I want to improve a collision system.
Right now I detect if 2 irregular objects collide if their bounding rectangles collide.
I want to obtain the for rectangle the corresponding ellipse while for the other one to use a circle. I found a method to obtain the ellipse coordinates but I have a problem when I try to detect if it intersects the circle.
Do you know a algorithm to test if a circle intersects an ellipse?
Short answer: Solving exactly for whether the two objects intersect is complicated enough to be infeasible for the purpose of collision detection. Discretize your ellipse as an n-sided polygon for some n (depending on how accurate you need to be) and do collision detection with that polygon.
Long answer: If you insist on determining if the smooth ellipse and circle intersect, there are two main approaches. Both involve solving first for the closest point to the circle's center on the ellipse, and then comparing that distance to the circle's radius.
Approach 1: Use a parametrization of the ellipse. Transform your coordinates so that the ellipse is at the origin, with its axes aligned to the x-y axes. That is:
Center of ellipse: (0,0)
Center of circle: c = (cx, cy)
Radius of circle: r
Radius of x-aligned axis of ellipse: a
Radius of y-aligned axis of ellipse: b.
The equation of the ellipse is then given by a cos(t), b sin(t). To find the closest point, we want to minimize the square distance
|| (a cos t, b sin t) - c ||^2. As Jean points out, this is "just calculus": take a derivative, and set it equal to 0. Unless I'm missing something, though, solving the resulting (quite nasty) equation for t is not possible analytically, and must be approximated using e.g. Newton's Method.
Plug in the t you find into the parametric equation to get the closest point.
Pro: Numerical solve is only in one variable, t.
Con: You must be able to write down a parametrization of the ellipse, or transform your coordinates so that you can. This shouldn't be too hard for any reasonable representation you have of the ellipse. However, I'm going to show you a second method, which is much more general and might be useful if you have to generalize your problem to, say, 3D.
Approach 2: Use multidimensional calculus. No change of coordinates is necessary.
Center of circle: c = (cx, cy)
Radius of cirlce: r
Ellipse is given by g(x, y) = 0 for a function g. For instance, per Curd's answer you might use g(x,y) = distance of (x,y) from focus 1 + distance of (x,y) from focus 2 - e.
Finding the point on the ellipse closest to the center of the circle can then be phrased as a constrained minimization problem:
Minimize ||(x,y) - c||^2 subject to g(x,y) = 0
(Minimizing the square distance is equivalent to minimizing the distance, and much more pleasant to deal with since it's a quadratic polynomial in x,y.)
To solve the constrained minimization problem, we introduce Lagrange multiplier lambda, and solve the system of equations
2 * [ (x,y) -c ] + lambda * Jg(x,y) = 0
g(x,y) = 0
Here Jg is the gradient of g. This is a system of three (nonlinear) equations in three unknowns: x, y, and lambda. We can solve this system using Newton's Method, and the (x,y) we get is the closest point to the circle's center.
Pro: No parametrization needs to be found
Pro: Method is very general, and works well whenever writing g is easier than finding a parametric equation (such as in 3D)
Con: Requires a multivariable Newton solve, which is very hairy if you don't have access to a numerical method package.
Caveat: both of these approaches technically solve for the point which extremizes the distance to the circle's center. Thus the point found might be the furthest point from the circle, and not the closest. For both methods, seeding your solve with a good initial guess (the center of the circle works well for Method 2; you're on your own for Method 1) will reduce this danger.
Potential Third Approach?: It may be possible to directly solve for the roots of the system of two quadratic equations in two variables representing the circle and ellipse. If a real root exists, the objects intersect. The most direct way of solving this system, again using a numerical algorithm like Newton's Method, won't help because lack of convergence does not necessary imply nonexistence of a real root. For two quadratic equations in two variables, however, there may exist a specialized method that's guaranteed to find real roots, if they exist. I myself can't think of a way of doing this, but you may want to research it yourself (or see if someone on stackoverflow can elaborate.)
An ellipse is defined a the set of points whose
sum of the distance to point A and the distance to point B is constant e.
(A and B are called the foci of the ellipse).
All Points P, whose sum AP + BP is less than e, lie within the ellipse.
A circle is defined as the set of points whose
distance to point C is r.
A simple test for intersection of circle and ellipse is following:
Find
P as the intersection of the circle and the line AC and
Q as the intersection of the circle and the line BC.
Circle and ellipse intersect (or the circle lies completely within the ellipse) if
AP + BP <= e or AQ + BQ <= e
EDIT:
After the comment of Martin DeMello and adapting my answer accordingly I thought more about the problem and found that the answer (with the 2nd check) still doesn't detect all intersections:
If circle and ellipse are intersecting only very scarcely (just a little more than being tangent) P and Q will not lie within the ellipse:
So the test described above detects collision only if the overlap is "big enough".
Maybe it is good enough for your practical purposes, although mathematically it is not perfect.
I know that it's too late but I hope it would help somebody. My approach to solve this problem was to interpolate the ellipse into an n-dimensions polygon, then to construct a line between every 2 points and find whether the circle intersects with any of the lines or not. This doesn't provide the best performance, but it is handy and easy to implement.
To interpolate the ellipse to an n-dimensions polygon, you can use:
float delta = (2 * PI) / n;
std::vector<Point*> interpolation;
for(float t = 0; t < (2 * PI); t += delta) {
float x = rx * cos(t) + c->get_x();
float y = ry * sin(t) + c->get_y();
interpolation.push_back(new Point(x, y));
}
c: The center of the ellipse.
rx: The radius of x-aligned axis of the ellipse.
ry: The radius of y-aligned axis of the ellipse.
Now we have the interpolation points, we can find the intersection between the circle and the lines between every 2 points.
One way to find the line-cricle intersection is described here,
an intersection occurs if an intersection occurred between any of the lines and the circle.
Hope this helps anybody.
find the point on the ellipse closest to the center of the circle
and then check if the distance from this point is smaller than the radius of the circle
if you need help doing this just comment, but it's simply calculus
edit: here's a ways towards the solution, since there is something wrong with curds
given center α β on the ellipse
and (for lack of remembering the term) x radius a, y radius b
the parametrization is
r(Θ) = (ab)/( ( (BcosΘ)^2 + (asinΘ)^2 )^.5)
x(Θ) = α + sin(Θ)r(Θ)
y(Θ) = β + cos(Θ)r(Θ)
and then just take the circle with center at (φ, ψ) and radius r
then the distance d(Θ) = ( (φ - x(Θ))^2 + (ψ - y(Θ) )^2)^.5
the minimum of this distance is when d'(Θ) = 0 (' for the derivative)
d'(Θ) = 1/d(Θ) * (-φx'(Θ) + x(Θ)x'(Θ) - ψy'(Θ) + y(Θ)y'(Θ) )
==>
x'(Θ) * (-φ + x(Θ)) = y'(Θ) * (ψ - y(Θ))
and keep going and going and hopefully you can solve for Θ
The framework you're working in might have things to help you solve this, and you could always take the easy way out and approximate roots via Newton's Method
if a circle and an ellipse collide, then either their boundaries intersect 1, 2, 3, or 4 times(or infinitely many in the case of a circular ellipse that coincides with the circle), or the circle is within the ellipse or vice versa.
I'm assuming the circle has an equation of (x - a)^2 + (y - b)^2 <= r^2 (1) and the ellipse has an equation of [(x - c)^2]/[d^2] + [(y - e)^2]/[f^2] <= 1 (2)
To check whether one of them is inside the other, you can evaluate the equation of the circle at the coordinates of the center of the ellipse(x=c, y=e), or vice versa, and see if the inequality holds.
to check the other cases in which their boundaries intersect, you have to check whether the system of equations described by (1) and (2) has any solutions.
you can do this by adding (1) and (2), giving you
(x - a)^2 + (y - b)^2 + [(x - c)^2]/[d^2] + [(y - e)^2]/[f^2] = r^2 + 1
next you multiply out the terms, giving
x^2 - 2ax + a^2 + y^2 - 2by + b^2 + x^2/d^2 - 2cx/d^2 + c^2/d^2 + y^2/f^2 - 2ey/f^2 + e^2/f^2 = r^2 + 1
collecting like terms, we get
(1 + 1/d^2)x^2 - (2a + 2c/d^2)x + (1 + 1/f^2)y^2 - (2b + 2e/f^2)y = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
now let m = (1 + 1/d^2), n = -(2a + 2c/d^2), o = (1 + 1/f^2), and p = -(2b + 2e/f^2)
the equation is now mx^2 + nx + oy^2 + py = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
now we need to complete the squares on the left hand side
m[x^2 + (n/m)x] + o[y^2 + (p/o)y] = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
m[x^2 + (n/m)x + (n/2m)^2 - (n/2m)^2] + o[y^2 + (p/o)y + (p/2o)^2 - (p/2o)^2] = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
m[(x + n/2m)^2 - (n/2m)^2] + o[(y + p/2o)^2 - (p/2o)^2] = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
m(x + n/2m)^2 - m(n/2m)^2 + o(y + p/2o)^2 - o(p/2o)^2 = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2
m(x + n/2m)^2 + o(y + p/2o)^2 = 1 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2 + m(n/2m)^2 + o(p/2o)^2
this system has a solution iff 11 + r^2 - a^2 - b^2 - c^2/d^2 - e^2/f^2 + m(n/2m)^2 + o(p/2o)^2 >= 0
There you have it, if I didn't make any algebraic mistakes. I don't know how much you can simplify the resulting expression, so this solution might be quite computationally expensive if you're going to check for many circles/ellipses
Enlarge the ellipse's major and minor radii by the radius of the circle. Then test if the center of the given circle is within this new larger ellipse by summing the distances to the foci of the enlarged ellipse.
This algorithm is quite efficient. You can early-out if the given circle doesn't intersect a circle which circumscribes the ellipse. This is slower than a bounding box test, but finding the bounding box of a non-axis-aligned ellipse is tricky.
Forget about a mathematical solution. As you can easily see by drawing, you can have up to four solutions, and thus likely a fourth grade polynomial.
Instead just do a binary search along the edge of one of the figures. It is easy to determine if a point lies within an ellipse and even more so in a circle (just see if distance is shorter than radius).
If you really want to go for the maths, Wolfram MathWorld has a nice article here: http://mathworld.wolfram.com/Circle-EllipseIntersection.html but be warned, you'll still have to write a polynomial equation solver, probably using something like binary search.
Supposing:
the ellipse is centred at the origin and with the semi-major
axis (of length a) oriented along the x axis, and with a semi-minor
axis of length b; E2 is the eccentricity squared, ie (aa-bb)/(a*a);
the circle is centred at X,Y and of radius r.
The easy cases are:
the circle centre is inside the ellipse (ie hypot(X/a, Y/b) <= 1)
so there is an intersection;
the circle centre is outside a circle centred at 0 of radius a+r
(ie hypot(X,Y) > a+r) so there isn't an intersection.
One approach for the other cases is to compute the geodetic
coordinates (latitude, height) of the circle centre. The circle
intersects the ellipse if and only if the height is less than the radius.
The geodetic latitude of a point on an ellipse is the angle
the normal to the ellipse at the point makes with the x axis, and
the height of a point outside the ellipse is the distance of the
point from the point on the ellipse closest to it. Note the geodetic latitude is not same as the polar angle from the ellipse centre to the point unless the
ellipse is in fact circular.
In formulae the conversion from geodetic coordinates lat,ht to
cartesian coordinates X,Y is
X = (nu+ht)*cos(lat), Y = (nu * (1-E2) + ht)*sin(lat)
where nu = a/sqrt( 1 - E2*sin(lat)sin(lat)).
The point on the ellipse closest to X,Y is the point
with the same latitude, but zero height, ie x = nucos(lat),
y = nu * (1-E2) * sin(lat).
Note that nu is a function of latitude.
Unfortunately the process of finding lat,ht from X,Y is an
iterative one. One approach is to first find the latitude, and then
the height.
A little algebra shows that the latitude satisfies
lat = atan2( Y+ E2*nusin(lat), X)
which can be used to compute successive approximations to the latitude,
starting at lat = atan2( Y, X(1.0-E2)), or (more efficiently) can be
solved using newton's method.
The larger E2 is, ie the flatter the ellipse is, the more
iterations will be required. For example if the ellipse is nearly
circular (say E2<0.1) then five iterations will get x,y below
to within a*1e-12, but if the ellipse is very flat, e.g. E2=0.999
you'll need around 300 iterations to get the same accuracy!
Finally, given the latitude, the height can be computed
by computing (x,y):
x = nucos(lat), y = nu(1-E2)*sin(lat)
and then h is the distance from x,y to the circle centre,
h = hypot( X-x, Y-y)
This isn't that hard. user168715's answer is generally right, but doing calculus isn't necessary. Just trigonometry.
Find the angle between the center of the two objects. Using this you can find the closest point to the circle's center on the ellipse using the polar-form:
(Taken from Wikipedia article on Ellipses)
Now compare the distance between the two object centers, subtracting the ellipse radius and circle radius.
Maybe I'm missing something; maybe ArcTan/Cos/Sin are slow -- but I don't think so, and there should be fast-approximations if needed.
I wanted to provide some input into the more general problem involving contact between two ellipses. Calculating the distance of closest approach of two ellipses was a long standing problem and was only solved analytically within the last ten years-it is by no means simple. The solution to the problem may be found here http://www.e-lc.org/docs/2007_01_17_00_46_52/.
The general method to determine if there is contact between two ellipses is to first calculate the distance of closest approach of the ellipses in their current configuration and then subtract this from their current magnitude of separation. If this result is less than or equal to 0, then they are in contact.
If anyone is interested I can post code that calculates the distance of closest approach--it's in C++. The code is for the general case of two arbitrary ellipses, but you can obviously do it for a circle and ellipse, since a circle is an ellipse with equal minor and major axes.
Related
I'm facing problem intersecting ray with triangle edges. Actually, I'm trying to pick/intersect with triangle, vertex, edge of a mesh using Mouse. So I made ray from the Mouse current position and then I intersect it with the mesh elements like triangle/polygon, vertex, edge etc to work with it. Basically, 3d modeling stuffs. Intersecting with triangle was easy and fun. And the vertex part was tricky.
But now, I don't know how to intersect/pick with triangle edges. I mean how I treat them when intersecting with the Mouse Ray? First I thought they can be treated like a 3D line. But eventually failed to do the Ray and the Line intersect. Searched on the Internet but not found any helpful info. Although I found some open source projects are using OpenGL built-in picking features to pick/intersect with Edge. But in my case, I can't use that. :(
My current edge picking code structure looks like the following:
void pickEdge(Ray ray, Scene scene)
{
for each object in scene
{
mesh = getMesh(object)
for each triangle in mesh
{
for each edge in triangle
{
v1 = getV1(edge)
v2 = getV2(edge)
// Do intersect with 'ray' and 'v1', 'v2'. But how?
}
}
}
}
So I'm stuck here and really need some help. Any idea, algorithm or a small help is greatly appreciated.
In your case problem of finding intersection between triangle and ray in 3D space can be boiled down to finding point location (INSIDE, OUTSIDE, ON BOUNDARY) in triangle in 2D space (plane). All you should do is project triangle on screen plane, find intersection on edge and perform reverse projection on edge. Position of point is position of mouse. The only problem is to treat degenerate cases like mapping triangle into line segment. But I think it will not be problem, because such cases can be easily coped.
Please give a look to the algorithms at the end of this page and more in general all the ones that this website offers: http://geomalgorithms.com/a05-_intersect-1.html
The first approach is to orthogonally project the edge (and the ray) to a plane perpendicular to the ray, and then to compute the distance of the projected ray to the projected edge.
Ie., first you determine two orthogonal vectors rdir1, rdir2 orthogonal to your ray.
Then you calculate the projection of your ray (its base point) to this plane, which will simply yield a 2d point rp.
Then you project the edge to that plane, by simply applying dot products:
pv1 = new Vector2(DotProduct(v1, rdir1), DotProduct(v1, rdir2))
pv2 = new Vector2(DotProduct(v2, rdir1), DotProduct(v2, rdir2))
Now you can compute the distance from this 2d line pv1, pv2 to the point rp.
Provided that the direction of the edge is taken from the view matrix's "forward" direction, then two vectors orthogonal to that would be the view matrix's left and right vectors.
Doing the above recipe will then yield something similar to projecting the edge to the screen. Hence, alternatively you could project the edge to the screen and work with those coordinates.
First of all, what is the distance between two geometric objects A and B ? It is the minimal distance between any two points on A and B, ie. dist(A,B) = min { EuclideanLength(x - y) | x in A, y in B}. (If it exists and is unique, which it does in your case.)
Here EuclideanLength((x,y,z)) = sqrt(x^2 + y^2 + z^2) as you already know. Because sqrt is strictly increasing it suffices to minimize SquareEuclideanLength((x,y,z)) = x^2 + y^2 + z^2, which greatly simplifies the problem.
In your question the objects are a line segment A := {v1 + t*(v2-v1) | 0 <= t <= 1} and a line B := {p + s*d | s is any real number}. (Don't worry that you asked about a ray, a line is really what you want.)
Now calculating the distance comes down to finding appropriate t and s such that SquareEuclideanLength(v1 + t*(v2-v1) - p - s*d) is minimal and then computing EuclideanLength(v1 + t*(v2-v1) - p - s*d) to get the real distance.
To solve this we need some analytic geometry. Because d is not zero, we can write each vector v as a sum of a part that is orthogonal to d and a part that is a multiple of d: v = Ov + Mv. For such an "orthogonal decomposition" it always holds SquareEuclideanLength(v) = SquareEuclideanLength(Ov) + SquareEuclideanLength(Mv).
Because of d = Md in the above
SquareEuclideanLength(v1 + t*(v2-v1) - p - s*d) =
SquareEuclideanLength(Ov1 + t*(Ov2-Ov1) - Op)
+ SquareEuclideanLength(Mv1 + t*(Mv2-Mv1) - Mp - s*d)
the left addend does not depend on s and however you chose t you can find an s such that the right addend is 0 ! (Remember that Mv1, Mv2, ... are multiples of d.)
Hence to find the minimum you just have to find such maps O, M as above and find the minimizer t.
Assuming that d is normalized, these are actually given by Ov := CrossProduct(v, d) and Mv := DotProduct(v, d)*d, but just believe me, that this also works if d is not normalized.
So the recipe for finding the distance is now: find 0 <= t <= 1 that minimizes
SquareEuclideanLength(Cross(v1 - p, d) + t*Cross(v2 - v1, d))
= SquareEuclideanLength(Cross(v1 - p, d))
+ 2*t*Dot(Cross(v1 - p, d), Cross(v2 - v1, d))
+ t^2 SquareEuclideanLength(Cross(v2 - v1, d)).
You will already know this formula from Point-Line distance calculation (that's what it is) and it is solved by differentiating with respect to t and equalling 0.
So the minimizer of this equation is
t = -Dot(Cross(v1 - p, d), Cross(v2 - v1, d))/SquareEuclideanLength(Cross(v2 - v1, d))
Using this t you calculate v1 + t*(v2 - v1), the point on the line segment A that is closest to line B and you can plug this into your point-line distance algorithm to find the sought after distance.
I hope this helps you !
I'm searching the way to efficiently find the point on an edge which is the closest point to some other point.
Let's say I know two points which are vertices of the edge. I can calculate the equation of the line that crosses those points.
What is the best way to calculate the point on the edge which is the closest point to some other point in the plane.
I would post an image but I don't have enough reputation points.
Let’s assume the line is defined by the two points (x1,y1), (x2,y2) and the “other point” is (a,b).
The point you’re looking for is (x,y).
You can easily find the equation of the black line. To find the blue line equation use the fact that m1*m2=-1 (m1 and m2 are the slopes of the two lines).
Clearly, the point you’re looking for is the intersection between the two lines.
There are two exceptions to what I was saying:
If x1=x2 then (x,y)=(x1,b).
If y1=y2 then (x,y)=(a,y1).
The following Python function finds the point (if you don’t know Python just think of it as a psudo-code):
def get_closest_point( x1,y1, x2,y2, a,b ):
if x1==x2: return (x1,b)
if y1==y2: return (a,y1)
m1 = (y2-y1)/(x2-x1)
m2 = -1/m1
x = (m1*x1-m2*a+b-y1) / (m1-m2)
y = m2*(x-a)+b
return (x,y)
You have three zones to consider. The "perpendicular" approach is for the zone in the middle:
For the other two zones the distance is the distance to the nearest segment endpoint.
The equation for the segment is:
y[x] = m x + b
Where
m -> -((Ay - By)/(-Ax + By)),
b -> -((-Ax By + Ay By)/(Ax - By))
And the perpendiculars have slope -1/m
The equations for the perpendicular passing thru A is:
y[x] = (-Ax + By)/(Ay - By) x + (Ax^2 + Ay^2 - Ax By - Ay By)/(Ay - By)
And the perpendicular passing thru B is the same exchanging the A's and B's in the equation above.
So you can know in which region lies your point introducing its x coordinate in the above equations and then comparing the y coordinate of the point with the result of y[x]
Edit
How to find in which region lies your point?
Let's suppose Ax ≤ Bx (if it's the other way, just change the point labels in the following formulae)
We will call your point {x0,y0}
1) Calculate
f[x0] = (-Ax + By)/(Ay - By) x0 + (Ax^2 + Ay^2 - Ax By - Ay By)/(Ay - By)
and compare with y0.
If y0 > f[x0], then your point lies in the green field in the figure above and the nearest point is A.
2) Else, Calculate
g[x0] = (-Bx + Ay)/(By - Ay) x0 + (Bx^2 + By^2 - Bx Ay - By Ay)/(By - Ay)
and compare with y0.
If y0 < g[x0], then your point lies in the yellow field in the figure above and the nearest point is B.
3) Else, you are in the "perpendicular light blue zone", and any of the other answer tell you how to calculate the nearest point and distance (I am not going to plagiarize :))
HTH!
I can describe what you want to do in geometric terms, but I don't have the algorithm at hand. Will that help?
Anyway, you want to draw a line which contains the stray point and is perpendicular to the edge. I think the slopes are a negative inverse relation between perpendicular lines, if that helps.
Then you want to find the intersection of the two lines.
Let's stick with the 2D case to save typing. It's been a while, so please forgive any elementary mistakes in my algebra.
The line forming the edge between the two points (x1, y1), (x2, y2) is represented as a function
y = mx + b
(You get to figure out m and b yourself, but it's elementary)
What you want to do is minimize the distance from your point (p1, p2) to a point on this line, i.e.
(p1-x)^2 + (p2-y)^2 (equation I)
subject to the equation
y = mx + b (equation II)
Substitute equation II into equation I and solve for x. You'll get two solutions; pick the one which gives the smaller value in equation I.
I have a quadratic bezier curve described as (startX, startY) to (anchorX, anchorY) and using a control point (controlX, controlY).
I have two questions:
(1) I want to determine y points on that curve based on an x point.
(2) Then, given a line-segment on my bezier (defined by two intermediary points on my bezier curve (startX', startY', anchorX', anchorY')), I want to know the control point for that line-segment so that it overlaps the original bezier exactly.
Why? I want this information for an optimization. I am drawing lots of horizontal beziers. When the beziers are larger than the screen, performance suffers because the rendering engine ends up rendering beyond the extents of what is visible. The answers to this question will let me just render what is visible.
Part 1
The formula for a quadratic Bezier is:
B(t) = a(1-t)2 + 2bt(1-t) + ct2
= a(1-2t+t2) + 2bt - 2bt2 + ct2
= (a-2b+c)t2+2(b-a)t + a
where bold indicates a vector. With Bx(t) given, we have:
x = (ax-2bx+cx)t2+2(bx-ax)t + ax
where vx is the x component of v.
According to the quadratic formula,
-2(bx-ax) ± 2√((bx-ax)2 - ax(ax-2bx+cx))
t = -----------------------------------------
2(ax-2bx+cx)
ax-bx ± √(bx2 - axcx)
= ----------------------
ax-2bx+cx
Assuming a solution exists, plug that t back into the original equation to get the other components of B(t) at a given x.
Part 2
Rather than producing a second Bezier curve that coincides with part of the first (I don't feel like crunching symbols right now), you can simply limit the domain of your parametric parameter to a proper sub-interval of [0,1]. That is, use part 1 to find the values of t for two different values of x; call these t-values i and j. Draw B(t) for t ∈ [i,j]. Equivalently, draw B(t(j-i)+i) for t ∈ [0,1].
The t equation is wrong, you need to use eq(1)
(1) x = (ax-2bx+cx)t2+2(bx-ax)t + ax
and solve it using the the quadratic formula for the roots (2).
-b ± √(b^2 - 4ac)
(2) x = -----------------
2a
Where
a = ax-2bx+cx
b = 2(bx-ax)
c = ax - x
Actually this is a classic problem as SO user Victor put it (in another SO question regarding which tasks to ask during an interview).
I couldn't do it in an hour (sigh) so what is the algorithm that calculates the number of integer points within a triangle?
EDIT: Assume that the vertices are at integer coordinates. (otherwise it becomes a problem of finding all points within the triangle and then subtracting all the floating points to be left with only the integer points; a less elegant problem).
Assuming the vertices are at integer coordinates, you can get the answer by constructing a rectangle around the triangle as explained in Kyle Schultz's An Investigation of Pick's Theorem.
For a j x k rectangle, the number of interior points is
I = (j – 1)(k – 1).
For the 5 x 3 rectangle below, there are 8 interior points.
(source: uga.edu)
For triangles with a vertical leg (j) and a horizontal leg (k) the number of interior points is given by
I = ((j – 1)(k – 1) - h) / 2
where h is the number of points interior to the rectangle that are coincident to the hypotenuse of the triangles (not the length).
(source: uga.edu)
For triangles with a vertical side or a horizontal side, the number of interior points (I) is given by
(source: uga.edu)
where j, k, h1, h2, and b are marked in the following diagram
(source: uga.edu)
Finally, the case of triangles with no vertical or horizontal sides can be split into two sub-cases, one where the area surrounding the triangle forms three triangles, and one where the surrounding area forms three triangles and a rectangle (see the diagrams below).
The number of interior points (I) in the first sub-case is given by
(source: uga.edu)
where all the variables are marked in the following diagram
(source: uga.edu)
The number of interior points (I) in the second sub-case is given by
(source: uga.edu)
where all the variables are marked in the following diagram
(source: uga.edu)
Pick's theorem (http://en.wikipedia.org/wiki/Pick%27s_theorem) states that the surface of a simple polygon placed on integer points is given by:
A = i + b/2 - 1
Here A is the surface of the triangle, i is the number of interior points and b is the number of boundary points. The number of boundary points b can be calculated easily by summing the greatest common divisor of the slopes of each line:
b = gcd(abs(p0x - p1x), abs(p0y - p1y))
+ gcd(abs(p1x - p2x), abs(p1y - p2y))
+ gcd(abs(p2x - p0x), abs(p2y - p0y))
The surface can also be calculated. For a formula which calculates the surface see https://stackoverflow.com/a/14382692/2491535 . Combining these known values i can be calculated by:
i = A + 1 - b/2
My knee-jerk reaction would be to brute-force it:
Find the maximum and minimum extent of the triangle in the x and y directions.
Loop over all combinations of integer points within those extents.
For each set of points, use one of the standard tests (Same side or Barycentric techniques, for example) to see if the point lies within the triangle. Since this sort of computation is a component of algorithms for detecting intersections between rays/line segments and triangles, you can also check this link for more info.
This is called the "Point in the Triangle" test.
Here is an article with several solutions to this problem: Point in the Triangle Test.
A common way to check if a point is in a triangle is to find the vectors connecting the point to each of the triangle's three vertices and sum the angles between those vectors. If the sum of the angles is 2*pi (360-degrees) then the point is inside the triangle, otherwise it is not.
Ok I will propose one algorithm, it won't be brilliant, but it will work.
First, we will need a point in triangle test. I propose to use the "Barycentric Technique" as explained in this excellent post:
http://www.blackpawn.com/texts/pointinpoly/default.html
Now to the algorithm:
let (x1,y1) (x2,y2) (x3,y3) be the triangle vertices
let ymin = floor(min(y1,y2,y3)) ymax = ceiling(max(y1,y2,y3)) xmin = floor(min(x1,x2,x3)) ymax = ceiling(max(x1,x2,3))
iterating from xmin to xmax and ymin to ymax you can enumerate all the integer points in the rectangular region that contains the triangle
using the point in triangle test you can test for each point in the enumeration to see if it's on the triangle.
It's simple, I think it can be programmed in less than half hour.
I only have half an answer for a non-brute-force method. If the vertices were integer, you could reduce it to figuring out how to find how many integer points the edges intersect. With that number and the area of the triangle (Heron's formula), you can use Pick's theorem to find the number of interior integer points.
Edit: for the other half, finding the integer points that intersect the edge, I suspect that it's the greatest common denominator between the x and y difference between the points minus one, or if the distance minus one if one of the x or y differences is zero.
Here's another method, not necessarily the best, but sure to impress any interviewer.
First, call the point with the lowest X co-ord 'L', the point with the highest X co-ord 'R', and the remaining point 'M' (Left, Right, and Middle).
Then, set up two instances of Bresenham's line algorithm. Parameterize one instance to draw from L to R, and the second to draw from L to M. Run the algorithms simultaneously for X = X[L] to X[M]. But instead of drawing any lines or turning on any pixels, count the pixels between the lines.
After stepping from X[L] to X[M], change the parameters of the second Bresenham to draw from M to R, then continue to run the algorithms simultaneously for X = X[M] to X[R].
This is very similar to the solution proposed by Erwin Smout 7 hours ago, but using Bresenham instead of a line-slope formula.
I think that in order to count the columns of pixels, you will need to determine whether M lies above or below the line LR, and of course special cases will arise when two points have the same X or Y co-ordinate. But by the time this comes up, your interviewer will be suitably awed and you can move on to the next question.
Quick n'dirty pseudocode:
-- Declare triangle
p1 2DPoint = (x1, y1);
p2 2DPoint = (x2, y2);
p3 2DPoint = (x3, y3);
triangle [2DPoint] := [p1, p2, p3];
-- Bounding box
xmin float = min(triangle[][0]);
xmax float = max(triangle[][0]);
ymin float = min(triangle[][1]);
ymax float = max(triangle[][1]);
result [[float]];
-- Points in bounding box might be inside the triangle
for x in xmin .. xmax {
for y in ymin .. ymax {
if a line starting in (x, y) and going in any direction crosses one, and only one, of the lines between the points in the triangle, or hits exactly one of the corners of the triangle {
result[result.count] = (x, y);
}
}
}
I have this idea -
Let A(x1, y1), B(x2, y2) and C(x3, y3) be the vertices of the triangle. Let 'count' be the number of integer points forming the triangle.
If we need the points on the triangle edges then using Euclidean Distance formula http://en.wikipedia.org/wiki/Euclidean_distance, the length of all three sides can be ascertained.
The sum of length of all three sides - 3, would give that count.
To find the number of points inside the triangle we need to use a triangle fill algorithm and instead of doing the actual rendering i.e. executing drawpixel(x,y), just go through the loops and keep updating the count as we loop though.
A triangle fill algorithm from
Fundamentals of Computer Graphics by
Peter Shirley,Michael Ashikhmin
should help. Its referred here http://www.gidforums.com/t-20838.html
cheers
I'd go like this :
Take the uppermost point of the triangle (the one with the highest Y coordinate). There are two "slopes" starting at that point. It's not the general solution, but for easy visualisation, think of one of both "going to the left" (decreasing x coordinates) and the other one "going to the right".
From those two slopes and any given Y coordinate less than the highest point, you should be able to compute the number of integer points that appear within the bounds set by the slopes. Iterating over decreasing Y coordinates, add all those number of points together.
Stop when your decreasing Y coordinates reach the second-highest point of the triangle.
You have now counted all points "above the second-highest point", and you are now left with the problem of "counting all the points within some (much smaller !!!) triangle, of which you know that its upper side parallels the X-axis.
Repeat the same procedure, but now with taking the "leftmost point" instead of the "uppermost", and with proceedding "by increasing x", instead of by "decreasing y".
After that, you are left with the problem of counting all the integer points within a, once again much smaller, triangle, of which you know that its upper side parallels the X-axis, and its left side parallels the Y-axis.
Keep repeating (recurring), until you count no points in the triangle you're left with.
(Have I now made your homework for you ?)
(wierd) pseudo-code for a bit-better-than-brute-force (it should have O(n))
i hope you understand what i mean
n=0
p1,p2,p3 = order points by xcoordinate(p1,p2,p3)
for int i between p1.x and p2.x do
a = (intersection point of the line p1-p2 and the line with x==i).y
b = (intersection point of the line p1-p3 and the line with x==i).y
n += number of integers between floats (a, b)
end
for i between p2.x+1 and p3.x do
a = (intersection point of the line p2-p3 and the line with x==i).y
b = (intersection point of the line p1-p3 and the line with x==i).y
n += number of integers between floats (a, b)
end
this algorithm is rather easy to extend for vertices of type float (only needs some round at the "for i.." part, with a special case for p2.x being integer (there, rounded down=rounded up))
and there are some opportunities for optimization in a real implementation
Here is a Python implementation of #Prabhala's solution:
from collections import namedtuple
from fractions import gcd
def get_points(vertices):
Point = namedtuple('Point', 'x,y')
vertices = [Point(x, y) for x, y in vertices]
a, b, c = vertices
triangle_area = abs((a.x - b.x) * (a.y + b.y) + (b.x - c.x) * (b.y + c.y) + (c.x - a.x) * (c.y + a.y))
triangle_area /= 2
triangle_area += 1
interior = abs(gcd(a.x - b.x, a.y - b.y)) + abs(gcd(b.x - c.x, b.y - c.y)) + abs(gcd(c.x - a.x, c.y - a.y))
interior /= 2
return triangle_area - interior
Usage:
print(get_points([(-1, -1), (1, 0), (0, 1)])) # 1
print(get_points([[2, 3], [6, 9], [10, 160]])) # 289
I found a quite useful link which clearly explains the solution to this problem. I am weak in coordinate geometry so I used this solution and coded it in Java which works (at least for the test cases I tried..)
Link
public int points(int[][] vertices){
int interiorPoints = 0;
double triangleArea = 0;
int x1 = vertices[0][0], x2 = vertices[1][0], x3 = vertices[2][0];
int y1 = vertices[0][1], y2 = vertices[1][1], y3 = vertices[2][1];
triangleArea = Math.abs(((x1-x2)*(y1+y2))
+ ((x2-x3)*(y2+y3))
+ ((x3-x1)*(y3+y1)));
triangleArea /=2;
triangleArea++;
interiorPoints = Math.abs(gcd(x1-x2,y1-y2))
+ Math.abs(gcd(x2-x3, y2-y3))
+ Math.abs(gcd(x3-x1, y3-y1));
interiorPoints /=2;
return (int)(triangleArea - interiorPoints);
}
Given a point (pX, pY) and a circle with a known center (cX,cY) and radius (r), what is the shortest amount of code you can come up with to find the point on the circle closest to (pX, pY) ?
I've got some code kind of working but it involves converting the circle to an equation of the form (x - cX)^2 + (y - cY)^2 = r^2 (where r is radius) and using the equation of the line from point (pX, pY) to (cX, cY) to create a quadratic equation to be solved.
Once I iron out the bugs it'll do, but it seems such an inelegant solution.
where P is the point, C is the center, and R is the radius, in a suitable "mathy" language:
V = (P - C); Answer = C + V / |V| * R;
where |V| is length of V.
OK, OK
double vX = pX - cX;
double vY = pY - cY;
double magV = sqrt(vX*vX + vY*vY);
double aX = cX + vX / magV * R;
double aY = cY + vY / magV * R;
easy to extend to >2 dimensions.
i would make a line from the center to the point, and calc where that graph crosses the circle oO i think not so difficult
Solve it mathematically first, then translate into code. Remember that the shortest line between a point and the edge of a circle will also pass through its center (as stated by #litb).
The shortest distance point lies at the intersection of circumference and line passing through the center and the input point. Also center, input and output points lie on a straight line
let the center be (xc, yc) and shortest point from input (xi, yi) be (x,y) then
sqrt((xc-x)^2 + (yc-y)^2) = r
since center, input and output points lie on a straight line, slope calculated between
any of two of these points should be same.
(yc-yi)/(xc-xi) = (y-yc)/(x-xc)
4.solving equations 2&3 should give us the shortest point.
Trig functions, multiply by r, and add pX or pY as appropriate.
Treat the centre of the circular as your origin, convert the co-ordinates of (pX, pY) to polar co-ordinates, (theta, r') replace r' with the original circle's r and convert back to cartesian co-ordinates (and adjust for the origin).
You asked for the shortest code, so here it is. In four lines it can be done, although there is still a quadratic.
I've considered the point to be outside the circle.
I've not considered what happens if the point is directly above or below the circle center, that is cX=pX.
m=(cY-pY)/(cX-pX); //slope
b=cY-m*cX; //or Py-m*Px. Now you have a line in the form y=m*x+b
X=( (2mcY)*((-2*m*cY)^2-4*(cY^2+cX^2-b^2-2*b*cY-r^2)*(-1-m^2))^(1/2) )/(2*(cY^2+cX^2-b^2-2*bc*Y-r^2));
Y=mX+b;
1] Get an equation for a line connecting the point and the circle center.
2] Move along the line a distance of one radius from the center to find the point on the circle. That is: radius=a^2+b^2 which is: r=((cY-Y)+(cX-X))^(1/2)
3] Solve quadratically. X=quadratic_solver(r=((cY-Y)+(cX-X))^(1/2),X) which if you substitute in Y=m*X+b you get that hell above.
4] X and Y are your results on the circle.
I am rather certain I have made an error somewhere, please leave a comment if anyone finds something. Of course it is degenerate, one answer is furthest from your point and the other is closest.
Easy way to think about it in terms of a picture, and easy to turn into code: Take the vector (pX - cX, pY - cY) from the center to the point. Divide by its length sqrt(blah blah blah), multiply by radius. Add this to (cX, cY).
Here is a simple method I use in unity... for the math kn00bs amongst us.
Its dependent on the transform orientation but it works nicely. I am doing a postion.z = 0 but just fatten the axis of the 2d circle you are not using.
//Find closest point on circle
Vector3 closestPoint = transform.InverseTransformPoint(m_testPosition.position);
closestPoint.z = 0;
closestPoint = closestPoint.normalized * m_radius;
Gizmos.color = Color.yellow;
Gizmos.DrawWireSphere(transform.TransformPoint(closestPoint), 0.01f);