I am reverse engineering a game from 1999 and I came across a function which looks to be checking if the player is within range of a 3d point for the triggering of audio sources. The decompiler mangles the code pretty bad but I think I understand it.
// Position Y delta
v1 = * (float * )(this + 16) - LocalPlayerZoneEntry - > y;
// Position X delta
v2 = * (float * )(this + 20) - LocalPlayerZoneEntry - > x;
// Absolute value
if (v1 < 0.0)
v1 = -v1;
// Absolute value
if (v2 < 0.0)
v2 = -v2;
// What is going on here?
if (v1 <= v2)
v1 = v1 * 0.5;
else
v2 = v2 * 0.5;
// Z position delta
v3 = * (float * )(this + 24) - LocalPlayerZoneEntry - > z;
// Absolute value
if (v3 < 0.0)
v3 = -v3;
result = v3 + v2 + v1;
// Radius
if (result > * (float * )(this + 28))
return 0.0;
return result;
Interestingly enough, when in game, it seemed like the triggering was pretty inconsistent and would sometimes be quite a bit off depending on from which side I approached the trigger.
Does anyone have any idea if this was a common algorithm used back in the day?
Note: The types were all added by me so they may be incorrect. I assume that this is a function of type bool.
The best way to visualize a distance function (a metric) is to plot its unit sphere (the set of points at unit distance from origin -- the metric in question is norm induced).
First rewrite it in a more mathematical form:
N(x,y,z) = 0.5*|x| + |y| + |z| when |x| <= |y|
= |x| + 0.5*|y| + |z| otherwise
Let's do that for 2d (assume that z = 0). The absolute values make the function symmetric in the four quadrants. The |x| <= |y| condition makes it symmetric in all the eight sectors. Let's focus on the sector x > 0, y > 0, x <= y. We want to find the curve when N(x,y,0) = 1. For that sector it reduces to 0.5x + y = 1, or y = 1 - 0.5x. We can go and plot that line. For when x > 0, y > 0, x > y, we get x = 1 - 0.5y. Plotting it all gives the following unit 'circle':
For comparison, here is an Euclidean unit circle overlaid:
In the third dimension it behaves like a taxicab metric, effectively giving you a 'diamond' shaped sphere:
So yes, it is a cheap distance function, though it lacks rotational symmetries.
Related
I'm trying to write an algorithm to determine if point is located inside a triangle or on it's edge in 3D coordinate space.
For example, I try to reach such results for different cases
I've figured out how to check if point P inside the triangle, I calculated normal vectors for triangles ABP, BCP, CAP and checked if they are similar.
Can someone explain how to check if a point is on the edge of a triangle (but not outside of a triangle)? You can provide formulas or code as you wish.
Make vectors:
r = p - A (r.x = p.x - A.x, r.y = p.y - A.y, r.z = p.z - A.z)
s = B - A
q = C - A
Calculate normal to ABC plane:
n = s x q (vector product)
Check if p lies in ABC plane using dot product:
dp = n.dot.r
If dp is zero (or has very small value like 1.0e-10 due to the floating point errors, then p is in the plane, and we can continue
Decompose vector p by base vectors s and q. At first check if z-component of normal (n.z) is non-zero. If so, use the next pair of equations (otherwise choose equations for x/z or y/z components):
px = a * sx + b * qx
py = a * sy + b * qy
Solve this system
a = (sy * qx - sx * qy) / (py * qx - px * qy)
b = (px - a * sx) / qx
If resulting coefficients a and b fulfill limits:
a >= 0
b >= 0
a + b <= 1.0
then point p lies in triangle plane inside it.
What I am trying to do:
1. scale uniformly an array of points around a point.
2. A point has to be an average point of array of points.
The code below, seems to work, but I do not know if it is the proper way of doing that.
I know that uniform scaling is simply multiplying points by some value, but this is scaling on 0,0,0 point, how to do it around mean point?
The code could be subdivided by following steps:
Get the average point of the array of points, by summing up all positions and dividing by a number of points.
Ratio is scaling value
Then I do vector subtraction to get a vector pointing from point to average point.
I normalize that vector (I get unit vector)
Then I add that normalized vector to current point multiplied by (1 - ratio)*0.5
This last bit 5th point came totally from checking total length of the value.
All examples I came up before was using matrices in math, and I really not capable of reading matrix operations.
Is it the correct uniform scaling method, if it's not could you point out what I am doing wrong?
//Get center of a curve
//That is average of all points
MatMxN curveCenter = MatMxN::Zero(2, 1); //This is just 1 vector/point with x and y coordinates
for (int i = 0; i < n; i++)
curveCenter += points.col(i);
curveCenter /= n;
//Scaling value
float ratio = 1.3;
//Get vector pointing to center and move by ratio
for (int i = 0; i < n; i++) {
MatMxN vector = curveCenter - points.col(i);
MatMxN unit = vector.normalized();
points.col(i) += unit*(1 - ratio)*0.5; //points.col(i) this is point in array
}
In order to scale points using a specific center point (other than 0), follow these steps:
Substract center from point MatMxN vector = points.col(i) - curveCenter;
Multiply vector by scaling factor vector *= ratio
Add center to the scaled vector to get new point points.col(i) = vector + curveCenter
This approach can be resolved to something remotely similar to your formula. Lets call the center C, the point to be scaled P0, the scaled point P1 and the scaling factor s. The above 3 steps can be written as:
v0 = P0 - C
v1 = s * v0
P1 = v1 + C
=>
P1 = s * P0 + C * (1 - s)
Now we define P1 = P0 + x for some x:
P0 + x = s * P0 + C * (1 - s)
=>
x = s * P0 + C * (1 - s) - P0
= C * (1 - s) - P0 * (1 - s)
= (C - P0) * (1 - s)
So the update could be written as follows instead of using the 3 steps mentioned:
MatMxN vector = curveCenter - points.col(i);
points.col(i) += vector * (1 - ratio);
However, I prefer to write the substractions in reverse, because it is closer to the original steps and easier to understand by intuition:
MatMxN vector = points.col(i) - curveCenter;
points.col(i) += vector * (ratio - 1);
I don't know where you found the normalize and *0.5 ideas.
I have a hexagon grid:
with template type coordinates T. How I can calculate distance between two hexagons?
For example:
dist((3,3), (5,5)) = 3
dist((1,2), (1,4)) = 2
First apply the transform (y, x) |-> (u, v) = (x, y + floor(x / 2)).
Now the facial adjacency looks like
0 1 2 3
0*-*-*-*
|\|\|\|
1*-*-*-*
|\|\|\|
2*-*-*-*
Let the points be (u1, v1) and (u2, v2). Let du = u2 - u1 and dv = v2 - v1. The distance is
if du and dv have the same sign: max(|du|, |dv|), by using the diagonals
if du and dv have different signs: |du| + |dv|, because the diagonals are unproductive
In Python:
def dist(p1, p2):
y1, x1 = p1
y2, x2 = p2
du = x2 - x1
dv = (y2 + x2 // 2) - (y1 + x1 // 2)
return max(abs(du), abs(dv)) if ((du >= 0 and dv >= 0) or (du < 0 and dv < 0)) else abs(du) + abs(dv)
Posting here after I saw a blog post of mine had gotten referral traffic from another answer here. It got voted down, rightly so, because it was incorrect; but it was a mischaracterization of the solution put forth in my post.
Your 'squiggly' axis - in terms of your x coordinate being displaced every other row - is going to cause you all sorts of headaches with trying to determine distances or doing pathfinding later on, if this is for a game of some sort. Hexagon grids lend themselves to three axes naturally, and a 'squared off' grid of hexagons will optimally have some negative coordinates, which allows for simpler math around distances.
Here's a grid with (x,y) mapped out, with x increasing to the lower right, and y increasing upwards.
By straightening things out, the third axis becomes obvious.
The neat thing about this, is that the three coordinates become interlinked - the sum of all three coordinates will always be 0.
With such a consistent coordinate system, the atomic distance between any two hexes is the largest change between the three coordinates, or:
d = max( abs(x1 - x2), abs(y1 -y2), abs( (-x1 + -y1) - (-x2 + -y2) )
Pretty straightforward. But you must fix your grid first!
The correct explicit formula for the distance, with your coordinate system, is given by:
d((x1,y1),(x2,y2)) = max( abs(x1 - x2),
abs((y1 + floor(x1/2)) - (y2 + floor(x2/2)))
)
Here is what a did:
Taking one cell as center (it is easy to see if you choose 0,0), cells at distance dY form a big hexagon (with “radius” dY). One vertices of this hexagon is (dY2,dY). If dX<=dY2 the path is a zig-zag to the ram of the big hexagon with a distance dY. If not, then the path is the “diagonal” to the vertices, plus an vertical path from the vertices to the second cell, with add dX-dY2 cells.
Maybe better to understand: led:
dX = abs(x1 - x2);
dY = abs(y1 - y2);
dY2= floor((abs(y1 - y2) + (y1+1)%2 ) / 2);
Then:
d = d((x1,y1),(x2,y2))
= dX < dY2 ? dY : dY + dX-dY2 + y1%2 * dY%2
First, you need to transform your coordinates to a "mathematical" coordinate system. Every two columns you shift your coordinates by 1 unit in the y-direction. The "mathamatical" coordinates (s, t) can be calculated from your coordinates (u,v) as follows:
s = u + floor(v/2)
t = v
If you call one side of your hexagons a, the basis vectors of your coordinate system are (0, -sqrt(3)a) and (3a/2, sqrt(3)a/2). To find the minimum distance between your points, you need to calculate the manhattan distance in your coordinate system, which is given by |s1-s2|+|t1-t2| where s and t are the coordinates in your system. The manhattan distance only covers walking in the direction of your basis vectors so it only covers walking like that: |/ but not walking like that: |\. You need to transform your vectors into another coordinate system with basis vectors (0, -sqrt(3)a) and (3a/2, -sqrt(3)a/2). The coordinates in this system are given by s'=s-t and t'=t so the manhattan distance in this coordinate system is given by |s1'-s2'|+|t1'-t2'|. The distance you are looking for is the minimum of the two calculated manhattan distances. Your code would look like this:
struct point
{
int u;
int v;
}
int dist(point const & p, point const & q)
{
int const ps = p.u + (p.v / 2); // integer division!
int const pt = p.v;
int const qs = q.u + (q.v / 2);
int const qt = q.v;
int const dist1 = abs(ps - qs) + abs(pt - qt);
int const dist2 = abs((ps - pt) - (qs - qt)) + abs(pt - qt);
return std::min(dist1, dist2);
}
(odd-r)(without z, only x,y)
I saw some problems with realizations above. Sorry, I didn't check it all but. But maybe my solution will be helpful for someone and maybe it's a bad and not optimized solution.
The main idea to go by diagonal and then by horizontal. But for that we need to note:
1) For example, we have 0;3 (x1=0;y1=3) and to go to the y2=6 we can handle within 6 steps to each point (0-6;6)
so: 0-left_border , 6-right_border
2)Calculate some offsets
#include <iostream>
#include <cmath>
int main()
{
//while(true){
int x1,y1,x2,y2;
std::cin>>x1>>y1;
std::cin>>x2>>y2;
int diff_y=y2-y1; //only up-> bottom no need abs
int left_x,right_x;
int path;
if( y1>y2 ) { // if Down->Up then swap
int temp_y=y1;
y1=y2;
y2=temp_y;
//
int temp_x=x1;
x1=x2;
x2=temp_x;
} // so now we have Up->Down
// Note that it's odd-r horizontal layout
//OF - Offset Line (y%2==1)
//NOF -Not Offset Line (y%2==0)
if( y1%2==1 && y2%2==0 ){ //OF ->NOF
left_x = x1 - ( (y2 - y1 + 1)/2 -1 ); //UP->DOWN no need abs
right_x = x1 + (y2 - y1 + 1)/2; //UP->DOWN no need abs
}
else if( y1%2==0 && y2%2==1 ){ // OF->NOF
left_x = x1 - (y2 - y1 + 1)/2; //UP->DOWN no need abs
right_x = x1 + ( (y2 - y1 + 1)/2 -1 ); //UP->DOWN no need abs
}
else{
left_x = x1 - (y2 - y1 + 1)/2; //UP->DOWN no need abs
right_x = x1 + (y2 - y1 + 1)/2; //UP->DOWN no need abs
}
/////////////////////////////////////////////////////////////
if( x2>=left_x && x2<=right_x ){
path = y2 - y1;
}
else {
int min_1 = std::abs( left_x - x2 );
int min_2 = std::abs( right_x - x2 );
path = y2 - y1 + std::min(min_1, min_2);
}
std::cout<<"Path: "<<path<<"\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n";
//}
return 0;
}
I believe the answer you seek is:
d((x1,y1),(x2,y2))=max(abs(x1-x2),abs(y1-y2));
You can find a good explanation on hexagonal grid coordinate-system/distances here:
http://keekerdc.com/2011/03/hexagon-grids-coordinate-systems-and-distance-calculations/
I have two points A (x1,y1) and B (x2,y2) that are given as an input to the program. I have to find a third point C that lies on the line AB and is at a distance 10 away from the point A.
I can easily get the slope of the line but that doesn't give me the full equation for the line. Even if I get the full equation, I am not sure using this equation, how would I find out a point that is x distance away from A.
Any suggestions on how to approach this?
There are always two points on each line:
get the vector from A to B (subtract the coordinates)
normalize the vector (divide by its length; pythagorean theorem)
multiply the vector by 10 or -10
add the vector to A to get C
Note that if A==B, the line is not defined, and this algorithm causes a division by zero. You may want to add a test for equality at the beginning.
You can use the sine or the cosine (times 10) of the angle of the line to get the horizontal or vertical distance of the point that is a distance of 10 from a given point. A shortcut is to use the horizontal or vertical distance divided by the direct distance between the points to get the sine or cosine.
You can do it using vectors like this:
Let D = the difference between B and A (D = B - A)
Then any point on the line can be described by this formula:
point = A + Dt
where t is a real number.
So just plug in any value for t to get another point. For example if you let t == 1 then the equation above reduces to point = B. If you let t = 0 then it reduces to point = A. So you can see that you can use this to find a point between A and B simply by let t range from 0 to 1. Additionally if you let t > 1, you will find a point past B.
You can see from the image that your given points are x1,y1 and x2,y2. You need to find an intermediate point at a distance 'R' from point x1,y1.
All you need to do is to find θ using
Tan θ = (y2-y1)/(x2-x1)
Then you can get the intermediate point as (R * cos θ),(R * Sin θ)
I have drawn this assuming positive slope.
Going on similar lines you can seek a solution for other special cases lile:
i. Horizontal line
ii. Vertical line
iii. Negative slope
Hope it clarifies.
I have done the calculation in Andengine using a Sprite object. I have two Array List x coordinates and y coordinates. Here i am just calculating using the last two values from these two array list to calculate the third point 800 pixel distant from Your point B. you can modify it using different values other than 800. Hope it will work.The coordinate system here is a little different where (0,0) on the top left corner of the screen. Thanks
private void addExtraCoordinate(CarSprite s) {
int x0, y0, x1, y1;
float x = 0f, y = 0f;
x0 = Math.round(xCoordinates.get(xCoordinates.size() - 2));
x1 = Math.round(xCoordinates.get(xCoordinates.size() - 1));
y0 = Math.round(yCoordinates.get(yCoordinates.size() - 2)) * (-1);
y1 = Math.round(yCoordinates.get(yCoordinates.size() - 1)) * (-1);
if (x1 == x0 && y1 == y0) {
return;
} else if (y1 == y0 && x1 != x0) {
if (x1 > x0) {
x = (float) x1 + 800f;
} else
x = (float) x1 - 800f;
y = Math.round(yCoordinates.get(yCoordinates.size() - 1));
} else if (y1 != y0 && x1 == x0) {
if (y1 > y0) {
y = (float) Math.abs(y1) - 800f;
} else
y = (float) Math.abs(y1) + 800f;
x = Math.round(xCoordinates.get(xCoordinates.size() - 1));
} else {
float m = (float) (yCoordinates.get(yCoordinates.size() - 1) * (-1) - yCoordinates
.get(yCoordinates.size() - 2) * (-1))
/ (float) (xCoordinates.get(xCoordinates.size() - 1) - xCoordinates
.get(xCoordinates.size() - 2));
if (x1 > x0) {
x = (float) ((float) x1 + 800f / (float) Math
.sqrt((double) ((double) 1f + (double) (m * m))));
} else
x = (float) ((float) x1 - 800f / (float) Math
.sqrt((double) ((double) 1f + (double) (m * m))));
if (y0 > y1) {
y = (float) ((float) Math.abs(y1) + 800f / (float) Math
.sqrt((double) (((double) 1f / (double) (m * m)) + (double) 1f)));
} else
y = (float) ((float) Math.abs(y1) - 800f / (float) Math
.sqrt((double) (((double) 1f / (double) (m * m)) + (double) 1f)));
}
xCoordinates.add(x);
yCoordinates.add(y);
}
I have a 2D bitmap-like array of let's say 500*500 values. I'm trying to create a linear gradient on the array, so the resulting bitmap would look something like this (in grayscale):
(source: showandtell-graphics.com)
The input would be the array to fill, two points (like the starting and ending point for the Gradient tool in Photoshop/GIMP) and the range of values which would be used.
My current best result is this:
alt text http://img222.imageshack.us/img222/1733/gradientfe3.png
...which is nowhere near what I would like to achieve. It looks more like a radial gradient.
What is the simplest way to create such a gradient? I'm going to implement it in C++, but I would like some general algorithm.
This is really a math question, so it might be debatable whether it really "belongs" on Stack Overflow, but anyway: you need to project the coordinates of each point in the image onto the axis of your gradient and use that coordinate to determine the color.
Mathematically, what I mean is:
Say your starting point is (x1, y1) and your ending point is (x2, y2)
Compute A = (x2 - x1) and B = (y2 - y1)
Calculate C1 = A * x1 + B * y1 for the starting point and C2 = A * x2 + B * y2 for the ending point (C2 should be larger than C1)
For each point in the image, calculate C = A * x + B * y
If C <= C1, use the starting color; if C >= C2, use the ending color; otherwise, use a weighted average:
(start_color * (C2 - C) + end_color * (C - C1))/(C2 - C1)
I did some quick tests to check that this basically worked.
In your example image, it looks like you have a radial gradient. Here's my impromtu math explanation for the steps you'll need. Sorry for the math, the other answers are better in terms of implementation.
Define a linear function (like y = x + 1) with the domain (i.e. x) being from the colour you want to start with to the colour your want to end with. You can think of this in terms of a range the within Ox0 to OxFFFFFF (for 24 bit colour). If you want to handle things like brightness, you'll have to do some tricks with the range (i.e. the y value).
Next you need to map a vector across the matrix you have, as this defines the direction that the colours will change in. Also, the colour values defined by your linear function will be assigned at each point along the vector. The start and end point of the vector also define the min and max of the domain in 1. You can think of the vector as one line of your gradient.
For each cell in the matrix, colours can be assigned a value from the vector where a perpendicular line from the cell intersects the vector. See the diagram below where c is the position of the cell and . is the the point of intersection. If you pretend that the colour at . is Red, then that's what you'll assign to the cell.
|
c
|
|
Vect:____.______________
|
|
I'll just post my solution.
int ColourAt( int x, int y )
{
float imageX = (float)x / (float)BUFFER_WIDTH;
float imageY = (float)y / (float)BUFFER_WIDTH;
float xS = xStart / (float)BUFFER_WIDTH;
float yS = yStart / (float)BUFFER_WIDTH;
float xE = xEnd / (float)BUFFER_WIDTH;
float yE = yEnd / (float)BUFFER_WIDTH;
float xD = xE - xS;
float yD = yE - yS;
float mod = 1.0f / ( xD * xD + yD * yD );
float gradPos = ( ( imageX - xS ) * xD + ( imageY - yS ) * yD ) * mod;
float mag = gradPos > 0 ? gradPos < 1.0f ? gradPos : 1.0f : 0.0f;
int colour = (int)( 255 * mag );
colour |= ( colour << 16 ) + ( colour << 8 );
return colour;
}
For speed ups, cache the derived "direction" values (hint: premultiply by the mag).
There are two parts to this problem.
Given two colors A and B and some percentage p, determine what color lies p 'percent of the way' from A to B.
Given a point on a plane, find the orthogonal projection of that point onto a given line.
The given line in part 2 is your gradient line. Given any point P, project it onto the gradient line. Let's say its projection is R. Then figure out how far R is from the starting point of your gradient segment, as a percentage of the length of the gradient segment. Use this percentage in your function from part 1 above. That's the color P should be.
Note that, contrary to what other people have said, you can't just view your colors as regular numbers in your function from part 1. That will almost certainly not do what you want. What you do depends on the color space you are using. If you want an RGB gradient, then you have to look at the red, green, and blue color components separately.
For example, if you want a color "halfway between" pure red and blue, then in hex notation you are dealing with
ff 00 00
and
00 00 ff
Probably the color you want is something like
80 00 80
which is a nice purple color. You have to average out each color component separately. If you try to just average the hex numbers 0xff0000 and 0x0000ff directly, you get 0x7F807F, which is a medium gray. I'm guessing this explains at least part of the problem with your picture above.
Alternatively if you are in the HSV color space, you may want to adjust the hue component only, and leave the others as they are.
void Image::fillGradient(const SColor& colorA, const SColor& colorB,
const Point2i& from, const Point2i& to)
{
Point2f dir = to - from;
if(to == from)
dir.x = width - 1; // horizontal gradient
dir *= 1.0f / dir.lengthQ2(); // 1.0 / (dir.x * dir.x + dir.y * dir.y)
float default_kx = float(-from.x) * dir.x;
float kx = default_kx;
float ky = float(-from.y) * dir.y;
uint8_t* cur_pixel = base; // array of rgba pixels
for(int32_t h = 0; h < height; h++)
{
for(int32_t w = 0; w < width; w++)
{
float k = std::clamp(kx + ky, 0.0f, 1.0f);
*(cur_pixel++) = colorA.r * (1.0 - k) + colorB.r * k;
*(cur_pixel++) = colorA.g * (1.0 - k) + colorB.g * k;
*(cur_pixel++) = colorA.b * (1.0 - k) + colorB.b * k;
*(cur_pixel++) = colorA.a * (1.0 - k) + colorB.a * k;
kx += dir.x;
}
kx = default_kx;
ky += dir.y;
}
}