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Hello, I've got a question which I cannot solve so I need a bit help.
In the picture above you can see an Oriented Bounding Box specified by 4 points (A, B, C, D). There is also a point in space called P. If I cast a ray from P against the OBB the ray is going to intersect the OBB at some point. This point of intersection is called Q in the picture. By the way the ray is always going to be x-axis aligned which means its directional vector is either (1, 0) or (-1,0) if normalized. My goal is to find the point of intersection - Q. Is there a way (if possible computationaly inexpensive) to do so?
Thanks in advance.
One way to do this is to consider each side of the bounding box to be a linear equation of the form y = ax + b, where a is the slope and b is the y-intercept. Then consider the ray from P to be an equation of the form y = c, where c is a constant. Then compare this equation to each of the four other equations to see where it intersects each one. One of these intersections will be our Q, if a Q exists; it's possible that the ray will miss the bounding box entirely. We will need to do a few checks:
Firstly, eliminate all potential Q's that are on the wrong side of P.
Secondly, check each of the four intersections to make sure they are within the bounds of the lines that they represent, and eliminate the ones that are not.
Finally, if any potential Q's remain, the one closest to P will be our Q. If no potential Q's remain, this means that the ray from P misses the bounding box entirely.
For example...
The line drawn from D to B would have a slope equal to (B.y - D.y) / (B.x - D.x) and a y-intercept equal to B.y - B.x * slope. Then the entire equation is y = (B.y - D.y) / (B.x - D.x) * x + B.y - B.x * (B.y - D.y) / (B.x - D.x). Set this equation equal to y = P.y and solve for x:
x = (P.y - B.y + B.x*(B.y - D.y)/(B.x - D.x))*(B.x - D.x)/(B.y - D.y)
The result of this equation will give you the x-value of the intersection. The y-value is P.y. Do this for each of the other 3 lines as well: A-B, A-C, C-D. I will refer to these intersections as Q(D-B), Q(A-B), Q(A-C), and Q(C-D) respectively.
Next, eliminate candidate-Q's that are on the wrong side of P. In our example, this eliminates Q(A-B), since it is way off to the right side of the screen. Mathematically, Q(A-B).x > P.x.
Then eliminate all candidate-Q's that are not on the line they represent. We can do this check by finding the lowest and highest x-values and y-values given by the two points that represent the line. For example, to check that Q(A-C) is on the line A-C, check that C.x <= Q(A-C).x <= A.x and C.y <= Q(A-C).y <= A.y. Q(A-C) passes the test, as well as Q(D-B). However, Q(C-D) does not pass, as it is way off to the left side of the screen, far from the box. Therefore, Q(C-D) is eliminated from candidacy.
Finally, of the two points that remain, Q(A-C) and Q(D-B), we choose Q(D-B) to be our winner, because it is closest to P.
We now can say that the ray from P hits the bounding box at Q(D-B).
Of course, when you implement this in code, you will need to account for divisions by zero. If a line is perfectly vertical, there does not exist a point-slope equation of the line, so you will need to create a separate formula for this case. If a line is perfectly horizontal, it's respective candidate-Q should be automatically eliminated from candidacy, as the ray from P will never touch it.
Edit:
It would be more efficient to only do this process with lines whose two points are on vertically opposite sides of point P. If both points of a line are above P, or they are both below P, they would be eliminated from candidacy from the beginning.
Find the two sides that straddle p on Y. (Test of the form (Ya < Yp) != (Yb < Yp)).
Then compute the intersection points of the horizontal by p with these two sides, and keep the first to the left of p.
If the ray points to the left(right) then it must intersect an edge that connects to the point in the OOB with max(min) x-value. We can determine which edge by simply comparing the y-value of the ray with the y value of the max(min) point and its neighbors. We also need to consider OBBs that are actually axis-aligned, and thus have two points with equal max(min) x-value. Once we have the edge it's simple to confirm that the ray does in fact intersect the OBB and calculate its x-value.
Here's some Java code to illustrate (ideone):
static double nearestX(Point[] obb, int y, int dir)
{
// Find min(max) point
int n = 0;
for(int i=1; i<4; i++)
if((obb[n].x < obb[i].x) == (dir == -1)) n = i;
// Determine next or prev edge
int next = (n+1) % 4;
int prev = (n+3) % 4;
int nn;
if((obb[n].x == obb[next].x) || (obb[n].y < y) == (obb[n].y < obb[next].y))
nn = next;
else
nn = prev;
// Check that the ray intersects the OBB
if(Math.abs(y) > Math.abs(obb[nn].y)) return Double.NaN;
// Standard calculation of x from y for line segment
return obb[n].x + (y-obb[n].y)*(obb[nn].x-obb[n].x)/(obb[nn].y-obb[n].y);
}
Test:
public static void main(String[] args)
{
test("Diamond", new Point[]{p(0, -2), p(2, 0), p(0, 2), p(-2,0)});
test("Square", new Point[]{p(-2, -2), p(2, -2), p(2, 2), p(-2,2)});
}
static void test(String label, Point[] obb)
{
System.out.println(label + ": " + Arrays.toString(obb));
for(int dir : new int[] {-1, 1})
{
for(int y : new int[] {-3, -2, -1, 0, 1, 2, 3})
System.out.printf("(% d, % d) = %.0f\n", y , dir, nearestX(obb, y, dir));
System.out.println();
}
}
Output:
Diamond: [(0,-2), (2,0), (0,2), (-2,0)]
(-3, -1) = NaN
(-2, -1) = 0
(-1, -1) = 1
( 0, -1) = 2
( 1, -1) = 1
( 2, -1) = 0
( 3, -1) = NaN
(-3, 1) = NaN
(-2, 1) = 0
(-1, 1) = -1
( 0, 1) = -2
( 1, 1) = -1
( 2, 1) = 0
( 3, 1) = NaN
Square: [(-2,-2), (2,-2), (2,2), (-2,2)]
(-3, -1) = NaN
(-2, -1) = 2
(-1, -1) = 2
( 0, -1) = 2
( 1, -1) = 2
( 2, -1) = 2
( 3, -1) = NaN
(-3, 1) = NaN
(-2, 1) = -2
(-1, 1) = -2
( 0, 1) = -2
( 1, 1) = -2
( 2, 1) = -2
( 3, 1) = NaN
I am trying to solve the grid unique paths problem. The problem involves finding the number of possible unique paths in a 2D grid starting from top left (0,0) to the bottom right (say A,B). One can only move right or down. Here is my initial attempt:
#include <stdio.h>
int count=0;
void uniquePathsRecur(int r, int c, int A, int B){
if(r==A-1 & c==B-1){
count++;
return;
}
if(r<A-1){
return uniquePathsRecur(r++,c,A,B);
}
if(c<B-1){
return uniquePathsRecur(r,c++,A,B);
}
}
int uniquePaths(int A, int B) {
if(B==1 | A==1){
return 1;
}
uniquePathsRecur(0,0,A,B);
return count;
}
int main(){
printf("%d", uniquePaths(5,3));
return 0;
}
I end up getting segmentation fault: 11 with my code. I tried to debug in gdb and i get the following:
lldb) target create "a.out"
Current executable set to 'a.out' (x86_64).
(lldb) r
Process 12171 launched: '<path to process>/a.out' (x86_64)
Process 12171 stopped
* thread #1: tid = 0x531b2e, 0x0000000100000e38 a.out`uniquePathsRecur + 8, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code=2, address=0x7fff5f3ffffc)
frame #0: 0x0000000100000e38 a.out`uniquePathsRecur + 8
a.out`uniquePathsRecur:
-> 0x100000e38 <+8>: movl %edi, -0x4(%rbp)
0x100000e3b <+11>: movl %esi, -0x8(%rbp)
0x100000e3e <+14>: movl %edx, -0xc(%rbp)
0x100000e41 <+17>: movl %ecx, -0x10(%rbp)
(lldb)
What is wrong with the above code?
I don't know the problem of your code. But you can solve the problem without using recursion.
Method 1: We can solve this problem with simple math skill. The
requirement is that you can only move either down or right at any
point. So it requires exact (m + n) steps from S to D and n out of (m
+ n) steps go down. Thus, the answer is C(m + n, n).
Method 2: Let us solve the issue in computer science way. This is a typical dynamic
programming problem. Let us assume the robot is standing at (i, j).
How did the robot arrive at (i, j)? The robot could move down from (i
- 1, j) or move right from (i, j - 1). So the path to (i, j) is equal to the sum of path to (i - 1, j) and path to (i, j - 1). We can use
another array to store the path to all node and use the equation
below: paths(i, j) = 1 // i == 0 or j == 0 paths(i, j) = paths(i - 1,
j) + paths(i, j - 1) // i != 0 and j != 0 However, given more
thoughts, you will find out that you don't actually need a 2D array to
record all the values since when the robot is at row i, you only need
the paths at (i - 1). So the equation is: paths(j) = 1 //j == 0 for
any i paths(j) = paths(j - 1) + paths(j) // j != 0 for any i
For more information, please see here: https://algorithm.pingzhang.io/DynamicProgramming/unique_path.html
I'm developing an application that involves getting the camera angle in a game. The angle can be anywhere from 0-359. 0 is North, 90 is East, 180 is South, etc. I'm using an API, which has a getAngle() method in Camera class.
How would I find the average between different camera angles. The real average of 0 and 359 is 179.5. As a camera angle, that would be South, but obviously 0 and 359 are both very close to North.
You can think of it in terms of vectors. Let θ1 and θ2 be your two angles expressed in radians. Then we can determine the x and y components of the unit vectors that are at these angles:
x1 = sin(θ1)
y1 = cos(θ1)
x2 = sin(θ2)
y2 = cos(θ2)
You can then add these two vectors, and determine the x and y components of the result:
x* = x1 + x2
y* = y1 + y2
Finally, you can determine the angle of this resulting vector:
θavg = tan-1(y*/x*)
or, even better, use atan2 (a function supported by many languages):
θavg = atan2(y*, x*)
You will probably have to separately handle the cases where y* = 0 and x* = 0, since this means the two vectors are pointing in exactly opposite directions (so what should the 'average' be?).
It depends what you mean by "average". But the normal definition is the bisector of the included acute angle. You must put both within 180 degrees of each other. There are many ways to do this, but a simple one is to increment or decrement one of the angles. If the angles are a and b, then this will do it:
if (a < b)
while (abs(a - b) > 180) a = a + 360
else
while (abs(a - b) > 180) a = a - 360
Now you can compute the simple average:
avg = (a + b) / 2
Of course you may want to normalize one more time:
while (avg < 0) avg = avg + 360
while (avg >= 360) avg = avg - 360
On your example, you'd have a=0, b=359. The first loop would increment a to 360. The average would be 359.5. Of course you could round that to an integer if you like. If you round up to 360, then the final set of loops will decrement to 0.
Note that if your angles are always normalized to [0..360) none of these loops ever execute more than once. But they're probably good practice so that a wild argument doesn't cause your code to fail.
You want to bisect the angles not average them. First get the distance between them, taking the shortest way around, then divide that in half and add to one of the angles. Eg:
A = 355
B = 5
if (abs(A - B) < 180) {
Distance = abs(A - B)
if (A < B) {
Bisect = A + Distance / 2
}
else {
Bisect = B + Distance / 2
}
}
else {
Distance = 360 - abs(A - B)
if (A < B) {
Bisect = A - Distance / 2
}
else {
Bisect = B - Distance / 2
}
}
Or something like that -- "Bisect" should come out to zero for the given inputs. There are probably clever ways to make the arithmetic come out with fewer if and abs operations.
In a comment, you mentioned that all "angles" to be averaged are within 90 degrees to each other. I am guessing that there is really only one camera, but it moves around a lot, and you are creating some sort of picture stability mechanism for the camera POV.
In any case, there is only the special case where the camera may be in the 270-359 quadrant and the 0-89 quadrant. For all other cases, you can just take a simple average. So, you just need to detect that special case, and when it happens, treat the angles in the 270-359 quadrant as -90 to -1 instead. Then, after computing the simple average, adjust it back into the 270-359 quadrant if necessary.
In C code:
int quadrant (int a) {
assert(0 <= a && a < 360);
return a/90;
}
double avg_rays (int rays[], int num) {
int i;
int quads[4] = { 0, 0, 0, 0 };
double sum = 0;
/* trivial case */
if (num == 1) return rays[0];
for (i = 0; i < num; ++i) ++quads[quadrant(rays[i])];
if (quads[0] == 0 || quads[3] == 0) {
/* simple case */
for (i = 0; i < num; ++i) sum += rays[i];
return sum/num;
}
/* special case */
for (i = 0; i < num; ++i) {
if (quadrant(rays[i]) == 3) rays[i] -= 360;
sum += rays[i];
}
return sum/num + (sum < 0) * 360;
}
This code can be optimized at the expense of clarity of purpose. When you detect the special case condition, you can fix up the sum after the fact. So, you can compute sum and figure out the special case and do the fix up in a single pass.
double avg_rays_opt (int rays[], int num) {
int i;
int quads[4] = { 0, 0, 0, 0 };
double sum = 0;
/* trivial case */
if (num == 1) return rays[0];
for (i = 0; i < num; ++i) {
++quads[quadrant(rays[i])];
sum += rays[i];
}
if (quads[0] == 0 || quads[3] == 0) {
/* simple case */
return sum/num;
}
/* special case */
sum -= quads[3]*360;
return sum/num + (sum < 0) * 360;
}
I am sure it can be further optimized, but it should give you a start.
I have 2 tables of values and want to scale the first one so that it matches the 2nd one as good as possible. Both have the same length. If both are drawn as graphs in a diagram they should be as close to each other as possible. But I do not want quadratic, but simple linear weights.
My problem is, that I have no idea how to actually compute the best scaling factor because of the Abs function.
Some pseudocode:
//given:
float[] table1= ...;
float[] table2= ...;
//wanted:
float factor= ???; // I have no idea how to compute this
float remainingDifference=0;
for(int i=0; i<length; i++)
{
float scaledValue=table1[i] * factor;
//Sum up the differences. I use the Abs function because negative differences are differences too.
remainingDifference += Abs(scaledValue - table2[i]);
}
I want to compute the scaling factor so that the remainingDifference is minimal.
Simple linear weights is hard like you said.
a_n = first sequence
b_n = second sequence
c = scaling factor
Your residual function is (sums are from i=1 to N, the number of points):
SUM( |a_i - c*b_i| )
Taking the derivative with respect to c yields:
d/dc SUM( |a_i - c*b_i| )
= SUM( b_i * (a_i - c*b_i)/|a_i - c*b_i| )
Setting to 0 and solving for c is hard. I don't think there's an analytic way of doing that. You may want to try https://math.stackexchange.com/ to see if they have any bright ideas.
However if you work with quadratic weights, it becomes significantly simpler:
d/dc SUM( (a_i - c*b_i)^2 )
= SUM( 2*(a_i - c*b_i)* -c )
= -2c * SUM( a_i - c*b_i ) = 0
=> SUM(a_i) - c*SUM(b_i) = 0
=> c = SUM(a_i) / SUM(b_i)
I strongly suggest the latter approach if you can.
I would suggest trying some sort of variant on Newton Raphson.
Construct a function Diff(k) that looks at the difference in area between your two graphs between fixed markers A and B.
mathematically I guess it would be integral ( x = A to B ){ f(x) - k * g(x) }dx
anyway realistically you could just subtract the values,
like if you range from X = -10 to 10, and you have a data point for f(i) and g(i) on each integer i in [-10, 10], (ie 21 datapoints )
then you just sum( i = -10 to 10 ){ f(i) - k * g(i) }
basically you would expect this function to look like a parabola -- there will be an optimum k, and deviating slightly from it in either direction will increase the overall area difference
and the bigger the difference, you would expect the bigger the gap
so, this should be a pretty smooth function ( if you have a lot of data points )
so you want to minimise Diff(k)
so you want to find whether derivative ie d/dk Diff(k) = 0
so just do Newton Raphson on this new function D'(k)
kick it off at k=1 and it should zone in on a solution pretty fast
that's probably going to give you an optimal computation time
if you want something simpler, just start with some k1 and k2 that are either side of 0
so say Diff(1.5) = -3 and Diff(2.9) = 7
so then you would pick a k say 3/10 of the way (10 = 7 - -3) between 1.5 and 2.9
and depending on whether that yields a positive or negative value, use it as the new k1 or k2, rinse and repeat
In case anyone stumbles upon this in the future, here is some code (c++)
The trick is to first sort the samples by the scaling factor that would result in the best fit for the 2 samples each. Then start at both ends iterate to the factor that results in the minimum absolute deviation (L1-norm).
Everything except for the sort has a linear run time => Runtime is O(n*log n)
/*
* Find x so that the sum over std::abs(pA[i]-pB[i]*x) from i=0 to (n-1) is minimal
* Then return x
*/
float linearFit(const float* pA, const float* pB, int n)
{
/*
* Algebraic solution is not possible for the general case
* => iterative algorithm
*/
if (n < 0)
throw "linearFit has invalid argument: expected n >= 0";
if (n == 0)
return 0;//If there is nothing to fit, any factor is a perfect fit (sum is always 0)
if (n == 1)
return pA[0] / pB[0];//return x so that pA[0] = pB[0]*x
//If you don't like this , use a std::vector :P
std::unique_ptr<float[]> targetValues_(new float[n]);
std::unique_ptr<int[]> indices_(new int[n]);
//Get proper pointers:
float* targetValues = targetValues_.get();//The value for x that would cause pA[i] = pB[i]*x
int* indices = indices_.get(); //Indices of useful (not nan and not infinity) target values
//The code above guarantees n > 1, so it is safe to get these pointers:
int m = 0;//Number of useful target values
for (int i = 0; i < n; i++)
{
float a = pA[i];
float b = pB[i];
float targetValue = a / b;
targetValues[i] = targetValue;
if (std::isfinite(targetValue))
{
indices[m++] = i;
}
}
if (m <= 0)
return 0;
if (m == 1)
return targetValues[indices[0]];//If there is only one target value, then it has to be the best one.
//sort the indices by target value
std::sort(indices, indices + m, [&](int ia, int ib){
return targetValues[ia] < targetValues[ib];
});
//Start from the extremes and meet at the optimal solution somewhere in the middle:
int l = 0;
int r = m - 1;
// m >= 2 is guaranteed => l > r
float penaltyFactorL = std::abs(pB[indices[l]]);
float penaltyFactorR = std::abs(pB[indices[r]]);
while (l < r)
{
if (l == r - 1 && penaltyFactorL == penaltyFactorR)
{
break;
}
if (penaltyFactorL < penaltyFactorR)
{
l++;
if (l < r)
{
penaltyFactorL += std::abs(pB[indices[l]]);
}
}
else
{
r--;
if (l < r)
{
penaltyFactorR += std::abs(pB[indices[r]]);
}
}
}
//return the best target value
if (l == r)
return targetValues[indices[l]];
else
return (targetValues[indices[l]] + targetValues[indices[r]])*0.5;
}
I have a 2-dimensional array that looks like this:
1 1 0 0 1
1 0 1 1 0
0 0 1 1 0
1 1 0 1 1
0 0 1 1 1
I'm trying to figure out a way to identify the longest contiguous chain of 1's going either across or down. In this case, it starts at column 4, row 2, and its length is 4, going down.
I was thinking of using recursion, but I'm running into some issues keeping track of position, especially when encountering a 0.
So far, I have something along the lines of this (for checking across only):
main() {
...
for(i = 0; i < n; i++)
for(j = 0; j < n; j++)
if (G[i][j] == 1) {
CheckAcross(i, j, n);
}
...
}
void CheckAcross (int i, int j, int n) {
if (i < 0 || i >= n || j < 0 || j >= n) return; // outside of grid
if (G[i][j] == 0 ) return; //0 encountered
G[i][j] = WordCount + 1;
CheckAcross(i, j + 1, n);
}
where G[][] is the 2-dimensional array containing the 1's and 0's, n is the number of rows/columns, i is the row number and j is the column number.
Thanks for any assistance in advance!
Your current answer will take O(n3) time; to evaluate a single line, you check every possible start and end position (O(n) possibilities for each), and there are n lines.
Your algorithm is correct, but let's see if we can improve on the running time.
The problem might become simpler if we break it into simpler problems, i.e. "What is the longest contiguous chain of 1s in this 1-dimensional array?". If we solve it 2n times, then we have our answer, so we just need to get this one down to smaller than O(n2) for an improvement.
Well, we can simply go through the line, remembering the position (start and end) and length of the longest sequence of 1s. This takes O(n) time, and is optimal (if the sequence is all 1s or 0s, we would have to read every element to know where the start/end of the longest sequence is).
Then we can simply solve this for every row and every column, in O(n2) time.
Create a new n-by-n matrix called V. This will store, for each cell, the number of 1s at that cell and immediately above it. This will be O(n^2).
checkAllVertical(int n) {
V = malloc(....) // create V, an n-by-n matrix initialized to zero
for(int r=0; r<n; r++) {
for(int c=0; c<n; c++) {
if(G[r][c]=1) {
if(r==0)
V[r][c] = 1;
else
V[r][c] = 1 + V[r][c];
}
}
}
}
You don't really need to allocate all of V. One row at a time would suffice.