I want to calculate a point on a given line that is perpendicular from a given point.
I have a line segment AB and have a point C outside line segment. I want to calculate a point D on AB such that CD is perpendicular to AB.
I have to find point D.
It quite similar to this, but I want to consider to Z coordinate also as it does not show up correctly in 3D space.
Proof:
Point D is on a line CD perpendicular to AB, and of course D belongs to AB.
Write down the Dot product of the two vectors CD.AB = 0, and express the fact D belongs to AB as D=A+t(B-A).
We end up with 3 equations:
Dx=Ax+t(Bx-Ax)
Dy=Ay+t(By-Ay)
(Dx-Cx)(Bx-Ax)+(Dy-Cy)(By-Ay)=0
Subtitute the first two equations in the third one gives:
(Ax+t(Bx-Ax)-Cx)(Bx-Ax)+(Ay+t(By-Ay)-Cy)(By-Ay)=0
Distributing to solve for t gives:
(Ax-Cx)(Bx-Ax)+t(Bx-Ax)(Bx-Ax)+(Ay-Cy)(By-Ay)+t(By-Ay)(By-Ay)=0
which gives:
t= -[(Ax-Cx)(Bx-Ax)+(Ay-Cy)(By-Ay)]/[(Bx-Ax)^2+(By-Ay)^2]
getting rid of the negative signs:
t=[(Cx-Ax)(Bx-Ax)+(Cy-Ay)(By-Ay)]/[(Bx-Ax)^2+(By-Ay)^2]
Once you have t, you can figure out the coordinates for D from the first two equations.
Dx=Ax+t(Bx-Ax)
Dy=Ay+t(By-Ay)
function getSpPoint(A,B,C){
var x1=A.x, y1=A.y, x2=B.x, y2=B.y, x3=C.x, y3=C.y;
var px = x2-x1, py = y2-y1, dAB = px*px + py*py;
var u = ((x3 - x1) * px + (y3 - y1) * py) / dAB;
var x = x1 + u * px, y = y1 + u * py;
return {x:x, y:y}; //this is D
}
There is a simple closed form solution for this (requiring no loops or approximations) using the vector dot product.
Imagine your points as vectors where point A is at the origin (0,0) and all other points are referenced from it (you can easily transform your points to this reference frame by subtracting point A from every point).
In this reference frame point D is simply the vector projection of point C on the vector B which is expressed as:
// Per wikipedia this is more efficient than the standard (A . Bhat) * Bhat
Vector projection = Vector.DotProduct(A, B) / Vector.DotProduct(B, B) * B
The result vector can be transformed back to the original coordinate system by adding point A to it.
A point on line AB can be parametrized by:
M(x)=A+x*(B-A), for x real.
You want D=M(x) such that DC and AB are orthogonal:
dot(B-A,C-M(x))=0.
That is: dot(B-A,C-A-x*(B-A))=0, or dot(B-A,C-A)=x*dot(B-A,B-A), giving:
x=dot(B-A,C-A)/dot(B-A,B-A) which is defined unless A=B.
What you are trying to do is called vector projection
Here i have converted answered code from "cuixiping" to matlab code.
function Pr=getSpPoint(Line,Point)
% getSpPoint(): find Perpendicular on a line segment from a given point
x1=Line(1,1);
y1=Line(1,2);
x2=Line(2,1);
y2=Line(2,1);
x3=Point(1,1);
y3=Point(1,2);
px = x2-x1;
py = y2-y1;
dAB = px*px + py*py;
u = ((x3 - x1) * px + (y3 - y1) * py) / dAB;
x = x1 + u * px;
y = y1 + u * py;
Pr=[x,y];
end
I didn't see this answer offered, but Ron Warholic had a great suggestion with the Vector Projection. ACD is merely a right triangle.
Create the vector AC i.e (Cx - Ax, Cy - Ay)
Create the Vector AB i.e (Bx - Ax, By - Ay)
Dot product of AC and AB is equal to the cosine of the angle between the vectors. i.e cos(theta) = ACx*ABx + ACy*ABy.
Length of a vector is sqrt(x*x + y*y)
Length of AD = cos(theta)*length(AC)
Normalize AB i.e (ABx/length(AB), ABy/length(AB))
D = A + NAB*length(AD)
For anyone who might need this in C# I'll save you some time:
double Ax = ;
double Ay = ;
double Az = ;
double Bx = ;
double By = ;
double Bz = ;
double Cx = ;
double Cy = ;
double Cz = ;
double t = ((Cx - Ax) * (Bx - Ax) + (Cy - Ay) * (By - Ay)) / (Math.Pow(Bx - Ax, 2) + Math.Pow(By - Ay, 2));
double Dx = Ax + t*(Bx - Ax);
double Dy = Ay + t*(By - Ay);
Here is another python implementation without using a for loop. It works for any number of points and any number of line segments. Given p_array as a set of points, and x_array , y_array as continues line segments or a polyline.
This uses the equation Y = mX + n and considering that the m factor for a perpendicular line segment is -1/m.
import numpy as np
def ortoSegmentPoint(self, p_array, x_array, y_array):
"""
:param p_array: np.array([[ 718898.941 9677612.901 ], [ 718888.8227 9677718.305 ], [ 719033.0528 9677770.692 ]])
:param y_array: np.array([9677656.39934991 9677720.27550726 9677754.79])
:param x_array: np.array([718895.88881594 718938.61392781 718961.46])
:return: [POINT, LINE] indexes where point is orthogonal to line segment
"""
# PENDIENTE "m" de la recta, y = mx + n
m_array = np.divide(y_array[1:] - y_array[:-1], x_array[1:] - x_array[:-1])
# PENDIENTE INVERTIDA, 1/m
inv_m_array = np.divide(1, m_array)
# VALOR "n", y = mx + n
n_array = y_array[:-1] - x_array[:-1] * m_array
# VALOR "n_orto" PARA LA RECTA PERPENDICULAR
n_orto_array = np.array(p_array[:, 1]).reshape(len(p_array), 1) + inv_m_array * np.array(p_array[:, 0]).reshape(len(p_array), 1)
# PUNTOS DONDE SE INTERSECTAN DE FORMA PERPENDICULAR
x_intersec_array = np.divide(n_orto_array - n_array, m_array + inv_m_array)
y_intersec_array = m_array * x_intersec_array + n_array
# LISTAR COORDENADAS EN PARES
x_coord = np.array([x_array[:-1], x_array[1:]]).T
y_coord = np.array([y_array[:-1], y_array[1:]]).T
# FILAS: NUMERO DE PUNTOS, COLUMNAS: NUMERO DE TRAMOS
maskX = np.where(np.logical_and(x_intersec_array < np.max(x_coord, axis=1), x_intersec_array > np.min(x_coord, axis=1)), True, False)
maskY = np.where(np.logical_and(y_intersec_array < np.max(y_coord, axis=1), y_intersec_array > np.min(y_coord, axis=1)), True, False)
mask = maskY * maskX
return np.argwhere(mask == True)
As Ron Warholic and Nicolas Repiquet answered, this can be solved using vector projection. For completeness I'll add a python/numpy implementation of this here in case it saves anyone else some time:
import numpy as np
# Define some test data that you can solve for directly.
first_point = np.array([4, 4])
second_point = np.array([8, 4])
target_point = np.array([6, 6])
# Expected answer
expected_point = np.array([6, 4])
# Create vector for first point on line to perpendicular point.
point_vector = target_point - first_point
# Create vector for first point and second point on line.
line_vector = second_point - first_point
# Create the projection vector that will define the position of the resultant point with respect to the first point.
projection_vector = (np.dot(point_vector, line_vector) / np.dot(line_vector, line_vector)) * line_vector
# Alternative method proposed in another answer if for whatever reason you prefer to use this.
_projection_vector = (np.dot(point_vector, line_vector) / np.linalg.norm(line_vector)**2) * line_vector
# Add the projection vector to the first point
projected_point = first_point + projection_vector
# Test
(projected_point == expected_point).all()
Since you're not stating which language you're using, I'll give you a generic answer:
Just have a loop passing through all the points in your AB segment, "draw a segment" to C from them, get the distance from C to D and from A to D, and apply pithagoras theorem. If AD^2 + CD^2 = AC^2, then you've found your point.
Also, you can optimize your code by starting the loop by the shortest side (considering AD and BD sides), since you'll find that point earlier.
Here is a python implementation based on Corey Ogburn's answer from this thread.
It projects the point q onto the line segment defined by p1 and p2 resulting in the point r.
It will return null if r falls outside of line segment:
def is_point_on_line(p1, p2, q):
if (p1[0] == p2[0]) and (p1[1] == p2[1]):
p1[0] -= 0.00001
U = ((q[0] - p1[0]) * (p2[0] - p1[0])) + ((q[1] - p1[1]) * (p2[1] - p1[1]))
Udenom = math.pow(p2[0] - p1[0], 2) + math.pow(p2[1] - p1[1], 2)
U /= Udenom
r = [0, 0]
r[0] = p1[0] + (U * (p2[0] - p1[0]))
r[1] = p1[1] + (U * (p2[1] - p1[1]))
minx = min(p1[0], p2[0])
maxx = max(p1[0], p2[0])
miny = min(p1[1], p2[1])
maxy = max(p1[1], p2[1])
is_valid = (minx <= r[0] <= maxx) and (miny <= r[1] <= maxy)
if is_valid:
return r
else:
return None
Related
I'm attempting to calculate a point in 3D space which is orthogonal/perpendicular to a line vector.
So I have P1 and P2 which form the line. I’m then trying to find a point with a radius centred at P1, which is orthogonal to the line.
I'd like to do this using trigonometry, without any programming language specific functions.
At the moment I'm testing how accurate this function is by potting a circle around the line vector.
I also rotate the line vector in 3D space to see what happens to the plotted circle, this is where my results vary.
Some of the unwanted effects include:
The circle rotating and passing through the line vector.
The circle's radius appearing to reducing to zero before growing as the line vector changes direction.
I used this question to get me started, and have since made some adjustments to the code.
Any help with this would be much appreciated - I've spent 3 days drawing circles now. Here's my code so far
//Define points which form the line vector
dx = p2x - p1x;
dy = p2y - p1y;
dz = p2z - p1z;
// Normalize line vector
d = sqrt(dx*dx + dy*dy + dz*dz);
// Line vector
v3x = dx/d;
v3y = dy/d;
v3z = dz/d;
// Angle and distance to plot point around line vector
angle = 123 * pi/180 //convert to radians
radius = 4;
// Begin calculating point
s = sqrt(v3x*v3x + v3y*v3y + v3z*v3z);
// Calculate v1.
// I have been playing with these variables (v1x, v1y, v1z) to try out different configurations.
v1x = s * v3x;
v1y = s * v3y;
v1z = s * -v3z;
// Calculate v2 as cross product of v3 and v1.
v2x = v3y*v1z - v3z*v1y;
v2y = v3z*v1x - v3x*v1z;
v2z = v3x*v1y - v3y*v1x;
// Point in space around the line vector
px = p1x + (radius * (v1x * cos(angle)) + (v2x * sin(angle)));
py = p1y + (radius * (v1y * cos(angle)) + (v2y * sin(angle)));
pz = p1z + (radius * (v1z * cos(angle)) + (v2z * sin(angle)));
EDIT
After wrestling with this for days while in lockdown, I've finally managed to get this working. I'd like to thank MBo and Futurologist for their invaluable input.
Although I wasn't able to get their examples working (more likely due to me being at fault), their answers pointed me in the right direction and to that eureka moment!
The key was in swapping the correct vectors.
So thank you to you both, you really helped me along with this. This is my final (working) code:
//Set some vars
angle = 123 * pi/180;
radius = 4;
//P1 & P2 represent vectors that form the line.
dx = p2x - p1x;
dy = p2y - p1y;
dz = p2z - p1z;
d = sqrt(dx*dx + dy*dy + dz*dz)
//Normalized vector
v3x = dx/d;
v3y = dy/d;
v3z = dz/d;
//Store vector elements in an array
p = [v3x, v3y, v3z];
//Store vector elements in second array, this time with absolute value
p_abs = [abs(v3x), abs(v3y), abs(v3z)];
//Find elements with MAX and MIN magnitudes
maxval = max(p_abs[0], p_abs[1], p_abs[2]);
minval = min(p_abs[0], p_abs[1], p_abs[2]);
//Initialise 3 variables to store which array indexes contain the (max, medium, min) vector magnitudes.
maxindex = 0;
medindex = 0;
minindex = 0;
//Loop through p_abs array to find which magnitudes are equal to maxval & minval. Store their indexes for use later.
for(i = 0; i < 3; i++) {
if (p_abs[i] == maxval) maxindex = i;
else if (p_abs[i] == minval) minindex = i;
}
//Find the remaining index which has the medium magnitude
for(i = 0; i < 3; i++) {
if (i!=maxindex && i!=minindex) {
medindex = i;
break;
}
}
//Store the maximum magnitude for now.
storemax = (p[maxindex]);
//Swap the 2 indexes that contain the maximum & medium magnitudes, negating maximum. Set minimum magnitude to zero.
p[maxindex] = (p[medindex]);
p[medindex] = -storemax;
p[minindex] = 0;
//Calculate v1. Perpendicular to v3.
s = sqrt(v3x*v3x + v3z*v3z + v3y*v3y);
v1x = s * p[0];
v1y = s * p[1];
v1z = s * p[2];
//Calculate v2 as cross product of v3 and v1.
v2x = v3y*v1z - v3z*v1y;
v2y = v3z*v1x - v3x*v1z;
v2z = v3x*v1y - v3y*v1x;
//For each circle point.
circlepointx = p2x + radius * (v1x * cos(angle) + v2x * sin(angle))
circlepointy = p2y + radius * (v1y * cos(angle) + v2y * sin(angle))
circlepointz = p2z + radius * (v1z * cos(angle) + v2z * sin(angle))
Your question is too vague, but I may suppose what you really want.
You have line through two point p1 and p2. You want to build a circle of radius r centered at p1 and perpendicular to the line.
At first find direction vector of this line - you already know how - normalized vector v3.
Now you need arbitrary vector perpendicular to v3: find components of v3 with the largest magnitude and with the second magnitude. For example, abs(v3y) is the largest and abs(v3x) has the second magnitude. Exchange them, negate the largest, and make the third component zero:
p = (-v3y, v3x, 0)
This vector is normal to v3 (their dot product is zero)
Now normalize it
pp = p / length(p)
Now get binormal vector as cross product of v3 and pp (I has unit length, no need to normalize), it is perpendicular to both v3 and pp
b = v3 x pp
Now build needed circle
circlepoint(theta) = p1 + radius * pp * Cos(theta) + radius * b * Sin(theta)
Aslo note that angle in radians is
angle = degrees * pi / 180
#Input:
# Pair of points which determine line L:
P1 = [x_P1, y_P1, z_P1]
P2 = [x_P1, y_P1, z_P1]
# Radius:
Radius = R
# unit vector aligned with the line passing through the points P1 and P2:
V3 = P1 - P2
V3 = V3 / norm(V3)
# from the three basis vectors, e1 = [1,0,0], e2 = [0,1,0], e3 = [0,0,1]
# pick the one that is the most transverse to vector V3
# this means, look at the entries of V3 = [x_V3, y_V3, z_V3] and check which
# one has the smallest absolute value and record its index. Take the coordinate
# vector that has 1 at that selected index. In other words,
# if min( abs(x_V3), abs(y_V)) = abs(y_V3),
# then argmin( abs(x_V3), abs(y_V3), abs(z_V3)) = 2 and so take e = [0,1,0]:
e = [0,0,0]
i = argmin( abs(V3[1]), abs(V3[2]), abs(V3[3]) )
e[i] = 1
# a unit vector perpendicular to both e and V3:
V1 = cross(e, V3)
V1 = V1 / norm(V1)
# third unit vector perpendicular to both V3 and V1:
V2 = cross(V3, V1)
# an arbitrary point on the circle (i.e. equation of the circle with parameter s):
P = P1 + Radius*( np.cos(s)*V1 + np.sin(s)*V2 )
# E.g. say you want to find point P on the circle, 60 degrees relative to vector V1:
s = pi/3
P = P1 + Radius*( cos(s)*V1 + sin(s)*V2 )
Test example in Python:
import numpy as np
#Input:
# Pair of points which determine line L:
P1 = np.array([1, 1, 1])
P2 = np.array([3, 2, 3])
Radius = 3
V3 = P1 - P2
V3 = V3 / np.linalg.norm(V3)
e = np.array([0,0,0])
e[np.argmin(np.abs(V3))] = 1
V1 = np.cross(e, V3)
V1 = V1 / np.linalg.norm(V3)
V2 = np.cross(V3, V1)
# E.g., say you want to rotate point P along the circle, 60 degrees along rel to V1:
s = np.pi/3
P = P1 + Radius*( np.cos(s)*V1 + np.sin(s)*V2 )
I've got a situation in which I have 2 circles (C1 and C2)
and i need to find the line equation for the line that is tangent to both of these circles.
So as far as i'm aware, given a single point (P1) and C2's point and radius it is possible to quite easily get 2 possible points of tangency for C2 and P1 to make 2 line equations. But as i don't have P1, only the knowledge that the point will be one of a possible 2 points on C1, i'm not sure how to calculate this.
I assume it will be something along the lines of getting the 2 tangent line equations of C1 that are equal to the same of C2.
Both circles can have any radius, they could be the same or they could be hugely different. They will also never overlap (they can still touch though). And I'm looking for the 2 possible internal tangents.
Oh, and also, visuals would be very helpful haha :)
Let O be the intersection point between the line through the centers and the tangent.
Let d be the distance between the centers and h1, h2 be the distances between O and the centers. By similarity, these are proportional to the radii.
Hence,
h1 / h2 = r1 / r2 = m,
h1 + h2 = d,
giving
h1 = m d / (1 + m),
h2 = d / (1 + m).
Then the coordinates of O are found by interpolating between the centers
xo = (h2.x1 + h1.x2) / d
yo = (h2.y1 + h1.y2) / d
and the angle of the tangent is that of the line through the centers plus or minus the angle between this line and the tangent,
a = arctan((y2 - y1)/(x2 - x1)) +/- arcsin(r1 / h1).
You can write the implicit equation of the tangent as
cos(a).y - sin(a).x = cos(a).yo - sin(a).xo.
(source: imag.fr)
So we are going to use a homothetic transformation. If the circles C and C' have respectively centres O and O', and radius r and r', then we know there exists a unique homothetic transformation with centre J and ratio a, such that :
a = |JO|/|JO'| = r/r'
Noting AB is the vector from A to B, and |z| the norm of a vector z.
Hence you get J, knowing that it is between O and O' which we both already know.
Then with u the projection of JR on JO', and v the decomposition on its orthogonal, and considering the sine s and cosine c of the angle formed by O'JR, we have
|u| = |JR| * c
|v| = |JR| * s
c^2 + s^2 = 1
And finally because the triangle JRO' is right-angled in R :
s = r' / |JO|'
Putting all of this together, we get :
J = O + OO' / |OO'| * a / (a+1)
if |OJ| == r and |O'J| == r' then
return the orthogonal line to (OO') passing through J
|JR| = √( |JO'|^ - r'^2 )
s = r' / |JO'|
c = √( 1 - s^2 )
u = c * |JR| * OO' / |OO'|
w = (-u.y, u.x) % any orthogonal vector to u
v = s * |JR| * w / |w|
return lines corresponding to parametric equations J+t*(u+v) and J+t*(u-v)
I am trying to create a binary tree from a lot of segments in 3d space sharing the same origin.
When merging two segments I want to have a specific angle between the lines to the child nodes.
The following image illustrates my problem. C shows the position of the parent node and A and B the child positions. N is the average vector of the vectors from C to A and C to B.
With a given angle, how can I determine point P?
Thanks for any help
P = C + t * ((A + B)/2 - C) t is unknown parameter
PA = A - P PA vector
PB = B - P PB vector
Tan(Fi) = (PA x PB) / (PA * PB) (cross product in the nominator, scalar product in the denominator)
Tan(Fi) * (PA.x*PB.x + PA.y*PB.y) = (PA.x*PB.y - PA.y*PB.x)
this is quadratic equation for t, after solving we will get two (for non-degenerate cases) possible positions of P point (the second one lies at other side of AB line)
Addition:
Let's ax = A.x - A point X-coordinate and so on,
abcx = (ax+bx)/2-cx, abcy = (ay+by)/2-cy
pax = ax-cx - t*abcx, pay = ay-cy - t*abcy
pbx = bx-cx - t*abcx, pby = by-cy - t*abcy
ff = Tan(Fi) , then
ff*(pax*pbx+pay*pby)-pax*pby+pay*pbx=0
ff*((ax-cx - t*abcx)*(bx-cx - t*abcx)+(ay-cy - t*abcy)*(by-cy - t*abcy)) -
- (ax-cx - t*abcx)*(by-cy - t*abcy) + (ay-cy - t*abcy)*(bx-cx - t*abcx) =
t^2 *(ff*(abcx^2+abcy^2)) +
t * (-2*ff*(abcx^2+abcy^2) + abcx*(by-ay) + abcy*(ax-bx) ) +
(ff*((ax-cx)*(bx-cx)+(ay-cy)*(by-cy)) - (ax-cx)*(by-cy)+(bx-cx)*(ay-cy)) =0
This is quadratic equation AA*t^2 + BB*t + CC = 0 with coefficients
AA = ff*(abcx^2+abcy^2)
BB = -2*ff*(abcx^2+abcy^2) + abcx*(by-ay) + abcy*(ax-bx)
CC = ff*((ax-cx)*(bx-cx)+(ay-cy)*(by-cy)) - (ax-cx)*(by-cy)+(bx-cx)*(ay-cy)
P.S. My answer is for 2d-case!
For 3d: It is probably simpler to use scalar product only (with vector lengths)
Cos(Fi) = (PA * PB) / (|PA| * |PB|)
Another solution could be using binary search on the vector N, whether P is close to C then the angle will be smaller and whether P is far from C then the angle will be bigger, being it suitable for a binary search.
I have a square bitmap of a circle and I want to compute the normals of all the pixels in that circle as if it were a sphere of radius 1:
The sphere/circle is centered in the bitmap.
What is the equation for this?
Don't know much about how people program 3D stuff, so I'll just give the pure math and hope it's useful.
Sphere of radius 1, centered on origin, is the set of points satisfying:
x2 + y2 + z2 = 1
We want the 3D coordinates of a point on the sphere where x and y are known. So, just solve for z:
z = ±sqrt(1 - x2 - y2).
Now, let us consider a unit vector pointing outward from the sphere. It's a unit sphere, so we can just use the vector from the origin to (x, y, z), which is, of course, <x, y, z>.
Now we want the equation of a plane tangent to the sphere at (x, y, z), but this will be using its own x, y, and z variables, so instead I'll make it tangent to the sphere at (x0, y0, z0). This is simply:
x0x + y0y + z0z = 1
Hope this helps.
(OP):
you mean something like:
const int R = 31, SZ = power_of_two(R*2);
std::vector<vec4_t> p;
for(int y=0; y<SZ; y++) {
for(int x=0; x<SZ; x++) {
const float rx = (float)(x-R)/R, ry = (float)(y-R)/R;
if(rx*rx+ry*ry > 1) { // outside sphere
p.push_back(vec4_t(0,0,0,0));
} else {
vec3_t normal(rx,sqrt(1.-rx*rx-ry*ry),ry);
p.push_back(vec4_t(normal,1));
}
}
}
It does make a nice spherical shading-like shading if I treat the normals as colours and blit it; is it right?
(TZ)
Sorry, I'm not familiar with those aspects of C++. Haven't used the language very much, nor recently.
This formula is often used for "fake-envmapping" effect.
double x = 2.0 * pixel_x / bitmap_size - 1.0;
double y = 2.0 * pixel_y / bitmap_size - 1.0;
double r2 = x*x + y*y;
if (r2 < 1)
{
// Inside the circle
double z = sqrt(1 - r2);
.. here the normal is (x, y, z) ...
}
Obviously you're limited to assuming all the points are on one half of the sphere or similar, because of the missing dimension. Past that, it's pretty simple.
The middle of the circle has a normal facing precisely in or out, perpendicular to the plane the circle is drawn on.
Each point on the edge of the circle is facing away from the middle, and thus you can calculate the normal for that.
For any point between the middle and the edge, you use the distance from the middle, and some simple trig (which eludes me at the moment). A lerp is roughly accurate at some points, but not quite what you need, since it's a curve. Simple curve though, and you know the beginning and end values, so figuring them out should only take a simple equation.
I think I get what you're trying to do: generate a grid of depth data for an image. Sort of like ray-tracing a sphere.
In that case, you want a Ray-Sphere Intersection test:
http://www.siggraph.org/education/materials/HyperGraph/raytrace/rtinter1.htm
Your rays will be simple perpendicular rays, based off your U/V coordinates (times two, since your sphere has a diameter of 2). This will give you the front-facing points on the sphere.
From there, calculate normals as below (point - origin, the radius is already 1 unit).
Ripped off from the link above:
You have to combine two equations:
Ray: R(t) = R0 + t * Rd , t > 0 with R0 = [X0, Y0, Z0] and Rd = [Xd, Yd, Zd]
Sphere: S = the set of points[xs, ys, zs], where (xs - xc)2 + (ys - yc)2 + (zs - zc)2 = Sr2
To do this, calculate your ray (x * pixel / width, y * pixel / width, z: 1), then:
A = Xd^2 + Yd^2 + Zd^2
B = 2 * (Xd * (X0 - Xc) + Yd * (Y0 - Yc) + Zd * (Z0 - Zc))
C = (X0 - Xc)^2 + (Y0 - Yc)^2 + (Z0 - Zc)^2 - Sr^2
Plug into quadratic equation:
t0, t1 = (- B + (B^2 - 4*C)^1/2) / 2
Check discriminant (B^2 - 4*C), and if real root, the intersection is:
Ri = [xi, yi, zi] = [x0 + xd * ti , y0 + yd * ti, z0 + zd * ti]
And the surface normal is:
SN = [(xi - xc)/Sr, (yi - yc)/Sr, (zi - zc)/Sr]
Boiling it all down:
So, since we're talking unit values, and rays that point straight at Z (no x or y component), we can boil down these equations greatly:
Ray:
X0 = 2 * pixelX / width
Y0 = 2 * pixelY / height
Z0 = 0
Xd = 0
Yd = 0
Zd = 1
Sphere:
Xc = 1
Yc = 1
Zc = 1
Factors:
A = 1 (unit ray)
B
= 2 * (0 + 0 + (0 - 1))
= -2 (no x/y component)
C
= (X0 - 1) ^ 2 + (Y0 - 1) ^ 2 + (0 - 1) ^ 2 - 1
= (X0 - 1) ^ 2 + (Y0 - 1) ^ 2
Discriminant
= (-2) ^ 2 - 4 * 1 * C
= 4 - 4 * C
From here:
If discriminant < 0:
Z = ?, Normal = ?
Else:
t = (2 + (discriminant) ^ 1 / 2) / 2
If t < 0 (hopefully never or always the case)
t = -t
Then:
Z: t
Nx: Xi - 1
Ny: Yi - 1
Nz: t - 1
Boiled farther still:
Intuitively it looks like C (X^2 + Y^2) and the square-root are the most prominent figures here. If I had a better recollection of my math (in particular, transformations on exponents of sums), then I'd bet I could derive this down to what Tom Zych gave you. Since I can't, I'll just leave it as above.
How can I draw a perpendicular on a line segment from a given point? My line segment is defined as (x1, y1), (x2, y2), If I draw a perpendicular from a point (x3,y3) and it meets to line on point (x4,y4). I want to find out this (x4,y4).
I solved the equations for you:
k = ((y2-y1) * (x3-x1) - (x2-x1) * (y3-y1)) / ((y2-y1)^2 + (x2-x1)^2)
x4 = x3 - k * (y2-y1)
y4 = y3 + k * (x2-x1)
Where ^2 means squared
From wiki:
In algebra, for any linear equation
y=mx + b, the perpendiculars will all
have a slope of (-1/m), the opposite
reciprocal of the original slope. It
is helpful to memorize the slogan "to
find the slope of the perpendicular
line, flip the fraction and change the
sign." Recall that any whole number a
is itself over one, and can be written
as (a/1)
To find the perpendicular of a given
line which also passes through a
particular point (x, y), solve the
equation y = (-1/m)x + b, substituting
in the known values of m, x, and y to
solve for b.
The slope of the line, m, through (x1, y1) and (x2, y2) is m = (y1 - y2) / (x1 - x2)
I agree with peter.murray.rust, vectors make the solution clearer:
// first convert line to normalized unit vector
double dx = x2 - x1;
double dy = y2 - y1;
double mag = sqrt(dx*dx + dy*dy);
dx /= mag;
dy /= mag;
// translate the point and get the dot product
double lambda = (dx * (x3 - x1)) + (dy * (y3 - y1));
x4 = (dx * lambda) + x1;
y4 = (dy * lambda) + y1;
You know both the point and the slope, so the equation for the new line is:
y-y3=m*(x-x3)
Since the line is perpendicular, the slope is the negative reciprocal. You now have two equations and can solve for their intersection.
y-y3=-(1/m)*(x-x3)
y-y1=m*(x-x1)
You will often find that using vectors makes the solution clearer...
Here is a routine from my own library:
public class Line2 {
Real2 from;
Real2 to;
Vector2 vector;
Vector2 unitVector = null;
public Real2 getNearestPointOnLine(Real2 point) {
unitVector = to.subtract(from).getUnitVector();
Vector2 lp = new Vector2(point.subtract(this.from));
double lambda = unitVector.dotProduct(lp);
Real2 vv = unitVector.multiplyBy(lambda);
return from.plus(vv);
}
}
You will have to implement Real2 (a point) and Vector2 and dotProduct() but these should be simple:
The code then looks something like:
Point2 p1 = new Point2(x1, y1);
Point2 p2 = new Point2(x2, y2);
Point2 p3 = new Point2(x3, y3);
Line2 line = new Line2(p1, p2);
Point2 p4 = getNearestPointOnLine(p3);
The library (org.xmlcml.euclid) is at:
http://sourceforge.net/projects/cml/
and there are unit tests which will exercise this method and show you how to use it.
#Test
public final void testGetNearestPointOnLine() {
Real2 p = l1112.getNearestPointOnLine(new Real2(0., 0.));
Real2Test.assertEquals("point", new Real2(0.4, -0.2), p, 0.0000001);
}
Compute the slope of the line joining points (x1,y1) and (x2,y2) as m=(y2-y1)/(x2-x1)
Equation of the line joining (x1,y1) and (x2,y2) using point-slope form of line equation, would be y-y2 = m(x-x2)
Slope of the line joining (x3,y3) and (x4,y4) would be -(1/m)
Again, equation of the line joining (x3,y3) and (x4,y4) using point-slope form of line equation, would be y-y3 = -(1/m)(x-x3)
Solve these two line equations as you solve a linear equation in two variables and the values of x and y you get would be your (x4,y4)
I hope this helps.
cheers
Find out the slopes for both the
lines, say slopes are m1 and m2 then
m1*m2=-1 is the condition for
perpendicularity.
Matlab function code for the following problem
function Pr=getSpPoint(Line,Point)
% getSpPoint(): find Perpendicular on a line segment from a given point
x1=Line(1,1);
y1=Line(1,2);
x2=Line(2,1);
y2=Line(2,1);
x3=Point(1,1);
y3=Point(1,2);
px = x2-x1;
py = y2-y1;
dAB = px*px + py*py;
u = ((x3 - x1) * px + (y3 - y1) * py) / dAB;
x = x1 + u * px;
y = y1 + u * py;
Pr=[x,y];
end
Mathematica introduced the function RegionNearest[] in version 10, 2014. This function could be used to return an answer to this question:
{x4,y4} = RegionNearest[Line[{{x1,y1},{x2,y2}}],{x3,y3}]
This is mostly a duplicate of Arnkrishn's answer. I just wanted to complete his section with a complete Mathematica code snippet:
m = (y2 - y1)/(x2 - x1)
eqn1 = y - y3 == -(1/m)*(x - x3)
eqn2 = y - y1 == m*(x - x1)
Solve[eqn1 && eqn2, {x, y}]
This is a C# implementation of the accepted answer. It's also using ArcGis to return a MapPoint as that's what we're using for this project.
private MapPoint GenerateLinePoint(double startPointX, double startPointY, double endPointX, double endPointY, double pointX, double pointY)
{
double k = ((endPointY - startPointY) * (pointX - startPointX) - (endPointX - startPointX) * (pointY - startPointY)) / (Math.Pow(endPointY - startPointY, 2)
+ Math.Pow(endPointX - startPointX, 2));
double resultX = pointX - k * (endPointY - startPointY);
double resultY = pointY + k * (endPointX - startPointX);
return new MapPoint(resultX, resultY, 0, SpatialReferences.Wgs84);
}
Thanks to Ray as this worked perfectly for me.
c#arcgis
Just for the sake of completeness, here is a solution using homogeneous coordinates.
The homogeneous points are:
p1 = (x1,y1,1), p2 = (x2,y2,1), p3 = (x3,y3,1)
a line through two points is their cross-product
l_12 := p1 x p2 = (y1-y2, x2-x1, x1*y2 - x2*y1)
The (signed) distance of a point to a line is their dot product.
d := l_12 * p3 = x3*(y1-y2) + y3*(x2-x1) + x1*y2 - x2*y1
The vector from p4 to p3 is d times the normal vector of l_12 divided by the squared length of the normal vector.
n2 := (y1-y2)^2 + (x2-x1)^2
p4 := p3 + d/n2*(y1-y2, x2-x1, 0)
Note: if you divide l_12 by the length of the normal vector
l_12 := l_12 / sqrt((y1-y2)^2 + (x2-x1)^2)
the distance d will be the euclidean distance.
First, calculate the linear function determined by the points
(x1,y2),(x2,y2).
We get:
y1 = mx+b1 where m and b1 are constants.
This step is easy to calculate by the formula of linear function between two points.
Then, calculate the linear function y that goes through (x3,y3).
The function slope is -m, where m is the slope of y1.
Then calculate the const b2 by the coordinates of the point (x3,y3).
We get y2 = -mx+b2 where m and b2 are constants.
The last thing to do is to find the intersection of y1, y2.
You can find x by solving the equation: -mx+b2 = mx+b1, then place x in one of the equations to find y.
This is a vectorized Matlab function for finding pairwise projections of m points onto n line segments. Here xp and yp are m by 1 vectors holding coordinates of m different points, and x1, y1, x2 and y2 are n by 1 vectors holding coordinates of start and end points of n different line segments.
It returns m by n matrices, x and y, where x(i, j) and y(i, j) are coordinates of projection of i-th point onto j-th line.
The actual work is done in first few lines and the rest of the function runs a self-test demo, just in case where it is called with no parameters. It's relatively fast, I managed to find projections of 2k points onto 2k line segments in less than 0.05s.
function [x, y] = projectPointLine(xp, yp, x1, y1, x2, y2)
if nargin > 0
xd = (x2-x1)';
yd = (y2-y1)';
dAB = xd.*xd + yd.*yd;
u = bsxfun(#rdivide, bsxfun(#times, bsxfun(#minus, xp, x1'), xd) + ...
bsxfun(#times, bsxfun(#minus, yp, y1'), yd), dAB);
x = bsxfun(#plus, x1', bsxfun(#times, u, xd));
y = bsxfun(#plus, y1', bsxfun(#times, u, yd));
else
nLine = 3;
nPoint = 2;
xp = rand(nPoint, 1) * 2 -1;
yp = rand(nPoint, 1) * 2 -1;
x1 = rand(nLine, 1) * 2 -1;
y1 = rand(nLine, 1) * 2 -1;
x2 = rand(nLine, 1) * 2 -1;
y2 = rand(nLine, 1) * 2 -1;
tic;
[x, y] = projectPointLine(xp, yp, x1, y1, x2, y2);
toc
close all;
plot([x1'; x2'], [y1'; y2'], '.-', 'linewidth', 2, 'markersize', 20);
axis equal;
hold on
C = lines(nPoint + nLine);
for i=1:nPoint
scatter(x(i, :), y(i, :), 100, C(i+nLine, :), 'x', 'linewidth', 2);
scatter(xp(i), yp(i), 100, C(i+nLine, :), 'x', 'linewidth', 2);
end
for i=1:nLine
scatter(x(:, i)', y(:, i)', 100, C(i, :), 'o', 'linewidth', 2);
end
end
end