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I have two data files, each of them contain a big number of 3-dimensional points (file A stores approximately 50,000 points, file B stores approximately 500,000 points). My goal is to find for every point (a) in file A the point (b) in file B which has the smallest distance to (a). I store the points in two lists like this:
List A nodes:
(ID X Y Z)
[ ['478277', -107.0, 190.5674, 128.1634],
['478279', -107.0, 190.5674, 134.0172],
['478282', -107.0, 190.5674, 131.0903],
['478283', -107.0, 191.9798, 124.6807],
... ]
List B data:
(X Y Z Data)
[ [-28.102, 173.657, 229.744, 14.318],
[-28.265, 175.549, 227.824, 13.648],
[-27.695, 175.925, 227.133, 13.142],
...]
My first approach was to simply iterate through the first and second list with a nested loop and compute the distance between every points like this:
outfile = open(job[0] + '/' + output, 'wb');
dist_min = float(job[5]);
dist_max = float(job[6]);
dists = [];
for node in nodes:
shortest_distance = 1000.0;
shortest_data = 0.0;
for entry in data:
dist = math.sqrt((node[1] - entry[0])**2 + (node[2] - entry[1])**2 + (node[3] - entry[2])**2);
if (dist_min <= dist <= dist_max) and (dist < shortest_distance):
shortest_distance = dist;
shortest_data = entry[3];
outfile.write(node[0] + ', ' + str('%10.5f' % shortest_data + '\n'));
outfile.close();
I recognized that the amount of loops Python has to run is way too big (~25,000,000,000), so I had to fasten my code. I tried to first calculate all distances with list comprehensions but the code still is too slow:
p_x = [row[1] for row in nodes];
p_y = [row[2] for row in nodes];
p_z = [row[3] for row in nodes];
q_x = [row[0] for row in data];
q_y = [row[1] for row in data];
q_z = [row[2] for row in data];
dx = [[(px - qx) for px in p_x] for qx in q_x];
dy = [[(py - qy) for py in p_y] for qy in q_y];
dz = [[(pz - qz) for pz in p_z] for qz in q_z];
dx = [[dxxx * dxxx for dxxx in dxx] for dxx in dx];
dy = [[dyyy * dyyy for dyyy in dyy] for dyy in dy];
dz = [[dzzz * dzzz for dzzz in dzz] for dzz in dz];
D = [[(dx[i][j] + dy[i][j] + dz[i][j]) for j in range(len(dx[0]))] for i in range(len(dx))];
D = [[(DDD**(0.5)) for DDD in DD] for DD in D];
To be honest, at this point, I do not know which of the two approaches is better, anyway, none of the two possibilities seem feasible. I'm not even sure if it is possible to write a code which calculates all distances in an acceptable time. Is there even another way to solve my problem without calculating all distances?
Edit: I forgot to mention that I am running on Python 2.5.1 and am not allowed to install or add any new libraries...
Just in case someone is interrested in the solution:
I found a way to speed up the whole process by not calculating all distances:
I created a 3D-list, representing a grid in the given 3D space, divided in X, Y and Z in a given step size (e.g. (Max. - Min.) / 1,000). Then I iterated over every 3D point to put it into my grid. After that I iterated over the points of set A again, looking if there are points from B in the same cube, if not I would increase the search radius, so the process is looking in the adjacent 26 cubes for points. The radius is increasing until there is at least one point found. The resulting list is comparatively small and can be ordered in short time and the nearest point is found.
The processing time went down to a couple minutes and it is working fine.
p_x = [row[1] for row in nodes];
p_y = [row[2] for row in nodes];
p_z = [row[3] for row in nodes];
q_x = [row[0] for row in data];
q_y = [row[1] for row in data];
q_z = [row[2] for row in data];
min_x = min(p_x + q_x);
min_y = min(p_y + q_y);
min_z = min(p_z + q_z);
max_x = max(p_x + q_x);
max_y = max(p_y + q_y);
max_z = max(p_z + q_z);
max_n = max(max_x, max_y, max_z);
min_n = min(min_x, min_y, max_z);
gridcount = 1000;
step = (max_n - min_n) / gridcount;
ruler_x = [min_x + (i * step) for i in range(gridcount + 1)];
ruler_y = [min_y + (i * step) for i in range(gridcount + 1)];
ruler_z = [min_z + (i * step) for i in range(gridcount + 1)];
grid = [[[0 for i in range(gridcount)] for j in range(gridcount)] for k in range(gridcount)];
for node in nodes:
loc_x = self.abatemp_get_cell(node[1], ruler_x);
loc_y = self.abatemp_get_cell(node[2], ruler_y);
loc_z = self.abatemp_get_cell(node[3], ruler_z);
if grid[loc_x][loc_y][loc_z] is 0:
grid[loc_x][loc_y][loc_z] = [[node[1], node[2], node[3], node[0]]];
else:
grid[loc_x][loc_y][loc_z].append([node[1], node[2], node[3], node[0]]);
for entry in data:
loc_x = self.abatemp_get_cell(entry[0], ruler_x);
loc_y = self.abatemp_get_cell(entry[1], ruler_y);
loc_z = self.abatemp_get_cell(entry[2], ruler_z);
if grid[loc_x][loc_y][loc_z] is 0:
grid[loc_x][loc_y][loc_z] = [[entry[0], entry[1], entry[2], entry[3]]];
else:
grid[loc_x][loc_y][loc_z].append([entry[0], entry[1], entry[2], entry[3]]);
out = [];
outfile = open(job[0] + '/' + output, 'wb');
for node in nodes:
neighbours = [];
radius = -1;
loc_nx = self.abatemp_get_cell(node[1], ruler_x);
loc_ny = self.abatemp_get_cell(node[2], ruler_y);
loc_nz = self.abatemp_get_cell(node[3], ruler_z);
reloop = True;
while reloop:
if neighbours:
reloop = False;
radius += 1;
start_x = 0 if ((loc_nx - radius) < 0) else (loc_nx - radius);
start_y = 0 if ((loc_ny - radius) < 0) else (loc_ny - radius);
start_z = 0 if ((loc_nz - radius) < 0) else (loc_nz - radius);
end_x = (len(ruler_x) - 1) if ((loc_nx + radius + 1) > (len(ruler_x) - 1)) else (loc_nx + radius + 1);
end_y = (len(ruler_y) - 1) if ((loc_ny + radius + 1) > (len(ruler_y) - 1)) else (loc_ny + radius + 1);
end_z = (len(ruler_z) - 1) if ((loc_nz + radius + 1) > (len(ruler_z) - 1)) else (loc_nz + radius + 1);
for i in range(start_x, end_x):
for j in range(start_y, end_y):
for k in range(start_z, end_z):
if not grid[i][j][k] is 0:
for grid_entry in grid[i][j][k]:
if not isinstance(grid_entry[3], basestring):
neighbours.append(grid_entry);
dists = [];
for n in neighbours:
d = math.sqrt((node[1] - n[0])**2 + (node[2] - n[1])**2 + (node[3] - n[2])**2);
dists.append([d, n[3]]);
dists = sorted(dists);
outfile.write(node[0] + ', ' + str(dists[0][-1]) + '\n');
outfile.close();
Function to get the position of a point:
def abatemp_get_cell(self, n, ruler):
for i in range(len(ruler)):
if i >= len(ruler):
return False;
if ruler[i] <= n <= ruler[i + 1]:
return i;
The gridcount variable gives one the chance to fasten the process, with a small gridcount the process of sorting the points into the grid is very fast, but the lists of neighbours in the search loop gets bigger and more time is needed for this part of the process. With a big gridcount more time is needed at the beginning, however the loop runs faster.
The only issue I have now is the fact, that there are cases when the process found neighbours but there are other points, which are not yet found, but are closer to the point (see picture). So far I solved this issue by incrementing the search radius another time when there are already neigbours. And still then I have points which are closer but not in the neighbours list, although it's a very small amount (92 out of ~100,000). I could solve this problem by increment the radius two times after finding neighbours, but this solution seems not very smart. Maybe you guys have an idea...
This is the first working draft of the process, I think it will be possible to improve it even more, just to give you an idea of how it is working...
It took me a bit of thought but at the end I think I found a solution for you.
Your problem is not in the code you wrote but in the algorithm it implements.
There is an algorithm called Dijkstra's algorithm and here is the gist of it: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm .
Now what you need to do is to use this algorithm in a clever way:
create a node S (stand for source).
Now link edges from S to all the nodes in B group.
After you done that you should link edges from each point b in B to each point a in A.
You should set the cost of the links from the source to 0 and the other to the distance between 2 points (only in 3D).
Now if we will use Dijkstra's algorithm the output we will get would be the cost to travel from S to each point in the graph (we are only interested in the distance to points in group A).
So since the cost is 0 to each point b in B and S is only connected to points in B so the road to any point a in A must include a node in B (actually exactly one since the shortest distance between to points is a single line).
I am not sure if this will fasten your code but as far as I know, a way to solve this problem without calculating all distances does not exist and this algorithm is the best time complexity one could hope for.
take a look at this generic 3D data structure:
https://github.com/m4nh/skimap_ros
it has a very fast RadiusSearch feature just ready to be used. This solution (similar to Octree but faster) avoids to you to create the Regular Grid first (you don't have to fix MAX/MIN size along each axis) and you save a lot of memory
I'm writing my own basic physic engine and now I come to a problem I can't solve. Probably because I don't how to google this problem.
So here is my problem. I hope this image can explain it:
Collision response
I have two objects. The gray one is fixed and don't move and the green one which falls from the top.
The green object has three vectors: a force, the acceleration and the velocity. It collides with the fixed gray object.
The real question is how can I get the rotation of the green object when it falls down?
It sounds like you may not have an understanding of the fundamental physics underlying rigid body dynamics. I say that only because you don't mention any of the terminology commonly used when talking about this kind of problem. You'll need to introduce the idea of orientation and angular velocity (the rotational analogs of position and linear velocity) to each dynamic body in the system, and compute all kinds of intermediate quantities like moment of inertia, angular acceleration, and torque.
Perhaps the best introductory reference for this is Chris Hecker's series of articles for Game Developer Magazine. Assuming you already have non-rotational dynamics (covered in part 1) and collision detection (not covered by this series) solved, you should begin with part 2 and proceed to part 3. They'll give you a solid foundation in the physics and mathematics necessary for implementing rotational collision response.
You do as described below once, when the objects collide.
Let us call the green rectangle "a", and the other one "b".
1.
First you need the rectangles "rotational mass", mass of inertia.
a.i = 4/3 * width * height * (width^2 + height^2) * a.density
2.
Then you need the vector pointing from the rectangle's center of mass (average position of all corners) to the contact position (where the rectangles collide), let us call it "r".
3.
Then you need to find the collision normal. This normal is the direction of an impulse being applied to a from b. The normal is a vector with length 1 unit. In your example the normal would probably point upwards. Let us call the normal vector "n".
4.
Now you will need the velocity of the contact point on a. If a is not rotating, the formula would be:
vp = a.vel
If a is rotating the formula would be:
vp = a.vel + cross(a.r_vel, r)
a.r_vel is a's rotational velocity given in radians and positive direction is counter clockwise.
cross() means cross product, the function is:
cross (v,i) = [-i * v.y , i * v.x]
The expanded formula would be:
vp = a.v + [-r * a.r_vel.y , r * a.r_vel.x]
5.
Now you need to calculate whether the objects are moving towards each other. Project the vp onto n.
vp_p = dot(vp, n)
dot (v1, v2) = v1.x * v2.x + v1.y * v2.y
vp_p is a scalar (a value, not a vector).
If vp_p is negative the obejcts are moving towards each other, if it is > 0 they are moving apart.
6.
Now you need to calculate the impulse to stop a from moving into b, the impulse is:
j = -vp_p / (
1/a.mass + cross(r,n)^2 / a.i
)
The cross product between two vectors are:
cross(v1,v2) = v1.x * v2.y - v1.y * v2.x
It returns a scalar.
Multiply the impulse with the normal to get the impulse vector:
jn = j * n
7.
Now you need to apply the impulse to a:
a.new_vel = a.old_vel + jn / a.mass;
a.new_r_vel = a.old_r_vel + cross(r,jn) / a.i;
If you want the collision to be fully elastic, you must multiply the impulse by 2. Let us call this multiplier "e". e needs to be between 1 and 2. 1 means no energy is conserved, 2 means all energy is conserved.
Example code:
var vp = a.vel + cross(a.r_vel, r);
var vp_p = dot(vp,n); // negative val = moving towards each other
if (vp_p >= 0) { // do they move apart?
return false;
}
// normal impulse
var j = - e * vp_p / (
1/a.mass + cross(r,n)^2 / a.i
);
var jn = j * n;
//
a.vel = a.vel + jn / a.mass;
a.r_vel = a.r_vel + cross(r,jn) / a.i;
If b is not static the algorithm will be slightly different:
a.r = vector pointing from a's center of mass to the contact position
var vp = a.vel + cross(a.r_vel, a.r) - b.vel - cross(b.r_vel, b.r);
var vp_p = dot(vp,n); // negative val = moving towards each other
if (vp_p >= 0) { // do they move apart?
return false;
}
// normal impulse
var j = - e * vp_p / (
1/a.mass + cross(a.r,n)^2 / a.i +
1/b.mass + cross(b.r,n)^2 / b.i
);
var jn = j * n;
//
a.vel = a.vel + jp / a.mass;
a.r_vel = a.r_vel + cross(a.r,jn) / a.i;
b.vel = b.vel - jp / b.mass;
b.r_vel = b.r_vel - cross(b.r,jn) / b.i;
How the formulas work / sources:
http://www.myphysicslab.com/collision.html#resting_contact
How can I programmatically detect whether or not two triangles touch each other, given their vertices on a 2D coordinate plane? This includes touching points or edges, as well as if one triangle is completely inside the other one.
You can prove that the two triangles do not collide by finding an edge (out of the total 6 edges that make up the two triangles) that acts as a separating line where all the vertices of one triangle lie on one side and the vertices of the other triangle lie on the other side. If you can find such an edge then it means that the triangles do not intersect otherwise the triangles are colliding.
Here is a Matlab implementation of the triangle collision function. You can find the theory of the sameside function here: http://www.blackpawn.com/texts/pointinpoly/default.html
function flag = triangle_intersection(P1, P2)
% triangle_test : returns true if the triangles overlap and false otherwise
% P1, P2: a 3 by 2 array (each), describing the vertices of a triangle,
% the first column corresponds to the x coordinates while the second column
% corresponds to the y coordinates
function flag = sameside(p1,p2,a,b)
% sameside : returns true if the p1,p1 lie on same sides of the
% edge ab and false otherwise
p1(3) = 0; p2(3) = 0; a(3) = 0; b(3) = 0;
cp1 = cross(b-a, p1-a);
cp2 = cross(b-a, p2-a);
if(dot(cp1, cp2) >= 0)
flag = true;
else
flag = false;
end
end
% Repeat the vertices for the loop
P1(4:5,:) = P1(1:2,:);
P2(4:5,:) = P2(1:2,:);
flag = true;
% Testing all the edges of P1
for i=1:3
if(~sameside(P1(i,:), P2(1,:), P1(i+1,:), P1(i+2,:)) ...
&& sameside(P2(1,:), P2(2,:), P1(i+1,:), P1(i+2,:)) ...
&& sameside(P2(2,:), P2(3,:), P1(i+1,:), P1(i+2,:)))
flag = false; return;
end
end
% Testing all the edges of P2
for i=1:3
if(~sameside(P2(i,:), P1(1,:), P2(i+1,:), P2(i+2,:)) ...
&& sameside(P1(1,:), P1(2,:), P2(i+1,:), P2(i+2,:)) ...
&& sameside(P1(2,:), P1(3,:), P2(i+1,:), P2(i+2,:)))
flag = false; return;
end
end
end
In short, Hassan's answer is fastest.
https://jsfiddle.net/eyal/gxw3632c/
This is the fastest code in javascript:
// check that all points of the other triangle are on the same side of the triangle after mapping to barycentric coordinates.
// returns true if all points are outside on the same side
var cross2 = function(points, triangle) {
var pa = points.a;
var pb = points.b;
var pc = points.c;
var p0 = triangle.a;
var p1 = triangle.b;
var p2 = triangle.c;
var dXa = pa.x - p2.x;
var dYa = pa.y - p2.y;
var dXb = pb.x - p2.x;
var dYb = pb.y - p2.y;
var dXc = pc.x - p2.x;
var dYc = pc.y - p2.y;
var dX21 = p2.x - p1.x;
var dY12 = p1.y - p2.y;
var D = dY12 * (p0.x - p2.x) + dX21 * (p0.y - p2.y);
var sa = dY12 * dXa + dX21 * dYa;
var sb = dY12 * dXb + dX21 * dYb;
var sc = dY12 * dXc + dX21 * dYc;
var ta = (p2.y - p0.y) * dXa + (p0.x - p2.x) * dYa;
var tb = (p2.y - p0.y) * dXb + (p0.x - p2.x) * dYb;
var tc = (p2.y - p0.y) * dXc + (p0.x - p2.x) * dYc;
if (D < 0) return ((sa >= 0 && sb >= 0 && sc >= 0) ||
(ta >= 0 && tb >= 0 && tc >= 0) ||
(sa+ta <= D && sb+tb <= D && sc+tc <= D));
return ((sa <= 0 && sb <= 0 && sc <= 0) ||
(ta <= 0 && tb <= 0 && tc <= 0) ||
(sa+ta >= D && sb+tb >= D && sc+tc >= D));
}
var trianglesIntersect4 = function(t0, t1) {
return !(cross2(t0,t1) ||
cross2(t1,t0));
}
I wrote the above fiddle to test a few different techniques and compare the speed. All the techniques are based on some combination of three different tools:
Barycentric point-in-triangle test: Convert a point from x,y space to u,v space where u,v are two sides of the triangle. Then test if the point is inside the triangle (0,0) (0,1) (1,0), which is easy.
Same-side point-in-triangle test: This test tells you if an angle is more or less than 180 degrees. If the triangle is a,b,c and your point is p, you check if the angle pab and bac are both more or both less than 180. You need to do this for ab, bc, and ca. If any are true, the point is outside. This test is slower than barycentric for one point.
Line segment intersection: Check if the line segment a,b intersects line segment c,d. To do that, you find the point where the two lines cross and then check that those lines are in the bounding box of a,b and b,c. This is about as fast as Barycentric.
Those are the tools. Now to find out if triangles intersect, there are 3 ways that I tested:
8 line intersection and 2 point-in-triangle: You only need 8 line intersection and not all 9 because there can't be just 1 intersection. After that, you need 2 point-in-triangle in case 1 triangle is entirely inside the other.
6 line intersection and 4 point-in-triangle: If you draw it out, you can see that you can completely ignore one side of one of the triangles for line intersection and then just do all the other triangles points for point-in-triangle. Because line intersection and barycentric are about the same speed, this isn't much better than #1. But if you used same-side, it will be faster because same side point-in-triangle is slower.
9 same-side point-in-triangle tests: You can use either barycentric or same side. For either, you modify the point-in-triangle to test 3 points at the same time and you don't want to just test that they are all three outside the triangle, you need to test that they are all 3 outside on the same side. Because we're re-using a lot of information, we can save time in calculations. Again, barycentric seems slightly faster than same side here, too.
Use Line Line intersection
https://www.topcoder.com/community/data-science/data-science-tutorials/geometry-concepts-line-intersection-and-its-applications/#line_line_intersection
Also consider the possibility that some vertex might be touching one of the sides of the other triangle.
http://www.blackpawn.com/texts/pointinpoly/default.html
function SameSide(p1,p2, a,b)
cp1 = CrossProduct(b-a, p1-a)
cp2 = CrossProduct(b-a, p2-a)
if DotProduct(cp1, cp2) >= 0 then return true
else return false
function PointInTriangle(p, a,b,c)
if SameSide(p,a, b,c) and SameSide(p,b, a,c)
and SameSide(p,c, a,b) then return true
else return false
Or look at this link and scroll down
http://compsci.ca/v3/viewtopic.php?t=6034
BOUNTY STATUS UPDATE:
I discovered how to map a linear lens, from destination coordinates to source coordinates.
How do you calculate the radial distance from the centre to go from fisheye to rectilinear?
1). I actually struggle to reverse it, and to map source coordinates to destination coordinates. What is the inverse, in code in the style of the converting functions I posted?
2). I also see that my undistortion is imperfect on some lenses - presumably those that are not strictly linear. What is the equivalent to-and-from source-and-destination coordinates for those lenses? Again, more code than just mathematical formulae please...
Question as originally stated:
I have some points that describe positions in a picture taken with a fisheye lens.
I want to convert these points to rectilinear coordinates. I want to undistort the image.
I've found this description of how to generate a fisheye effect, but not how to reverse it.
There's also a blog post that describes how to use tools to do it; these pictures are from that:
(1) : SOURCE Original photo link
Input : Original image with fish-eye distortion to fix.
(2) : DESTINATION Original photo link
Output : Corrected image (technically also with perspective correction, but that's a separate step).
How do you calculate the radial distance from the centre to go from fisheye to rectilinear?
My function stub looks like this:
Point correct_fisheye(const Point& p,const Size& img) {
// to polar
const Point centre = {img.width/2,img.height/2};
const Point rel = {p.x-centre.x,p.y-centre.y};
const double theta = atan2(rel.y,rel.x);
double R = sqrt((rel.x*rel.x)+(rel.y*rel.y));
// fisheye undistortion in here please
//... change R ...
// back to rectangular
const Point ret = Point(centre.x+R*cos(theta),centre.y+R*sin(theta));
fprintf(stderr,"(%d,%d) in (%d,%d) = %f,%f = (%d,%d)\n",p.x,p.y,img.width,img.height,theta,R,ret.x,ret.y);
return ret;
}
Alternatively, I could somehow convert the image from fisheye to rectilinear before finding the points, but I'm completely befuddled by the OpenCV documentation. Is there a straightforward way to do it in OpenCV, and does it perform well enough to do it to a live video feed?
The description you mention states that the projection by a pin-hole camera (one that does not introduce lens distortion) is modeled by
R_u = f*tan(theta)
and the projection by common fisheye lens cameras (that is, distorted) is modeled by
R_d = 2*f*sin(theta/2)
You already know R_d and theta and if you knew the camera's focal length (represented by f) then correcting the image would amount to computing R_u in terms of R_d and theta. In other words,
R_u = f*tan(2*asin(R_d/(2*f)))
is the formula you're looking for. Estimating the focal length f can be solved by calibrating the camera or other means such as letting the user provide feedback on how well the image is corrected or using knowledge from the original scene.
In order to solve the same problem using OpenCV, you would have to obtain the camera's intrinsic parameters and lens distortion coefficients. See, for example, Chapter 11 of Learning OpenCV (don't forget to check the correction). Then you can use a program such as this one (written with the Python bindings for OpenCV) in order to reverse lens distortion:
#!/usr/bin/python
# ./undistort 0_0000.jpg 1367.451167 1367.451167 0 0 -0.246065 0.193617 -0.002004 -0.002056
import sys
import cv
def main(argv):
if len(argv) < 10:
print 'Usage: %s input-file fx fy cx cy k1 k2 p1 p2 output-file' % argv[0]
sys.exit(-1)
src = argv[1]
fx, fy, cx, cy, k1, k2, p1, p2, output = argv[2:]
intrinsics = cv.CreateMat(3, 3, cv.CV_64FC1)
cv.Zero(intrinsics)
intrinsics[0, 0] = float(fx)
intrinsics[1, 1] = float(fy)
intrinsics[2, 2] = 1.0
intrinsics[0, 2] = float(cx)
intrinsics[1, 2] = float(cy)
dist_coeffs = cv.CreateMat(1, 4, cv.CV_64FC1)
cv.Zero(dist_coeffs)
dist_coeffs[0, 0] = float(k1)
dist_coeffs[0, 1] = float(k2)
dist_coeffs[0, 2] = float(p1)
dist_coeffs[0, 3] = float(p2)
src = cv.LoadImage(src)
dst = cv.CreateImage(cv.GetSize(src), src.depth, src.nChannels)
mapx = cv.CreateImage(cv.GetSize(src), cv.IPL_DEPTH_32F, 1)
mapy = cv.CreateImage(cv.GetSize(src), cv.IPL_DEPTH_32F, 1)
cv.InitUndistortMap(intrinsics, dist_coeffs, mapx, mapy)
cv.Remap(src, dst, mapx, mapy, cv.CV_INTER_LINEAR + cv.CV_WARP_FILL_OUTLIERS, cv.ScalarAll(0))
# cv.Undistort2(src, dst, intrinsics, dist_coeffs)
cv.SaveImage(output, dst)
if __name__ == '__main__':
main(sys.argv)
Also note that OpenCV uses a very different lens distortion model to the one in the web page you linked to.
(Original poster, providing an alternative)
The following function maps destination (rectilinear) coordinates to source (fisheye-distorted) coordinates. (I'd appreciate help in reversing it)
I got to this point through trial-and-error: I don't fundamentally grasp why this code is working, explanations and improved accuracy appreciated!
def dist(x,y):
return sqrt(x*x+y*y)
def correct_fisheye(src_size,dest_size,dx,dy,factor):
""" returns a tuple of source coordinates (sx,sy)
(note: values can be out of range)"""
# convert dx,dy to relative coordinates
rx, ry = dx-(dest_size[0]/2), dy-(dest_size[1]/2)
# calc theta
r = dist(rx,ry)/(dist(src_size[0],src_size[1])/factor)
if 0==r:
theta = 1.0
else:
theta = atan(r)/r
# back to absolute coordinates
sx, sy = (src_size[0]/2)+theta*rx, (src_size[1]/2)+theta*ry
# done
return (int(round(sx)),int(round(sy)))
When used with a factor of 3.0, it successfully undistorts the images used as examples (I made no attempt at quality interpolation):
Dead link
(And this is from the blog post, for comparison:)
If you think your formulas are exact, you can comput an exact formula with trig, like so:
Rin = 2 f sin(w/2) -> sin(w/2)= Rin/2f
Rout= f tan(w) -> tan(w)= Rout/f
(Rin/2f)^2 = [sin(w/2)]^2 = (1 - cos(w))/2 -> cos(w) = 1 - 2(Rin/2f)^2
(Rout/f)^2 = [tan(w)]^2 = 1/[cos(w)]^2 - 1
-> (Rout/f)^2 = 1/(1-2[Rin/2f]^2)^2 - 1
However, as #jmbr says, the actual camera distortion will depend on the lens and the zoom. Rather than rely on a fixed formula, you might want to try a polynomial expansion:
Rout = Rin*(1 + A*Rin^2 + B*Rin^4 + ...)
By tweaking first A, then higher-order coefficients, you can compute any reasonable local function (the form of the expansion takes advantage of the symmetry of the problem). In particular, it should be possible to compute initial coefficients to approximate the theoretical function above.
Also, for good results, you will need to use an interpolation filter to generate your corrected image. As long as the distortion is not too great, you can use the kind of filter you would use to rescale the image linearly without much problem.
Edit: as per your request, the equivalent scaling factor for the above formula:
(Rout/f)^2 = 1/(1-2[Rin/2f]^2)^2 - 1
-> Rout/f = [Rin/f] * sqrt(1-[Rin/f]^2/4)/(1-[Rin/f]^2/2)
If you plot the above formula alongside tan(Rin/f), you can see that they are very similar in shape. Basically, distortion from the tangent becomes severe before sin(w) becomes much different from w.
The inverse formula should be something like:
Rin/f = [Rout/f] / sqrt( sqrt(([Rout/f]^2+1) * (sqrt([Rout/f]^2+1) + 1) / 2 )
I blindly implemented the formulas from here, so I cannot guarantee it would do what you need.
Use auto_zoom to get the value for the zoom parameter.
def dist(x,y):
return sqrt(x*x+y*y)
def fisheye_to_rectilinear(src_size,dest_size,sx,sy,crop_factor,zoom):
""" returns a tuple of dest coordinates (dx,dy)
(note: values can be out of range)
crop_factor is ratio of sphere diameter to diagonal of the source image"""
# convert sx,sy to relative coordinates
rx, ry = sx-(src_size[0]/2), sy-(src_size[1]/2)
r = dist(rx,ry)
# focal distance = radius of the sphere
pi = 3.1415926535
f = dist(src_size[0],src_size[1])*factor/pi
# calc theta 1) linear mapping (older Nikon)
theta = r / f
# calc theta 2) nonlinear mapping
# theta = asin ( r / ( 2 * f ) ) * 2
# calc new radius
nr = tan(theta) * zoom
# back to absolute coordinates
dx, dy = (dest_size[0]/2)+rx/r*nr, (dest_size[1]/2)+ry/r*nr
# done
return (int(round(dx)),int(round(dy)))
def fisheye_auto_zoom(src_size,dest_size,crop_factor):
""" calculate zoom such that left edge of source image matches left edge of dest image """
# Try to see what happens with zoom=1
dx, dy = fisheye_to_rectilinear(src_size, dest_size, 0, src_size[1]/2, crop_factor, 1)
# Calculate zoom so the result is what we wanted
obtained_r = dest_size[0]/2 - dx
required_r = dest_size[0]/2
zoom = required_r / obtained_r
return zoom
I took what JMBR did and basically reversed it. He took the radius of the distorted image (Rd, that is, the distance in pixels from the center of the image) and found a formula for Ru, the radius of the undistorted image.
You want to go the other way. For each pixel in the undistorted (processed image), you want to know what the corresponding pixel is in the distorted image.
In other words, given (xu, yu) --> (xd, yd). You then replace each pixel in the undistorted image with its corresponding pixel from the distorted image.
Starting where JMBR did, I do the reverse, finding Rd as a function of Ru. I get:
Rd = f * sqrt(2) * sqrt( 1 - 1/sqrt(r^2 +1))
where f is the focal length in pixels (I'll explain later), and r = Ru/f.
The focal length for my camera was 2.5 mm. The size of each pixel on my CCD was 6 um square. f was therefore 2500/6 = 417 pixels. This can be found by trial and error.
Finding Rd allows you to find the corresponding pixel in the distorted image using polar coordinates.
The angle of each pixel from the center point is the same:
theta = arctan( (yu-yc)/(xu-xc) ) where xc, yc are the center points.
Then,
xd = Rd * cos(theta) + xc
yd = Rd * sin(theta) + yc
Make sure you know which quadrant you are in.
Here is the C# code I used
public class Analyzer
{
private ArrayList mFisheyeCorrect;
private int mFELimit = 1500;
private double mScaleFESize = 0.9;
public Analyzer()
{
//A lookup table so we don't have to calculate Rdistorted over and over
//The values will be multiplied by focal length in pixels to
//get the Rdistorted
mFisheyeCorrect = new ArrayList(mFELimit);
//i corresponds to Rundist/focalLengthInPixels * 1000 (to get integers)
for (int i = 0; i < mFELimit; i++)
{
double result = Math.Sqrt(1 - 1 / Math.Sqrt(1.0 + (double)i * i / 1000000.0)) * 1.4142136;
mFisheyeCorrect.Add(result);
}
}
public Bitmap RemoveFisheye(ref Bitmap aImage, double aFocalLinPixels)
{
Bitmap correctedImage = new Bitmap(aImage.Width, aImage.Height);
//The center points of the image
double xc = aImage.Width / 2.0;
double yc = aImage.Height / 2.0;
Boolean xpos, ypos;
//Move through the pixels in the corrected image;
//set to corresponding pixels in distorted image
for (int i = 0; i < correctedImage.Width; i++)
{
for (int j = 0; j < correctedImage.Height; j++)
{
//which quadrant are we in?
xpos = i > xc;
ypos = j > yc;
//Find the distance from the center
double xdif = i-xc;
double ydif = j-yc;
//The distance squared
double Rusquare = xdif * xdif + ydif * ydif;
//the angle from the center
double theta = Math.Atan2(ydif, xdif);
//find index for lookup table
int index = (int)(Math.Sqrt(Rusquare) / aFocalLinPixels * 1000);
if (index >= mFELimit) index = mFELimit - 1;
//calculated Rdistorted
double Rd = aFocalLinPixels * (double)mFisheyeCorrect[index]
/mScaleFESize;
//calculate x and y distances
double xdelta = Math.Abs(Rd*Math.Cos(theta));
double ydelta = Math.Abs(Rd * Math.Sin(theta));
//convert to pixel coordinates
int xd = (int)(xc + (xpos ? xdelta : -xdelta));
int yd = (int)(yc + (ypos ? ydelta : -ydelta));
xd = Math.Max(0, Math.Min(xd, aImage.Width-1));
yd = Math.Max(0, Math.Min(yd, aImage.Height-1));
//set the corrected pixel value from the distorted image
correctedImage.SetPixel(i, j, aImage.GetPixel(xd, yd));
}
}
return correctedImage;
}
}
I found this pdf file and I have proved that the maths are correct (except for the line vd = *xd**fv+v0 which should say vd = **yd**+fv+v0).
http://perception.inrialpes.fr/CAVA_Dataset/Site/files/Calibration_OpenCV.pdf
It does not use all of the latest co-efficients that OpenCV has available but I am sure that it could be adapted fairly easily.
double k1 = cameraIntrinsic.distortion[0];
double k2 = cameraIntrinsic.distortion[1];
double p1 = cameraIntrinsic.distortion[2];
double p2 = cameraIntrinsic.distortion[3];
double k3 = cameraIntrinsic.distortion[4];
double fu = cameraIntrinsic.focalLength[0];
double fv = cameraIntrinsic.focalLength[1];
double u0 = cameraIntrinsic.principalPoint[0];
double v0 = cameraIntrinsic.principalPoint[1];
double u, v;
u = thisPoint->x; // the undistorted point
v = thisPoint->y;
double x = ( u - u0 )/fu;
double y = ( v - v0 )/fv;
double r2 = (x*x) + (y*y);
double r4 = r2*r2;
double cDist = 1 + (k1*r2) + (k2*r4);
double xr = x*cDist;
double yr = y*cDist;
double a1 = 2*x*y;
double a2 = r2 + (2*(x*x));
double a3 = r2 + (2*(y*y));
double dx = (a1*p1) + (a2*p2);
double dy = (a3*p1) + (a1*p2);
double xd = xr + dx;
double yd = yr + dy;
double ud = (xd*fu) + u0;
double vd = (yd*fv) + v0;
thisPoint->x = ud; // the distorted point
thisPoint->y = vd;
This can be solved as an optimization problem. Simply draw on curves in images that are supposed to be straight lines. Store the contour points for each of those curves. Now we can solve the fish eye matrix as a minimization problem. Minimize the curve in points and that will give us a fisheye matrix. It works.
It can be done manually by adjusting the fish eye matrix using trackbars! Here is a fish eye GUI code using OpenCV for manual calibration.
Okay, this all takes place in a nice and simple 2D world... :)
Suppose I have a static object A at position Apos, and a linearly moving object B at Bpos with bVelocity, and an ammo round with velocity Avelocity...
How would I find out the angle that A has to shoot, to hit B, taking into account B's linear velocity and the speed of A's ammo ?
Right now the aim's at the current position of the object, which means that by the time my projectile gets there the unit has moved on to safer positions :)
I wrote an aiming subroutine for xtank a while back. I'll try to lay out how I did it.
Disclaimer: I may have made one or more silly mistakes anywhere in here; I'm just trying to reconstruct the reasoning with my rusty math skills. However, I'll cut to the chase first, since this is a programming Q&A instead of a math class :-)
How to do it
It boils down to solving a quadratic equation of the form:
a * sqr(x) + b * x + c == 0
Note that by sqr I mean square, as opposed to square root. Use the following values:
a := sqr(target.velocityX) + sqr(target.velocityY) - sqr(projectile_speed)
b := 2 * (target.velocityX * (target.startX - cannon.X)
+ target.velocityY * (target.startY - cannon.Y))
c := sqr(target.startX - cannon.X) + sqr(target.startY - cannon.Y)
Now we can look at the discriminant to determine if we have a possible solution.
disc := sqr(b) - 4 * a * c
If the discriminant is less than 0, forget about hitting your target -- your projectile can never get there in time. Otherwise, look at two candidate solutions:
t1 := (-b + sqrt(disc)) / (2 * a)
t2 := (-b - sqrt(disc)) / (2 * a)
Note that if disc == 0 then t1 and t2 are equal.
If there are no other considerations such as intervening obstacles, simply choose the smaller positive value. (Negative t values would require firing backward in time to use!)
Substitute the chosen t value back into the target's position equations to get the coordinates of the leading point you should be aiming at:
aim.X := t * target.velocityX + target.startX
aim.Y := t * target.velocityY + target.startY
Derivation
At time T, the projectile must be a (Euclidean) distance from the cannon equal to the elapsed time multiplied by the projectile speed. This gives an equation for a circle, parametric in elapsed time.
sqr(projectile.X - cannon.X) + sqr(projectile.Y - cannon.Y)
== sqr(t * projectile_speed)
Similarly, at time T, the target has moved along its vector by time multiplied by its velocity:
target.X == t * target.velocityX + target.startX
target.Y == t * target.velocityY + target.startY
The projectile can hit the target when its distance from the cannon matches the projectile's distance.
sqr(projectile.X - cannon.X) + sqr(projectile.Y - cannon.Y)
== sqr(target.X - cannon.X) + sqr(target.Y - cannon.Y)
Wonderful! Substituting the expressions for target.X and target.Y gives
sqr(projectile.X - cannon.X) + sqr(projectile.Y - cannon.Y)
== sqr((t * target.velocityX + target.startX) - cannon.X)
+ sqr((t * target.velocityY + target.startY) - cannon.Y)
Substituting the other side of the equation gives this:
sqr(t * projectile_speed)
== sqr((t * target.velocityX + target.startX) - cannon.X)
+ sqr((t * target.velocityY + target.startY) - cannon.Y)
... subtracting sqr(t * projectile_speed) from both sides and flipping it around:
sqr((t * target.velocityX) + (target.startX - cannon.X))
+ sqr((t * target.velocityY) + (target.startY - cannon.Y))
- sqr(t * projectile_speed)
== 0
... now resolve the results of squaring the subexpressions ...
sqr(target.velocityX) * sqr(t)
+ 2 * t * target.velocityX * (target.startX - cannon.X)
+ sqr(target.startX - cannon.X)
+ sqr(target.velocityY) * sqr(t)
+ 2 * t * target.velocityY * (target.startY - cannon.Y)
+ sqr(target.startY - cannon.Y)
- sqr(projectile_speed) * sqr(t)
== 0
... and group similar terms ...
sqr(target.velocityX) * sqr(t)
+ sqr(target.velocityY) * sqr(t)
- sqr(projectile_speed) * sqr(t)
+ 2 * t * target.velocityX * (target.startX - cannon.X)
+ 2 * t * target.velocityY * (target.startY - cannon.Y)
+ sqr(target.startX - cannon.X)
+ sqr(target.startY - cannon.Y)
== 0
... then combine them ...
(sqr(target.velocityX) + sqr(target.velocityY) - sqr(projectile_speed)) * sqr(t)
+ 2 * (target.velocityX * (target.startX - cannon.X)
+ target.velocityY * (target.startY - cannon.Y)) * t
+ sqr(target.startX - cannon.X) + sqr(target.startY - cannon.Y)
== 0
... giving a standard quadratic equation in t. Finding the positive real zeros of this equation gives the (zero, one, or two) possible hit locations, which can be done with the quadratic formula:
a * sqr(x) + b * x + c == 0
x == (-b ± sqrt(sqr(b) - 4 * a * c)) / (2 * a)
+1 on Jeffrey Hantin's excellent answer here. I googled around and found solutions that were either too complex or not specifically about the case I was interested in (simple constant velocity projectile in 2D space.) His was exactly what I needed to produce the self-contained JavaScript solution below.
The one point I would add is that there are a couple special cases you have to watch for in addition to the discriminant being negative:
"a == 0": occurs if target and projectile are traveling the same speed. (solution is linear, not quadratic)
"a == 0 and b == 0": if both target and projectile are stationary. (no solution unless c == 0, i.e. src & dst are same point.)
Code:
/**
* Return the firing solution for a projectile starting at 'src' with
* velocity 'v', to hit a target, 'dst'.
*
* #param ({x, y}) src position of shooter
* #param ({x, y, vx, vy}) dst position & velocity of target
* #param (Number) v speed of projectile
*
* #return ({x, y}) Coordinate at which to fire (and where intercept occurs). Or `null` if target cannot be hit.
*/
function intercept(src, dst, v) {
const tx = dst.x - src.x;
const ty = dst.y - src.y;
const tvx = dst.vx;
const tvy = dst.vy;
// Get quadratic equation components
const a = tvx * tvx + tvy * tvy - v * v;
const b = 2 * (tvx * tx + tvy * ty);
const c = tx * tx + ty * ty;
// Solve quadratic
const ts = quad(a, b, c); // See quad(), below
// Find smallest positive solution
let sol = null;
if (ts) {
const t0 = ts[0];
const t1 = ts[1];
let t = Math.min(t0, t1);
if (t < 0) t = Math.max(t0, t1);
if (t > 0) {
sol = {
x: dst.x + dst.vx * t,
y: dst.y + dst.vy * t
};
}
}
return sol;
}
/**
* Return solutions for quadratic
*/
function quad(a, b, c) {
let sol = null;
if (Math.abs(a) < 1e-6) {
if (Math.abs(b) < 1e-6) {
sol = Math.abs(c) < 1e-6 ? [0, 0] : null;
} else {
sol = [-c / b, -c / b];
}
} else {
let disc = b * b - 4 * a * c;
if (disc >= 0) {
disc = Math.sqrt(disc);
a = 2 * a;
sol = [(-b - disc) / a, (-b + disc) / a];
}
}
return sol;
}
// For example ...
const sol = intercept(
{x:2, y:4}, // Starting coord
{x:5, y:7, vx: 2, vy:1}, // Target coord and velocity
5 // Projectile velocity
)
console.log('Fire at', sol)
First rotate the axes so that AB is vertical (by doing a rotation)
Now, split the velocity vector of B into the x and y components (say Bx and By). You can use this to calculate the x and y components of the vector you need to shoot at.
B --> Bx
|
|
V
By
Vy
^
|
|
A ---> Vx
You need Vx = Bx and Sqrt(Vx*Vx + Vy*Vy) = Velocity of Ammo.
This should give you the vector you need in the new system. Transform back to old system and you are done (by doing a rotation in the other direction).
Jeffrey Hantin has a nice solution for this problem, though his derivation is overly complicated. Here's a cleaner way of deriving it with some of the resultant code at the bottom.
I'll be using x.y to represent vector dot product, and if a vector quantity is squared, it means I am dotting it with itself.
origpos = initial position of shooter
origvel = initial velocity of shooter
targpos = initial position of target
targvel = initial velocity of target
projvel = velocity of the projectile relative to the origin (cause ur shooting from there)
speed = the magnitude of projvel
t = time
We know that the position of the projectile and target with respect to t time can be described with some equations.
curprojpos(t) = origpos + t*origvel + t*projvel
curtargpos(t) = targpos + t*targvel
We want these to be equal to each other at some point (the point of intersection), so let's set them equal to each other and solve for the free variable, projvel.
origpos + t*origvel + t*projvel = targpos + t*targvel
turns into ->
projvel = (targpos - origpos)/t + targvel - origvel
Let's forget about the notion of origin and target position/velocity. Instead, let's work in relative terms since motion of one thing is relative to another. In this case, what we now have is relpos = targetpos - originpos and relvel = targetvel - originvel
projvel = relpos/t + relvel
We don't know what projvel is, but we do know that we want projvel.projvel to be equal to speed^2, so we'll square both sides and we get
projvel^2 = (relpos/t + relvel)^2
expands into ->
speed^2 = relvel.relvel + 2*relpos.relvel/t + relpos.relpos/t^2
We can now see that the only free variable is time, t, and then we'll use t to solve for projvel. We'll solve for t with the quadratic formula. First separate it out into a, b and c, then solve for the roots.
Before solving, though, remember that we want the best solution where t is smallest, but we need to make sure that t is not negative (you can't hit something in the past)
a = relvel.relvel - speed^2
b = 2*relpos.relvel
c = relpos.relpos
h = -b/(2*a)
k2 = h*h - c/a
if k2 < 0, then there are no roots and there is no solution
if k2 = 0, then there is one root at h
if 0 < h then t = h
else, no solution
if k2 > 0, then there are two roots at h - k and h + k, we also know r0 is less than r1.
k = sqrt(k2)
r0 = h - k
r1 = h + k
we have the roots, we must now solve for the smallest positive one
if 0<r0 then t = r0
elseif 0<r1 then t = r1
else, no solution
Now, if we have a t value, we can plug t back into the original equation and solve for the projvel
projvel = relpos/t + relvel
Now, to the shoot the projectile, the resultant global position and velocity for the projectile is
globalpos = origpos
globalvel = origvel + projvel
And you're done!
My implementation of my solution in Lua, where vec*vec represents vector dot product:
local function lineartrajectory(origpos,origvel,speed,targpos,targvel)
local relpos=targpos-origpos
local relvel=targvel-origvel
local a=relvel*relvel-speed*speed
local b=2*relpos*relvel
local c=relpos*relpos
if a*a<1e-32 then--code translation for a==0
if b*b<1e-32 then
return false,"no solution"
else
local h=-c/b
if 0<h then
return origpos,relpos/h+targvel,h
else
return false,"no solution"
end
end
else
local h=-b/(2*a)
local k2=h*h-c/a
if k2<-1e-16 then
return false,"no solution"
elseif k2<1e-16 then--code translation for k2==0
if 0<h then
return origpos,relpos/h+targvel,h
else
return false,"no solution"
end
else
local k=k2^0.5
if k<h then
return origpos,relpos/(h-k)+targvel,h-k
elseif -k<h then
return origpos,relpos/(h+k)+targvel,h+k
else
return false,"no solution"
end
end
end
end
Following is polar coordinate based aiming code in C++.
To use with rectangular coordinates you would need to first convert the targets relative coordinate to angle/distance, and the targets x/y velocity to angle/speed.
The "speed" input is the speed of the projectile. The units of the speed and targetSpeed are irrelevent, as only the ratio of the speeds are used in the calculation. The output is the angle the projectile should be fired at and the distance to the collision point.
The algorithm is from source code available at http://www.turtlewar.org/ .
// C++
static const double pi = 3.14159265358979323846;
inline double Sin(double a) { return sin(a*(pi/180)); }
inline double Asin(double y) { return asin(y)*(180/pi); }
bool/*ok*/ Rendezvous(double speed,double targetAngle,double targetRange,
double targetDirection,double targetSpeed,double* courseAngle,
double* courseRange)
{
// Use trig to calculate coordinate of future collision with target.
// c
//
// B A
//
// a C b
//
// Known:
// C = distance to target
// b = direction of target travel, relative to it's coordinate
// A/B = ratio of speed and target speed
//
// Use rule of sines to find unknowns.
// sin(a)/A = sin(b)/B = sin(c)/C
//
// a = asin((A/B)*sin(b))
// c = 180-a-b
// B = C*(sin(b)/sin(c))
bool ok = 0;
double b = 180-(targetDirection-targetAngle);
double A_div_B = targetSpeed/speed;
double C = targetRange;
double sin_b = Sin(b);
double sin_a = A_div_B*sin_b;
// If sin of a is greater than one it means a triangle cannot be
// constructed with the given angles that have sides with the given
// ratio.
if(fabs(sin_a) <= 1)
{
double a = Asin(sin_a);
double c = 180-a-b;
double sin_c = Sin(c);
double B;
if(fabs(sin_c) > .0001)
{
B = C*(sin_b/sin_c);
}
else
{
// Sin of small angles approach zero causing overflow in
// calculation. For nearly flat triangles just treat as
// flat.
B = C/(A_div_B+1);
}
// double A = C*(sin_a/sin_c);
ok = 1;
*courseAngle = targetAngle+a;
*courseRange = B;
}
return ok;
}
Here's an example where I devised and implemented a solution to the problem of predictive targeting using a recursive algorithm: http://www.newarteest.com/flash/targeting.html
I'll have to try out some of the other solutions presented because it seems more efficient to calculate it in one step, but the solution I came up with was to estimate the target position and feed that result back into the algorithm to make a new more accurate estimate, repeating several times.
For the first estimate I "fire" at the target's current position and then use trigonometry to determine where the target will be when the shot reaches the position fired at. Then in the next iteration I "fire" at that new position and determine where the target will be this time. After about 4 repeats I get within a pixel of accuracy.
I just hacked this version for aiming in 2d space, I didn't test it very thoroughly yet but it seems to work. The idea behind it is this:
Create a vector perpendicular to the vector pointing from the muzzle to the target.
For a collision to occur, the velocities of the target and the projectile along this vector (axis) should be the same!
Using fairly simple cosine stuff I arrived at this code:
private Vector3 CalculateProjectileDirection(Vector3 a_MuzzlePosition, float a_ProjectileSpeed, Vector3 a_TargetPosition, Vector3 a_TargetVelocity)
{
// make sure it's all in the horizontal plane:
a_TargetPosition.y = 0.0f;
a_MuzzlePosition.y = 0.0f;
a_TargetVelocity.y = 0.0f;
// create a normalized vector that is perpendicular to the vector pointing from the muzzle to the target's current position (a localized x-axis):
Vector3 perpendicularVector = Vector3.Cross(a_TargetPosition - a_MuzzlePosition, -Vector3.up).normalized;
// project the target's velocity vector onto that localized x-axis:
Vector3 projectedTargetVelocity = Vector3.Project(a_TargetVelocity, perpendicularVector);
// calculate the angle that the projectile velocity should make with the localized x-axis using the consine:
float angle = Mathf.Acos(projectedTargetVelocity.magnitude / a_ProjectileSpeed) / Mathf.PI * 180;
if (Vector3.Angle(perpendicularVector, a_TargetVelocity) > 90.0f)
{
angle = 180.0f - angle;
}
// rotate the x-axis so that is points in the desired velocity direction of the projectile:
Vector3 returnValue = Quaternion.AngleAxis(angle, -Vector3.up) * perpendicularVector;
// give the projectile the correct speed:
returnValue *= a_ProjectileSpeed;
return returnValue;
}
I made a public domain Unity C# function here:
http://ringofblades.com/Blades/Code/PredictiveAim.cs
It is for 3D, but you can easily modify this for 2D by replacing the Vector3s with Vector2s and using your down axis of choice for gravity if there is gravity.
In case the theory interests you, I walk through the derivation of the math here:
http://www.gamasutra.com/blogs/KainShin/20090515/83954/Predictive_Aim_Mathematics_for_AI_Targeting.php
I've seen many ways to solve this problem mathematically, but this was a component relevant to a project my class was required to do in high school, and not everyone in this programming class had a background with calculus, or even vectors for that matter, so I created a way to solve this problem with more of a programming approach. The point of intersection will be accurate, although it may hit 1 frame later than in the mathematical computations.
Consider:
S = shooterPos, E = enemyPos, T = targetPos, Sr = shooter range, D = enemyDir
V = distance from E to T, P = projectile speed, Es = enemy speed
In the standard implementation of this problem [S,E,P,Es,D] are all givens and you are solving either to find T or the angle at which to shoot so that you hit T at the proper timing.
The main aspect of this method of solving the problem is to consider the range of the shooter as a circle encompassing all possible points that can be shot at any given time. The radius of this circle is equal to:
Sr = P*time
Where time is calculated as an iteration of a loop.
Thus to find the distance an enemy travels given the time iteration we create the vector:
V = D*Es*time
Now, to actually solve the problem we want to find a point at which the distance from the target (T) to our shooter (S) is less than the range of our shooter (Sr). Here is somewhat of a pseudocode implementation of this equation.
iteration = 0;
while(TargetPoint.hasNotPassedShooter)
{
TargetPoint = EnemyPos + (EnemyMovementVector)
if(distanceFrom(TargetPoint,ShooterPos) < (ShooterRange))
return TargetPoint;
iteration++
}
Basically , intersection concept is not really needed here, As far as you are using projectile motion, you just need to hit at a particular angle and instantiate at the time of shooting so that you get the exact distance of your target from the Source and then once you have the distance, you can calculate the appropriate velocity with which it should shot in order to hit the Target.
The following link makes teh concept clear and is considered helpful, might help:
Projectile motion to always hit a moving target
I grabbed one of the solutions from here, but none of them take into account movement of the shooter. If your shooter is moving, you might want to take that into account (as the shooter's velocity should be added to your bullet's velocity when you fire). Really all you need to do is subtract your shooter's velocity from the target's velocity. So if you're using broofa's code above (which I would recommend), change the lines
tvx = dst.vx;
tvy = dst.vy;
to
tvx = dst.vx - shooter.vx;
tvy = dst.vy - shooter.vy;
and you should be all set.