I am trying to implement Bezier Curves for an assignment. I am trying to move a ball (using bezier curves) by giving my function an array of key frames. The function should give me all the frames in between the key frames ... or control points ... but although I'm using the formula found on wikipedia... it is not really working :s
her's my code:
private void interpolate(){
float x,y,b, t = 0;
frames = new Frame[keyFrames.length];
for(int i =0;i<keyFrames.length;++i){
t+=0.001;
b = Bint(i,keyFrames.length,t);
x = b*keyFrames[i].x;
y = b*keyFrames[i].y;
frames[i] = new Frame(x,y);
}
}
private float Bint(int i, int n, float t){
float Cni = fact(n)/(fact(i) * fact(n-i));
return Cni * pow(1-t,n-i) * pow(t,i);
}
Also I've noticed that the frames[] array should be much bigger but I can't find any other text which is more programmer friendly
Thanks in advance.
There are lots of things that don't look quite right here.
Doing it this way, your interpolation will pass exactly through the first and last control points, but not through the others. Is that what you want?
If you have lots of key frames, you're using a very-high-degree polynomial for your interpolation. Polynomials of high degree are notoriously badly-behaved, you may get your position oscillating wildly in between the key frame positions. (This is one reason why the answer to question 1 should probably be no.)
Assuming for the sake of argument that you really do want to do this, your value of t should go from 0 at the start to 1 at the end. Do you happen to have exactly 1001 of these key frames? If not, you'll be doing the wrong thing.
Evaluating these polynomials with lots of calls to fact and pow is likely to be inefficient, especially if n is large.
I'm reluctant to go into much detail about what you should do without knowing more about the scope of your assignment -- it will do no one any good for Stack Overflow to do your homework for you! What have you already been told about Bezier curves? What exactly does your assignment ask you to do?
EDITED to add:
The simplest way to do interpolation using Bezier curves is probably this. Have one (cubic) Bezier curve between each pair of key-points. The endpoints (first and last control points) of each Bezier curve are those keypoints. You need two more control points. For motion to be smooth as you move through a given keypoint, you need (keypoint minus previous control point) = (next control point minus keypoint). So you're choosing a single vector at each keypoint, which will determine where the previous and subsequent control points go. As you move through each keypoint, you'll be moving in the direction of that vector, and the longer the vector is the faster you'll be moving. (If the vector is zero then your cubic Bezier degenerates into a simple straight-line path.)
Choosing that vector so that everything looks nice is highly nontrivial, but you probably aren't really being asked to do that at this stage. So something pretty simple will probably be good enough. You might, e.g., take the vector to be proportional to (next keypoint minus previous keypoint). You'll need to do something a bit different at the start and end of your path if you do that.
Finally got What I needed! Here's what I did:
private void interpolate() {
float t = 0;
float x,y,b;
for(int f =0;f<frames.length;f++) {
x=0;
y=0;
for(int i = 0; i<keyFrames.length; i++) {
b = Bint(i,keyFrames.length-1,map(t,0,time,0,1));
x += b*keyFrames[i].x;
y += b*keyFrames[i].y;
}
frames[f] = new Frame(x,y);
t+=partialTime;
}
}
private void createInterpolationData() {
time = keyFrames[keyFrames.length-1].time -
keyFrames[0].time;
noOfFrames = 60*time;
partialTime = time/noOfFrames;
frames = new Frame[ceil(noOfFrames)];
}
Related
I'am trying to create some snake-like movement, but i cant implement algorithm to move one body part straight by another and so on.
I wanna to have some auto-moved snake which consists of separate blocks ( spheres ). This snake should move along some path. I generate path with bezier spline and have already implemented one future snake's part along it. Point for head is obtained from spline by next api:
class BezierSpline
{
Vector3 GetPoint(float progress) // 0 to 1
}
And than I have SnakeMovement script
public class SnakeMovement : MonoBehaviour
{
public BezierSpline Path;
public List<Transform> Parts;
public float minDistance = 0.25f;
public float speed = 1;
//.....
void Update()
{
Vector3 position = Path.GetPoint(progress);
Parts.First().localPosition = position;
Parts.First().LookAt(position + Path.GetDirection(progress));
for (int i = 1; i < Parts.Count; i++)
{
Transform curBody = Parts[i];
Transform prevBody = Parts[i - 1];
float dist = Vector3.Distance(prevBody.position, curBody.position);
Vector3 newP = prevBody.position;
newP.y = Parts[0].position.y;
float t = Time.deltaTime * dist / minDistance * curspeed;
curBody.position = Vector3.Slerp(curBody.position, newP, t);
curBody.rotation = Quaternion.Slerp(curBody.rotation, prevBody.rotation, t);
}
//....
}
For now, if I stopped head movement all parts dont preserve distance and keep moving to the head position. Another problem with above algorithm is that parts don't exectly follow the head path. They can "cut" corners while turning.
The main idea is to have user/ai control for only head(first body part) and each followed part should exectly repeat head path and preserve distance between its neighbours.
For a snake like motion you are likely to get lots of strange behaviours if you treat spheres as seperate objects. While i can imagine its possible to get it to work, I think this is not the best approach.
First solution that comes to mind is to create a List, onto which you would add to index 0, on every frame, the position of the head of the snake.
The list would grow, and all the other segments would wait their turn, so lag x frames, and on each update segment y would have position of list[x*y]
If Count() of the list is greater than number_of_segments*lag, you RemoveAt(Count()-1)
This can be optimized as changing the list is somewhat costly (a ring buffer would be better suited, but a Queue could also work. For starters i find Lists much easier to follow and you can always optimize later). This may behave a bit awkward if your framerate varies a lot but should be very stable in general (as in - no unpredictable motion, we only re-use the same values over and over)
Second method:
You mentioned using a bezier spline to generate a path. beziers are parametrized by a float t so you have something like
SplineAt(t).
if you take your bezier_path_length and distance_between_segments, than segment n should have position of
SplineAt(t-n*distance_between_segments/bezier_path_length)
I'm coding a game using Box2D and SFML, and I'd like to let my users import their own textures to use as physics polygons. The polygons are created using the images' alpha layer. It doesn't need to be pixel perfect, and this is where my problem is. If it's pixel-perfect, it's going to be way too buggy when the player gets stuck between two rather complex shapes. I have a working edge-detection algorithm, and it produces something like this. It's pixel per pixel (and the shape it's tracing is simply a square with an dip). After that, I have a simplifying algorithm that produces this. It works fine to me, but if every single corner is traced like that, I'm going to have some problems. The code for the vector-simplifying is this:
//borders is a std::vector containing simple Box2D b2Vec2 (2D vector class containing an x and a y)
//vector shortener
for(unsigned int i = 0; i < borders.size(); i++)
{
int x = 0, y = 0;
int counter = 0;
//get the values for x and y that need to be added to check whether in a line or not
x = borders[i].x - borders[i-1].x;
y = borders[i].y - borders[i-1].y;
//while points are aligned..
while((borders[i].x + x*counter == borders[i + counter].x) && (borders[i].y + y*counter == borders[i+counter].y))
{
counter++;
}
if(counter-1 > i)
{
borders.erase(borders.begin() + i, borders.begin() + i + counter -1);
}
}
So my question is, how can I transform the previous set of vectors into something a bit less precise? Are there any rounding algorithms out there? If so, which is best? Any tips you can give me? It doesn't matter whether the resulting polygon is convex or concave, I'm triangulating it anyways.
Thanks,
AsterAlff
Probably an easy question, but I could not find an easy solution so far. I'm working on a simple image recognition software for a very specific use case.
Given is a bunch of points that are supposedly on a straight line. However, some of the points are mistakenly placed and away from the line. Especially near the ends of the line, points may happen to be more or less inaccurate.
Example:
X // this guy is off
X // this one even more
X // looks fine
X
X
X // a mistake in the middle
X
X // another mistake, not as bad as the previous
X
X
X
X
X // we're off the line again
The general direction of the line is known, in this case, it's vertical. The actual line in the example is in fact vertical with slight diagonal slope.
I'm only interested in the infinite line (that is, it's slope and offset), the position of the endpoints is not important.
As additional information (not sure if it is important), it is impossible for 2 points to lie next to each other horizontally. Example:
X
X
X
X X // cannot happen
X
X
Performance is not important. I'm working in C#, but I'm fine with any language or just a generic idea, too.
I think you're looking for Least squares fit via Linear Regression
Linear regression (as mentioned by others) is good if you know you do not have outliers.
If you do have outliers, then one of my favorite methods is the median median line method:
http://education.uncc.edu/droyster/courses/spring00/maed3103/Median-Median_Line.htm
Basically you sort the points by the X values and then split the points up into three equal sized groups (smallest values, medium values, and largest values). The final slope is the slope of the line going through the median of the small group and through the median of the large group. The median of the middle group is used with the other medians to calculate the final offset/intercept.
This is a simple algorithm that can be found on several graphing calculators.
By taking the three medians, you are completely ignoring any outliers (either on the far left, far right, far up, or far down).
The image below shows the linear regression and median-median lines for a set of data with a couple of large outliers.
Mike is spot on! Use the following:
double[] xVals = {...};
double[] yVals = {...};
double xMean = 0;
double yMean = 0;
double Sxy = 0;
double Sxx = 0;
double beta0, beta1;
int i;
for (i = 0; i < xVals.Length; i++)
{
xMean += xVals[i]/xVals.Length;
yMean += yVals[i]/yVals.Length;
}
for (i = 0; i < xVals.Length; i++)
{
Sxy += (xVals[i]-xMean)*(yVals[i]-yMean);
Sxx += (xVals[i]-xMean)*(xVals[i]-xMean);
}
beta1 = Sxy/Sxx;
beta0 = yMean-beta1*xMean;
Use beta1 as the slope and beta0 as the y-intercept!
standard scaling using the center of an image as the pivot point and is uniform in all dimensions. I'd like to figure out a way to scale an image from an arbitrary pivot point such that points closer to the pivot point scale less than points away from that point.
Well, I don't know what framework/library you're using but you can think of it as:
translation to make your pivot point the center point
standard scaling
opposite transalation to make the center point the original pivot point
Translation and scaling are isomorphismes so you can represent them as matrix. Each transformation is a matrix and you can multiply them for find the combined transformation matrix. So:
T = transformation
S = scalling
T' = opposite transformation
If you apply T.x being x a point vector it gives you the new coordinates. The same for S.x.
So if you want to do that operations you have to do: T'. (S. (T.x))
I think you can associate operations so it's the same as (T'.S.T).x
If you are using a framework apply three operations (or combine operations and apply).
If you are using crude math... go the matrix way :)
PS: If you are doing this by hand. I know that if you are scaling you will want to find the coordinates of the original point given a transformed point. So you can iterate over the resulting points (each of the pixels) and see what coordinates (or point in between) from the original image you have to use. In that case what you need is the inverse matrix. So instead of using S you want to use S^(-1). If you know that you want to apply T'.S.T you can find this resulting matrix and next find (T'.S.T)^(-1). Then you have your inverse matrix to find original points given the resulting points.
This is an oversimplification, but should help you get started. For one, since standard resampling is uniform, there isn't really a concept of a pivot-point. If anything, they usually just start from a corner, as it's easier to run the for loops that way.
Generally the algorithm is something like this pseudo-code
function resample (srcImg, dstSize) {
dstImg = makeImage(dstSize)
for (r = 0; r < dstSize.height; ++r) {
for (c = 0; r < dstSize.width; ++c) {
// getResampleLoc returns float coordinate
resampleLoc = getResampleLoc(c, r, dstImg.size, srcImg.size)
color = getColor(srcImg, resampleLoc)
dstImg.setColor(c, r, color)
}
}
return dstImage
}
For uniform resampling, getResampleLoc is just a simple scale of x and y from the dstImg size to the srcImg size. It returns float coordinates, which are passed to getColor. The implementation of getColor is what determines the various resampling algorithms. Basically, it blends the pixels surrounding the coordinate in some ratio. In reality, there are optimizations that can be done to make information generated inside of getColor shared between calls, but don't worry about that.
For you, you would need something like:
function resample (srcImg, dstSize, pivotPt) {
dstImg = makeImage(dstSize)
for (r = 0; r < dstSize.height; ++r) {
for (c = 0; r < dstSize.width; ++c) {
// getResampleLoc returns float coordinate
resampleLoc = getResampleLoc(c, r, dstImg.size, srcImg.size, pivotPt)
color = getColor(srcImg, resampleLoc)
dstImg.setColor(c, r, color)
}
}
return dstImage
}
And then you just need to implement getResampleLoc to take pivotPt into account. Probably the simplest thing is to log-scale the distance to the edge.
I have a device that records GPS data. A reading is taken every 2-10 seconds. For an activity taking 2 hours there are a lot of GPS points.
Does anyone know of an algorithm for compressing the dataset by removing redundant data points. i.e. If a series of data points are all in a straight line then only the start and end point are required.
check out the Douglas Peucker Algorithm which is used to simplify a polygon. i´ve used this successfully to reduce the amount of gps waypoints when trasmitted to clients for displaying purposes.
You probably want to approximate your path x(t), y(t) with a polynomial approximation of it. Are you looking for something like this: http://www.youtube.com/watch?v=YtcZXlKbDJY ???
You can remove redundant points by performing a very basic simplification based on calculation of slope between subsequent points.
Here is a bit of but not complete C++ code presenting possible algorithm:
struct Point
{
double x;
double y;
};
double calculate_slope(Point const& p1, Point const& p2)
{
// dy y2 - y1
// m = ---- = ---------
// dx x2 - x1
return ((p2.y - p1.y) / (p2.x - p1.x));
}
// 1. Read first two points from GPS stream source
Point p0 = ... ;
Point p1 = ... ;
// 2. Accept p0 as it's first point
// 3. Calculate slope
double m0 = calculate_slope(p0, p1);
// 4. next point is now previous
p0 = p1;
// 5. Read another point
p1 = ... ;
double m1 = calculate_slope(p0, p1);
// 6. Eliminate Compare slopes
double const tolerance = 0.1; // choose your tolerance
double const diff = m0 - m1;
bool if (!((diff <= tolerance) && (diff >= -tolerance)))
{
// 7. Accept p0 and eliminate p1
m0 = m1;
}
// Repeat steps from 4 to 7 for the rest of points.
I hope it helps.
There is a research paper on Compressing GPS Data on Mobile Devices.
Additionally, you can look at this CodeProject article on Writing GPS Applications. I think the problem you will have is not for straight points, but curved roads. It all depends on how precise you want your path to be.
The code given above has a couple of issues that might make it unsuitable:
"same slope" tolerance measures difference in gradient rather than angle, so NNE to NNW is considered a much bigger difference than NE to SE (assuming y axis runs North-South).
One way of addressing this would be for the tolerance to measure how the dot product of two segments compares with the product of their lengths. (It might help understanding to remember that dot product of two vectors is the product of their lengths and the cosine of the angle between them.) However, see next point.
Considers only slope error rather than position error, so a long ENE segment followed by long ESE segment is just as likely to be compressed to a single segment as a string of short segments alternating between ENE and ESE.
The approach that I was thinking of would be to look at what vector graphics applications do to convert a list of cursor coordinates into a sequence of curves. E.g. see lib2geom's bezier-utils.cpp. Note that (i) it's almost entirely position-based rather than direction-based; and (ii) it gives cubic bézier curves as output rather than a polyline, though you could use the same approach to give polyline output if that's preferred (in which case the Newton-Raphson step becomes essentially just a simple dot product).