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I need to find the indices of the polygon nearest to a point
So in this case the ouput would be 4 and 0. Such that if the red point is added I know to where to place the vertex in the array. Does anyone know where to start?
(Sorry if the title is misleading, I wasnt sure how to phrase it properly)
In this case the ouput would be 0 and 1, rather than the closest 4.
Point P lies on the segment AB, if two simple conditions are met together:
AP x PB = 0 //cross product, vectors are collinear or anticollinear, P lies on AB line
AP . PB > 0 //scalar product, exclude anticollinear case to ensure that P is inside the segment
So you can check all sequential vertice pairs (pseudocode):
if (P.X-V[i].X)*(V[i+1].Y-P.Y)-(P.Y-V[i].Y)*(V[i+1].X-P.X)=0 then
//with some tolerance if point coordinates are float
if (P.X-V[i].X)*(V[i+1].X-P.X)+(P.Y-V[i].Y)*(V[i+1].Y-P.Y)>0
then P belongs to (i,i+1) segment
This is fast direct (brute-force) method.
Special data structures exist in computer geometry to quickly select candidate segments - for example, r-tree. But these complicated methods will gain for long (many-point) polylines and for case where the same polygon is used many times (so pre-treatment is negligible)
I'll assume that the new point is to be added to an edge. So you are given the coordinates of a point a = (x, y) and you want to find the indices of the edge on which it lies. Let's call the vertices of that edge b, c. Observe that the area of the triangle abc is zero.
So iterate over all edges and choose the one that minimizes area of triangle abc where a is your point and bc is current edge.
a = input point
min_area = +infinity
closest_edge = none
n = number of vertices in polygon
for(int i = 1; i <= n; i++)
{ b = poly[ i - 1 ];
c = poly[ i % n ];
if(area(a, b, c) < min_area)
{ min_area = area(a, b, c);
closest_edge = bc
}
}
You can calculate area using:
/* Computes area x 2 */
int area(a, b, c)
{ int ans = 0;
ans = (a.x*b.y + b.x*x.y + c.x*a.y) - (a.y*b.x + b.y*c.x + c.y*a.x);
return ABS(ans);
}
I think you would be better off trying to compare the distance from the actual point to a comparable point on the line. The closest comparable point would be the one that forms a perpendicular line like this. a is your point in question and b is the comparable point on the line line between the two vertices that you will check distance to.
However there's another method which I think might be more optimal for this case (as it seems most of your test points lie pretty close to the desired line already). Instead of find the perpendicular line point we can simply check the point on the line that has the same X value like this. b in this case is a lot easier to calculate:
X = a.X - 0.X;
Slope = (1.Y - 0.Y) / (1.X - 0.X);
b.X = 0.X + X;
b.Y = 0.Y + (X * Slope);
And the distance is simply the difference in Y values between a and b:
distance = abs(a.Y - b.Y);
One thing to keep in mind is that this method will become more inaccurate as the slope increases as well as become infinite when the slope is undefined. I would suggest flipping it when the slope > 1 and checking for a b that lies at the same y rather than x. That would look like this:
Y = a.Y - 0.Y;
Inverse_Slope = (1.X - 0.X) / (1.Y - 0.Y);
b.Y = 0.Y + Y;
b.X = 0.Y + (Y * Inverse_Slope);
distance = abs(a.X - b.X);
Note: You should also check whether b.X is between 0.X and 1.X and b.Y is between 0.Y and 1.Y in the second case. That way we are not checking against points that dont lie on the line segment.
I admit I don't know the perfect terminology when it comes to this kind of thing so it might be a little confusing, but hope this helps!
Rather than checking if the point is close to an edge with a prescribed tolerance, as MBo suggested, you can fin the edge with the shortest distance to the point. The distance must be computed with respect to the line segment, not the whole line.
How do you compute this distance ? Let P be the point and Q, R two edge endpoints.
Let t be in range [0,1], you need to minimize
D²(P, QR) = D²(P, Q + t QR) = (PQ + t QR)² = PQ² + 2 t PQ.QR + t² QR².
The minimum is achieved when the derivative cancels, i.e. t = - PQ.QR / QR². If this quantity exceeds the range [0,1], just clamp it to 0 or 1.
To summarize,
if t <= 0, D² = PQ²
if t >= 1, D² = PR²
otherwise, D² = PQ² - t² QR²
Loop through all the vertices, calculate the distance of that vertex to the point, find the minimum.
double min_dist = Double.MAX_VALUE;
int min_index=-1;
for(int i=0;i<num_vertices;++i) {
double d = dist(vertices[i],point);
if(d<min_dist) {
min_dist = d;
min_index = i;
}
}
Related
I have this problem I can't figure out and need help.
The problem is about calculating how many balloons are hit by a pellet gun. Balloons positions are described by 3D coordinates (X,Y,Z) and radius R. The gunshot is defined by 3D location of the end of the barrel "p" (Px,Py,Pz) and vector "v" (Vx, Vy, Vz) describing the direction barrel is pointing to.
I've tried to implement the solution suggested here: https://math.stackexchange.com/questions/1939423/calculate-if-vector-intersects-sphere
// C = center of sphere
// r = radius of sphere
// P = point on line
// U = unit vector in direction of line
Q = P - C;
a = U*U; // should be = 1
b = 2*U*Q
c = Q*Q - r*r;
d = b*b - 4*a*c; // discriminant of quadratic
if d < 0 then solutions are complex, so no intersections
if d >= 0 then solutions are real, so there are intersections
But the problem with this is that I get intersection with balloons that are positioned behind the gun. How can I modify this algorithm in order to produce the correct result? Or is my approach maybe wrong?
You need to actually solve the quadratic equation defined by your variables a, b and c.
Often, there are math libraries to do this, something like:
(t1,t2) = QuadraticSolve(a, b, c);
You can also do it manually for each parameter:
t1 = (-b + sqrt(b*b - 4*a*c)) / (2*a)
t2 = (-b - sqrt(b*b - 4*a*c)) / (2*a)
If t1 or t2 is positive then that intersection is in front of your gun.
I'm trying to code a general algorithm that can find a polygon from the area swept out by a circle (red line) that follows some known path (green line), and where the circle gets bigger as it moves further down the known path. Basically, can anyone point me down a direction to solve this, please? I can't seem to nail down which tangent points are part of the polygon for any point (and thus circle) on the path.
Any help is appreciated.
Well, the easiest is to approximate your path by small segments on which your path is linear, and your circle grows linearly.
Your segments and angles will likely be small, but for the sake of the example, let's take bigger (and more obvious) angles.
Going through the geometry
Good lines for the edges of your polygon are the tangents to both circles. Note that there aren't always close to the lines defined by the intersections between the circles and the orthogonal line to the path, especially with stronger growth speeds. See the figure below, where (AB) is the path, we want the (OE) and (OF) lines, but not the (MN) one for example :
The first step is to identify the point O. It is the only point that defines a homothetic transformation between both circles, with a positive ratio.
Thus ratio = OA/OB = (radius C) / (radius C') and O = A + AB/(1-ratio)
Now let u be the vector from O to A normalized, and v a vector orthogonal to u (let us take it in the direction from A to M).
Let us call a the vector from O to E normalized, and beta the angle EOA. Then, since (OE) and (AE) are perpendicular, sin(beta) = (radius C) / OA. We also have the scalar product a.u = cos(beta) and since the norm of a is 1, a = u * cos(beta) + v * sin(beta)
Then it comes easily that with b the vector from O to F normalized, b = u * cos(beta) - v * sin(beta)
Since beta is an angle less than 90° (otherwise the growth of the circle would be so much faster than it going forward, that the second circle contains the first completely), we know that cos(beta) > 0.
Pseudo-code-ish solution
For the first and last circles you can do something closer to them -- fort the sake of simplicity, I'm just going to use the intersection between the lines I'm building and the tangent to the circle that's orthogonal to the first (or last) path, as illustrated in the first figure of this post.
Along the path, you can make your polygon arbitrarily close to the real swept area by making the segments smaller.
Also, I assume you have a function find_intersection that, given two parametric equations of two lines, returns the point of intersection between them. First of all, it makes it trivial to see if they are parallel (which they should never be), and it allows to easily represent vertical lines.
w = 1; // width of the first circle
C = {x : 0, y : 0}; // first circle center
while( (new_C, new_w) = next_step )
{
// the vector (seg_x, seg_y) is directing the segment
seg = new_C - C;
norm_seg = sqrt( seg.x * seg.x + seg.y * seg.y );
// the vector (ortho_x, ortho_y) is orthogonal to the segment, with same norm
ortho = { x = -seg.y, y = seg.x };
// apply the formulas we devised : get ratio-1
fact = new_w / w - 1;
O = new_C - seg / fact;
sin_beta = w * fact / norm_seg;
cos_beta = sqrt(1 - sin_beta * sin_beta);
// here you know the two lines, parametric equations are O+t*a and O+t*b
a = cos_beta * seg + sin_beta * ortho;
b = cos_beta * seg - sin_beta * ortho;
if( first iteration )
{
// initialize both "old lines" to a line perpendicular to the first segment
// that passes through the opposite side of the circle
old_a = ortho;
old_b = -ortho;
old_O = C - seg * (w / norm_seg);
}
P = find_intersection(old_O, old_a, O, a);
// add P to polygon construction clockwise
Q = find_intersection(old_O, old_b, O, b);
// add Q to polygon construction clockwise
old_a = a;
old_b = b;
old_O = O;
w = new_w;
C = new_C;
}
// Similarly, finish with line orthogonal to last direction, that is tangent to last circle
O = C + seg * (w / norm_seg);
a = ortho;
b = -ortho;
P = find_intersection(old_O, old_a, O, a);
// add P to polygon construction clockwise
Q = find_intersection(old_O, old_b, O, b);
// add Q to polygon construction clockwise
Let's suppose the centers are along the positive x-axis, and the lines in the envelope are y=mx and y=-mx for some m>0. The distance from (x,0) to y=mx is mx/sqrt(1+m^2). So, if the radius is increasing at a rate of m/sqrt(1+m^2) times the distance moved along the x-axis, the enveloping lines are y=mx and y=-mx.
Inverting this, if you put a circle of radius cx at the center of (x,0), then c=m/sqrt(1+m^2) so
m = c/sqrt(1-c^2).
If c=1 then you get a vertical line, and if c>1 then every point in the plane is included in some circle.
This is how you can tell how much faster than sound a supersonic object is moving from the Mach angle of the envelope of the disturbed medium.
You can rotate this to nonhorizontal lines. It may help to use the angle formulation mu = arcsin(c), where mu is the angle between the envelope and the path, and the Mach number is 1/c.
I'm working on a project in Matlab and need to find the area between two lines inside of the square [-1,+1]x[-1,+1] intersecting in a point (xIntersection,yIntersection). So the idea is to subtract the two lines and integrate between [-1, xIntersection] and [xIntersection, +1], sum the results and if it's negative, change its sign.
For details on how I find the intersection of the two lines check this link.
I'm using Matlab's function integral(), here a snippet of my code:
xIntersection = ((x_1 * y_2 - y_1 * x_2) * (x_3 - x_4) - (x_1 - x_2) * (x_3 * y_4 - y_3 * x_4) ) / ((x_1 - x_2) * (y_3 - y_4) - (y_1 - y_2) * (x_3 - x_4));
d = #(x) g(x) - f(x);
result = integral(d, -1, xIntersection) - int( d, xIntersection, 1)
if(result < 0),
result = result * -1;
end
Note that I defined previously in the code g(x) and f(x) but haven't reported it in the snippet.
The problem is that I soon realized that the lines could intersect either inside or outside of the square, furthermore they could intersect the square on any of its sides and the number of possible combinations grows up very quickly.
I.e.:
These are just 4 cases, but considering that f(+1), f(-1), g(+1), g(-1) could be inside the interval [-1,+1], above it or under it and that the intersection could be inside or outside of the square the total number is 3*3*3*3*2 = 162.
Obviously in each case the explicit function to integrate in order to get the area between the two lines is different, but I can't possibly think of writing a switch case for each one.
Any ideas?
I think my answer to your previous question still applies, for the most part.
If you want to compute the area of the region bounded by the smaller angle of the two lines and the boundaries of the square, then you can forget about the intersection, and forget about all the different cases.
You can just use the fact that
the area of the square S is equal to 4
the value of this integral
A = quadgk(#(x) ...
abs( max(min(line1(x),+1),-1) - max(min(line2(x),+1),-1) ), -1, +1);
gives you the area between the lines (sometimes the large angle, sometimes the small angle)
the value of of min(A, S-A) is the correct answer (always the small angle).
Assuming “between the lines” means “inside the smaller angle formed by the lines”:
With the lines l and h, S := [-1,+1]x[-1,+1] and B as the border of S.
Transform l to the form l_1 + t*l_2 with l_1 and l_2 beeing vectors. Do the same for h.
If the intersection is not inside S, find the 4 intersections of l and h with B. Sort them so you get a convex quadrilateral. Calculate its area.
Else:
Find the intersection point p and find the intersection angle α between l_2 and h_2. and then check:
If α in [90°,180°] or α in [270°,360°], swap l and h.
If α > 180°, set l_2 = −l_2
Set l_1 := h_1 := p. Do once for positive t and negative t
(forwards and backwards along l_2 and h_2 from p):
Find intersections s_l and s_h of l and h with B.
If on same border of B: compute area of triangle s_l, s_h, p
If on adjacent borders of B: find the corner c of B in between the hit borders and once again sort the four points s_l, s_h, p and c so you get an convex quadrilateral and calculate it’s area.
If on opposite borders, find the matching side of B (you can do this by looking at the direction of s_l-p). Using its two corner points c_1 and c_2, you now have 5 points that form a polygon. Sort them so the polygon is convex and calculate its area.
This is still quite a few cases, but I don’t think there’s a way around that. You can simplify this somewhat by also using the polygon formula for the triangle and the quadrilateral.
How to sort the points so you get a convex polygon/quadrilateral: select any one of them as p_1 and then sort the rest of the points according to angle to p_1.
If you define an intersection function and a polygon_area that takes a list of points, sorts them and returns the area, this algorithm should be rather simple to implement.
edit: Image to help explain the comment:
Hey guys thanks for your answers, I also thought of an empirical method to find the area between the lines and wanted to share it for the sake of discussion and completeness.
If you take a large number of random points within the square [-1,+1]x[-1,+1] you can measure the area as the fraction of the points which fall in the area between the two lines.
Here's a little snippet and two images to show the different accuracy of empirical result obtained with different number of points.
minX = -1;
maxX = +1;
errors = 0;
size = 10000;
for j=1:size,
%random point in [-1,+1]
T(j,:) = minX + (maxX - minX).*rand(2,1);
%equation of the two lines is used to compute the y-value
y1 = ( ( B(2) - A(2) ) / ( B(1) - A(1) ) ) * (T(j,1) - A(1)) + A(2);
y2 = (- W(1) / W(2)) * T(j,1) -tresh / W(2);
if(T(j,2) < y1),
%point is under line one
Y1 = -1;
else
%point is above line one
Y1 = +1;
end
if(T(j,2) < y2),
%point is under line two
Y2 = -1;
else
%point is above line two
Y2 = +1;
end
if(Y1 * Y2 < 0),
errors = errors + 1;
scatter(T(j,1),T(j,2),'fill','r')
else
scatter(T(j,1),T(j,2),'fill','g')
end
end
area = (errors / size) / 4;
And here are two images, it sure takes longer than the solution posted by #Rody but as you can see you can make it accurate.
Number of points = 2000
Number of points = 10000
We have a ray that starts at point A(X, Y) and goes on forever through given point B(X, Y) != A. We have a rectangle defined by points K,L,M,N each with its (X, Y).
I wonder how to detect if our ray intersects with any point of our rectangle (get a bool, not precice coordinates)? What is algorithm for calculating such value?
Let me get this straight. You have a vector v headed off in direction (b_x - a_x, b_y - a_y) and starting at (a_x, a_y).
Consider the vector w = (b_y - a_y, a_x - b_x). It is at right angles to the first. (Verify with a dot product.) Therefore for any point (p_x, p_y) you can easily tell which side of the vector it is on by taking a dot product of (p_x - a_x, p_y - a_y) with w and looking at the sign.
So take that dot product with all 4 corners of your rectangle. If any give a 0 dot product, they are on the vector, if the signs change there is an intersection, if the sign is always the same there is no intersection.
You can use the sweep line algorithm to do so.
http://en.wikipedia.org/wiki/Sweep_line_algorithm
A less clever, but conceptually simpler approach: the ray intersects the rectangle if and only if it intersects at least one of the sides. So for each side of the rectangle, find the intersection (if any) of the line passing through the endpoints with the ray AB; then it's simply a range check to determine if that intersection lies is part of the line segment on the boundary of the rectangle, or if it is outside.
You probably want to compute the segment (if any) of the ray AB that intersects the rectangle. If your rectangle is axis-aligned, this will be easier to compute in a numerical sense, but the logic should be similar.
You can represent a directed line L as [a, b, c] such that, if point P is (X, Y):
let L(P) = a*X + b*Y + c
then, if L(P) == 0, point P is on L
if L(P) > 0, point P is to the left of L
if L(P) < 0, point P is to the right of L
Note that this is redundant in the sense that, given any k > 0, [k*a, k*b, k*c] represents the same line (this property makes it a homogeneous coordinate system). We can also represent points with homogeneous coordinates by augmenting them with a third coordinate:
2D point P = (X, Y)
-> homogeneous coordinates [x, y, w] for P are [X, Y, 1]
L(P) = L.a*P.x + L.b*P.y + L.c*P.w == a*X + b*Y + c*1
In any case, given two corners of your rectangle (say, P and Q), you can compute the homogeneous coordinates of the line through P and Q using a 3-D cross-product of their homogeneous coordinates:
homogeneous coordinates for line PQ are: [P.X, P.Y, 1] cross [Q.X, Q.Y, 1]
-> PQ.a = P.Y - Q.Y
PQ.b = Q.X - P.X
PQ.c = P.X*Q.Y - Q.X*P.Y
You can verify mathematically that points P and Q are both on the above-described line PQ.
To represent the segment of line AB that intersects the rectangle, first compute vector V = B - A, as in #btilly's answer. For homogeneous coordinates, this works as follows:
A = [A.X, A.Y, 1]
B = [B.X, B.Y, 1]
-> V = B - A = [B.X-A.X, B.Y-A.Y, 0]
for any point C on AB: homogeneous coordinates for C = u*A + v*V
(where u and v are not both zero)
Point C will be on the ray part of the line only if u and v are both non-negative. (This representation may seem obscure, compared to the usual formulation of C = A + lambda * V, but doing it this way avoids unnecessary divide-by-zero cases...)
Now, we can compute the ray intersection: we represent a segment of the line AB by the parametric [u,v] coordinates of each endpoint: { start = [start.u, start.v]; end = [end.u, end.v] }.
We compute the edges of the rectangle in the counterclockwise direction, so that points inside the rectangle are on the left/positive side (L(P)>0) of every edge.
Starting segment is entire ray:
start.u = 1; start.v = 0
end.u = 0; end.v = 1
for each counterclockwise-directed edge L of the rectangle:
compute:
L(A) = L.a*A.X + L.b*A.Y + L.c
L(V) = L.a*V.X + L.b*V.Y
L(start) = start.u * L(A) + start.v * L(V)
L(end) = end.u * L(A) + end.v * L(V)
if L(start) and L(end) are both less than zero:
exit early: return "no intersection found"
if L(start) and L(end) are both greater or equal to zero:
do not update the segment; continue with the next line
else, if L(start) < 0:
update start coordinates:
start.u := L(V)
start.v := -L(A)
else, if L(end) < 0:
update end coordinates:
end.u := -L(V)
end.v := L(A)
on normal loop exit, the ray does intersect the rectangle;
the part of the ray inside the rectangle is the segment between points:
homog_start = start.u * A + start.v * V
homog_end = end.u * A + end.v * V
return "intersection found":
intersection_start.X = homog_start.x/homog_start.w
intersection_start.Y = homog_start.y/homog_start.w
intersection_end.X = homog_end.x/homog_end.w
intersection_end.Y = homog_end.y/homog_end.w
Note that this will work for arbitrary convex polygons, not just rectangles; the above is actually a general ray/convex polygon intersection algorithm. For a rectangle, you can unroll the for-loop; and, if the rectangle is axis-aligned, you can drastically simplify the arithmetic. However, the 4-case decision in the inner loop should remain the same for each edge.
From the man page for XFillPolygon:
If shape is Complex, the path may self-intersect. Note that contiguous coincident points in the path are not treated as self-intersection.
If shape is Convex, for every pair of points inside the polygon, the line segment connecting them does not intersect the path. If known by the client, specifying Convex can improve performance. If you specify Convex for a path that is not convex, the graphics results are undefined.
If shape is Nonconvex, the path does not self-intersect, but the shape is not wholly convex. If known by the client, specifying Nonconvex instead of Complex may improve performance. If you specify Nonconvex for a self-intersecting path, the graphics results are undefined.
I am having performance problems with fill XFillPolygon and, as the man page suggests, the first step I want to take is to specify the correct shape of the polygon. I am currently using Complex to be on the safe side.
Is there an efficient algorithm to determine if a polygon (defined by a series of coordinates) is convex, non-convex or complex?
You can make things a lot easier than the Gift-Wrapping Algorithm... that's a good answer when you have a set of points w/o any particular boundary and need to find the convex hull.
In contrast, consider the case where the polygon is not self-intersecting, and it consists of a set of points in a list where the consecutive points form the boundary. In this case it is much easier to figure out whether a polygon is convex or not (and you don't have to calculate any angles, either):
For each consecutive pair of edges of the polygon (each triplet of points), compute the z-component of the cross product of the vectors defined by the edges pointing towards the points in increasing order. Take the cross product of these vectors:
given p[k], p[k+1], p[k+2] each with coordinates x, y:
dx1 = x[k+1]-x[k]
dy1 = y[k+1]-y[k]
dx2 = x[k+2]-x[k+1]
dy2 = y[k+2]-y[k+1]
zcrossproduct = dx1*dy2 - dy1*dx2
The polygon is convex if the z-components of the cross products are either all positive or all negative. Otherwise the polygon is nonconvex.
If there are N points, make sure you calculate N cross products, e.g. be sure to use the triplets (p[N-2],p[N-1],p[0]) and (p[N-1],p[0],p[1]).
If the polygon is self-intersecting, then it fails the technical definition of convexity even if its directed angles are all in the same direction, in which case the above approach would not produce the correct result.
This question is now the first item in either Bing or Google when you search for "determine convex polygon." However, none of the answers are good enough.
The (now deleted) answer by #EugeneYokota works by checking whether an unordered set of points can be made into a convex polygon, but that's not what the OP asked for. He asked for a method to check whether a given polygon is convex or not. (A "polygon" in computer science is usually defined [as in the XFillPolygon documentation] as an ordered array of 2D points, with consecutive points joined with a side as well as the last point to the first.) Also, the gift wrapping algorithm in this case would have the time-complexity of O(n^2) for n points - which is much larger than actually needed to solve this problem, while the question asks for an efficient algorithm.
#JasonS's answer, along with the other answers that follow his idea, accepts star polygons such as a pentagram or the one in #zenna's comment, but star polygons are not considered to be convex. As
#plasmacel notes in a comment, this is a good approach to use if you have prior knowledge that the polygon is not self-intersecting, but it can fail if you do not have that knowledge.
#Sekhat's answer is correct but it also has the time-complexity of O(n^2) and thus is inefficient.
#LorenPechtel's added answer after her edit is the best one here but it is vague.
A correct algorithm with optimal complexity
The algorithm I present here has the time-complexity of O(n), correctly tests whether a polygon is convex or not, and passes all the tests I have thrown at it. The idea is to traverse the sides of the polygon, noting the direction of each side and the signed change of direction between consecutive sides. "Signed" here means left-ward is positive and right-ward is negative (or the reverse) and straight-ahead is zero. Those angles are normalized to be between minus-pi (exclusive) and pi (inclusive). Summing all these direction-change angles (a.k.a the deflection angles) together will result in plus-or-minus one turn (i.e. 360 degrees) for a convex polygon, while a star-like polygon (or a self-intersecting loop) will have a different sum ( n * 360 degrees, for n turns overall, for polygons where all the deflection angles are of the same sign). So we must check that the sum of the direction-change angles is plus-or-minus one turn. We also check that the direction-change angles are all positive or all negative and not reverses (pi radians), all points are actual 2D points, and that no consecutive vertices are identical. (That last point is debatable--you may want to allow repeated vertices but I prefer to prohibit them.) The combination of those checks catches all convex and non-convex polygons.
Here is code for Python 3 that implements the algorithm and includes some minor efficiencies. The code looks longer than it really is due to the the comment lines and the bookkeeping involved in avoiding repeated point accesses.
TWO_PI = 2 * pi
def is_convex_polygon(polygon):
"""Return True if the polynomial defined by the sequence of 2D
points is 'strictly convex': points are valid, side lengths non-
zero, interior angles are strictly between zero and a straight
angle, and the polygon does not intersect itself.
NOTES: 1. Algorithm: the signed changes of the direction angles
from one side to the next side must be all positive or
all negative, and their sum must equal plus-or-minus
one full turn (2 pi radians). Also check for too few,
invalid, or repeated points.
2. No check is explicitly done for zero internal angles
(180 degree direction-change angle) as this is covered
in other ways, including the `n < 3` check.
"""
try: # needed for any bad points or direction changes
# Check for too few points
if len(polygon) < 3:
return False
# Get starting information
old_x, old_y = polygon[-2]
new_x, new_y = polygon[-1]
new_direction = atan2(new_y - old_y, new_x - old_x)
angle_sum = 0.0
# Check each point (the side ending there, its angle) and accum. angles
for ndx, newpoint in enumerate(polygon):
# Update point coordinates and side directions, check side length
old_x, old_y, old_direction = new_x, new_y, new_direction
new_x, new_y = newpoint
new_direction = atan2(new_y - old_y, new_x - old_x)
if old_x == new_x and old_y == new_y:
return False # repeated consecutive points
# Calculate & check the normalized direction-change angle
angle = new_direction - old_direction
if angle <= -pi:
angle += TWO_PI # make it in half-open interval (-Pi, Pi]
elif angle > pi:
angle -= TWO_PI
if ndx == 0: # if first time through loop, initialize orientation
if angle == 0.0:
return False
orientation = 1.0 if angle > 0.0 else -1.0
else: # if other time through loop, check orientation is stable
if orientation * angle <= 0.0: # not both pos. or both neg.
return False
# Accumulate the direction-change angle
angle_sum += angle
# Check that the total number of full turns is plus-or-minus 1
return abs(round(angle_sum / TWO_PI)) == 1
except (ArithmeticError, TypeError, ValueError):
return False # any exception means not a proper convex polygon
The following Java function/method is an implementation of the algorithm described in this answer.
public boolean isConvex()
{
if (_vertices.size() < 4)
return true;
boolean sign = false;
int n = _vertices.size();
for(int i = 0; i < n; i++)
{
double dx1 = _vertices.get((i + 2) % n).X - _vertices.get((i + 1) % n).X;
double dy1 = _vertices.get((i + 2) % n).Y - _vertices.get((i + 1) % n).Y;
double dx2 = _vertices.get(i).X - _vertices.get((i + 1) % n).X;
double dy2 = _vertices.get(i).Y - _vertices.get((i + 1) % n).Y;
double zcrossproduct = dx1 * dy2 - dy1 * dx2;
if (i == 0)
sign = zcrossproduct > 0;
else if (sign != (zcrossproduct > 0))
return false;
}
return true;
}
The algorithm is guaranteed to work as long as the vertices are ordered (either clockwise or counter-clockwise), and you don't have self-intersecting edges (i.e. it only works for simple polygons).
Here's a test to check if a polygon is convex.
Consider each set of three points along the polygon--a vertex, the vertex before, the vertex after. If every angle is 180 degrees or less you have a convex polygon. When you figure out each angle, also keep a running total of (180 - angle). For a convex polygon, this will total 360.
This test runs in O(n) time.
Note, also, that in most cases this calculation is something you can do once and save — most of the time you have a set of polygons to work with that don't go changing all the time.
To test if a polygon is convex, every point of the polygon should be level with or behind each line.
Here's an example picture:
The answer by #RoryDaulton
seems the best to me, but what if one of the angles is exactly 0?
Some may want such an edge case to return True, in which case, change "<=" to "<" in the line :
if orientation * angle < 0.0: # not both pos. or both neg.
Here are my test cases which highlight the issue :
# A square
assert is_convex_polygon( ((0,0), (1,0), (1,1), (0,1)) )
# This LOOKS like a square, but it has an extra point on one of the edges.
assert is_convex_polygon( ((0,0), (0.5,0), (1,0), (1,1), (0,1)) )
The 2nd assert fails in the original answer. Should it?
For my use case, I would prefer it didn't.
This method would work on simple polygons (no self intersecting edges) assuming that the vertices are ordered (either clockwise or counter)
For an array of vertices:
vertices = [(0,0),(1,0),(1,1),(0,1)]
The following python implementation checks whether the z component of all the cross products have the same sign
def zCrossProduct(a,b,c):
return (a[0]-b[0])*(b[1]-c[1])-(a[1]-b[1])*(b[0]-c[0])
def isConvex(vertices):
if len(vertices)<4:
return True
signs= [zCrossProduct(a,b,c)>0 for a,b,c in zip(vertices[2:],vertices[1:],vertices)]
return all(signs) or not any(signs)
I implemented both algorithms: the one posted by #UriGoren (with a small improvement - only integer math) and the one from #RoryDaulton, in Java. I had some problems because my polygon is closed, so both algorithms were considering the second as concave, when it was convex. So i changed it to prevent such situation. My methods also uses a base index (which can be or not 0).
These are my test vertices:
// concave
int []x = {0,100,200,200,100,0,0};
int []y = {50,0,50,200,50,200,50};
// convex
int []x = {0,100,200,100,0,0};
int []y = {50,0,50,200,200,50};
And now the algorithms:
private boolean isConvex1(int[] x, int[] y, int base, int n) // Rory Daulton
{
final double TWO_PI = 2 * Math.PI;
// points is 'strictly convex': points are valid, side lengths non-zero, interior angles are strictly between zero and a straight
// angle, and the polygon does not intersect itself.
// NOTES: 1. Algorithm: the signed changes of the direction angles from one side to the next side must be all positive or
// all negative, and their sum must equal plus-or-minus one full turn (2 pi radians). Also check for too few,
// invalid, or repeated points.
// 2. No check is explicitly done for zero internal angles(180 degree direction-change angle) as this is covered
// in other ways, including the `n < 3` check.
// needed for any bad points or direction changes
// Check for too few points
if (n <= 3) return true;
if (x[base] == x[n-1] && y[base] == y[n-1]) // if its a closed polygon, ignore last vertex
n--;
// Get starting information
int old_x = x[n-2], old_y = y[n-2];
int new_x = x[n-1], new_y = y[n-1];
double new_direction = Math.atan2(new_y - old_y, new_x - old_x), old_direction;
double angle_sum = 0.0, orientation=0;
// Check each point (the side ending there, its angle) and accum. angles for ndx, newpoint in enumerate(polygon):
for (int i = 0; i < n; i++)
{
// Update point coordinates and side directions, check side length
old_x = new_x; old_y = new_y; old_direction = new_direction;
int p = base++;
new_x = x[p]; new_y = y[p];
new_direction = Math.atan2(new_y - old_y, new_x - old_x);
if (old_x == new_x && old_y == new_y)
return false; // repeated consecutive points
// Calculate & check the normalized direction-change angle
double angle = new_direction - old_direction;
if (angle <= -Math.PI)
angle += TWO_PI; // make it in half-open interval (-Pi, Pi]
else if (angle > Math.PI)
angle -= TWO_PI;
if (i == 0) // if first time through loop, initialize orientation
{
if (angle == 0.0) return false;
orientation = angle > 0 ? 1 : -1;
}
else // if other time through loop, check orientation is stable
if (orientation * angle <= 0) // not both pos. or both neg.
return false;
// Accumulate the direction-change angle
angle_sum += angle;
// Check that the total number of full turns is plus-or-minus 1
}
return Math.abs(Math.round(angle_sum / TWO_PI)) == 1;
}
And now from Uri Goren
private boolean isConvex2(int[] x, int[] y, int base, int n)
{
if (n < 4)
return true;
boolean sign = false;
if (x[base] == x[n-1] && y[base] == y[n-1]) // if its a closed polygon, ignore last vertex
n--;
for(int p=0; p < n; p++)
{
int i = base++;
int i1 = i+1; if (i1 >= n) i1 = base + i1-n;
int i2 = i+2; if (i2 >= n) i2 = base + i2-n;
int dx1 = x[i1] - x[i];
int dy1 = y[i1] - y[i];
int dx2 = x[i2] - x[i1];
int dy2 = y[i2] - y[i1];
int crossproduct = dx1*dy2 - dy1*dx2;
if (i == base)
sign = crossproduct > 0;
else
if (sign != (crossproduct > 0))
return false;
}
return true;
}
For a non complex (intersecting) polygon to be convex, vector frames obtained from any two connected linearly independent lines a,b must be point-convex otherwise the polygon is concave.
For example the lines a,b are convex to the point p and concave to it below for each case i.e. above: p exists inside a,b and below: p exists outside a,b
Similarly for each polygon below, if each line pair making up a sharp edge is point-convex to the centroid c then the polygon is convex otherwise it’s concave.
blunt edges (wronged green) are to be ignored
N.B
This approach would require you compute the centroid of your polygon beforehand since it doesn’t employ angles but vector algebra/transformations
Adapted Uri's code into matlab. Hope this may help.
Be aware that Uri's algorithm only works for simple polygons! So, be sure to test if the polygon is simple first!
% M [ x1 x2 x3 ...
% y1 y2 y3 ...]
% test if a polygon is convex
function ret = isConvex(M)
N = size(M,2);
if (N<4)
ret = 1;
return;
end
x0 = M(1, 1:end);
x1 = [x0(2:end), x0(1)];
x2 = [x0(3:end), x0(1:2)];
y0 = M(2, 1:end);
y1 = [y0(2:end), y0(1)];
y2 = [y0(3:end), y0(1:2)];
dx1 = x2 - x1;
dy1 = y2 - y1;
dx2 = x0 - x1;
dy2 = y0 - y1;
zcrossproduct = dx1 .* dy2 - dy1 .* dx2;
% equality allows two consecutive edges to be parallel
t1 = sum(zcrossproduct >= 0);
t2 = sum(zcrossproduct <= 0);
ret = t1 == N || t2 == N;
end