I have xy grid, where points have whole-numbered, positive coordinates. These points are "neighbours" if both their x and y coordinates differ by less than 2 - they are simply next to each other.
In this grid, I have found path that encloses some area. Every point in the path is neighbour to the previous point and the next point, so it is sorted like you walked around the enclosed area. It is also shortest path around that enclosed area, so there are no steps back and forth. The enclosed area does not need to be convex, so when you connect two random points on the path with one line, that line can lie completely out of that area.
The problem is, I need to find at least one point in enclosed area, that is neighbour to any point in enclosing path.
It might sound easy, but I have not found solid algorithm to determine it.
* I'm sorry, I did not explain that well enough. There are no "empty parts" in enclosed area. If there are three points in enclosed area, the path around is the smallest path that captures those three points. In this picture, red path is shortest, black is too long and I never need to detect those.
Observations to check that I understand the question:
It's possible that there are no such interior points.
Given that consecutive path point are neighbors, the connection
angles will be at multiples of 45°.
The basic idea is to walk the path and keep track of interior and exterior neighbors.
To determine which side is the interior, walk the path and keep track of the cumulative turn angle. The final amount will be 360° or -360° which will indicate the interior is on the left or the right, depending on which way you defined positive and negative angles.
During that first pass, also collect a hash set of the points on the path, onPathPoints.
Initialize two other hash sets to empty: exteriorPoints and possibleInteriorPoints.
Walk the path and for each point:
a. categorize the 8 neighbors as on-path, interior-side or exterior-side based on the relative positions of the previous and next points on the path.
b. for each point in onPathPoints, ignore it.
c. for any point on the interior side and distance 1 away, return that point as the result.
d. for each points on the interior side with distance > 1 (diagonal neighbor), add it to possibleInteriorPoints.
e. for each point on the exterior side and distance 1 away, add it to exteriorPoints.
At the end of the walk, remove from possibleInteriorPoints any point in exteriorPoints (set subtraction). Return any point remaining in possibleInteriorPoints as an interior point.
Otherwise, there are no interior points.
The possibleInteriorPoints represent diagonally neighboring points that are in the interior unless the path loops back between the original point and it (in which case it will be an exterior point to the new path part.
For example in the image below, when visiting (2,2) and going toward (3,3) the interior is on the right. (3,1) is a possible interior point, but later in the walk while visiting (4,1), (3,1) is noted to be an exterior point and so would get removed from possibleInteriorPoints.
Technically, in this example the algorithm would have stopped when visiting (4,3) and noting (4,2) as an interior point at distance 1.
Related
How to place n points in a plane so that the distance between every two points is unique and at the same time we could choose one of them that is the closest for all others n-1 points.
I tried to draw it. But I was able to draw it only for max n=5. I drew two perpendicular lines. One point I was intersection of the lines and the others points lie on the line so that they formed a quadrilateral and point I was inside. With more points it seems impossible to me but I can't prove why
I have 5 {x,y} points randomly placed on a grid
Each of the points do not know the {x,y} coordinates of the other points
Each of the points do know the distance of each of the other points from their {x,y} position
Each of the points exchanges this distance information with every other point
So every point knows every distance of every other point
Using this distance information every point can calculate (by finding the angles) triangles for every other point using itself as a reference point
Example, point 1 can calculate the following triangles:
1-2-3,
1-2-4,
1-2-5,
1-3-4,
1-3-5,
1-4-5,
and using the distance data recieved from the other points it can also calculate
2-3-4,
2-3-5,
2-4-5,
3-4-5
I would like to build a map of the location of every other point relative to a single point
How should I go about doing this? I am asuming it would be some kind of triangulation algorithm but these mainly seem to compute the location of a point from three other points, not the other way around where the other points {x,y} coordinates are discovered based on only the distance information.
I have tried plotting the two possible triangles for every 3 triangle points and then rotating them on a fixed known point to try and align them, but I think this avenue will end up with too many possibilities and errors
Ultimately I would like every point to end up with {x,y} coordinates of every other point relative to itself
You know the distance from one point to every other, dij. Thus, point 2 lies in a circumference of center point 1 and radius = d12. Point 3 lies in a circumference of center point 1 and R=d13 and it also lies in another circumference of center point 2 and R=d23.
See this picture:
I've set point 2 in X-axis for simplicity.
As you see, point 3 is on the intersection of two cicrcumferences centered at P1 and P2. There is a second intersection, P3a. Let's choose the one that is upwards and continue.
For P4 we can use three circumferences, centered at P1, P2 and P3. Again we get two solutions.
The same process can be done with the rest of points. For Pn you have n-1 circumferences.
I'm sure you can find the maths for circle-circle intersection.
Some remarks must be observed:
1) The construction is simpler if you first sort the points by distance to P1.
2) Not all distances generate a solution. For example, increase d13 an there's no intersection between the two circumferences for P3. Or increase d14 and now the three circumferences don't intersect in just the two expected points 4 and 4a.
3) This fact can be overworked by considering the average of intersections and the distance from each solution to this average. You can set a tolerance in these distances and tell if the average is a solution or else some dij is wrong. Since two solutions are possible, you must consider two averages.
4) The two possible triangulations are symmetric, over X-axis in the case I've drawn.
The real solution is obtained by a rotation around P1. To calculate the angle of rotation you need the {x,y} coordinates of another point.
I have a set of 2D points, unorganized, and I want to find the "contour" of this set (not the convex hull). I can't use alpha shapes because I have a speed objective (less than 10ms on an average computer).
My first approach was to compute a grid and find the outline squares (squares which have an empty square as a neighbor). So I think I downsized efficiently my numbers of points (from 22000 to 3000 roughly). But I still need to refine this new set.
My question is : how do I find the real outlines points among my green points ?
After a weekend full of reflexions, I may have found a convenient solution.
So we need a grid, we need to fill it with our points, no difficulty here.
We have to decide which squares are considered as "Contour". Our criteria is : at least one empty neighbor and at least 3 non empty neighbors.
We lack connectivity information. So we choose a "Contour" square which as 2 "Contour" neighbors or less. We then pick one of the neighbor. From that, we can start the expansion. We just circle around the current square to find the next "Contour" square, knowing the previous "Contour" squares. Our contour criteria prevent us from a dead end.
We now have vectors of connected squares, and normally if our shape doesn't have a hole, only one vector of connected squares !
Now for each square, we need to find the best point for the contour. We select the one which is farther from the barycenter of our plane. It works for most of the shapes. Another technique is to compute the barycenter of the empty neighbors of the selected square and choose the nearest point.
The red points are the contour of the green one. The technique used is the plane barycenter one.
For a set of 28000 points, this techniques take 8 ms. CGAL's Alpha shapes would take an average 125 ms for 28000 points.
PS : I hope I made myself clear, English is not my mothertongue :s
You really should use the alpha shapes. Maybe use only green points as inputs of the alpha alpha algorithm.
I'm experimenting with a vector based graphics style with objects represented as series of line segments with a given width(it would probably be easier to think of these as rectangles). The problem is that these segments are connected at the center and leave a gap (shown below). I've determined that the most efficient way to cover this gap is simply to cover it with a triangle, and since I'm working in OpenGL, all I need are the points of the two points that don't overlap with the other rectangle, the third point being the center point where the two line segments(rectangles) are connected. How can I determine which points I need to use for the triangle, given that I have all of the points from both rectangles?
EDIT: I will also accept alternative solutions, as long as they cover up that gap.
EDIT 2: Nevermind, I solved it. I'll post code once I have better Internet connection.
Maybe I'm misunderstanding the question... but if you zoom in on the top corner of your red pentagon, you get something like this, am I right?
where A and B are nodes on the rectangle for edge1 and C and D are nodes on the rectangle for edge2. You say you already know these coordinates. And from what you say, the edges meet at the centre, which is halfway between A and B, and also halfway between C and D. So call this point X, and you can calculate its coordinates easily I guess.
So all you need to do is draw the missing triangle AXC, right? So one way would be to determine that A and C are on the "outside" of the polygon (and therefore need filling) and B and D are on the "inside" and therefore don't. But it's probably easier to just draw both, as it doesn't hurt. So if you fill AXC and BXD, you'd get this:
The solution I found assumes that there are 3 basic cases:
First, the three unique center points for the two rectangle proceed upward (positive y direction) so the gap is either on the left or right of the connection. In my code, I had the corner points of the rectangle organized by their orientation to the left or right of the center point, so if the bottom rectangle's left point is below the top rectangle's left point, then the gap is between the left points of the two rectangles, otherwise the gap is between the right points.
Second, the three unique center points have a maximum at the center most of the center points, so the gap is on the top. The gap is then between the two points with the maximum y values.
Third, the three unique center points have a minimum at the center most of the center points, so the gap is on the bottom. The gap is then between the two points with the minimum y values.
[I'll post pictures of the example cases if it is requested]
In an infinite 2D space there are a set of lines, each line having a start and end point, and a time of creation: Line(p0, p1, t).
I want to find the lines that should be rendered in a top-down view of this 2D space (higher values of t show up closer to the viewport, not that it should be relevant.)
The intuitive answer is "check if either point is within the viewport coordinates," but this falls down when the points are further apart than the viewport area covers.
The other idea I had was using something like geohash, this would limit precision i.e. maximum zoom level of the viewport. The idea is enumerating the hashes of the cells intersected and storing them. This way querying is a matter of asking the right question.
Are there any ideal solutions? Has this been solved before?
I think you need to check two conditions: one that the rectangle of viewport overlaps the rectangle with corners (p0,p1) and the second that some corners of viewport rectangle are on the different sides of the whole line which contains line segment (p0,p1).
The task of finding rectangle overlap can be solved very effectively for very large number of rectangles using R-trees (http://en.wikipedia.org/wiki/R-tree).
The second task can be reduced to checking signs of the cross product of (p1-p0) x (corner_coordinate-p0)
(all three quantities taken as 3-d vectors with third coordinate equal to zero, the result will be vector along the perpendicular direction). There should be corners with the opposite sign of this cross product.