I have a triangle defined by its three vertices. The vertices are of type Point = CGAL::Point_2<K> with a Simple_cartersian<double> kernel.
I want to randomly sample this triangle and for that I use a formula (https://math.stackexchange.com/questions/18686/uniform-random-point-in-triangle-in-3d) which adds the three vertices of the triangle multiplied by some random factors.
Point = Point(0, 0) + //
(1 - std::sqrt(r1)) * (standardTriangle[0] - Point(0, 0)) + //
(std::sqrt(r1) * (1 - r2)) * (standardTriangle[1] - Point(0, 0)) +
(r2 * std::sqrt(r1)) * (standardTriangle[2] - Point(0, 0)));
This looks very cumbersome, as I need to convert the points to vector by substracting Point(0,0) and then I need to add everything to a Point on the origin.
Looks more natural to just do something like the following
Point = (1 - std::sqrt(r1)) * standardTriangle[0] + //
(std::sqrt(r1) * (1 - r2)) * standardTriangle[1] +
(r2 * std::sqrt(r1)) * standardTriangle[2]);
Adding and removing points from the origin is really the only way to sum points, even though mathematically this is not correct?
You might want to use the barycenter() function.
In case you need another sampling there is one available in CGAL. See here
Related
What I have tried already: d = |v||PQ|sin("Theta")
Now, I need to determine what theta is, so I set up a position on a makeshift graph, the graph I made was on the xy plane only as the z plane complicates things needlessly for finding theta. So, I ended up with an acute angle, and if the angle is acute, then I have to find theta which according to dot product facts is greater than 0.
I do not have access to theta, so I used the same princples from cross dots. u * v = |u||v|cos("theta") but in this case, u and v are PQ and v. A vector is a vector, right?
so now I have theta = acos((v*PQ)/(|v||PQ))
with that I get (4sqrt(10))/15 = 32.5125173162 in degrees, so the angle is 32.5125173162 degrees.
So, now that I have theta, I plug it into my distance formula |v||PQ|sin(32.5125173162)
3*sqrt(10)*sin(32.5125173162) = 5.0990195136
or for the sake of simplicity, 5.1
I however want to know if this question is correct.
If it is NOT correct, what can I do to correct it? At what points did I use incorrect information?
This is not a question with a definitive answer in the back of the book, its a question on the side of a page that said: "try this!"
There are a couple of problems with this question.
From the context it looks like you mean for both v and PQ to be vectors. The "distance" between two vectors is an awkward (not well defined) question because vectors are not position bound.
You are using the cross product formula and I have no idea why:
|AxB| = |A||B|Sin(theta)
I think what you are actually trying to do is calculate the distance between the terminal points of the vectors, (2, 1, 2) and (1, 0, 3). Just use the Pythagorean Theorem (extended to 3D) for this.
d = sqrt( (x1 - x2)^2 + (y1 - y2)^2 + (z1 - z2)^2 )
d = sqrt( (2 - 1)^2 + (1 - 2)^2 + (2 - 3)^2 )
d = sqrt( 1^2 + (-1)^2 + (-1)^2 )
d = sqrt(3)
Edit:
If what you need really is the magnitude of the cross product, |AxB| then just find the cross product (using the determinant) and then calculate the magnitude of the result. There is no need for the formula you were using.
I keep stumbling into game/simulation solutions for finding distance while time is running, and it's not what I'm looking for.
I'm looking for an O(1) formula to calculate the (0 or 1 or 2) clock time(s) in which two circles are exactly r1+r2 distance from each other. Negative time is possible. It's possible two circles don't collide, and they may not have an intersection (as in 2 cars "clipping" each other while driving too close to the middle of the road in opposite directions), which is messing up all my mx+b solutions.
Technically, a single point collision should be possible.
I'm about 100 lines of code deep, and I feel sure there must be a better way, and I'm not even sure whether my test cases are correct or not. My initial setup was:
dist( x1+dx1*t, y1+dy1*t, x2+dx2*t, y2+dy2*t ) == r1+r2
By assuming the distance at any time t could be calculated with Pythagoras, I would like to know the two points in time in which the distance from the centers is precisely the sum of the radii. I solved for a, b, and c and applied the quadratic formula, and I believe that if I'm assuming they were phantom objects, this would give me the first moment of collision and the final moment of collision, and I could assume at every moment between, they are overlapping.
I'm working under the precondition that it's impossible for 2 objects to be overlapping at t0, which means infinite collision of "stuck inside each other" is not possible. I'm also filtering out and using special handling for when the slope is 0 or infinite, which is working.
I tried calculating the distance when, at the moment object 1 is at the intersection point, it's distance from object 2, and likewise when o2 is at the intersection point, but this did not work as it's possible to have collision when they are not at their intersection.
I'm having problems for when the slopes are equal, but different magnitude.
Is there a simple physics/math formula for this already?
Programming language doesn't matter, pseudcode would be great, or any math formula that doesn't have complex symbols (I'm not a math/physics person)... but nothing higher order (I assume python probably has a collide(p1, p2) method already)
There is a simple(-ish) solution. You already mentioned using the quadratic formula which is a good start.
First define your problem where the quadratic formula can be useful, in this case, distance between to centers, over time.
Let's define our time as t
Because we are using two dimensions we can call our dimensions x & y
First let's define the two center points at t = 0 of our circles as a & b
Let's also define our velocity at t = 0 of a & b as u & v respectively.
Finally, assuming a constant acceleration of a & b as o & p respectively.
The equation for a position along any one dimension (which we'll call i) with respect to time t is as follows: i(t) = 1 / 2 * a * t^2 + v * t + i0; with a being constant acceleration, v being initial velocity, and i0 being initial position along dimension i.
We know the distance between two 2D points at any time t is the square root of ((a.x(t) - b.x(t))^2 + (a.y(t) - b.y(t))^2)
Using the formula of position along a dimensions we can substitute everything in the distance equation in terms of just t and the constants we defined earlier. For shorthand we will call the function d(t);
Finally using that equation, we will know that the t values where d(t) = a.radius + b.radius are where collision starts or ends.
To put this in terms of quadratic formula we move the radius to the left so we get d(t) - (a.radius + b.radius) = 0
We can then expand and simplify the resulting equation so everything is in terms of t and the constant values that we were given. Using that solve for both positive & negative values with the quadratic formula.
This will handle errors as well because if you get two objects that will never collide, you will get an undefined or imaginary number.
You should be able to translate the rest into code fairly easily. I'm running out of time atm and will write out a simple solution when I can.
Following up on #TinFoilPancakes answer and heavily using using WolframAlpha to simplify the formulae, I've come up with the following pseudocode, well C# code actually that I've commented somewhat:
The Ball class has the following properties:
public double X;
public double Y;
public double Xvel;
public double Yvel;
public double Radius;
The algorithm:
public double TimeToCollision(Ball other)
{
double distance = (Radius + other.Radius) * (Radius + other.Radius);
double a = (Xvel - other.Xvel) * (Xvel - other.Xvel) + (Yvel - other.Yvel) * (Yvel - other.Yvel);
double b = 2 * ((X - other.X) * (Xvel - other.Xvel) + (Y - other.Y) * (Yvel - other.Yvel));
double c = (X - other.X) * (X - other.X) + (Y - other.Y) * (Y - other.Y) - distance;
double d = b * b - 4 * a * c;
// Ignore glancing collisions that may not cause a response due to limited precision and lead to an infinite loop
if (b > -1e-6 || d <= 0)
return double.NaN;
double e = Math.Sqrt(d);
double t1 = (-b - e) / (2 * a); // Collison time, +ve or -ve
double t2 = (-b + e) / (2 * a); // Exit time, +ve or -ve
// b < 0 => Getting closer
// If we are overlapping and moving closer, collide now
if (t1 < 0 && t2 > 0 && b <= -1e-6)
return 0;
return t1;
}
The method will return the time that the Balls collide, which can be +ve, -ve or NaN, NaN means they won't or didn't collide.
Further points to note are, we can check the discriminant against <zero to bail out early which will be most of the time, and avoid the Sqrt. Also since I'm using this in a continuous collision detection system, I'm ignoring collisions (glancing) that will have little or no impact since it's possible the response to the collision won't change the velocities and lead to the same situation being checked infinitely, freezing the simulation.
The 'b' variable can used for this check since luckily it's similar to the dot product. If b is >-1e-6 ie. they're not moving closer fast enough we return NaN, ie. they don't collide. You can tweak this value to avoid freezes, smaller will allow closer glancing collisions but increase the chance of a freeze when they happen like when a bunch of circles are packed tightly together. Likewise to avoid Balls moving through each other we signal an immediate collison if they're already overlapping and moving closer.
I'm using Postgres 9.5 and I've just installed PostGIS for some extended functions. I have a table with (x,y) points and I want to find the rectangle that fits the maximum number of points. The constraint is that the rectangle side lenghts are fixed. So far I'm counting how many points are in the box without rotation. My points are centered around the origin, (0,0).
SELECT Sum(CASE
WHEN x > -5
AND x < 5
AND y > -10
AND y < 10 THEN 1
ELSE 0
END) AS inside_points,
Count(1) AS total_points
FROM track_t;
This query gives me the count of points inside a rectangle with origin (0,0) and lenghts x = 10 and y = 20.
From here I would create a helper table of rotated rectangle corner points (angle, x1, y1, x2, y2), then cross join to my data, and count over the points per angle, while GROUP BY angle. Then I can select which angle gives me the most points inside the rectangle.
But this seems a little old fashioned, and perhaps non-performant. Additionally, counting points inside a rotated rectangle is not a trivial calculation.
Are there more efficient and elegant ways, perhaps using Postgres Geometric Datatypes or PostGIS Box2D, to rotate a rectangle with fixed side lenghts, and then to count the number of points inside? The geometric functions look good, but they seem to provide minimum bounding boxes and not the other way around.
In addition to Postgresql, I'm using a Python framework that could be used in case SQL can't make this work.
Update: One thing I tried is to use Geometric Types, specifically BOX
SELECT deg, Box(Point(-5, -10), Point(5, 10)) * Point(1, Radians(deg))
FROM Generate_series(0, 360, 90) AS deg
Unforunately, the Rotate function by a Point doesn't work for Polygons.
I ended up by generating rectangle vertices, rotating those vertices, and then comparing the area of the rectangle (constant) with the area of the 4 triangles that are made by including the test point.
This technique is based on the parsimonious answer:
Make triangle. Suppose, abcd is the rectangle and x is the point then if area(abx)+area(bcx)+area(cdx)+area(dax) equals area(abcd) then the point is inside it.
The rectangles are defined by
A bottom left (-x/2,-y/2)
B top left (-x/2,+y/2)
C top right (+x/2,+y/2)
D bottom right (+x/2,-y/2)
This code then checks if point (qx,qy) is inside a rectangle of width x=10 and height y=20, which is rotated around the origin (0,0) by an angle with range of 0 to 180, by 10 degrees.
Here's the code. It's taking 9 minutes to check 750k points, so there is definite room for improvement. Additionally, It can be parallelized once I upgrade to 9.6
with t as (select 10*0.5 as x, 20*0.5 as y, 17.0 as qx, -3.0 as qy)
select
z.angle
-- ABC area
--,abs(0.5*(z.ax*(z.by-z.cy)+z.bx*(z.cy-z.ay)+z.cx*(z.ay-z.by)))
-- CDA area
--,abs(0.5*(z.cx*(z.dy-z.ay)+z.dx*(z.ay-z.cy)+z.ax*(z.cy-z.dy)))
-- ABCD area
,abs(0.5*(z.ax*(z.by-z.cy)+z.bx*(z.cy-z.ay)+z.cx*(z.ay-z.by))) + abs(0.5*(z.cx*(z.dy-z.ay)+z.dx*(z.ay-z.cy)+z.ax*(z.cy-z.dy))) as abcd_area
-- ABQ area
--,abs(0.5*(z.ax*(z.by-z.qx)+z.bx*(z.qy-z.ay)+z.qx*(z.ay-z.by)))
-- BCQ area
--,abs(0.5*(z.bx*(z.cy-z.qx)+z.cx*(z.qy-z.by)+z.qx*(z.by-z.cy)))
-- CDQ area
--,abs(0.5*(z.cx*(z.dy-z.qx)+z.dx*(z.qy-z.cy)+z.qx*(z.cy-z.dy)))
-- DAQ area
--,abs(0.5*(z.dx*(z.ay-z.qx)+z.ax*(z.qy-z.dy)+z.qx*(z.dy-z.ay)))
-- total area of triangles with question point (ABQ + BCQ + CDQ + DAQ)
,abs(0.5*(z.ax*(z.by-z.qx)+z.bx*(z.qy-z.ay)+z.qx*(z.ay-z.by)))
+ abs(0.5*(z.bx*(z.cy-z.qx)+z.cx*(z.qy-z.by)+z.qx*(z.by-z.cy)))
+ abs(0.5*(z.cx*(z.dy-z.qx)+z.dx*(z.qy-z.cy)+z.qx*(z.cy-z.dy)))
+ abs(0.5*(z.dx*(z.ay-z.qx)+z.ax*(z.qy-z.dy)+z.qx*(z.dy-z.ay))) as point_area
from
(
SELECT
a.id as angle
-- bottom left (A)
,(-t.x) * cos(radians(a.id)) - (-t.y) * sin(radians(a.id)) as ax
,(-t.x) * sin(radians(a.id)) + (-t.y) * cos(radians(a.id)) as ay
--top left (B)
,(-t.x) * cos(radians(a.id)) - (t.y) * sin(radians(a.id)) as bx
,(-t.x) * sin(radians(a.id)) + (t.y) * cos(radians(a.id)) as by
--top right (C)
,(t.x) * cos(radians(a.id)) - (t.y) * sin(radians(a.id)) as cx
,(t.x) * sin(radians(a.id)) + (t.y) * cos(radians(a.id)) as cy
--bottom right (D)
,(t.x) * cos(radians(a.id)) - (-t.y) * sin(radians(a.id)) as dx
,(t.x) * sin(radians(a.id)) + (-t.y) * cos(radians(a.id)) as dy
-- point to check (Q)
,t.qx as qx
,t.qy as qy
FROM generate_series(0,180,10) AS a(id), t
) z
;
the results then are
angle;abcd_area;point_area
0;200;340
10;200;360.6646055963
20;200;373.409049054212
30;200;377.846096908265
40;200;373.84093170467
50;200;361.515248361426
60;200;341.243556529821
70;200;313.641801308188
80;200;279.548648061772
90;200;240
*100;200;200*
*110;200;200*
*120;200;200*
*130;200;200*
*140;200;200*
150;200;237.846096908265
160;200;277.643408923024
170;200;312.04311584956
180;200;340
Where the rotations of angles 100, 110, 120, 130 and 140 degrees then includes the test-point (indicated with *)
I have a problem and please let me know if my solution is correct.
I have a known point, at location A(x1,y1,z1) and the origin O(0,0,0) and I would like to find the coordinates of a point B(x2,y2,z2) that is located on the line OA, and the distance OB is 1.2 times greater then OA.
So, my idea is to obtain the equation of the line formed by points O and A.
The direction of OA is (-x1, -y1, -z1), so the equation of the line is:
x = -x1*t;
y = -y1*t;
z = -z1*t;
Distance OA is sqrt( (x1-0)^2 + (y1-0)^2 + (z1-0)^2). KNOWN
Distance OB is sqrt( (x2-0)^2 + (y2-0)^2 + (z2-0)^2). UNKNOWN
I can replace the x, y, z points determined for the line equation in the distance OB, and the result should be 1.2 times greater then the distance OA.
So, sqrt( (-x1*t-0)^2 + (-y1*t-0)^2 + (-z1*t-0)^2) = 1.2 * dist(OA).
I find t from here, solving the quadratic equation and I obtain the coordinates of the point by replacing the t in the equation of the line.
Is this correct?
Thank you for your time.
EDIT:
This is my code:
rangeRatio = 1.114;
norm = sqrt((P2(1) - P1(1))^2 + (P2(2) - P1(2))^2 + (P2(3) - P1(3))^2);
P3(1) = P1(1) + ((P2(1,1) - P1(1)) /norm) * rangeRatio;
P3(2) = P1(2) + ((P2(1,2) - P1(2)) /norm) * rangeRatio;
P3(3) = P1(3) + ((P2(1,3) - P1(3)) /norm) * rangeRatio;
I tried also norm = 1, and i get slightly different results but still not always colinear.
Thank you
It is even a lot easier; you can just multiply a, b and c by 1.2. This gives a line that is 1.2 times the size of the original line.
I have successfully drawn a Quad2D or Bezier curve in java. I have the equation for the same. But I need to determine whether a particular point(x,y) lies on the curve or not. I tried using Quad2D.contains and a few GeneralPath APIs, I could not get the result accurately.
Can someone help to find out the solution to this?
I think you've meant QuadCurve2D class, which is quadratic Bezier curve.
There seems to be no ready-made method for that, and the problem comes to the distance from the point to the Bezier curve.
Let's P0 will be your point, P1 is a start point, P2 - a control point and P3 is an end point of your curve.
Then point on the curve will be given by
P = B(t)
There is such t, for which distance between P and P0 will be minimal.
F(t) = (B(t)_x - P0_x)^2 + (B(t)_y - P0_y)^2 -> min
If distance is 0 or less than certain error, then P0 is on the curve.
t can be found with Netwon's iterative method by minimizing cost function
t_n = t_n-1 + F'(t) / F''(t)
where F' is first derivative of cost function and F'' is its second derivative.
F'(t) = 2 * (B(t)_x - P0_x) * B'(t)_x + 2 * (B(t)_y - P0_y) * B'(t)_y
F''(t) = 2 * B'(t)_x * B'(t)_x + 2 * (B(t)_x - P0_x) * B''(t)_x +
2 * B'(t)_y * B'(t)_y + 2 * (B(t)_y - P0_x) * B''(t)_y
First derivative of quadratic Bezier curve B'(t) is a line segment with a start point (P2 - P1) and end point (P3 - P2). Second derivative B''(t) is a point P3 - 2 * P2 + P1.
Plugging everything together will give a formula to t for which F(t) is minimal.