In the latest IEEE Xtreme Competition, a problem I've tried to solve is this,
Input Two points p1(x1,y1) , p2(x2,y2) you must find the length of shortest path from p1 to p2,
for example if p1(1,1) , p2(4,4) then the shortest path has lenght of 9 edges,
I did something like depth first search, it works great if the distance between the two point is small, and take long time for example for the points (1,1) & (10,10),
And there is a limit on the points the maximum point is (12,12).
my approach is to convert the above picture to an undirected graph with all weights to 1, and then find the shortest path.
here are my function that finds the shortest path:
int minCost;
vector<int> path;
multimap<int,int> Connections;
typedef multimap<int,int>::iterator mmit;
void shortestPath(int cs){
if(cs > minCost)
return;
if(path.back() == Target){
if(cs < minCost)
minCost = cs;
return;
}
pair<mmit,mmit> it = Connections.equal_range(path.back());
mmit mit = it.first;
for( ; mit != it.second ; ++mit){
if(isVisited(mit->second))
continue;
markVisited(mit->second);
path.push_back(mit->second);
shortestPath(cs+1);
markUnvisited(mit->second);
path.pop_back();
}
}
Is there any way faster than this ?? could i use dijkstra for this undirected graph ??
Using Dijkstra or any kind of graph-based search seems like total overkill here. At each vertex, you just need to choose the next vertex that brings you closer to your target.
So you start in the centre of (1,1). You need to choose the starting vertex. Obviously this is the one in the south-east.
From there, you have three choices: move west, move north-east or move south-east. This is in fact the choice you have at every second vertex. You choose the direction that brings you closer (ie, subtends smallest angle with your target).
In fact, you can represent all your vertex co-ordinates in a sort of cheaty way. Notice that they are all roughly half-way between the hexagon coordinates. So you could say the first vertex is at (1.5,1.33).
Your possible moves at the first co-ordinate are the directions:
west = (0, -0.67)
north-east = (-0.5, 0.33)
south-east = (0.5, 0.33)
Let's call that the odd-movement. Now, the even-movement you have these choices:
east = (0, 0.67)
north-west = (-0.5, -0.33)
south-west = (0.5, -0.33)
So all you have to do is, for each possible direction, test the new distance (as the crow flies -- ie pythagoras) to your target. Choose the new vertex that has the smallest distance of the three choices. Obviously you don't have to compute the actual distance -- distance squared is fine.
You can figure out the initial and final moves, I'm sure.
One final point, obviously I'm using double arithmetic which is imprecise. You can scale all your directions (and of course your hexagon co-ordinates) by 6 and use integers.
Related
I have N points in a 2D cartesian space loaded in a boost:rtree.
Given a random point P(x,y) not in the tree, I need to find an effective way to identify the nearest point for each of the four quadrant of generated by the local csys centered in P and parallel to the main csys
As shown in the image (linked above), given the red point I need to find the four purple points.
I tried this naive approach:
namespace bg = boost::geometry;
typedef bg::model::box<point> box;
vector<item> result_s;
vector<item> result_p;
int xres = 10; /*this is a fixed amount that is loosely related to the points distribution*/
int yres = 10; /*as for xres*/
int range = 10;
int maxp = 30;
/*
* .. filling the tree
*/
box query_box2(point(lat, lon), point(lat-range*yres, lon+range*xres));
rtree.query(bgi::intersects(query_box2) && bgi::nearest(p, maxp), std::back_inserter(result_p));
if(result_p.size()>0) result_s.push_back(result_p[0]);
result_p.clear();
box query_box1(point(lat, lon), point(lat+range*yres, lon+range*xres));
rtree.query(bgi::intersects(query_box1) && bgi::nearest(p, maxp), std::back_inserter(result_p));
if(result_p.size()>0) result_s.push_back(result_p[0]);
result_p.clear();
box query_box3(point(lat, lon), point(lat+range*yres, lon-range*xres));
rtree.query(bgi::intersects(query_box3) && bgi::nearest(p, maxp), std::back_inserter(result_p));
if(result_p.size()>0) result_s.push_back(result_p[0]);
result_p.clear();
box query_box4(point(lat, lon), point(lat-range*yres, lon-range*xres));
rtree.query(bgi::intersects(query_box4) && bgi::nearest(p, maxp), std::back_inserter(result_p));
if(result_p.size()>0) result_s.push_back(result_p[0]);
result_p.clear();
if(result_s.size()>3)
cout << "OK!" << endl;
else
cout << "KO" << endl;
but often it end up with an empty result (KO)
Any suggestion or address will be very appreciated.
Tnx.
I would do an iterated nearest query.
It will produce nearest points ordered by distance ascending.
You can cancel it after you received at least 1 point in all quadrants.
In principle the time complexity of this approach is MUCH lower because it involves only a single query.
Worst case behaviour would iterate all points in the tree e.g.
if one quadrant doesn't contain any points, or
when all the points in one quadrant are actually closer than the closest point in another quadrant.
Seems like the former might not be possible in your model (?) and the latter is statistically unlikely with normal distributions. You'd have to check your domains expected point distributions.
Or, and this always applies: MEASURE and compare the effective performance
Use a modified distance function. More precisely, use four.
The main idea is to use a distance such that
d(v1,v2) = infinity if v2.x < v1.x
d(v1,v2) = infinity if v2.y < v1.y
d(v1,v2) = (v1.x-v2.x)²+(v1.y-v2.y)² otherwise
If you search for the nearest point with this distance, it must be in the top right quadrant.
You'll need to extend this logic to minDist when searching the tree.
The benefit is that it can stop searching a quadrant when it has found a point. Pages that overlap the "axes" may be expanded twice though.
In CGAL I need to compute the exact intersection points between a set of lines and a set o circles. Starting from the circles (which can have irrational radius but rational squared_radius) I should compute the vertical line passing through the x_extremal_points of each circle (not segment but lines) and calculate the intersection point of each circle with each line.
I’m using CircularKernel and Circle_2 for the circles and Line_2 for the lines.
Here’s an example of how I compute the circles and the lines and how I check if they intersect.
int main()
{
Point_2 a = Point_2(250.5, 98.5);
Point_2 b = Point_2(156, 139);
//Radius is half distance ab
Circular_k::FT aRad = CGAL::squared_distance(a, b);
Circle_2 circle_a = Circle_2(a, aRad/4);
Circular_arc_point_2 a_left_point = CGAL::x_extremal_point(circle_a, false);
Circular_arc_point_2 a_right_point = CGAL::x_extremal_point(circle_a, true);
//for example use only left extremal point of circle a
CGAL::Bbox_2 a_left_point_bb = a_left_point.bbox();
Line_2 a_left_line = Line_2(Point_2(a_left_point_bb.xmin(), a_left_point_bb.ymin()),
Point_2(a_left_point_bb.xmin(), a_left_point_bb.ymax()));
if ( do_intersect(a_left_line, circle_a) ) {
std::cout << "intersect";
}
else {
std::cout << " do not intersect ";
}
return 0;
}
This flow rises this exception:
CGAL error: precondition violation!
Expression : y != 0
File : c:\dev\cgal-4.7\include\cgal\gmp\gmpq_type.h
Line : 371
Explanation:
Refer to the bug-reporting instructions at http://www.cgal.org/bug_report.html
I can’t figure out how I can calculate the intersection points.
Also, Is there a better way to compute the lines? I know abot the x_extremal_point function but it returns the Circular_arc_point point and I’m not able to construct a vertical line passing through them directly without using Bounding box.
In your code, you seem to compute the intersection of a circle with the vertical line that passes through the extremal point of the circle (I forget the bounding box). Well, then the (double) intersection is the extremal point itself...
More globally, you say in your text of introduction that you want to compute exact intersections. Then you should certainly not use bounding boxes, which by definition introduce some approximation.
If I understand your text correctly,
* for testing the intersection of your vertical lines with the other circles, you don't need to construct the lines, you only need to compare the abscissae of the extremal points of two circles, which you can do with the CGAL circular kernel.
* for computing the intersection of a vertical line that has non-rational coefficients (since its equation is of the form x= +-sqrt(r)) with another circle, then the CGAL circular kernel will not give you a pre-cooked solution. That kernel will help, but you must still compute a few things by hand.
If you don't want to bother, then you can also just take a standard CGAL kernel with Core::Expr as underlying number type. It can do "anything", but it will be slower.
For efficiency, you should look at the underlying 1D problem: projecting the lines and the circle on the X axis, you have a set of points and a set of intervals [Xc-R, Xc+R].
If the L points are sorted increasingly, you can locate the left bound of an interval in time Lg(L) by dichotomy, and scan the list of points until the right bound. This results in a O(Lg(L).C + I) behavior (C circle intervals), where I is the number of intersections reported.
I guess that with a merge-like process using an active list, if the interval bounds are also sorted you can lower to O(L + C + I).
The extension to 2D is elementary.
Im currently having a small problems , because i need to find the coordinates corresponded to my min distance.
For example i have a person and three potential stations. I need to find the distance between them and then take the min distance and in the next i have to take the coordinates related to that min distance.
Im sending you my loop just for that part of my program.
for (newCoordinate = newPoints.begin(); newCoordinate != newPoints.end(); newCoordinate++)// here are my three potential stations
{
distanceBetweenBusCoordinateAndNewPoint = sqrt(pow((busRequest->x - newCoordinate->x), 2) + (pow((busRequest->y - newCoordinate->y), 2)));// find min
distances.push_back(distanceBetweenBusCoordinateAndNewPoint);//add to the vector
}
newdis = min_element(distances.begin(), distances.end());// find the min of min
Thank you for you help!
I would treat this as finding a minimum value in a std::vector.
One variable contains the minimum distance; initialized to the largest value.
Two Point variables define the straight line associated with the minimum distance.
Here's an algorithm:
Calculate distance.
if distance < minimum distance
set Point1 and Point2 to the ordinates used in the distance calculation.
set minimum distance to the calculated distance.
At the end of the loop, the variables Point1 and Point2 will be the coordinates associated with the minimum distance.
I'am trying to find out an algorithm to recognize circle in array of points.
Lets say that I've got points array where circle could or could not be stored (that also means array doesn't have to store only circle's points, there could be some "extra" points before or after circle's data).
I've already tried some algorithms but none of them work properly with those "extra" points. Have you got any ideas how to deal with this problem?
EDIT// I didn't mention that before. I want this algorithm to be used on circle gesture recognition. I've thought I would have data in array (for last few seconds) and by analysing this data in every tracking frame I would be able to say if there was or was not a circle gesture.
First I calculate the geometric mean (not the aritmetic mean) for each X and Y component.
I choose geometric mean because one feature is that small values (with respect to the arithmetic mean ) of the values are much more influential than the large values.
This lead me to the theoretical center of all points: circ_center
Then I calculate the standard deviation of distance of each point to center: stddev. This gives me the "indicator" to quantify the amount of variation. One property of circle is that all circumference point is at the same distance of it's center. With standard dev I try to test if your points are (with max variance threshold: max_dispersion) equally distance.
Last I calculates the average distance of points inside max_dispersion threshold from center, this give me the radius of the circle: avg_dist.
Parameters:
max_dispersion represents the "cicle precision". Smaller means more precise.
min_points_needed is the minimun number of points valid to be considered as circumference.
This is just an attempt, I have not tried. Let me know.
I will try this (in pseudo language)
points_size = 100; //number_of_user_points
all_poins[points_size]; //coordinates of points
//thresholds to be defined by user
max_dispersion = 20; //value of max stddev accepted, expressed in geometric units
min_points_needed = 5; //minimum number of points near the circumference
stddev = 0; //standard deviation of points from center
circ_center; //estimated circumference center, using Geometric mean
num_ok_points = 0; //points with distance under standard eviation
avg_dist = 0; //distance from center of "ok points"
all_x = 1; all_y = 1;
for(i = 0 ; i < points_size ; i++)
{
all_x = all_x * all_poins[i].x;
all_y = all_y * all_poins[i].y;
}
//pow(x, 1/y) = nth root
all_x = pow(all_x, 1 / points_size); //Geometric mean
all_y = pow(all_y, 1 / points_size); //Geometric mean
circ_center = make_point(all_x, all_y);
for(i = 0 ; i < points_size ; i++)
{
dist = distance(all_poins[i], circ_center);
stddev = stddev + (dist * dist);
}
stddev = square_root(stddev / points_size);
for(i = 0 ; i < points_size ; i++)
{
if( distance(all_poins[i], circ_center) < max_dispersion )
{
num_ok_points++;
avg_dist = avg_dist + distance(all_poins[i], circ_center);
}
}
avg_dist = avg_dist / num_ok_points;
if(stddev <= max_dispersion && num_ok_points >= min_points_needed)
{
circle recognized; it's center is circ_center; it's radius is avg_dist;
}
Can we assume the array of points are mostly on or near to the circumference of the circle?
A circle has a center and radius. If you can determine the circle's center coordinates, via the intersection of perpendiculars of two chords, then all the true circle points should be equidistant(r), from the center point.
The false points can be eliminated by not being equidistant (+-)tolerance from the center point.
The weakness of this approach is how well can you determine the center and radius? You may want to try a least squares approach to computing the center coordinates.
To answer the initially stated question, my approach would be to iterate through the points and derive the center of a circle from each consecutive set of three points. Then, take the longest contiguous subset of points that create circles with centers that fall within some absolute range. Then determine if the points wind consistently around the average of the circles. You can always perform some basic heuristics on any discarded data to determine if a circle is actually what the user wanted to make though.
Now, since you say that you want to perform gesture recognition, I would suggest you think of a completely different method. Personally, I would first create a basic sort of language that can be used to describe gestures. It should be very simple; the only words I would consider having are:
Start - Denotes the start of a stroke
Angle - The starting angle of the stroke. This should be one of the eight major cardinal directions (N, NW, W, SW, S, SE, E, NE) or Any for unaligned gestures. You could also add combining mechanisms, or perhaps "Axis Aligned" or other such things.
End - Denotes the end of a stroke
Travel - Denotes a straight path in the stroke
Distance - The percentage of the total length of the path that this particular operation will consume.
Turn - Denotes a turn in the stroke
Direction - The direction to turn in. Choices would be Left, Right, Any, Previous, or Opposite.
Angle - The angle of the turn. I would suggest you use just three directions (90 deg, 180 deg, 270 deg)
Tolerance - The maximum tolerance for deviation from the specified angle. This should have a default of somewhere around 45 degrees in either direction for a high chance of matching the angle in a signature.
Type - Hard or Radial. Radial angles would be a stroke along a radius. Hard angles would be a turn about a point.
Radius - If the turn is radial, this is the radius of the turn (units are in percentage of total path length, with appropriate conversions of course)
Obviously you can make the angles much more fine, but the coarser the ranges are, the more tolerant of input error it can be. Being too tolerant can lead to misinterpretation though.
If you apply some fuzzy logic, it wouldn't be hard to break just about any gesture down into a language like this. You could then create a bunch of gesture "signatures" that describe various gestures that can be performed. For instance:
//Circle
Start Angle=Any
Turn Type=Radial Direction=Any Angle=180deg Radius=50%
Turn Type=Radial Direction=Previous Angle=180deg Radius=50%
End
//Box
Start Angle=AxisAligned
Travel Distance=25%
Turn Type=Hard Direction=Any Angle=90deg Tolerance=10deg
Travel Distance=25%
Turn Type=Hard Direction=Previous Angle=90deg Tolerance=10deg
Travel Distance=25%
Turn Type=Hard Direction=Previous Angle=90deg Tolerance=10deg
Travel Distance=25%
End
If you want, I could work on an algorithm that could take a point cloud and degenerate it into a series of commands like this so you can compare them with pre-generated signatures.
I have a parametric curve, say two vectors of doubles where the parameter is the index, and I have to calculate the angle of the tangent to this curve at any given point (index).
Any suggestion or link about how to do that?
Thanks.
Here's a short formula, equivalent (I think) to pau.estalella's answer:
m[i] = (y[i+1] - y[i-1]) / (x[i+1] - x[i-1])
this approximates, reasonably well, the slope at the point (x[i], y[i]).
Your question mentions the "angle of the tangent". The tangent line, having slope m[i], makes angle arctangent(m[i]) with the positive x axis. If this is what you're after, you might use the two-argument arctangent, if it's available:
angle[i] = atan2(y[i+1] - y[i-1], x[i+1] - x[i-1])
this will work correctly, even when x[i+1] == x[i-1].
I suggest you check out the Wikipedia article on numerical differentiation for a start. Before you go much further than that, decide what purposes you want the tangent for and decide whether or not you need to try more complex schemes than the simple ones in the article.
The first problem you run into is to even define the tangent in one of the vertexes of the curve. Consider e.g. that you have the two arrays:
x = { 1.0, 2.0, 2.0 };
y = { 1.0, 1.0, 2.0 };
Then at the second vertex you have a 90-degree change of direction of the line. In that place the tangent isn't even defined mathematically.
Answer to gregseth's comment below
I guess in your example the "tangent" at the second point would be the line parallel to (P0,P2) passing through P1... which kind of give me the answer : for any point of index N the parallel to (N-1, N+1) passing through N. Would that be a not-too-bad approximation?
It depends on what you are using it for. Consider for example:
x = { 1.0, 2.0, 2.0 };
y = { 1.0, 1000000, 1000000 };
That is basically an L shape with a very high vertical line. In your suggestion it would give you an almost vertical tangent. Is that what you want, or do you rather want a 45-degree tangent in that case? It also depends on your input data how you sould define it.
One solution is get the two vectors connection to the vertex, normalize them and then use your algorithm. That way you would get a 45-degree tangent in the above example.
Compute the first derivative: dy/dx. That gives you the tangent.
The tangent to a smooth curve at a point P is the parametric straight line P + tV, where V is the derivative of the curve with respect to "the parameter". But here the parameter is just the index of an array, and numerical differentiation is a difficult problem, hence to approximate the tangent I would use (weighted) least squares approximation.
In other words, choose three or five points of the curve around your point of interest P (i.e. P[i-2], P[i-1], P[i], P[i+1], and P[i+2], if P==P[i]), and approximate them with a straight line, in the least squares sense. The more weight you assign to the middle point P, the more close the line will be to P; on the other hand, the more weight you assign to the extremal points, the more "tangent" the straight line will be, that is, the more nicely it will approximate you curve in the neighborhood of P.
For example, with respect to the following points:
x = [-1, 0, 1]
y = [ 0, 1, 0]
for which the tangent is not defined (as in Anders Abel's answer),
this approach should yield a horizontal straight line close to the point (0,1).
You can try to compute the tangent of an interpolating curve that passes through the given points (I'm thinking of a cubic spline, which is pretty easy to derive) or compute the tangent directly from the data points.
You can find a rough approximation of the derivative in the following manner
Let a curve C pass through points p1,p2 and p3. At point p2 you have two possible tangents: t1=p2-p1 and t2=p3-p2. You can combine them by simply computing their average: 0.5*(t1+t2)
or you can combine them according to their lengths (or their reciprocal 1/length)
Remember to normalize the resulting tangent.
In order to compute the angle between the tangent and the curve, remember that the dot product of two unit vectors gives the cosine of the angle between them. Take the resulting tangent t and the unit vector v2=|p3-p2|, and acos(dot(t,v2)) gives the angle you need.