How to find the biggest possible polygon of a group of points?
E.g. the following points are given:
0 | 3
4 | 0
1 | 1
0 | 0
-> Use the 1st, 2nd and 4th point to build a polygon (3rd point is useless)
What you seems to be looking for is the Convex Hull.
Example:
The Gift Wrapping Algorithm is the easiest way to calculate the Convex Hull, but it isn't optimal.
Chan's algorithm is probably the simplest optimal algorithm.
HTH!
Related
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'm currently drawing up a mock database schema with two tables: Booking and Waypoint.
Booking stores the taxi booking information.
Waypoint stores the pickup and drop off points during the journey, along with the lat lon position. Each sequence is a stop in the journey.
How would I calculate the distance between the different stops in each journey (using the lat/lon data) in Excel?
Is there a way to programmatically define this in Excel, i.e. so that a formula can be placed into the mileage column (Booking table), lookup the matching sequence (via bookingId) for that journey in the Waypoint table and return a result?
Example 1:
A journey with 2 stops:
1 1 1 MK4 4FL, 2, Levens Hall Drive, Westcroft, Milton Keynes 52.002529 -0.797623
2 1 2 MK2 2RD, 55, Westfield Road, Bletchley, Milton Keynes 51.992571 -0.72753
4.1 miles according to Google, entry made in mileage column in Booking table where id = 1
Example 2:
A journey with 3 stops:
6 3 1 MK7 7DT, 2, Spearmint Close, Walnut Tree, Milton Keynes 52.017486 -0.690113
7 3 2 MK18 1JL, H S B C, Market Hill, Buckingham 52.000674 -0.987062
8 3 1 MK17 0FE, 1, Maids Close, Mursley, Milton Keynes 52.040622 -0.759417
27.7 miles according to Google, entry made in mileage column in Booking table where id = 3
If you want to find the distance between two points just use this formula and you will get the result in Km, just convert to miles if needed.
Point A: LAT1, LONG1
Point B: LAT2, LONG2
ACOS(COS(RADIANS(90-Lat1)) *COS(RADIANS(90-Lat2)) +SIN(RADIANS(90-Lat1)) *SIN(RADIANS(90-lat2)) *COS(RADIANS(long1-long2)))*6371
Regards
Until quite recently, accurate maps were constructed by triangulation, which in essence is the application of Pythagoras’s Theorem. For the distance between any pair of co-ordinates take the square root of the sum of the square of the difference in x co-ordinates and the square of the difference in y co-ordinates. The x and y co-ordinates must however be in the same units (eg miles) which involves factoring the latitude and longitude values. This can be complicated because the factor for longitude depends upon latitude (walking all round the North Pole is less far than walking around the Equator) but in your case a factor for 52o North should serve. From this the results (which might be checked here) are around 20% different from the examples you give (in the second case, with pairing IDs 6 and 7 and adding that result to the result from pairing IDs 7 and 8).
Since you say accuracy is not important, and assuming distances are small (say less than 1000 miles) you can use the loxodromic distance.
For this, compute the difference of latitutes (dlat) and difference of longitudes (dlon). If there were any chance (unlikely) that you're crossing meridian 180º, take modulo 360º to ensure the difference of longitudes is between -180º and 180º. Also compute average latitude (alat).
Then compute:
distance= 60*sqrt(dlat^2 + (dlon*cos(alat))^2)
This distance is in nautical miles. Apply conversions as needed.
EXPLANATION: This takes advantage of the fact that one nautical mile is, by definition, always equal to one minute-arc of latitude. The cosine corresponds to the fact that meridians get closer to each other as they approach the poles. The rest is just application of Pythagoras theorem -- which requires that the relevant portion of the globe be flat, which is of course only a good approximation for small distances.
It all depends on what the distance is and what accuracy you require. Calculations based on "Earth locally flat" model will not provide great results for long distances but for short distance they may be ok. Models assuming Earth is a perfect sphere (e.g. Haversine formula) give better accuracy but they still do not produce geodesic grade results.
See Geodesics on an ellipsoid for more details.
One of the high accuracy (fraction of a mm) solutions is known as Vincenty's formulae. For my Excel VBA implementation look here https://github.com/tdjastrzebski/Vincenty-Excel
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 found many implementations that deal with splitting a polygon by a given line, but I only need to split a Quad (rectangle with 4 vertexes).
Is there an algorithm optimized for this task? Simplicity is valued over performance.
I narrowed down 4 types of intersection:
Adjacent
Where the line enters one side and leaves through an adjacent side.
This will generate 1 polygon with 3 points and 1 polygon with 5 points.
Opposite
Where the line enters one side and leaves through the opposite side.
This will generate 1 polygon with 4 points and 1 polygon with 4 points.
Diagonal Opposite
Where the line enters one corner and leaves through the opposite corner.
This will generate 1 polygon with 3 points and 1 polygon with 3 points.
Diagonal Adjacent
Where the line enters one corner and leaves through an adjacent side.
This will generate 1 polygon with 3 points and 1 polygon with 4 points.
But so far I was unable to come up with a good simple algorithm.
There are a lot of effective algorithms to clip a line by rectangular window.
I've used Liang-Barski one for my purposes (check "External Links" section for effective implementation)
Where would i go to look for algorithms that take a 2d grid of values that are either 0 or 1 as input and then identifies all possible non-overlapping rectangles in it?
In a more practical explanation: I am drawing a grid that is represented by a number of squares, and i wish to find a way to combine as many adjacent squares into rectangles as possible, in order to cut down on the time spent on cycling through each square and drawing it.
Maximum efficiency is not needed, speed is more important.
Addendum: Apparently what i am looking for seems to be a technique called Tesselation. Now i only need to find a good description for this specific case.
Addendum 2: The boundary of the "1" squares will be irregular and in some cases not even connected, as the distribution of "1" squares will be completely random. I need these irregular shapes to be identified and split up into regular rectangles.
Correct answer: To get the best balance between speed and efficiency it is optimal to use the grid data to fill a quad-tree with each node having a status value of either empty/partly filled/filled.
I've done something similar for a quick-and-dirty voxel visualization of 3d boxes with OpenGL.
I started from the top left box and stored the empty/filled flag. Then I tried to expand the rectangle to the right until I hit a box with a different flag. I did the same in the down direction.
Draw the rectangle, if it is filled.
If there are boxes remaing, recursivly repeat the procedure for all three remaing rectangles induced by the last rectangle, which are right, bottom and bottom right:
xxxx 1111
xxxx 1111
xxxx 1111
2222 3333
2222 3333
2222 3333
Have a look at this article from Dr Dobb's Portal on finding a maximal rectangle in your situation. It is a very detailed discussion of an extremely efficient algorithm, and I think that repeating it iteratively would possibly solve your problem.
As you are not looking for the minimum number of squares I would suggest using a compromise that still keeps your algorithm simple.
What the best solution is depends on your data, but one simple alternative is to just collect boxes along one row. I.e:
0 0 1 1 1 0 0 0 1 1 1 1 0
Will result in:
skip 2
draw 3
skip 3
draw 4
skip 1
This will reduce the number of calls to draw box without any need of caching (i.e you can build your boxes on the fly).
If you want to create bigger boxes I would suggest a backtracking algorithm there you find the first 1 and try to expand the box in all directions. Build a list of boxes and clear the 1:s as you have used them.
So you are looking for the rectangular boundary of the 'ON' squares?
Do you want the inner or outer bound?
ie. Must the boundary only have 'ON' squares or do you want the rectangle to contain all the 'ON' squares in a group?
I had to solve a similar problem, my algorithm supports jagged arrays, I have heavily tested and commented it but it's slower than joel-in-gö's suggestion :
https://stackoverflow.com/a/13802336