I have some lines that their intersection describes a polygon, like this:
I know the order of the lines, and their equations.
To find the internal angles, I found each lines orientations. But I've got confused as subtracting two lines orientation would give two different angles, even if I do it in the order of polygon's sides.
For example, in the following image, if I just subtract the orientation of the lines, I would get any of the following angles:
What made me more confused, is when the polygon is not convex, I will have angles greater than 180, and using my approach I don't get the correct angle at all:
And I found out that this way of approaching the problem is wrong.
So, What is the best way of finding the internal angles using just the lines? I know for a convex polygon, I may find vectors and then find the angle between them, but even for P6 in my example the vector approach fails.
Anyway, I prefer a method that won't include a conditional case for solving that concavity problem.
Thanks.
With ordered lines it is possible to find points of intersection (polygon vertexes) in clockwise order. Then you can calculate internal angles:
Angle[i] = Pi + ArcTan2(V[i] x V[i+1], V[i] * V[i+1])
(crossproduct and dotproduct of incoming and outgoing vectors for every vertex)
or
Angle[i] = Pi + ArcTan2( dx_in*dy_out-dx_out*dy_in, dx_in*dx_out+dy_in*dy_out2 )
Note: change plus sign after Pi to minus for anti-clockwise direction.
Edit:
Note that crossproduct and dotproduct are scalars, not vectors.
Example for your data:
dx1 = 5; dy1 = -15; dx2 = -15; dy2 = 5
Angle = Pi + ArcTan2(5*5-15*15, -5*15-5*15) = Pi - 2.11 radians ~ 59 degrees
Example for vectors:
(0,-1) (1,0) (L-curve)
Angle = Pi + ArcTan2(1, 0) = 270 degrees
Related
I need to offset a curve, which by the simplest way is just shifting the points perpendicularly. I can access each point to calculate angle of each line along given path, for now I use atan2. Then I take those two angle and make average of it. It returns the shortest angle, not what I need in this case.
How can I calculate angle of each connection? Concerning that I am not interested in the shortest angle but the one that would create parallel offset curve.
Assuming 2D case...
So do a cross product of direction vectors of 2 neighboring lines the sign of z coordinate of the result will tell you if the lines are CW/CCW
So if you got 3 consequent control points on the polyline: p0,p1,p2 then:
d1 = p1-p0
d2 = p2-p1
if you use some 3D vector math then convert them to 3D by setting:
d1.z=0;
d2.z=0;
now compute 3D cross:
n = cross(d1,d2)
which returns vector perpendicular to both vectors of size equals to the area of quad (parallelogram) constructed with d1,d2 as base vectors. The direction (from the 2 possible) is determined by the winding rule of the p0,p1,p2 so inspecting z of the result is enough.
The n.x,n.y are not needed so you can compute directly without doing full cross product:
n.z=(d1.x*d2.y)-(d1.y*d2.x)
if (n.z>0) case1
if (n.z<0) case2
if the case1 is CW or CCW depends on your coordinate system properties (left/right handness). This approach is very commonly used in CG fur back face culling of polygons ...
if n.z is zero it means that your vectors/lines are either parallel or at lest one of them is zero.
I think these might interest you:
draw outline for some connected lines
How can I create an internal spiral for a polygon?
Also in 2D you do not need atan2 to get perpendicular vector... You can do instead this:
u = (x,y)
v = (-y,x)
w = (x,-y)
so u is any 2D vector and v,w are the 2 possible perpendicular vectors to u in 2D. they are the result of:
cross((x,y,0),(0,0,1))
cross((0,0,1),(x,y,0))
I'm interested in a fast way to calculate the rotation-independent center of a simple, convex, (non-intersecting) 2D polygon.
The example below (on the left) shows the mean center (sum of all points divided by the total), and the desired result on the right.
Some options I've already considered.
bound-box center (depends on rotation, and ignores points based on their relation to the axis).
Straight skeleton - too slow to calculate.
I've found a way which works reasonably well, (weight the points by the edge-lengths) - but this means a square-root call for every edge - which I'd like to avoid.(Will post as an answer, even though I'm not entirely satisfied with it).
Note, I'm aware of this questions similarity with:What is the fastest way to find the "visual" center of an irregularly shaped polygon?
However having to handle convex polygons increases the complexity of the problem significantly.
The points of the polygon can be weighted by their edge length which compensates for un-even point distribution.
This works for convex polygons too but in that case the center point isn't guaranteed to be inside the polygon.
Psudo-code:
def poly_center(poly):
sum_center = (0, 0)
sum_weight = 0.0
for point in poly:
weight = ((point - point.next).length +
(point - point.prev).length)
sum_center += point * weight
sum_weight += weight
return sum_center / sum_weight
Note, we can pre-calculate all edge lengths to halve the number of length calculations, or reuse the previous edge-length for half+1 length calculations. This is just written as an example to show the logic.
Including this answer for completeness since its the best method I've found so far.
There is no much better way than the accumulation of coordinates weighted by the edge length, which indeed takes N square roots.
If you accept an approximation, it is possible to skip some of the vertices by curve simplification, as follows:
decide of a deviation tolerance;
start from vertex 0 and jump to vertex M (say M=N/2);
check if the deviation along the polyline from 0 to M exceeds the tolerance (for this, compute the height of the triangle formed by the vertices 0, M/2, M);
if the deviation is exceeded, repeat recursively with 0, M/4, M/2 and M/2, 3M/4, M;
if the deviation is not exceeded, assume that the shape is straight between 0 and M.
continue until the end of the polygon.
Where the points are dense (like the left edge on your example), you should get some speedup.
I think its easiest to do something with the center of masses of the delaunay triangulation of the polygon points. i.e.
def _centroid_poly(poly):
T = spatial.Delaunay(poly).simplices
n = T.shape[0]
W = np.zeros(n)
C = 0
for m in range(n):
sp = poly[T[m,:],:]
W[m] = spatial.ConvexHull(sp).volume
C += W[m] +np.mean(sp, axis = 0)
return C / np.sum(W)
This works well for me!
I'm trying to find the best way to calculate this. On a 2D plane I have fixed points all with an instantaneous measurement value. The coordinates of these points is known. I want to predict the value of a movable point between these fixed points. The movable point coodinates will be known. So the distance betwwen the points is known as well.
This could be comparable to temperature readings or elevation on topography. I this case I'm wanting to predict ionospheric TEC of the mobile point from the fixed point measurements. The fixed point measurements are smoothed over time however I do not want to have to store previous values of the mobile point estimate in RAM.
Would some sort of gradient function be the way to go here?
This is the same algorithm for interpolating the height of a point from a triangle.
In your case you don't have z values for heights, but some other float value for each triangle vertex, but it's the same concept, still 3D points.
Where you have 3D triangle points p, q, r and test point pt, then pseudo code from the above mathgem is something like this:
Vector3 v1 = q - p;
Vector3 v2 = r - p;
Vector3 n = v1.CrossProduct(v2);
if n.z is not zero
return ((n.x * (pt.x - p.x) + n.y * (pt.y - p.y)) / -n.z) + p.z
As you indicate in your comment to #Phpdevpad, you do have 3 fixed points so this will work.
You can try contour plots especially contour lines. Simply use a delaunay triangulation of the points and a linear transformation along the edges. You can try my PHP implementations https://contourplot.codeplex.com for geographic maps. Another algorithm is conrec algorithm from Paul Bourke.
I have the plane equation describing the points belonging to a plane in 3D and the origin of the normal X, Y, Z. This should be enough to be able to generate something like a 3D arrow. In pcl this is possible via the viewer but I would like to actually store those 3D points inside the cloud. How to generate them then ? A cylinder with a cone on top ?
To generate a line perpendicular to the plane:
You have the plane equation. This gives you the direction of the normal to the plane. If you used PCL to get the plane, this is in ModelCoefficients. See the details here: SampleConsensusModelPerpendicularPlane
The first step is to make a line perpendicular to the normal at the point you mention (X,Y,Z). Let (NORMAL_X,NORMAL_Y,NORMAL_Z) be the normal you got from your plane equation. Something like.
pcl::PointXYZ pnt_on_line;
for(double distfromstart=0.0;distfromstart<LINE_LENGTH;distfromstart+=DISTANCE_INCREMENT){
pnt_on_line.x = X + distfromstart*NORMAL_X;
pnt_on_line.y = Y + distfromstart*NORMAL_Y;
pnt_on_line.z = Z + distfromstart*NORMAL_Z;
my_cloud.points.push_back(pnt_on_line);
}
Now you want to put a hat on your arrow and now pnt_on_line contains the end of the line exactly where you want to put it. To make the cone you could loop over angle and distance along the arrow, calculate a local x and y and z from that and convert them to points in point cloud space: the z part would be converted into your point cloud's frame of reference by multiplying with the normal vector as with above, the x and y would be multiplied into vectors perpendicular to this normal vectorE. To get these, choose an arbitrary unit vector perpendicular to the normal vector (for your x axis) and cross product it with the normal vector to find the y axis.
The second part of this explanation is fairly terse but the first part may be the more important.
Update
So possibly the best way to describe how to do the cone is to start with a cylinder, which is an extension of the line described above. In the case of the line, there is (part of) a one dimensional manifold embedded in 3D space. That is we have one variable that we loop over adding points. The cylinder is a two dimensional object so we have to loop over two dimensions: the angle and the distance. In the case of the line we already have the distance. So the above loop would now look like:
for(double distfromstart=0.0;distfromstart<LINE_LENGTH;distfromstart+=DISTANCE_INCREMENT){
for(double angle=0.0;angle<2*M_PI;angle+=M_PI/8){
//calculate coordinates of point and add to cloud
}
}
Now in order to calculate the coordinates of the new point, well we already have the point on the line, now we just need to add it to a vector to move it away from the line in the appropriate direction of the angle. Let's say the radius of our cylinder will be 0.1, and let's say an orthonormal basis that we have already calculated perpendicular to the normal of the plane (which we will see how to calculate later) is perpendicular_1 and perpendicular_2 (that is, two vectors perpendicular to each other, of length 1, also perpendicular to the vector (NORMAL_X,NORMAL_Y,NORMAL_Z)):
//calculate coordinates of point and add to cloud
pnt_on_cylinder.x = pnt_on_line.x + 0.1 * perpendicular_1.x * 0.1 * cos(angle) + perpendicular_2.x * sin(angle)
pnt_on_cylinder.y = pnt_on_line.y + perpendicular_1.y * 0.1 * cos(angle) + perpendicular_2.y * 0.1 * sin(angle)
pnt_on_cylinder.z = pnt_on_line.z + perpendicular_1.z * 0.1 * cos(angle) + perpendicular_2.z * 0.1 * sin(angle)
my_cloud.points.push_back(pnt_on_cylinder);
Actually, this is a vector summation and if we were to write the operation as vectors it would look like:
pnt_on_line+perpendicular_1*cos(angle)+perpendicular_2*sin(angle)
Now I said I would talk about how to calculate perpendicular_1 and perpendicular_2. Let K be any unit vector that is not parallel to (NORMAL_X,NORMAL_Y,NORMAL_Z) (this can be found by trying e.g. (1,0,0) then (0,1,0)).
Then
perpendicular_1 = K X (NORMAL_X,NORMAL_Y,NORMAL_Z)
perpendicular_2 = perpendicular_1 X (NORMAL_X,NORMAL_Y,NORMAL_Z)
Here X is the vector cross product and the above are vector equations. Note also that the original calculation of pnt_on_line involved a vector dot product and a vector summation (I am just writing this for completeness of the exposition).
If you can manage this then the cone is easy just by changing a couple of things in the double loop: the radius just changes along its length until it is zero at the end of the loop and in the loop distfromstart will not start at 0.
I am trying to reconstruct original graphics primitives from Postscript/SVG paths. Thus an original circle is rendered (in SVG markup) as:
<path stroke-width="0.5" d="M159.679 141.309
C159.679 141.793 159.286 142.186 158.801 142.186
C158.318 142.186 157.925 141.793 157.925 141.309
C157.925 140.825 158.318 140.432 158.801 140.432
C159.286 140.432 159.679 140.825 159.679 141.309" />
This is an approximation using 4 Beziers curves to create a circle.In other places circular arcs are approximated by linked Bezier curves.
My question is whether there is an algorithm I can use to recognize this construct and reconstruct the "best" circle. I don't mind small errors - they will be second-order at worst.
UPDATE: Note that I don't know a priori that this is a circle or an arc - it could be anything. And there could be 2, 3 4 or possibly even more points on the curve. So I'd really like a function of the sort:
error = getCircleFromPath(path)
where error will give an early indication of whether this is likely to be a circle.
[I agree that if I know it's a circle it's an easier problem.]
UPDATE: #george goes some way towards answering my problem but I don't think it's the whole story.
After translation to the origin and normalization I appear to have the following four points on the curve:
point [0, 1] with control point at [+-d,1] // horizontal tangent
point [1, 0] with control point at [1,+-d] // vertical tangent
point [0, -1] with control point at [+-d,-1] // horizontal tangent
point [-1, 0] with control point at [-1,+-d] // vertical tangent
This guarantees that the tangent at each point is "parallel" to the path direction at the point. It also guarantees the symmetry (4-fold axis with reflection. But it does not guarantee a circle. For example a large value of d will give a rounded box and a small value a rounded diamond.
My value of d appears to be about 0.57. This might be 1/sqrt(3.) or it might be something else.It is this sort of relationship I am asking for.
#george gives midpoint of arc as;
{p1,(p1 + 3 (p2 + p3) + p4)/8,p4}
so in my example (for 1,0 to 0,1) this would be:
[[1,0]+3[1,d]+3[d,1]+[0,1]] / 8
i.e.
[0.5+3d/8, 3d/8+0.5]
and if d =0.57, this gives 0.71, so maybe d is
(sqrt(0.5)-0.5)*8./3.
This holds for a square diamond, but for circular arcs the formula must be more general and I'd be grateful if anyone has it. For example, I am not familiar with Bezier math, so #george's formula was new to me
enter code here
Without doing all the math for you.. this may help:
there are always 4 control points on a bezier.
Your curve is 4 beziers linked together with points 1-4 , 4-7 , 7-10 , and 10-13 the control points
for each part. Points 1 , 4 , 7 and 10 (&13==1) lie exactly on the curve. To see if you have a nice circle calculate:
center = ( p1+p7 )/2 =( {159.679, 141.309} + {157.925, 141.309} ) / 2
= {158.802, 141.309}
verify you get the same result using points 4+10 -> {158.801, 141.309}
Once you know the center you can sample points along the curve and see if you have a constant distance.
If you only have a single bezier arc with 4 points a useful formula is that the midpoint is at
(p1 + 3 (p2 + p3) + p4)/8. So you can find the circle passing through three points:
{p1,(p1 + 3 (p2 + p3) + p4)/8,p4}
and again sample other points on the curve to decide if you indeed have a near circular arc.
Edit
the bezier formula is this:
x=(1-t)^3 p1 + 3 (1-t)^2 t p2 + 3 (1-t) t^2 p3 + t^3 p4 with parameter 0 < t < 1
so for example at t=1/4 you have
x=( 27 p1 + 27 p2 + 9 p3 + 1 p4 ) / 64
so once you find the center you can readily check a few points and calculate their distance.
I suspect if you only want to detect nearly exact circular arcs then checking two extra points with a tight tolerance will do the job. If you want to detect things that are approximately circular I would compute a bunch of points and use the average error as a criteria.
If all your elements are circle-like then you can just get the dimensions through path.getBBox() and generate a circle from there. In this case I'm considering ellipses, but you can easily translate it to actual circle elements:
var path = document.getElementById("circle_path");
var bbox = path.getBBox();
var rx = bbox.width/2;
var ry = bbox.height/2;
var cx = bbox.x + rx;
var cy = bbox.y + ry;
var ellipse = document.createElementNS(xmlns, "ellipse");
ellipse.setAttribute("fill", "none");
ellipse.setAttribute("stroke", "red");
ellipse.setAttribute("stroke-width", 0.1);
ellipse.setAttribute("cx", cx);
ellipse.setAttribute("cy", cy);
ellipse.setAttribute("rx", rx);
ellipse.setAttribute("ry", ry);
svg.appendChild(ellipse);
You can see a demo here:
http://jsfiddle.net/nwHm6/
The endpoints of the Bézier curves are probably on the circle. If so, it's easy to reconstruct the original circle.
Another possibility is to take the barycenter of the control points as the center of the circle because the control points are probably laid out symmetrically around the center. From the center, you get the radius as the average distance of the four control points closest to the center.
One can define an ellipse as a unit circle centred on (0,0), translated (2 params), scaled (2 params), and rotated (1 param). So on each arc take five points (t=0 ¼ ½ ¾ 1) and solve for these five parameters. Next take the in-between four points (t=⅛ ⅜ ⅝ ⅞), and test whether these lie on the same transformed circle. If yes, whoopee!, this is (part of) a transformed circle.
Immediately before and after might be another arc or arcn. Are these the same ellipse? If yes, and the subtended angles touch, then join together your descriptions of the pieces.