Turning a line into a ring is a simple task in graphics programs such as GIMP:
(source: adamhaskell.net)
I'm trying to work out if it's possible to produce the same effect in CSS.
So I worked out the following:
The above algorithm maps x to r and y to θ
To do this, x is scaled to the range of [0,w/2], with w being the width of the image
Also, y is scaled to the range of [0,2π]
To transform polar coordinates back into Cartesian: xc = rp*cos(θp) and yc = rp*sin(θp)
The result is then translated so the origin is in the centre of the image.
So we have:
x' = (x/2)*cos(y/h*2π) + w/2;
y' = (x/2)*sin(y/h*2π) + h/2;
This is all fine and dandy, but how can I produce such a transform in CSS? Presumably none of the keywords are useful, so it has to be a Matrix transform. Well, I have no idea how to build a matrix from the two equations above, much less how to represent it in a CSS transform.
Can anyone help me on this last step?
I never worked with CSS transform matrices, but I think what you want is not possible.
Using transform matrices you do a linear transformation. Linear transformations ALWAYS map a straight line to a straight line or to 0. Take a look at Wikipedia for more information.
So it's impossible to map a straight line to a circle using matrices.
At least you can make 2 symmetrical third order Bezier curve
Using
Y(t) = (t^3,t^2,t,1) * M * (P0,P1,P2,P3)
t - time
P0 - P3 control points coordinates. This vector must be vertical.I do not know how to make vertical vector in this editor.
Y(t) - curve coordinate
M - 4*4 matrix row 1 (-1,3,-3,1) row 2 (3,-6,-3,0) row 3 (-3,3,0,0) row 4 (1,0,0,0)
Now you need only a function what defines control points from your line coordinate.
Related
I have the same rectangle in an untransformed form and in a perspective form.
Both, the coordinates from the untransformed form as well as from the perspective form are in the untransformed coordinate system.
Is there a way to reconstruct the transformation matrix which leads to this transformation?
I think it should be possible to do that by solving the 4 equations given by the 4 corners but I am not sure where to start.
// Edit:
It looks like I am victim of a xy problem here. All answers are based in a 3d environment. But I have a rectangle on an image + I know the real dimensions of this rectangle. What I need to know is how to transform other known points onto the perspective image
Note that you have 8 pairs of corresponding parameters (x and y for every point), and need to calculate 8 parameters of matrix using 8 equations
//four pairs of such equaions:
x' = (A * x + B * y + C) / (G * x + H * y + 1.0)
y' = (D * x + E * y + F) / (G * x + H * y + 1.0)
Theory of finding perspective transformation matrix is described in Paul Heckbert article.
C++ implementation could be found in antigrain library (file agg_trans_perspective.h)
One way is to plug in the values given in the transformation matrix. That has the merit of being easy and working, but it won't help you to understand it.
To understand the transform, draw a diagram with the screen vertical and looking top-down. Draw the eye. The line from the eye to the screen should be perpendicular. Then draw a point, somewhere off the screen. Then draw a line from the eye to the point, going through the screen.
It's then an exercise in highschool-level trigonometry to work out the projection of the point onto the screen.
I am working on a project of interpolating sample data {(x_i,y_i)} where the input domain for x_i locates in 4D space and output y_i locates in 3D space. I need generate two look up tables for both directions. I managed to generate the 4D -> 3D table. But the 3D -> 4D one is tricky. The sample data are not on regular grid points, and it is not one to one mapping. Is there any known method to treat this situation? I did some search online, but what I found is only for 3D -> 3D mapping, which are not suitable for this case. Thank you!
To answer the questions of Spektre:
X(3D) -> Y(4D) is the case 1X -> nY
I want to generate a table that for any given X, we can find the value for Y. The sample data is not occupy all the domain of X. But it's fine, we only need accuracy for point inside the domain of sample data. For example, we have sample data like {(x1,x2,x3) ->(y1,y2,y3,y4)}. It is possible we also have a sample data {(x1,x2,x3) -> (y1_1,y2_1,y3_1,y4_1)}. But it is OK. We need a table for any (a,b,c) in space X, it corresponds to ONE (e,f,g,h) in space Y. There might be more than one choice, but we only need one. (Sorry for the symbol confusing if any)
One possible way to deal with this: Since I have already established a smooth mapping from Y->X, I can use Newton's method or any other method to reverse search the point y for any given x. But it is not accurate enough, and time consuming. Because I need do search for each point in the table, and the error is the sum of the model error with the search error.
So I want to know it is possible to find a mapping directly to interpolate the sample data instead of doing such kind of search in 3.
You are looking for projections/mappings
as you mentioned you have projection X(3D) -> Y(4D) which is not one to one in your case so what case it is (1 X -> n Y) or (n X -> 1 Y) or (n X -> m Y) ?
you want to use look-up table
I assume you just want to generate all X for given Y the problem with non (1 to 1) mappings is that you can use lookup table only if it has
all valid points
or mapping has some geometric or mathematic symmetry (for example distance between points in X and Yspace is similar,and mapping is continuous)
You can not interpolate between generic mapped points so the question is what kind of mapping/projection you have in mind?
First the 1->1 projections/mappings interpolation
if your X->Y projection mapping is suitable for interpolation
then for 3D->4D use tri-linear interpolation. Find closest 8 points (each in its axis to form grid hypercube) and interpolate between them in all 4 dimensions
if your X<-Y projection mapping is suitable for interpolation
then for 4D->3D use quatro-linear interpolation. Find closest 16 points (each in its axis to form grid hypercube) and interpolate between them in all 3 dimensions.
Now what about 1->n or n->m projections/mappings
That solely depends on the projection/mapping properties which I know nothing of. Try to provide an example of your datasets and adding some image would be best.
[edit1] 1 X <- n Y
I still would use quatro-linear interpolation. You still will need to search your Y table but if you group it like 4D grid then it should be easy enough.
find 16 closest points in Y-table to your input Y point
These points should be the closest points to your Y in each +/- direction of all axises. In 3D it looks like this:
red point is your input Y point
blue points are the found closest points (grid) they do not need to be so symmetric as on image .
Please do not want me to draw 4D example that make sense :) (at least for sober mind)
interpolation
find corresponding X points. If there is more then one per point chose the closer one to the others ... Now you should have 16 X points and 16+1 Y points. Then from Y points you need just to calculate the distance along lines from your input Y point. These distances are used as parameter for linear interpolations. Normalize them to <0,1> where
0 means 'left' and 1 means 'right' point
0.5 means exact middle
You will need this scalar distance in each of Y-domain dimension. Now just compute all the X points along the linear interpolations until you get the corresponding red point in X-domain.
With tri-linear interpolation (3D) there are 4+2+1=7 linear interpolations (as on image). For quatro-linear interpolation (4D) there are 8+4+2+1=15 linear interpolations.
linear interpolation
X = X0 + (X1-X0)*t
X is interpolated point
X0,X1 are the 'left','right' points
t is the distance parameter <0,1>
I'm learning Unity3d + some basic maths I've forgotten by messing around.
Heres what I'm doing now..
As you can probably tell the sides of this shape form a parabola.
The distance they are out from the centre is the base radius + the height squared * by a constant (0.05 in this image)
The code generating this is very simple..
for (int changer = 1; changer > -2; changer-=2) {
Vector3 newPos = new Vector3(
transform.position.x
,transform.position.y + currentheight*changer
,transform.position.z - RadiusAtZero -(Mathf.Pow(currentheight,2)*CurveMultiplier)
);
var newFleck = Instantiate(Fleck, newPos, Quaternion.identity)as GameObject;
newFleck.transform.RotateAround(transform.position,Vector3.up,angle*changer);
FleckList.Add(newFleck );
}
Btw the for loop and 'changer' mirror everything so 'currentheight' is really just the distance from the centreline of the parabola.
Anyway I'd like to make the cubes (or flecks as I've called them) be angled so that they are tangentional to the parabola I have made.
I need to determine the angle of a tangent to the parabola at particular point.
I found this
to find the line tangent to y=x^2 -3 at (1, -2) we can simultaneously solve
y=x^2 -3 and y+2=m(x-1) and set the discriminant equal to zero
But I dont know how to implement this. Also I reckon my 'CurveMultiplier' constant makes my parabola equation different from that one.
Can someone write some code that determines the angle? (and also maybe explain it)
Update.
Here is fixed version using the derivative of the equation. (Also I have changed from boxes to tetrahedrons and few other superficial things)
The easiest solution is to use a derivative for the parabolic equation.
In your picture then I'll assume Y is vertical, X horizontal, and Z in/out of the screen. Then the parabola being rotated, based upon your description, is:
f(h) = 0.05*h^2 + R
(h is height, R is base radius). If you imagine a plane containing the Y axis, you can rotate the plane around the Y axis at any angle and the dual parabola looks the same.
The derivative of a parabolic equation of the form f(x) = C*h^2 + R is f'(x) = 2*C*h, which is the slope of the tangent at h. In this specific case, that would be:
f'(h) = 0.1*h
Since the cross-sectional plane has an angle relative to X and Z axes, then that tangent will also have the same angular component (you have a rotated parabola).
Depending upon the units given for the constants in f(h), particularly the 0.05 value, you may have to adjust this for the correct results.
I get a series of square binary images as in the picture below,
I want to find the red point, which is the point of intersection of four blocks (2 black and 2 white). For doing so, I use to get the sum of all pixel values along the diagonal directions of the square image, which is 45 deg and 135 deg respectively. The intersection of maximum pixel sum 45 deg line and minimum pixel sum 135 deg line is where my red point is.
Now that I get the co-ordinate of the red point in 45 deg-135 deg co-ordinate system, how to I transform them to earth co-ordinates?
In other words, say I have a point in 45deg-135deg co-ordinate system; How do I find the corresponding co-ordinate values in x-y co-ordinate system? What is the transformation matrix?
some more information that might help:
1) if the image is a 60x60 image, I get 120 values in 45deg-135deg system, since i scan each row followed by column to add the pixels.
I don't know much about matlab, but in general all you need to do is rotate your grid by 45 degrees.
Here's a helpful link; shows you the rotation matrix you need
wikipedia rotation matrix article
The new coordinates for a point after 2D rotation look like this:
x' = x \cos \theta - y \sin \theta.
y' = x \sin \theta + y \cos \theta.
replace theta with 45 (or maybe -45) and you should be all set.
If your red dot starts out at (x,y), then after the -45 degree rotation it will have the new coordinates (x',y'), which are defined as follows:
x' = x cos(-45) - y sin (-45)
y' = x sin (-45) + y cos (-45)
Sorry when I misunderstood your question but why do you rotate the image? The x-value of your red point is just the point where the derivative in x-direction has the maximum absolute value. And for the y-direction it is the same with the derivative in y-direction.
Assume you have the following image
If you take the first row of the image it has at the beginning all 1 and the for most of the width zeroes. The plot of the first column looks like this.
Now you convolve this line with the kernel {-1,1} which is only one nested loop over your line and you get
Going now through this result and extracting the position of the point with the highest value gets you 72. Therefore the x-position of the red point is 73 (since the kernel of the convolution finds the derivative one point too soon).
Therefore, if data is the image matrix of the above binary image then extracting your red point position is near to one line in Mathematica
Last[Transpose[Position[ListConvolve[{-1, 1}, #] & /#
{data[[1]],Transpose[data][[1]]}, 1 | -1]]] + 1
Here you get {73, 86} which is the correct position if y=0 is the top row. This method should be implemented in a few minutes in any language.
Remarks:
The approximated derivative which is the result of the convolution can either be negative or positive. This depends whether it is a change from 0 to 1 or vice versa. If you want to search for the highest value, you have to take the absolute value of the convolution result.
Remember that the first row in the image matrix is not always in top position of the displayed image. This depends on the software you are using. If you get wrong y values be aware of that.
I am developing a rotate-around-axis algorithm in 3 dimensions. My inputs are
the axis I am revolving around, as a vector from my center point
the center point (obviously)
the angle I wish to rotate around
my current position
I am wondering if there is a way to do this without trigonometry, just with vector operations. Does anyone have a potential solution?
EDIT: Is there a way that I could rotate by pi/4 radians (45 degrees) each time, rather than an inputted angle theta? This might simplify things a bit, I don't know.
Rotations are inherently well-described by and .
It's a handy trick that unit quaternions nicely represent 3-D rotations just as well as (and in some senses, better than) rotation matrices. Converting a rotation by angle about a normal axis where , does require a little bit of trigonometry: .
But from there on it's simple arithmetic.
A quaternion can be directly applied to rotate a vector with , or converted to a rotation matrix .
This is a rotation around the origin, of course. To rotate around an arbitrary point in space, simply translate by to the origin, rotate, then translate by to return.
use matrices: http://en.wikipedia.org/wiki/Rotation_matrix#Rotations_in_three_dimensions
If this is some sort of dumb homework problem, you can use Taylor Series approximation of the sine/consine functions. Whether or not this "counts" as trigonometry is I guess up for debate. You could then use these values in a rotation matrix or quarternion, if you want to use vector operations.
But again, there's no practical reason to do this.
Are there other techniques that don't use trig functions? Possibly, but there are no know efficient, general (i.e. for arbitrary angles) ways to perform rotations without use of trig functions.
However, based on your edit, you can precompute the sin and cos for a collection of angles you're interested in and store them in a lookup table. You need not be constrained in such a circumstance to π/4 increments, but you can do π/256 or π/1024 increments if you want. Also, you don't need two tables, since cos(θ) = sin(θ+π/2).
From there, you can use any of a number of interpolation methods to include simple rounding, linear interpolation or some sort of polynomial interpolation based on your needs.
You would then use either the matrix or quaternion based transformation to compute the rotated vector.
This will be faster than computing the sin and cos for general angles, though will require some additional space, and there will be an accuracy penalty as well. But if it satisfies your needs...
Theres a cheaper way than matrices, I think ive got it to sum count of adders.
The perimetre box of the vector is as good as an angle, if you step in partitions of the box size. (thats only a binary shift if its a power of 2.)
Then that would be a "box rotate" then just use the side report to give you how far along the diagonal you would be then you can split it up into so many gradients, the circle shape.
Id like to see someone proove that u can rotate without matrices or any trig like that too.
Is it possible to rotate without trigonometry? Yes.
Is it useful to rotate without using trigonometry? Probably not.
The first option is a problem-level solution: Change your coordinate system to spherical or cylindrical coordinates.
Since you rotate around an axis cylindrical coordinates of the form (alpha, radius, x3) will work.
Naming your center point O (for origin) and the point to rotate P, you can get the vector between them v=P-O. You also know the normal vector n of your plane of rotation (the vector you rotate around). With this, you can get the components of v that are parallel and orthogonal to n using a vector projection.
You have the freedom to choose how your new coordinate frame is rotated (relative to your original frame), so you can measure angles from the projection of v onto the plane of rotation. You also have the freedom to choose between degree and radians.
From there, you can now rotate to your heart's content using addition and subtraction.
Using dot(.,.) to denote the scalar product it would look something like this in code
v_parallel = dot(v, n) / dot(n, n) * n
radius = norm(v - v_parallel)
x3 = norm(v_parallel)
new_axis = (v - v_parallel) / norm(v - v_parallel)
P_polar = (0, radius, x3)
# P rotated by 90 degrees
P_polar = (pi/2, radius, x3)
# P rotated by -10 degrees
P_polar = (-pi/36, radius, x3)
However, if you want to change back to a standard basis you will have to use trigonometry again. Hence why I said this approach exists, but may not be too useful in practice.
Another approach comes from the cool observation that you can describe any planar rotation using two reflections along two given axis (represented by two vectors). The plane of rotation is the plane that is spun up by the two vectors and the angle of rotation is twice the angle between the two vectors.
You can reflect a vector using the vector projection from above; hence, you can do the entire process without trigonometry if you know the two vectors (let's call them x1 and x2).
tmp = v - 2 * dot(v, x1) / dot(x1, x1) * x1
v_rotated = tmp - 2 * dot(tmp, x2) / dot(x2, x2) * x2
The problem then turns into finding two vectors that are orthogonal to n and have an enclosing angle of alpha/2. How to do this is specific to your problem. For arbitrary alpha this is again the point where you can't dodge the trigonometry bullet; hence, it is again possible, but maybe not so viable in practice.
With help from Mathematica, it looks like we can rotate a point around a vector without Sin/Cos if you are willing to specify the amount of rotation as a number between -1 and 1, rather than an angle in radians.
The below starts with Mathematica's RotationTransform of a point {x,y,z} around a vector {u,v,w} by c radians (which contains many instances of Cos[c] and Sin[c]). It then substitutes all the Cos[c] with "c" and Sin[c] with Sqrt[1-c^2] (a trig identity for Sin in terms of Cos). Everything is simplified with the assumption that the rotation vector is normalized. The resulting equation produces the rotated point without any trig operations.
Note: as c ranges from -1 to 1 the point will only rotate through half a circle, the other half of the rotation can be achieved by flipping the signs on {u,v,w}.