2D-tree (kd-tree with k=2)).
I'd store for each vertex which lines it belongs to (it's easier to assign an ID to each vertex and line instead of comparing each time the coordinates of vertices).
vertex(1)=(2.5,3.97) <- vertex coordinates
vertex(2)=( 13.453 , 24.687 )
lines_for_Vertex(1)= {1,5} if vertex 1 is member of lines 1 and 5
lines_for_Vertex(3)={2,5,7} if vertex 3 is member of lines 2,5 and 7
lines_for_vertex(9)={4} if vertex 9 is member of line 4 (edge or segment not connected)
lines_for_vertex(3)={} if vertex 3 is not connected (not member of any segment)
(maybe some cases are impossible for you)
You can assign ID to lines with position inside your collection of lines.
In any case, if you do this changes or keep your collections of lines, inside the nested loops you have to collect information of point to be deleted without changing anything.
So instead of doing:
if vertices are aligned then remove the vertex in the middle
you fill a list 'to_remove' with this information:
to_remove.add(vertex in the middle) <- with the ID is easier
Then when the two loops end, you can remove all the vertices collected in the list. If you have the array 'lines_for_vertex' it's easy to find the two segments to be collapsed into one (eg. if vertex to remove is 1, the collapsing lines are 1 and 5).
If you build a structure even for lines, referring to ID of its vertices
e.g. line(5)={1,3} if line with ID=5 connects vertices 1 and 3
(compare with lines_for_vertex above), it's easier to know how to collapse lines.
You need to retrieve the topology of the polygon. The means to rearrange the vertices in a closed loop. By comparing the endpoint coordinates, you can find those that match and obtain a graph with edges between endpoints, and endpoints merged.
From this representation you can easily detect and remove the alignments.
Related
I am working in Unity3D an am trying to draw a border based on an image. The code finds a set of positions that are the edges, and saves them in two lists, one for the x cords, one for the y cords. Now I want to input these coordinates into a LineRenderer, which would draw a line between every point you put into it. But, this creates a giant mess. This is because of the order of the coordinates: first every point with the same x-coordinate, then the next x-coordinate, etc. This generates a huge mess: lots of unconnected horizontal lines that are then connected by diagonal lines by the Unity software...
So what I am looking for: what algoritm or function can I use that I can input into my points, and returns the correct order of the points (so either a right-handed stroll through all the points or a left-handed stroll) so I can put it into the linerenderer. Every point has at least one neighbour that is also in the list, but if allowing backtracking is too hard I can remove that so each point has at least two neighbours in the lists.
Below a bit of my code:
List<int> xs = new List<int>();
List<int> ys = new List<int>();
for loops that fill the lists, identical index means the points belong together
var orderedpoints = OrderPointsForLineRenderer(xs,ys);
...
I have some photos of white pages with some black points drawn on, like this:
photo (the points aren't very circular, I can draw them better),
and I would find the coordinates of these points.
I can binarize the images (the previous photo binarized: image), but how can I find the coordinates of these black points? I need only the coordinates of one pixel for each point, the approximate center.
This is for a school assignment.
Since its for school work I will only provide you with a high level algorithm.
Since the background is guarantee to be white, you are in luck.
First you need to define a threshold on the level black which you want to consider as the black dot's color.
#ffffff is pure white and #000000 is pure black. I would suggest some where like #383838 to be your threshold.
Then you make a two dimensional bool array to keep track of which pixel you have visited already.
Now we can start looking at the picture.
You read the pixel one at the time horizontally and see if the pixel is > threshold. If yes then you do a DFS or BFS to find the entire area where the pixel's neighbor is also > threshold.
During the process you will be marking the bool array we created earlier to indicate that you have already visited the pixel.
since its a circle point you can just take the min, max of x and y coordinate and calculate the center point.
Once you are done with one point you would keep looping thru the picture's pixel and find the points that you have not visited (false in the bool array)
Since the points you have on the photo contains some small dots on the edge which is not connected to the large point, you might have to do some math to see if the radius is > some number to consider that a valid point. Or instead of a radius 1 neighbor you do a 5 - 10 pixel neighbor BFS/DFS to include the ones that are really close to the main point.
The basics for processing image data can be found in other questions, so I won't go into deeper detail about that, but for the threshold check specifically, I'd do it by gathering the red, green and blue bytes of each pixel (as indicated in the answer I linked), and then just combine them to a Color c = Color.FromArgb(r,g,b) and testing that to be "dark" using c.GetBrightness() < brightnessThreshold. A value of 0.4 was a good threshold for your test image.
You should store the result of this threshold detection in an array in which each item is a value that indicates whether the threshold check passed or failed. This means you can use something as simple as a two-dimensional Boolean array with the original image's height and width.
If you already have methods of doing all that, all the better. Just make sure you got some kind of array in which you can easily look up the result of that binarization. If the method you have gives you the result as image, you will be more likely to end up with a simple one-dimensional byte array, but then your lookups will simply be of a format like imagedata[y * stride + x]. This is functionally identical to how internal lookups in a two-dimensional array happen, so it won't be any less efficient.
Now, the real stuff in here, as I said in my comment, would be an algorithm to detect which pixels should be grouped together to one "blob".
The general usage of this algorithm is to loop over every single pixel on the image, then check if A) it cleared the threshold, and B) it isn't already in one of your existing detected blobs. If the pixel qualifies, generate a new list of all threshold-passed pixels connected to this one, and add that new list to your list of detected blobs. I used the Point class to collect coordinates, making each of my blobs a List<Point>, and my collection of blobs a List<List<Point>>.
As for the algorithm itself, what you do is make two collections of points. One is the full collection of neighbouring points you're building up (the points list), the other is the current edge you're scanning (the current edge list). The current edge list will start out containing your origin point, and the following steps will loop as long as there are items in your current edge list:
Add all items from the current edge list into the full points list.
Make a new collection for your next edge (the next edge list).
For each point in your current edge list, get a list of its directly neighbouring points (excluding any that would fall outside the image bounds), and check for all of these points if they clear the threshold, and if they are not already in either the points list or the next edge list. Add the points that pass the checks to the next edge list.
After this loop through the current edge list ends, replace the original current edge list by the next edge list.
...and, as I said, loop these steps as long as your current edge list after this last step is not empty.
This will create an edge that expands until it matches all threshold-clearing pixels, and will add them all to the list. Eventually, as all neighbouring pixels end up in the main list, the new generated edge list will become empty, and the algorithm will end. Then you add your new points list to the list of blobs, and any pixels you loop over after that can be detected as already being in those blobs, so the algorithm is not repeated for them.
There are two ways of doing the neighbouring points; you either get the four points around it, or all eight. The difference is that using four will not make the algorithm do diagonal jumps, while using eight will. (An added effect is that one causes the algorithm to expand in a diamond shape, while the other expands in a square.) Since you seem to have some stray pixels around your blobs, I advise you to get all eight.
As Steve pointed out in his answer, a very quick way of doing checks to see if a point is present in a collection is to create a two-dimensional Boolean array with the dimensions of the image, e.g. Boolean[,] inBlob = new Boolean[height, width];, which you keep synchronized with the actual points list. So whenever you add a point, you also mark the [y, x] position in the Boolean array as true. This will make rather heavy checks of the if (collection.contains(point)) type as simple as if (inBlob[y,x]), which requires no iterations at all.
I had a List<Boolean[,]> inBlobs which I kept synced with the List<List<Point>> blobs I built, and in the expanding-edge algorithm I kept such a Boolean[,] for both the next edge list and the points list (the latter of which was added to inBlobs at the end).
As I commented, once you have your blobs, just loop over the points inside them per blob and get the minimums and maximums for both X and Y, so you end up with the boundaries of the blob. Then just take the averages of those to get the center of the blob.
Extras:
If all your dots are guaranteed to be a significant distance apart, a very easy way to get rid of floating edge pixels is to take the edge boundaries of each blob, expand them all by a certain threshold (I took 2 pixels for that), and then loop over these rectangles and check if any intersect, and merge those that do. The Rectangle class has both an IntersectsWith() for easy checks, and a static Rectangle.Inflate for increasing a rectangle's size.
You can optimise the memory usage of the fill method by only storing the edge points (threshold-matching points with non-matching neighbours in any of the four main directions) in the main list. The final boundaries, and thus the center, will remain the same. The important thing to remember then is that, while you exclude a bunch of points from the blob list, you should mark all of them in the Boolean[,] array that's used for checking the already-processed pixels. This doesn't take up any extra memory anyway.
The full algorithm, including optimisations, in action on your photo, using 0.4 as brightness threshold:
Blue are the detected blobs, red is the detected outline (by using the memory-optimised method), and the single green pixels indicate the center points of all blobs.
[Edit]
Since it's been almost a year since I posted this, I guess I might as well link to the implementation I made of this. I actually managed to use it myself about a month after I wrote it, when recreating the video compression algorithm of an old DOS game which used chunked up diff frames.
Given a Delaunay Triangulation of a point set, how should I index my triangulation to do quick point localization?
I'm currently looping over all the triangles. For each triangle, I'm checking if the given point is within triangle's bounding rectangle. If it is, I then check the triangle using geometry equations.
This is slow. Any ideas of how to make this search more efficient?
Mission accomplished, that's the way I ended up doing it:
1) Check if the point lies within triangle bounding rectangle.
2) Assign the point as the start of a horizontal line, ending at max width.
3) Check intersections from the triangles found in (1) with the line from (2).
4) If triangle intersect, check how many times the horizontal line intersect with the triangle.
5) If intersects 1 time, means point in triangle. Else, not in triangle.
Reference:
Fast generation of points inside triangulated objects obtained by cross-sectional contours
Ranging from quick and practical to theoretically robust, here are three approaches you could use:
Construct a regular grid where each cell contains a list of triangles that intersect it. Given a query point, in constant time determine the cell that contains it, then compare your query point against only those triangles that are in that cell's list.
Construct a quadtree where each leaf cell contains the triangles that intersect it. Localizing the query point to a quadtree leaf takes logtime, but this can be more efficient in both speed and memory overall.
Sweep a horizontal line down across all the triangles. Points in your point sets correspond to events. At each event, some triangles begin intersecting the sweepline, and other triangles stop intersecting the sweepline. You can use an immutable (aka persistent) sorted map data structure to efficiently represent this. map<double, sweepstate>, where the key is the y-intercept of the sweepline at an event and sweepstate is a sorted list of line segment pairs (corresponding to the left and right sides of triangles). Given a query point, you first use its y value to lookup a sweepstate, and then you do a single trapezoid containment test. (Two horizontal sweeplines and two line segments between them form a trapezoid.)
A common approach to solve this point location problem is the efficient Trapezoidal Decomposition. It reduces the query time to O(Log(N)) per point, after O(N.Log(N)) preprocessing time, using O(N) space.
It could also be that the distribution of your query points allows alternative/simpler approaches.
A solution is a hierarchical tree, I.e. dendogram or hierarchical cluster. For example use the euklidian distance:http://en.m.wikipedia.org/wiki/Hierarchical_clustering. Or you can use a metric tree.
[edit: I tried to rewrote my question a bit because it seems, that nobody understands what I want... and I thought, that it is a hard algorithm only for me :) ]
Problem I am facing is joining of individual polygons. Each is a 4-point polygon. The final result is then a merge / union of two polygons.
Following image shows one version of possible result (results may vary, because that black filled part can be different for each result).
I start with something like:
Polygon one = [A,B,C,D]; // (A/B/C/D) might look like : new Point {x = 10, y = 15}
Polygon two = [E,F,G,H];
And I need an algorithm for calculating union of these two sets, so I will get result like:
Polygon total = [I,J,K,L,M,N]; // = new points
I don't have to visualize it (even when I do..), I just need the set of points defining new polygon (union of those two), because my final result will be a centroid of that merged polygon.
I already have algorithm to calculate centroid based on set of input points. But I need to get the right points first.
So far, I have found mentions about convex-hull algorithm, but I am afraid that it would generate following polygon (which is wrong):
EDIT:
So different way, how to look at this problem:
I have a program, that is able to work with objects, that are represented by 4 points. Each point has two attributes (x coordinate, y coordinate).
Then the program is able to draw lines between these points. These lines will then look like a square, rectangle or polygon.. this result depends on given coordinates, but I know, that I will be always using points, that will generate polygons. Once the points are connected, the program is able to fill this connected area. Once this is drawn, you can see following image:
BUT: The program doesn't know, that he just made a polygon. He only knows, that he got 4 points from me, he connected them and filled them.
Then I have second object (=polygon), which is defined by another set of points (different coordinates). Program again doesn't know that he's creating a filled polygon.. he just did some operations with 4 given points. Result in this case is another polygon:
Now, we just draw two polygons at display.. and we gave them such coordinates, that they overlap each other. The result looks like this (considering only the filled area):
My program just draw two polygons. Fine. You can see at your screen only one polygon (because there are two overlaping = they look like one piece) and I need to count the centroid of that ONE piece.
I already have an algorithm, that will accept a set of points (representing a points forming polygon) and counting a centroid from these points. But I can't use the algorithm now, because I can't give him the needed points, because I do not know them.
Here are the points, that I want as a result:
So my algorithm should start with points A,B,C,D,E,F,G,H and he should give me points I,J,K,L,M,N as a result.
Summary: I need to count a centroid of polygon which is result of union/merge of two individual polygons, that are overlapping.
And I thought, that union of two polygons would be enough to explain :)
Here http://www.codeproject.com/KB/recipes/Wykobi.aspx is a collection of Computational Geometry algorithms. At least you can start from there.
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.