I have some square board .
I would like it to be inside a rounded corners square box.
Question seems to be basic, but how do you calculate how much length you have to add to your square board ,in order to make a rounded square box with specific radius that cover it exactly ?
For example i have a square 30x30mm board , and i would like to cover it with a square box that has corner radius of 6mm , how much length do i have to add to the 30mm , to create that box so it can filled with the original board ( so the board can exactly "live" inside that box )
Agree that the OP application may be carpentry but it seems like an interesting enough geometry problem that it could have programming/graphics applications. Here's what I come up with:
Let I = inner square side length (30mm)
O = outer rounded square side length
r = corner radius (6mm)
Decompose the outer rounded square into 9 sections:
1 center square (with diagonal length d and side length c)
4 side squares (left, right, above, and below)
4 round corners
Since the inner square fits into the outer square,
the outer rounded square diagonal is I * sqrt(2)
Since there are 2 rounded corners the diagonal of the center square is
d = I sqrt(2) - 2 * r
The diagonal of the center square is sqrt(2) times its side
d = c * sqrt(2)
Equating these two
c = I - r * sqrt(2)
The side length of the outer square is
O = c + 2 * r
Substituting for c
O = I + r * (2 - sqrt(2))
Your question asked for the amount of length to add which would be
length to add = O - I = r * (2 - sqrt(2))
In your case, 6mm * (2 - sqrt(2)) = 3.515mm (approximately)
Related
i have some X/Y coordinates that represent the center of a circle somewhere in an image. From that circle, i want to compoute the mean of all the point contained inside the circle.
currently, i compute the mean of a square patche as follow, but a square is not relevent for the project. Ideally, i would like to do it only with numpy. but if it is not possible, i would concidere something else.
mean = np.mean(image[Y - margin : Y + margin, X - margin, X + margin])
As I understood,
YOU HAVE: (x,y) of the center of the circle
YOU WANT: mean of all the points contained in the circle
Since all the points on the right side should be equal to the number of points on the left side of the center, Wouldn't the mean be the same as the center of the circle !?
i found a solution where I compute all indexes contained in a centered disk.
I calculate the squarred euclidean distance of each X/Y coordinate of a squarre array.
I compare it to the squarred radius of the circle.
If it is superior, the point is not contained in the circle. Mark it as 0, 1 otherwise
I extract indexes where the array is equal to one (disk-shaped)
I center the computed indexes. To use them, i add the X/Y coordinate of a specific image point.
NOTE: I used the squarred euclidean distance because the square root function is monotonic (i.e constantly increasing). So, it saves compational power to keep with the squarred version.
radius = 4
size = 2 * radius + 1
radiusSquarred= radius**2
mask = np.zeros((size, size))
distance = lambda x, y: (x-radius)**2 + (y-radius)**2
for i in range(size):
for j in range (2 * radius+ 1):
if distance(i, j) <= radiusSquarred:
mask[i, j] = 1
index = np.where(mask == 1)
diskIndexes = (index[0] - radius, index[1] - radius)
X, Y = 100, 150
np.mean(image[diskIndexes[0] + Y, diskIndexes[1] + X])
I have an infinite grid of hexagons, defined by a cubic (x y z) coordinate system like so:
I also have a viewport -- a rectangular canvas where I will draw the hexagons.
My issue is this. Because the grid of hexagons is infinite in all directions, I can't feasibly draw all of them at once. Therefore, I need to draw all the hexagons that are in the viewport, and ONLY those hexagons.
This image summarizes what I want to do:
In this image, purple-colored hexagons are those I want to render, while white-colored hexagons are those I don't want to render. The black rectangle is hte viewport -- all the hexagons that intersect with it are to be drawn. How would I find which hexagons to render (IE their xyz coordinates)?
Some other info:
I have a function that can recall a hexagon tile and draw it centered at position(x,y) in the viewport, given its cubic xyz coordinates. Therefore, all I should need is the xyz coords of each rectangle to draw, and I can draw them. This might simplify the problem.
I have formulas to convert from cubic hexagon coordinates to x/y coordinates, and back. Given the above diagram, r/g/b being the axes for the cubic coords with the image above, x and y being the cartesian coordinates, and s being the length of a hexagon's edge...
y = 3/2 * s * b
b = 2/3 * y / s
x = sqrt(3) * s * ( b/2 + r)
x = - sqrt(3) * s * ( b/2 + g )
r = (sqrt(3)/3 * x - y/3 ) / s
g = -(sqrt(3)/3 * x + y/3 ) / s
r + b + g = 0
Let's X0, Y0 are coordinates of top left corner, RectWidth is rectangle width, HexWidth = s * Sqrt(3/2) is hexagon width.
Find center of the closest hexagon r0, g0, b0, HX0, HY0. (Rect corner lies in this hexagon, because hexagons are Voronoy diagram cells). Remember horizontal and vertical shift DX = X0 - HX0, DY = Y0 - HY0
Draw horizontal row of Ceil(RectWidth/HexWidth) hexagons, incrementing r coordinate, decrementing f, and keeping b the same, ROWINC=(1,-1,0).
Note that if DY > HexWidth/2, you need extra top row with initial coordinates shifted up (r0, g0-1, b0+1)
Shift starting point by L=(0, 1, -1) if the DX < 0, or by R=(1, 0, -1) otherwise. Draw another horizontal row with the same ROWINC
Shift row starting point by alternative way (L after R, R after L). Draw horizontal rows until bottom edge is reached.
Check whether extra row is needed in the bottom.
You can think of the rectangular box in terms of constraints on an axis.
In the diagram, the horizontal lines correspond to b and your constraints will be of the form somenumber ≤ b and b ≤ somenumber. For example the rectangle might be in the range 3 ≤ b ≤ 7.
The vertical lines are a little trickier, but they are a “diagonal” that corresponds to r-g. Your constraints will be of the form somenumber ≤ r-g and r-g ≤ somenumber. For example it might be the range -4 ≤ r-g ≤ 5.
Now you have two axes with constraints on them, and you can form a loop. The easiest thing will be to have the outer loop use b:
for (b = 3; b ≤ 7; b++) {
…
}
The inner loop is a little trickier, because that's the diagonal constraint. Since we know r+g+b=0, and we know the value of b from the outer loop, we can rewrite the two-variable constraint on r-g. Express r+g+b=0 as g=0-r-b. Now substitute into r-g and get r-(0-r-b). Simplify r-(0-r-b) to 2*r-b. Instead of -4 ≤ r-g we can say -4 ≤ 2*r-b or -4+b ≤ 2*r or (-4+b)/2 ≤ r. Similarly, we can rearrange r-g ≤ 5 to 2*r-b ≤ 5 to r ≤ (5+b)/2. This gives us our inner loop:
for (b = 3; b ≤ 7; b++) {
for (r = (-4+b)/2; r ≤ (5+b)/2; r++) {
g = 0-b-r;
…
}
}
The last bit is to generalize, replacing the constants 3,7,-4,5 with the actual bounds for your rectangle.
My trigonometry needs a little help.
How would I go about calculating the point of the nearest possible intersection with a line along a rounded corner?
Take this image:
What I would like to know is, given that I know point a, and the dimensions of the rectangle, how would I find point b when the edges of the rectangle are curved?
So far, as you can see, I've only managed to calculate the nearest edge of the rectangle as if it had right-angled corners.
If it matters, I'm doing this in ActionScript 3. But example sudo-code will suffice.
Calculate the vector from the midpoint M of the corner to A:
v_x = a_x - m_x
v_y = a_y - m_y
then go radius of the corner r times towards A to get to the intersection point I
i_x = m_x + r*v_x
i_y = m_y + r*v_y
This obviously only works if the nearest intersection is on the rounded corner. Just calculate the other intersections with the edges, too, and then check which has the nearest distance to A.
You need to know the radius R of the circle that generates the round corner and the coordinates (Xr,Yr) of the point where the two sides of a non rounded rectangle cross each other.
Then the coordinates for the center of the circle that generates the round corner are (Xc, Yc) = (Xr-R, Yr-R)
From here, it's a matter of solving the equation of the cross point between the segment line defined by point A=(Xa, Ya) and point (Xc, Yc), whose parametric equation is:
x = Xa + p*(Xc-Xa)
y = Ya + p*(Yc-Ya)
and the circle whose equation is
(x-Xc)^2 + (y-Yc)^2 = R^2
Substitute values for x and y from the parametric euation of the line in the equation of the circle, and you will have an equation with only one unkown: p. Solve the equation, and if there are more than one solution, choose the one that is in the range [0,1]. Substitute the found value of p in the parametric equation of the line to get the point of intersection.
Graphically:
If you know the radius and center of the corner as R and C=(Xc, Yc), then the nearest point on the corner to the given point A=(Xa, Ya) is the intersection point of the corner and the line defined by the given point and the center. This point can be directly expressed as
X = Xc + R*(Xa-Xc)/|AC|
Y = Yc + R*(Ya-Yc)/|AC|
where |AC| = Sqrt((Xa-Xc)^2 + (Ya-Yc)^2)
Two points P and Q with coordinates (Px, Py) and (Qx, Qy) satisfy the following properties:
The coordinates Px, Py, Qx, and Qy are integers.
Px = −Qx.
The line PQ is tangent to a circle with center (0, 0) and radius r
0 < Px ≤ a for some integer limit a.
How do I find all such pairs of points P and Q?
For example, in the image below I have a circle with radius r=2 and the limit a = 6. The pair of points P = (6, 2) and Q = (−6, −7) are a solution, because:
The coordinates of P and Q are integers.
Px = −Qx.
The line PQ is tangent to the circle.
0 < Px ≤ 6.
But this is just one pair. I need to find all such pairs. There are a finite number of solutions.
So, is there a way to check if coordinates of points are tangent to the circle and are integers, then to list them all? I've looked at slope equations and shortest path from the center of the circle to the line equations, however, in the first case it requires coordinates to be known (which I could do by brute forcing every single digit, but I cannot see the pattern, because my guts tell me there should be some sort of equation I should apply), and in the second case I have to know the slope equation.
This is the algorithm I came up with but I don't think it is correct or good enough:
Find the slope equation y = mx + b for all 1 ≤ Px ≤ a and −a ≤ Qx ≤ −1.
For every y = mx + b check if it is tangent to the circle (how to do that???)
If true, return the pair
Line PQ has equation:
(x-Px)/(Qx-Px)=(y-Py)/(Qy-Py) or
(x-Px)*(Qy-Py)-(y-Py)*(Qx-Px)=0 or
x*(Qy-Py)+y*(Px-Qx)-Px*Qy+Px*Py+Py*Qx-Py*Px =
x*(Qy-Py)+y*2*Px-Px*(Qy+Py)=0
Distance from zero point to this line (circle radius) is
r=Px*(Qy+Py)/Sqrt((2*Px)^2+(Qy-Py)^2)
Note that radius and nominator are integers, so denominator must be integer too. It is possible when 2*Px and |Qy-Py| are members of some Pythagorean triple (for your example - 12^2+9^2 =15^2).So you can use "Generating a triple" method from the above link to significantly reduce the search and find all the possible point pairs (with radius checking)
Px = k * m * n (for m>=n, radius < k*m*n <= a)
|Qy-Py| = k * (m^2 - n^2)
With a=6 max value of n is 2, and your example corresponds to (k, m, n) set of (3, 2, 1)
I've got a shape consisting of four points, A, B, C and D, of which the only their position is known. The goal is to transform these points to have specific angles and offsets relative to each other.
For example: A(-1,-1) B(2,-1) C(1,1) D(-2,1), which should be transformed to a perfect square (all angles 90) with offsets between AB, BC, CD and AD all being 2. The result should be a square slightly rotated counter-clockwise.
What would be the most efficient way to do this?
I'm using this for a simple block simulation program.
As Mark alluded, we can use constrained optimization to find the side 2 square that minimizes the square of the distance to the corners of the original.
We need to minimize f = (a-A)^2 + (b-B)^2 + (c-C)^2 + (d-D)^2 (where the square is actually a dot product of the vector argument with itself) subject to some constraints.
Following the method of Lagrange multipliers, I chose the following distance constraints:
g1 = (a-b)^2 - 4
g2 = (c-b)^2 - 4
g3 = (d-c)^2 - 4
and the following angle constraints:
g4 = (b-a).(c-b)
g5 = (c-b).(d-c)
A quick napkin sketch should convince you that these constraints are sufficient.
We then want to minimize f subject to the g's all being zero.
The Lagrange function is:
L = f + Sum(i = 1 to 5, li gi)
where the lis are the Lagrange multipliers.
The gradient is non-linear, so we have to take a hessian and use multivariate Newton's method to iterate to a solution.
Here's the solution I got (red) for the data given (black):
This took 5 iterations, after which the L2 norm of the step was 6.5106e-9.
While Codie CodeMonkey's solution is a perfectly valid one (and a great use case for the Lagrangian Multipliers at that), I believe that it's worth mentioning that if the side length is not given this particular problem actually has a closed form solution.
We would like to minimise the distance between the corners of our fitted square and the ones of the given quadrilateral. This is equivalent to minimising the cost function:
f(x1,...,y4) = (x1-ax)^2+(y1-ay)^2 + (x2-bx)^2+(y2-by)^2 +
(x3-cx)^2+(y3-cy)^2 + (x4-dx)^2+(y4-dy)^2
Where Pi = (xi,yi) are the corners of the fitted square and A = (ax,ay) through D = (dx,dy) represent the given corners of the quadrilateral in clockwise order. Since we are fitting a square we have certain contraints regarding the positions of the four corners. Actually, if two opposite corners are given, they are enough to describe a unique square (save for the mirror image on the diagonal).
Parametrization of the points
This means that two opposite corners are enough to represent our target square. We can parametrise the two remaining corners using the components of the first two. In the above example we express P2 and P4 in terms of P1 = (x1,y1) and P3 = (x3,y3). If you need a visualisation of the geometrical intuition behind the parametrisation of a square you can play with the interactive version.
P2 = (x2,y2) = ( (x1+x3-y3+y1)/2 , (y1+y3-x1+x3)/2 )
P4 = (x4,y4) = ( (x1+x3+y3-y1)/2 , (y1+y3+x1-x3)/2 )
Substituting for x2,x4,y2,y4 means that f(x1,...,y4) can be rewritten to:
f(x1,x3,y1,y3) = (x1-ax)^2+(y1-ay)^2 + ((x1+x3-y3+y1)/2-bx)^2+((y1+y3-x1+x3)/2-by)^2 +
(x3-cx)^2+(y3-cy)^2 + ((x1+x3+y3-y1)/2-dx)^2+((y1+y3+x1-x3)/2-dy)^2
a function which only depends on x1,x3,y1,y3. To find the minimum of the resulting function we then set the partial derivatives of f(x1,x3,y1,y3) equal to zero. They are the following:
df/dx1 = 4x1-dy-dx+by-bx-2ax = 0 --> x1 = ( dy+dx-by+bx+2ax)/4
df/dx3 = 4x3+dy-dx-by-bx-2cx = 0 --> x3 = (-dy+dx+by+bx+2cx)/4
df/dy1 = 4y1-dy+dx-by-bx-2ay = 0 --> y1 = ( dy-dx+by+bx+2ay)/4
df/dy3 = 4y3-dy-dx-2cy-by+bx = 0 --> y3 = ( dy+dx+by-bx+2cy)/4
You may see where this is going, as simple rearrangment of the terms leads to the final solution.
Final solution