Related
(More info at end)----->
I am trying to render a small picture-in-picture display over my scene. The PiP is just a smaller texture, but it is intended to reveal secret objects in the scene when it is placed over them.
To do this, I want to render my scene, then render the SAME scene on the smaller texture, but with the exact same positioning as the main scene. The intended result would be something like this:
My problem is... I cannot get the scene on the smaller texture to match up 1:1. I keep trying various kludges, but ultimately I suspect that I need to do something to the projection matrix to pan it over to the location of the frame. I can get it to zoom correctly...just can't get it to pan.
Can anyone suggest what I need to do to my projection matrix to render my scene 1:1 (but panned by x,y) onto a smaller texture?
The data I have:
Resolution of the full-screen framebuffer
Resolution of the smaller texture
XY coordinate where I want to draw the smaller texture as an overlay sprite
The world/view/projection matrices from the original full-screen scene
The viewport from the original full-screen scene
(Edit)
Here is the function I use to produce the 3D camera:
void Make3DCamera(Vector theCameraPos, Vector theLookAt, Vector theUpVector, float theFOV, Point theRez, Matrix& theViewMatrix,Matrix& theProjectionMatrix)
{
Matrix aCombinedViewMatrix;
Matrix aViewMatrix;
aCombinedViewMatrix.Scale(1,1,-1);
theCameraPos.mZ*=-1;
theLookAt.mZ*=-1;
theUpVector.mZ*=-1;
aCombinedViewMatrix.Translate(-theCameraPos);
Vector aLookAtVector=theLookAt-theCameraPos;
Vector aSideVector=theUpVector.Cross(aLookAtVector);
theUpVector=aLookAtVector.Cross(aSideVector);
aLookAtVector.Normalize();
aSideVector.Normalize();
theUpVector.Normalize();
aViewMatrix.mData.m[0][0] = -aSideVector.mX;
aViewMatrix.mData.m[1][0] = -aSideVector.mY;
aViewMatrix.mData.m[2][0] = -aSideVector.mZ;
aViewMatrix.mData.m[3][0] = 0;
aViewMatrix.mData.m[0][1] = -theUpVector.mX;
aViewMatrix.mData.m[1][1] = -theUpVector.mY;
aViewMatrix.mData.m[2][1] = -theUpVector.mZ;
aViewMatrix.mData.m[3][1] = 0;
aViewMatrix.mData.m[0][2] = aLookAtVector.mX;
aViewMatrix.mData.m[1][2] = aLookAtVector.mY;
aViewMatrix.mData.m[2][2] = aLookAtVector.mZ;
aViewMatrix.mData.m[3][2] = 0;
aViewMatrix.mData.m[0][3] = 0;
aViewMatrix.mData.m[1][3] = 0;
aViewMatrix.mData.m[2][3] = 0;
aViewMatrix.mData.m[3][3] = 1;
if (gG.mRenderToSprite) aViewMatrix.Scale(1,-1,1);
aCombinedViewMatrix*=aViewMatrix;
// Projection Matrix
float aAspect = (float) theRez.mX / (float) theRez.mY;
float aNear = gG.mZRange.mData1;
float aFar = gG.mZRange.mData2;
float aWidth = gMath.Cos(theFOV / 2.0f);
float aHeight = gMath.Cos(theFOV / 2.0f);
if (aAspect > 1.0) aWidth /= aAspect;
else aHeight *= aAspect;
float s = gMath.Sin(theFOV / 2.0f);
float d = 1.0f - aNear / aFar;
Matrix aPerspectiveMatrix;
aPerspectiveMatrix.mData.m[0][0] = aWidth;
aPerspectiveMatrix.mData.m[1][0] = 0;
aPerspectiveMatrix.mData.m[2][0] = gG.m3DOffset.mX/theRez.mX/2;
aPerspectiveMatrix.mData.m[3][0] = 0;
aPerspectiveMatrix.mData.m[0][1] = 0;
aPerspectiveMatrix.mData.m[1][1] = aHeight;
aPerspectiveMatrix.mData.m[2][1] = gG.m3DOffset.mY/theRez.mY/2;
aPerspectiveMatrix.mData.m[3][1] = 0;
aPerspectiveMatrix.mData.m[0][2] = 0;
aPerspectiveMatrix.mData.m[1][2] = 0;
aPerspectiveMatrix.mData.m[2][2] = s / d;
aPerspectiveMatrix.mData.m[3][2] = -(s * aNear / d);
aPerspectiveMatrix.mData.m[0][3] = 0;
aPerspectiveMatrix.mData.m[1][3] = 0;
aPerspectiveMatrix.mData.m[2][3] = s;
aPerspectiveMatrix.mData.m[3][3] = 0;
theViewMatrix=aCombinedViewMatrix;
theProjectionMatrix=aPerspectiveMatrix;
}
Edit to add more information:
Just playing and tweaking numbers, I have come to a "close" result. However the "close" result requires a multiplication by some kludge numbers, that I don't understand.
Here's what I'm doing to to perspective matrix to produce my close result:
//Before calling Make3DCamera, adjusting FOV:
aFOV*=smallerTexture.HeightF()/normalRenderSize.HeightF(); // Zoom it
aFOV*=1.02f // <- WTH is this?
//Then, to pan the camera over to the x/y position I want, I do:
Matrix aPM=GetCurrentProjectionMatrix();
float aX=(screenX-normalRenderSize.WidthF()/2.0f)/2.0f;
float aY=(screenY-normalRenderSize.HeightF()/2.0f)/2.0f;
aX*=1.07f; // <- WTH is this?
aY*=1.07f; // <- WTH is this?
aPM.mData.m[2][0]=-aX/normalRenderSize.HeightF();
aPM.mData.m[2][1]=-aY/normalRenderSize.HeightF();
SetCurrentProjectionMatrix(aPM);
When I do this, my new picture is VERY close... but not exactly perfect-- the small render tends to drift away from "center" the further the "magic window" is from the center. Without the kludge number, the drift away from center with the magic window is very pronounced.
The kludge numbers 1.02f for zoom and 1.07 for pan reduce the inaccuracies and drift to a fraction of a pixel, but those numbers must be a ratio from somewhere, right? They work at ANY RESOLUTION, though-- so I have have a 1280x800 screen and a 256,256 magic window texture... if I change the screen to 1024x768, it all still works.
Where the heck are these numbers coming from?
If you don't care about sub-optimal performance (i.e., drawing the whole scene twice) and if you don't need the smaller scene in a texture, an easy way to obtain the overlay with pixel perfect precision is:
Set up main scene (model/view/projection matrices, etc.) and draw it as you are now.
Use glScissor to set the rectangle for the overlay. glScissor takes the screen-space x, y, width, and height and discards anything outside that rectangle. It looks like you have those four data items already, so you should be good to go.
Call glEnable(GL_SCISSOR_TEST) to actually turn on the test.
Set the shader variables (if you're using shaders) for drawing the greyscale scene/hidden objects/etc. You still use the same view and projection matrices that you used for the main scene.
Draw the greyscale scene/hidden objects/etc.
Call glDisable(GL_SCISSOR_TEST) so you won't be scissoring at the start of the next frame.
Draw the red overlay border, if desired.
Now, if you actually need the overlay in its own texture for some reason, this probably won't be adequate...it could be made to work either with framebuffer objects and/or pixel readback, but this would be less efficient.
Most people completely overcomplicate such issues. There is absolutely no magic to applying transformations after applying the projection matrix.
If you have a projection matrix P (and I'm assuming default OpenGL conventions here where P is constructed in a way that the vector is post-multiplied to the matrix, so for an eye space vector v_eye, we get v_clip = P * v_eye), you can simply pre-multiply some other translate and scale transforms to cut out any region of interest.
Assume you have a viewport of size w_view * h_view pixels, and you want to find a projection matrix which renders only a tile w_tile * h_tile pixels , beginning at pixel location (x_tile, y_tile) (again, assuming default GL conventions here, window space origin is bottom left, so y_tile is measured from the bottom). Also note that the _tile coordinates are to be interpreted relative to the viewport, in the typical case, that would start at (0,0) and have the size of your full framebuffer, but this is by no means required nor assumed here.
Since after applying the projection matrix we are in clip space, we need to transform our coordinates from window space pixels to clip space. Note that clip space is a 4D homogeneous space, but we can use any w value we like (except 0) to represent any point (as a point in the 3D space we care about forms a line in the 4D space we work in), so let's just use w=1 for simplicity's sake.
The view volume in clip space is denoted by the [-w,w] range, so in the w=1 hyperplane, it is [-1,1]. Converting our tile into this space yields:
x_clip = 2 * (x_tile / w_view) -1
y_clip = 2 * (y_tile / h_view) -1
w_clip = 2 * (w_tile / w_view) -1
h_clip = 2 * (h_tile / h_view) -1
We now just need to translate the objects such that the center of the tile is moved to the center of the view volume, which by definition is the origin, and scale the w_clip * h_clip sized region to the full [-1,1] extent in each dimension.
That means:
T = translate(-(x_clip + 0.5*w_clip), -(y_clip + 0.5 *h_clip), 0)
S = scale(2.0/w_clip, 2.0/h_clip, 1.0)
We can now create the modified projection matrix P' as P' = S * T * P, and that's all there is. Rendering with P' instead of P will render exactly the region of your tile to whatever viewport you are using, so for it to be pixel-exact with respect to your original viewport, you must now render with a viewport which is also w_tile * h_tile pixels big.
Note that there is also another approach: The viewport is not clamped against the framebuffer you're rendering to. It is actually valid to provide negative values for x and y. If your framebuffer for rendering your tile into is exactly w_tile * h_tile pixels, you simply could set glViewport(-x_tile, -y_tile, x_tile + w_tile, y_tile + h_tile) and render with the unmodified projection matrix P instead.
I want to create a graphic calculator and Im stuck with the graph bit. I want to know how to plot a graph for sin(x) cos(x) tan(x). I have made the grid already. I dont want to use core plot framework.
Any help would be appreciated.
Thanks.
To actually plot the function, do like you would with paper and pencil: evaluate the function for a number of inputs. Then draw lines to connect the resulting points.
Not that I would actually do this (I would look at Core Plot), but you could plot such a graph using a Core Image generator filter, like this:
//wavelength and magnitude are distances in destination pixels. Think of them as the width and height of each wave.
kernel vec4 sineWave(float wavelength, float magnitude, __color color)
{
vec2 coord = destCoord();
coord.y -= magnitude;
coord /= vec2(wavelength, magnitude / 2.0);
float pi = radians(180.0);
float value = sin(coord.x * pi);
//Smaller threshold = finer wave line. For a gradient, replace the comparison with 1.0 - abs(…).
float threshold = 0.1;
float alpha = abs(coord.y - value) <= threshold;
return color * alpha;
}
Here is some pseudo-code that could answer your question:
for i = xmin to xmax do
{
draw XY point at X=(i*x_scale_factor+x_offset) and Y=(sin(i)*y_scale_factor+Y_offset);
}
And beware: don't use floats in for loops
EDIT in response to comments
The easiest way to proceed, IMHO, would be to get the bounds of your view, to get the min and max values of your data on both X and Y axis.
You then can use a NSAffineTransform instance to transform the coordinates of your drawings. So everything can be done in your graphic coordinates, which is easier. You can write a label at coordinates (4.6, 3.2*10-7) if you wish to. This is a key point to get you started. The road is long. But using NSAffineTransform will make it easier.
I'm not sure how to approach this problem. I'm not sure how complex a task it is. My aim is to have an algorithm that generates any polygon. My only requirement is that the polygon is not complex (i.e. sides do not intersect). I'm using Matlab for doing the maths but anything abstract is welcome.
Any aid/direction?
EDIT:
I was thinking more of code that could generate any polygon even things like this:
I took #MitchWheat and #templatetypedef's idea of sampling points on a circle and took it a bit farther.
In my application I need to be able to control how weird the polygons are, ie start with regular polygons and as I crank up the parameters they get increasingly chaotic. The basic idea is as stated by #templatetypedef; walk around the circle taking a random angular step each time, and at each step put a point at a random radius. In equations I'm generating the angular steps as
where theta_i and r_i give the angle and radius of each point relative to the centre, U(min, max) pulls a random number from a uniform distribution, and N(mu, sigma) pulls a random number from a Gaussian distribution, and clip(x, min, max) thresholds a value into a range. This gives us two really nice parameters to control how wild the polygons are - epsilon which I'll call irregularity controls whether or not the points are uniformly space angularly around the circle, and sigma which I'll call spikeyness which controls how much the points can vary from the circle of radius r_ave. If you set both of these to 0 then you get perfectly regular polygons, if you crank them up then the polygons get crazier.
I whipped this up quickly in python and got stuff like this:
Here's the full python code:
import math, random
from typing import List, Tuple
def generate_polygon(center: Tuple[float, float], avg_radius: float,
irregularity: float, spikiness: float,
num_vertices: int) -> List[Tuple[float, float]]:
"""
Start with the center of the polygon at center, then creates the
polygon by sampling points on a circle around the center.
Random noise is added by varying the angular spacing between
sequential points, and by varying the radial distance of each
point from the centre.
Args:
center (Tuple[float, float]):
a pair representing the center of the circumference used
to generate the polygon.
avg_radius (float):
the average radius (distance of each generated vertex to
the center of the circumference) used to generate points
with a normal distribution.
irregularity (float):
variance of the spacing of the angles between consecutive
vertices.
spikiness (float):
variance of the distance of each vertex to the center of
the circumference.
num_vertices (int):
the number of vertices of the polygon.
Returns:
List[Tuple[float, float]]: list of vertices, in CCW order.
"""
# Parameter check
if irregularity < 0 or irregularity > 1:
raise ValueError("Irregularity must be between 0 and 1.")
if spikiness < 0 or spikiness > 1:
raise ValueError("Spikiness must be between 0 and 1.")
irregularity *= 2 * math.pi / num_vertices
spikiness *= avg_radius
angle_steps = random_angle_steps(num_vertices, irregularity)
# now generate the points
points = []
angle = random.uniform(0, 2 * math.pi)
for i in range(num_vertices):
radius = clip(random.gauss(avg_radius, spikiness), 0, 2 * avg_radius)
point = (center[0] + radius * math.cos(angle),
center[1] + radius * math.sin(angle))
points.append(point)
angle += angle_steps[i]
return points
def random_angle_steps(steps: int, irregularity: float) -> List[float]:
"""Generates the division of a circumference in random angles.
Args:
steps (int):
the number of angles to generate.
irregularity (float):
variance of the spacing of the angles between consecutive vertices.
Returns:
List[float]: the list of the random angles.
"""
# generate n angle steps
angles = []
lower = (2 * math.pi / steps) - irregularity
upper = (2 * math.pi / steps) + irregularity
cumsum = 0
for i in range(steps):
angle = random.uniform(lower, upper)
angles.append(angle)
cumsum += angle
# normalize the steps so that point 0 and point n+1 are the same
cumsum /= (2 * math.pi)
for i in range(steps):
angles[i] /= cumsum
return angles
def clip(value, lower, upper):
"""
Given an interval, values outside the interval are clipped to the interval
edges.
"""
return min(upper, max(value, lower))
#MateuszKonieczny here is code to create an image of a polygon from a list of vertices.
vertices = generate_polygon(center=(250, 250),
avg_radius=100,
irregularity=0.35,
spikiness=0.2,
num_vertices=16)
black = (0, 0, 0)
white = (255, 255, 255)
img = Image.new('RGB', (500, 500), white)
im_px_access = img.load()
draw = ImageDraw.Draw(img)
# either use .polygon(), if you want to fill the area with a solid colour
draw.polygon(vertices, outline=black, fill=white)
# or .line() if you want to control the line thickness, or use both methods together!
draw.line(vertices + [vertices[0]], width=2, fill=black)
img.show()
# now you can save the image (img), or do whatever else you want with it.
There's a neat way to do what you want by taking advantage of the MATLAB classes DelaunayTri and TriRep and the various methods they employ for handling triangular meshes. The code below follows these steps to create an arbitrary simple polygon:
Generate a number of random points equal to the desired number of sides plus a fudge factor. The fudge factor ensures that, regardless of the result of the triangulation, we should have enough facets to be able to trim the triangular mesh down to a polygon with the desired number of sides.
Create a Delaunay triangulation of the points, resulting in a convex polygon that is constructed from a series of triangular facets.
If the boundary of the triangulation has more edges than desired, pick a random triangular facet on the edge that has a unique vertex (i.e. the triangle only shares one edge with the rest of the triangulation). Removing this triangular facet will reduce the number of boundary edges.
If the boundary of the triangulation has fewer edges than desired, or the previous step was unable to find a triangle to remove, pick a random triangular facet on the edge that has only one of its edges on the triangulation boundary. Removing this triangular facet will increase the number of boundary edges.
If no triangular facets can be found matching the above criteria, post a warning that a polygon with the desired number of sides couldn't be found and return the x and y coordinates of the current triangulation boundary. Otherwise, keep removing triangular facets until the desired number of edges is met, then return the x and y coordinates of triangulation boundary.
Here's the resulting function:
function [x, y, dt] = simple_polygon(numSides)
if numSides < 3
x = [];
y = [];
dt = DelaunayTri();
return
end
oldState = warning('off', 'MATLAB:TriRep:PtsNotInTriWarnId');
fudge = ceil(numSides/10);
x = rand(numSides+fudge, 1);
y = rand(numSides+fudge, 1);
dt = DelaunayTri(x, y);
boundaryEdges = freeBoundary(dt);
numEdges = size(boundaryEdges, 1);
while numEdges ~= numSides
if numEdges > numSides
triIndex = vertexAttachments(dt, boundaryEdges(:,1));
triIndex = triIndex(randperm(numel(triIndex)));
keep = (cellfun('size', triIndex, 2) ~= 1);
end
if (numEdges < numSides) || all(keep)
triIndex = edgeAttachments(dt, boundaryEdges);
triIndex = triIndex(randperm(numel(triIndex)));
triPoints = dt([triIndex{:}], :);
keep = all(ismember(triPoints, boundaryEdges(:,1)), 2);
end
if all(keep)
warning('Couldn''t achieve desired number of sides!');
break
end
triPoints = dt.Triangulation;
triPoints(triIndex{find(~keep, 1)}, :) = [];
dt = TriRep(triPoints, x, y);
boundaryEdges = freeBoundary(dt);
numEdges = size(boundaryEdges, 1);
end
boundaryEdges = [boundaryEdges(:,1); boundaryEdges(1,1)];
x = dt.X(boundaryEdges, 1);
y = dt.X(boundaryEdges, 2);
warning(oldState);
end
And here are some sample results:
The generated polygons could be either convex or concave, but for larger numbers of desired sides they will almost certainly be concave. The polygons are also generated from points randomly generated within a unit square, so polygons with larger numbers of sides will generally look like they have a "squarish" boundary (such as the lower right example above with the 50-sided polygon). To modify this general bounding shape, you can change the way the initial x and y points are randomly chosen (i.e. from a Gaussian distribution, etc.).
For a convex 2D polygon (totally off the top of my head):
Generate a random radius, R
Generate N random points on the circumference of a circle of Radius R
Move around the circle and draw straight lines between adjacent points on the circle.
As #templatetypedef and #MitchWheat said, it is easy to do so by generating N random angles and radii. It is important to sort the angles, otherwise it will not be a simple polygon. Note that I am using a neat trick to draw closed curves - I described it in here. By the way, the polygons might be concave.
Note that all of these polygons will be star shaped. Generating a more general polygon is not a simple problem at all.
Just to give you a taste of the problem - check out
http://www.cosy.sbg.ac.at/~held/projects/rpg/rpg.html
and http://compgeom.cs.uiuc.edu/~jeffe/open/randompoly.html.
function CreateRandomPoly()
figure();
colors = {'r','g','b','k'};
for i=1:5
[x,y]=CreatePoly();
c = colors{ mod(i-1,numel(colors))+1};
plotc(x,y,c);
hold on;
end
end
function [x,y]=CreatePoly()
numOfPoints = randi(30);
theta = randi(360,[1 numOfPoints]);
theta = theta * pi / 180;
theta = sort(theta);
rho = randi(200,size(theta));
[x,y] = pol2cart(theta,rho);
xCenter = randi([-1000 1000]);
yCenter = randi([-1000 1000]);
x = x + xCenter;
y = y + yCenter;
end
function plotc(x,y,varargin)
x = [x(:) ; x(1)];
y = [y(:) ; y(1)];
plot(x,y,varargin{:})
end
Here is a working port for Matlab of Mike Ounsworth solution. I did not optimized it for matlab. I might update the solution later for that.
function [points] = generatePolygon(ctrX, ctrY, aveRadius, irregularity, spikeyness, numVerts)
%{
Start with the centre of the polygon at ctrX, ctrY,
then creates the polygon by sampling points on a circle around the centre.
Randon noise is added by varying the angular spacing between sequential points,
and by varying the radial distance of each point from the centre.
Params:
ctrX, ctrY - coordinates of the "centre" of the polygon
aveRadius - in px, the average radius of this polygon, this roughly controls how large the polygon is, really only useful for order of magnitude.
irregularity - [0,1] indicating how much variance there is in the angular spacing of vertices. [0,1] will map to [0, 2pi/numberOfVerts]
spikeyness - [0,1] indicating how much variance there is in each vertex from the circle of radius aveRadius. [0,1] will map to [0, aveRadius]
numVerts - self-explanatory
Returns a list of vertices, in CCW order.
Website: https://stackoverflow.com/questions/8997099/algorithm-to-generate-random-2d-polygon
%}
irregularity = clip( irregularity, 0,1 ) * 2*pi/ numVerts;
spikeyness = clip( spikeyness, 0,1 ) * aveRadius;
% generate n angle steps
angleSteps = [];
lower = (2*pi / numVerts) - irregularity;
upper = (2*pi / numVerts) + irregularity;
sum = 0;
for i =1:numVerts
tmp = unifrnd(lower, upper);
angleSteps(i) = tmp;
sum = sum + tmp;
end
% normalize the steps so that point 0 and point n+1 are the same
k = sum / (2*pi);
for i =1:numVerts
angleSteps(i) = angleSteps(i) / k;
end
% now generate the points
points = [];
angle = unifrnd(0, 2*pi);
for i =1:numVerts
r_i = clip( normrnd(aveRadius, spikeyness), 0, 2*aveRadius);
x = ctrX + r_i* cos(angle);
y = ctrY + r_i* sin(angle);
points(i,:)= [(x),(y)];
angle = angle + angleSteps(i);
end
end
function value = clip(x, min, max)
if( min > max ); value = x; return; end
if( x < min ) ; value = min; return; end
if( x > max ) ; value = max; return; end
value = x;
end
I'm attempting to use Java3D to rotate a vector. My goal is create a transform that will make the vector parallel with the y-axis. To do this, I calculated the angle between the original vector and an identical vector except that it has a z value of 0 (original x, original y, 0 for z-value). I then did the same thing for the y-axis (original x, 0 for y-value, original z). I then used each angle to create two Transform3D objects, multiply them together and apply to the vector. My code is as follows:
Transform3D yRotation = new Transform3D();
Transform3D zRotation = new Transform3D();
//create new normal vector
Vector3f normPoint = new Vector3f (normal.getX(), normal.getY(), normal.getZ());
//****Z rotation methods*****
Vector3f newNormPointZ = new Vector3f(normal.getX(), normal.getY(),0.0F);
float zAngle = normPoint.angle(newNormPointZ);
zRotation.rotZ(zAngle);
//****Y rotation methods*****
Vector3f newNormPointY = new Vector3f(normal.getX(),0.0F, normal.getZ());
float yAngle = normPoint.angle(newNormPointY);
yRotation.rotY(yAngle);
//combine the two rotations
yRotation.mul(zRotation);
System.out.println("before trans normal = " +normPoint.x + ", "+normPoint.y+", "+normPoint.z);
//PRINT STATEMENT RETURNS: before trans normal = 0.069842085, 0.99316376, 0.09353002
//perform transform
yRotation.transform(normPoint);
System.out.println("normal trans = " +normPoint.x + ", "+normPoint.y+", "+normPoint.z);
//PRINT STATEMENT RETURNS: normal trans = 0.09016449, 0.99534255, 0.03411238
I was hoping the transform would produce x and z values of or very close to 0. While the logic makes sense to me, I'm obviously missing something..
If your goal is to rotate a vector parallel to the y axis, why can't you just manually set it using the magnitude of the vector and setting your vector to <0, MAGNITUDE, 0>?
Also, you should know that rotating a vector to be directly pointing +Y or -Y can cause some rotation implementations to break, since they operate according to the "world up" vector, or, <0,1,0>. You can solve this by building your own rotation system and using the "world out" vector <0,0,1> when rotating directly up.
If you have some other purpose for this, fastgraph helped me with building rotation matrices.
It's best to understand the math of what's going on so that you know what to do in the future.
I have the need to determine the bounding rectangle for a polygon at an arbitrary angle. This picture illustrates what I need to do:
alt text http://kevlar.net/RotatedBoundingRectangle.png
The pink rectangle is what I need to determine at various angles for simple 2d polygons.
Any solutions are much appreciated!
Edit:
Thanks for the answers, I got it working once I got the center points correct. You guys are awesome!
To get a bounding box with a certain angle, rotate the polygon the other way round by that angle. Then you can use the min/max x/y coordinates to get a simple bounding box and rotate that by the angle to get your final result.
From your comment it seems you have problems with getting the center point of the polygon. The center of a polygon should be the average of the coordinate sums of each point. So for points P1,...,PN, calculate:
xsum = p1.x + ... + pn.x;
ysum = p1.y + ... + pn.y;
xcenter = xsum / n;
ycenter = ysum / n;
To make this complete, I also add some formulas for the rotation involved. To rotate a point (x,y) around a center point (cx, cy), do the following:
// Translate center to (0,0)
xt = x - cx;
yt = y - cy;
// Rotate by angle alpha (make sure to convert alpha to radians if needed)
xr = xt * cos(alpha) - yt * sin(alpha);
yr = xt * sin(alpha) + yt * cos(alpha);
// Translate back to (cx, cy)
result.x = xr + cx;
result.y = yr + cx;
To get the smallest rectangle you should get the right angle. This can acomplished by an algorithm used in collision detection: oriented bounding boxes.
The basic steps:
Get all vertices cordinates
Build a covariance matrix
Find the eigenvalues
Project all the vertices in the eigenvalue space
Find max and min in every eigenvalue space.
For more information just google OBB "colision detection"
Ps: If you just project all vertices and find maximum and minimum you're making AABB (axis aligned bounding box). Its easier and requires less computational effort, but doesn't guarantee the minimum box.
I'm interpreting your question to mean "For a given 2D polygon, how do you calculate the position of a bounding rectangle for which the angle of orientation is predetermined?"
And I would do it by rotating the polygon against the angle of orientation, then use a simple search for its maximum and minimum points in the two cardinal directions using whatever search algorithm is appropriate for the structure the points of the polygon are stored in. (Simply put, you need to find the highest and lowest X values, and highest and lowest Y values.)
Then the minima and maxima define your rectangle.
You can do the same thing without rotating the polygon first, but your search for minimum and maximum points has to be more sophisticated.
To get a rectangle with minimal area enclosing a polygon, you can use a rotating calipers algorithm.
The key insight is that (unlike in your sample image, so I assume you don't actually require minimal area?), any such minimal rectangle is collinear with at least one edge of (the convex hull of) the polygon.
Here is a python implementation for the answer by #schnaader.
Given a pointset with coordinates x and y and the degree of the rectangle to bound those points, the function returns a point set with the four corners (and a repetition of the first corner).
def BoundingRectangleAnglePoints(x,y, alphadeg):
#convert to radians and reverse direction
alpha = np.radians(alphadeg)
#calculate center
cx = np.mean(x)
cy = np.mean(y)
#Translate center to (0,0)
xt = x - cx
yt = y - cy
#Rotate by angle alpha (make sure to convert alpha to radians if needed)
xr = xt * np.cos(alpha) - yt * np.sin(alpha)
yr = xt * np.sin(alpha) + yt * np.cos(alpha)
#Find the min and max in rotated space
minx_r = np.min(xr)
miny_r = np.min(yr)
maxx_r = np.max(xr)
maxy_r = np.max(yr)
#Set up the minimum and maximum points of the bounding rectangle
xbound_r = np.asarray([minx_r, minx_r, maxx_r, maxx_r,minx_r])
ybound_r = np.asarray([miny_r, maxy_r, maxy_r, miny_r,miny_r])
#Rotate and Translate back to (cx, cy)
xbound = (xbound_r * np.cos(-alpha) - ybound_r * np.sin(-alpha))+cx
ybound = (xbound_r * np.sin(-alpha) + ybound_r * np.cos(-alpha))+cy
return xbound, ybound