Related
The Problem
I am making a game where enemies appear at some point on the screen then follow a smooth curvy path and disappear at some point. I can make them follow a straight path but can't figure out the way to make them follow the paths depicted in the image.
Attempts
I started with parabolic curve and implemented them successfully. I just used the equation of parabola to calculate the coordinates gradually. I have no clue what is the equation for desired paths supposed to be.
What I want
I am not asking for the code.I just want someone to explain me the general technique.If you still want to show some code then I don't have special preference for programming language for this particular question you can use C,Java or even pseudo-code.
First you need to represent each curve with a set of points over time, For example:
-At T(0) the object should be at (X0, Y0).
-At T(1) the object should be at (X1, Y1).
And the more points you have, the more smooth curve you will get.
Then you will use those set of points to generate two formulas-one for X, and another one for Y-, using any Interpolation method, like The La-grange's Interpolation Formula:
Note that you should replace 'y' with the time T, and replace 'x' with your X for X formula, and Y for Y formula.
I know you hoped for a simple equation, but unfortunately this is will take from you a huge effort to simplify each equation, and my advise DON'T do it unless it's worth it.
If you are seeking for a more simple equation to perform well in each frame in your game you should read about SPline method, In this method is about splitting your curve into a smaller segments, and make a simple equation for every segment, for example:
Linear Spline:
Every segment contains 2 points, this will draw a line between every two points.
The result will be some thing like this:
Or you could use quadratic spline, or cubic spline for more smooth curves, but it will slow your game performance. You can read more about those methods here.
I think linear spline will be great for you with reasonable set of points for each curve.
Please change the question title to be more generic.
If you want to generate a spiral path you need.
Total time
How many full rotations
Largest radius
So, total time T_f = 5sec, rotations R_f = 2.5 * 2 * PI, the final distance from the start D_f = 200px
function SpiralEnemy(spawnX, spawnY, time) {
this.startX = spawnX;
this.startY = spawnY;
this.startTime = time;
// these will change and be used for rendering
this.x = this.startX;
this.y = this.startY;
this.done = false;
// constants we figured out above
var TFinal = 5.0;
var RFinal = -2.6 * 2 * Math.PI;
var RStart = -Math.PI / 2;
var DFinal = 100;
// the update function called every animation tick with the current time
this.update = function(t) {
var delta = t - this.startTime;
if(delta > TFinal) {
this.done = true;
return;
}
// find out how far along you are in the animation
var percent = delta / TFinal;
// what is your current angle of rotation (in radians)
var angle = RStart + RFinal * percent;
// how far from your start point should you be
var dist = DFinal * percent;
// update your coordinates
this.x = this.startX + Math.cos(angle) * dist;
this.y = this.startY + Math.sin(angle) * dist;
};
}
EDIT Here's a jsfiddle to mess with http://jsfiddle.net/pxb3824z/
EDIT 2 Here's a loop (instead of spiral) version http://jsfiddle.net/dpbLxuz7/
The loop code splits the animation into 2 parts the beginning half and the end half.
Beginning half : angle = Math.tan(T_percent) * 2 and dist = Speed + Speed * (1 - T_percent)
End half : angle = -Math.tan(1 - T_percent) * 2 and dist = **Speed + Speed * T_percent
T_percent is normalized to (0, 1.0) for both halfs.
function LoopEnemy(spawnX, spawnY, time) {
this.startX = spawnX;
this.startY = spawnY;
this.startTime = time;
// these will change and be used for rendering
this.x = this.startX;
this.y = this.startY;
this.last = time;
this.done = false;
// constants we figured out above
var TFinal = 5.0;
var RFinal = -2 * Math.PI;
var RStart = 0;
var Speed = 50; // px per second
// the update function called every animation tick with the current time
this.update = function(t) {
var delta = t - this.startTime;
if(delta > TFinal) {
this.done = true;
return;
}
// find out how far along you are in the animation
var percent = delta / TFinal;
var localDelta = t - this.last;
// what is your current angle of rotation (in radians)
var angle = RStart;
var dist = Speed * localDelta;
if(percent <= 0.5) {
percent = percent / 0.5;
angle -= Math.tan(percent) * 2;
dist += dist * (1 - percent);
} else {
percent = (percent - 0.5) / 0.5;
angle -= -Math.tan(1 - percent) * 2;
dist += dist * percent;
}
// update your coordinates
this.last = t;
this.x = this.x + Math.cos(angle) * dist;
this.y = this.y + Math.sin(angle) * dist;
};
}
Deriving the exact distance traveled and the height of the loop for this one is a bit more work. I arbitrarily chose a Speed of 50px / sec, which give a final x offset of ~+145 and a loop height of ~+114 the distance and height will scale from those values linearly (ex: Speed=25 will have final x at ~73 and loop height of ~57)
I don't understand how you give a curve. If you need a curve depicted on the picture, you can find a curve is given analytically and use it. If you have not any curves you can send me here: hedgehogues#bk.ru and I will help find you. I leave e-mail here because I don't get any messages about answers of users from stackoverflow. I don't know why.
If you have some curves in parametric view in [A, B], you can write a code like this:
struct
{
double x, y;
}SPoint;
coord = A;
step = 0.001
eps = 1e-6;
while (coord + step - eps < B)
{
SPoint p1, p2;
p1.x = x(coord);
p1.y = y(coord);
coord += step;
p2.x = x(coord);
p2.y = y(coord);
drawline(p1, p2);
}
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.
I have read in an image file to MATLAB and I am trying to stretch it in one direction, but a variable amount (sinusoidal). This would create an accordion affect on the image. I have toyed around with imresize, however that only resizes the image linearly. I would like the amount of "stretch" to vary for each image line. I tried to convey this with the following code:
periods = 10; % Number of "stretch" cycles
sz = size(original_image,2)/periods;
s = 0;
x = 0;
for index = 1:periods
B = original_image(:,round(s+1:s+sz));
if mod(index,2) == 0
amp = 1.5;
else
amp = 0.75;
end
xi = size(B,2)*amp;
new_image(:,x+1:x+xi) = imresize(B, [size(B,1) size(B,2)*amp]);
s = s + sz;
x = x+xi;
end
You can see that segments of the image are stretched, then compressed, then stretched, etc, like an accordion. However, each segment has a uniform amount of stretch, whereas I'd like it to be increasing then decreasing as you move along the image.
I have also looked at MATLAB's example of Applying a Sinusoidal Transformation to a Checkerboard which seems very applicable to my problem, however I have been trying and I cannot get this to produce the desired result for my image.
Any help is much appreciated.
UPDATE:
Thank you for Answer #1. I was unable to get it to work for me, but also realized it would resulted in loss of data, as the code only called for certian lines in the original image, and other lines would have been ignored.
After experimenting further, I developed the code below. I used a checkerboard as an example. While combersome, it does get the job done. However, upon trying the script with an actual high-resolution image, it was extremely slow and ended up failing due to running out of memory. I believe this is because of the excessive number of "imresize" commands that are used in loop.
I = checkerboard(10,50);
I = imrotate(I,90);
[X Y] = size(I);
k = 4; % Number of "cycles"
k = k*2;
x = 1;
y = 2;
z = 2;
F = [];
i = 1;
t = 0;
s = 0;
for j = 1:k/2
t = t + 1;
for inc = round(s+1):round(Y/k*t)
Yi = i + 1;
F(:,(x:y)) = imresize(I(:,(inc:inc)),[X Yi]);
x = y + 1;
y = x + z;
z = z + 1;
i = i + 1;
end
y = y - 2;
z = z - 4;
for inc = round(Y/k*t+1):round(Y/k*(t+1))
Yi = i - 1;
F(:,(x:y)) = imresize(I(:,(inc:inc)),[X Yi]);
x = y + 1;
y = x + z;
z = z - 1;
i = i - 1;
end
y = y + 2;
z = z + 4;
s = Y/k*(t+1);
t = t + 1;
end
Fn = imresize(F, [X Y]);
imshow(Fn);
Does anyone know of a simpler way to achieve this? If you run the code above, you can see the effect I am trying to achieve. Unfortunately, my method above does not allow me to adjust the amplitude of the "stretch" either, only the number of "cycles," or frequency. Help on this would also be appreciated. Much thanks!
Here is how I would approach it:
Determine how the coordinate of each point in your Final image F maps into your Initial image I of size (M,N)
Since you want to stretch horizontally only, given a point (xF,yF) in your final image, that point would be (xI,yI) in your initial image where xI and yI can be obtained as follows:
yI = yF;
xI = xF + Lsin(xFK);
Notes:
these equations do not guarantee that xI remains within the range [1:N] so cropping needs to be added
K controls the how many wrinkles you want to have in your accordion effect. For example, if you only want one wrinkle, K would be 2*pi/N
L controls how much stretching you want to apply
Then simply express your image F from image I with the transforms you have in 1.
Putting it all together, the code below creates a sample image I and generates the image F as follows:
% Generate a sample input image
N=500;
xF=1:N;
I=(1:4)'*xF/N*50;
% Set the parameters for your accordion transform
K=2*pi/N;
L=100;
% Apply the transform
F=I(:, round(min(N*ones(1,N), max(ones(1,N), (xF + L*sin(xF*K))))) );
% Display the input and output images side by side
image(I);
figure;
image(F);
If you run this exact code you get:
As you can see, the final image on the right stretches the center part of the image on the left, giving you an accordion effect with one wrinkle.
You can fiddle with K and L and adjust the formula to get the exact effect you want, but note how by expressing the transform in a matrix form MATLAB executes the code in a fraction of second. If there is one take away for you is that you should stay away from for loops and complex processing whenever you can.
Have fun!
I'm using linear interpolation for animating an object between two 2d coordinates on the screen. This is pretty close to what I want, but because of rounding, I get a jagged motion. In ASCII art:
ooo
ooo
ooo
oo
Notice how it walks in a Manhattan grid, instead of taking 45 degree turns. What I'd like is linear interpolation along the line which Bresenham's algorithm would have created:
oo
oo
oo
oo
For each x there is only one corresponding y. (And swap x/y for a line that is steep)
So why don't I just use Bresenham's algorithm? I certainly could, but that algorithm is iterative, and I'd like to know just one coordinate along the line.
I am going to try solving this by linearly interpolating the x coordinate, round it to the pixel grid, and then finding the corresponding y. (Again, swap x/y for steep lines). No matter how that solution pans out, though, I'd be interested in other suggestion and maybe previous experience.
Bresenham's algorithm for lines was introduced to draw a complete line a bit faster than usual approaches. It has two major advantages:
It works on integer variables
It works iteratively, which is fast, when drawing the complete line
The first advantage is not a great deal, if you calculate only some coordinates. The second advantage turns out as a disadvantage when calculating only some coordinates. So after all, there is no need to use Bresenham's algorithm.
Instead, you can use a different algorithm, which results in the same line. For example the DDA (digital differential analyzer). This is basically, the same approach you mentioned.
First step: Calculate the slope.
m = (y_end - y_start) / (x_end - x_start)
Second step: Calculate the iteration step, which is simply:
i = x - x_start
Third step: Calculate the coresponding y-value:
y = y_start + i * m
= y_start + (x - x_start) * (y_end - y_start) / (x_end - x_start)
Here's the solution I ended up with:
public static Vector2 BresenhamLerp(Vector2 a, Vector2 b, float percent)
{
if (a.x == b.x || Math.Abs(a.x - b.x) < Math.Abs(a.y - b.y))
{
// Didn't do this part yet. Basically, we just need to recurse
// with x/y swapped and swap result on return
}
Vector2 result;
result.x = Math.Round((1-percent) * a.x + percent * b.x);
float adjustedPercent = (result.x - a.x + 0.5f) / (b.x - a.x);
result.y = Math.Round((1-adjustedPercent) * a.y + adjustedPercent * b.y);
return result;
}
This is what I just figured out would work. Probably not the most beautiful interpolations, but it is just a 1-2 float additions per iteration on the line with a one-time precalculation. Works by calculating the number of steps on a manhattan matrix.
Ah, and it does not yet catch the case when the line is vertical (dx = 0)
This is the naive bresenham, but the iterations could in theory only use integers as well. If you want to get rid of the float color value, things are going to get harder because the line might be longer than the color difference, so delta-color < 1.
void Brepolate( uint8_t* pColorBuffer, uint8_t cs, float xs, float ys, float zs, uint8_t ce, float xe, float ye, float ze )
{
float nColSteps = (xe - xs) + (ye - ys);
float fColInc = ((float)cs - (float)ce) / nColSteps;
float fCol = cs;
float dx = xe - xs;
float dy = ye - ys;
float fCol = cs;
if (dx > 0.5)
{
float de = fabs( dy / dx );
float re = de - 0.5f;
uint32_t iY = ys;
uint32_t iX;
for ( uint32_t iX = xs;
iX <= xe;
iX++ )
{
uint32_t off = surf.Offset( iX, iY );
pColorBuffer[off] = fCol;
re += de;
if (re >= 0.5f)
{
iY++;
re -= 1.0f;
fCol += fColInc;
}
fCol += fColInc;
}
}
}
I need an algorithm to convert the HCL color to RGB and backward RGB to HCL keeping in mind that these color spaces have different gamuts (I need to constrain the HCL colors to those that can be reproduced in RGB color space). What is the algorithm for this (the algorithm is intended to be implemented in Wolfram Mathematica that supports natively only RGB color)? I have no experience in working with color spaces.
P.S. Some articles about HCL color:
M. Sarifuddin (2005). A new perceptually uniform color space with associated color similarity measure for content–based image and video retrieval.
Zeileis, Hornik and Murrell (2009): Escaping RGBland: Selecting Colors for Statistical Graphics // Computational Statistics & Data Analysis Volume 53, Issue 9, 1 July 2009, Pages 3259-3270
UPDATE:
As pointed out by Jonathan Jansson, in the above two articles different color spaces are described by the name "HCL": "The second article uses LCh(uv) which is the same as Luv* but described in polar coordiates where h(uv) is the angle of the u* and v* coordinate and C* is the magnitude of that vector". So in fact I need an algorithm for converting RGB to Luv* and backward.
I was just learing about the HCL colorspace too. The colorspace used in the two articles in your question seems to be different color spaces though.
The second article uses L*C*h(uv) which is the same as L*u*v* but described in polar coordiates where h(uv) is the angle of the u* and v* coordiate and C* is the magnitude of that vector.
The LCH color space in the first article seems to describe another color space than that uses a more algorithmical conversion. There is also another version of the first paper here: http://isjd.pdii.lipi.go.id/admin/jurnal/14209102121.pdf
If you meant to use the CIE L*u*v* you need to first convert sRGB to CIE XYZ and then convert to CIE L*u*v*. RGB actually refers to sRGB in most cases so there is no need to convert from RGB to sRGB.
All source code needed
Good article about how conversion to XYZ works
Nice online converter
But I can't answer your question about how to constrain the colors to the sRGB space. You could just throw away RGB colors which are outside the range 0 to 1 after conversion. Just clamping colors can give quite weird results. Try to go to the converter and enter the color RGB 0 0 255 and convert to L*a*b* (similar to L*u*v*) and then increase L* to say 70 and convert it back and the result is certainly not blue anymore.
Edit: Corrected the URL
Edit: Merged another answer into this answer
HCL is a very generic name there are a lot of ways to have a hue, a chroma, and a lightness. Chroma.js for example has something it calls HCL which is polar coord converted Lab (when you look at the actual code). Other implementations, even ones linked from that same site use Polar Luv. Since you can simply borrow the L factor and derive the hue by converting to polar coords these are both valid ways to get those three elements. It is far better to call them Polar Lab and Polar Luv, because of the confusion factor.
M. Sarifuddin (2005)'s algorithm is not Polar Luv or Polar Lab and is computationally simpler (you don't need to derive Lab or Luv space first), and may actually be better. There are some things that seem wrong in the paper. For example applying a Euclidean distance to a CIE L*C*H* colorspace. The use of a Hue means it's necessarily round, and just jamming that number into A²+B²+C² is going to give you issues. The same is true to apply a hue-based colorspace to D94 or D00 as these are distance algorithms with built in corrections specific to Lab colorspace. Unless I'm missing something there, I'd disregard figures 6-8. And I question the rejection tolerances in the graphics. You could set a lower threshold and do better, and the numbers between color spaces are not normalized. In any event, despite a few seeming flaws in the paper, the algorithm described is worth a shot. You might want to do Euclidean on RGB if it doesn't really matter much. But, if you're shopping around for color distance algorithms, here you go.
Here is HCL as given by M. Sarifuddin implemented in Java. Having read the paper repeatedly I cannot avoid the conclusion that it scales the distance by a factor of between 0.16 and 180.16 with regard to the change in hue in the distance_hcl routine. This is such a profound factor that it almost cannot be right at all. And makes the color matching suck. I have the paper's line commented out and use a line with only the Al factor. Scaling Luminescence by constant ~1.4 factor isn't going to make it unusable. With neither scale factor it ends up being identical to cycldistance.
http://w3.uqo.ca/missaoui/Publications/TRColorSpace.zip is corrected and improved version of the paper.
static final public double Y0 = 100;
static final public double gamma = 3;
static final public double Al = 1.4456;
static final public double Ach_inc = 0.16;
public void rgb2hcl(double[] returnarray, int r, int g, int b) {
double min = Math.min(Math.min(r, g), b);
double max = Math.max(Math.max(r, g), b);
if (max == 0) {
returnarray[0] = 0;
returnarray[1] = 0;
returnarray[2] = 0;
return;
}
double alpha = (min / max) / Y0;
double Q = Math.exp(alpha * gamma);
double rg = r - g;
double gb = g - b;
double br = b - r;
double L = ((Q * max) + ((1 - Q) * min)) / 2;
double C = Q * (Math.abs(rg) + Math.abs(gb) + Math.abs(br)) / 3;
double H = Math.toDegrees(Math.atan2(gb, rg));
/*
//the formulae given in paper, don't work.
if (rg >= 0 && gb >= 0) {
H = 2 * H / 3;
} else if (rg >= 0 && gb < 0) {
H = 4 * H / 3;
} else if (rg < 0 && gb >= 0) {
H = 180 + 4 * H / 3;
} else if (rg < 0 && gb < 0) {
H = 2 * H / 3 - 180;
} // 180 causes the parts to overlap (green == red) and it oddly crumples up bits of the hue for no good reason. 2/3H and 4/3H expanding and contracting quandrants.
*/
if (rg < 0) {
if (gb >= 0) H = 90 + H;
else { H = H - 90; }
} //works
returnarray[0] = H;
returnarray[1] = C;
returnarray[2] = L;
}
public double cycldistance(double[] hcl1, double[] hcl2) {
double dL = hcl1[2] - hcl2[2];
double dH = Math.abs(hcl1[0] - hcl2[0]);
double C1 = hcl1[1];
double C2 = hcl2[1];
return Math.sqrt(dL*dL + C1*C1 + C2*C2 - 2*C1*C2*Math.cos(Math.toRadians(dH)));
}
public double distance_hcl(double[] hcl1, double[] hcl2) {
double c1 = hcl1[1];
double c2 = hcl2[1];
double Dh = Math.abs(hcl1[0] - hcl2[0]);
if (Dh > 180) Dh = 360 - Dh;
double Ach = Dh + Ach_inc;
double AlDl = Al * Math.abs(hcl1[2] - hcl2[2]);
return Math.sqrt(AlDl * AlDl + (c1 * c1 + c2 * c2 - 2 * c1 * c2 * Math.cos(Math.toRadians(Dh))));
//return Math.sqrt(AlDl * AlDl + Ach * (c1 * c1 + c2 * c2 - 2 * c1 * c2 * Math.cos(Math.toRadians(Dh))));
}
I'm familiar with quite a few color spaces, but this one is new to me. Alas, Mathematica's ColorConvert doesn't know it either.
I found an rgb2hcl routine here, but no routine going the other way.
A more comprehensive colorspace conversion package can be found here. It seems to be able to do conversions to and from all kinds of color spaces. Look for the file colorspace.c in colorspace_1.1-0.tar.gz\colorspace_1.1-0.tar\colorspace\src. Note that HCL is known as PolarLUV in this package.
As mentioned in other answers, there are a lot of ways to implement an HCL colorspace and map that into RGB.
HSLuv ended up being what I used, and has MIT-licensed implementations in C, C#, Go, Java, PHP, and several other languages. It's similar to CIELUV LCh but fully maps to RGB. The implementations are available on GitHub.
Here's a short graphic from the website describing the HSLuv color space, with the implementation output in the right two panels:
I was looking to interpolate colors on the web and found HCL to be the most fitting color space, I couldn't find any library making the conversion straightforward and performant so I wrote my own.
There's many constants at play, and some of them vary significantly depending on where you source them from.
My target being the web, I figured I'd be better off matching the chromium source code. Here's a minimized snippet written in Typescript, the sRGB XYZ matrix is precomputed and all constants are inlined.
const rgb255 = (v: number) => (v < 255 ? (v > 0 ? v : 0) : 255);
const b1 = (v: number) => (v > 0.0031308 ? v ** (1 / 2.4) * 269.025 - 14.025 : v * 3294.6);
const b2 = (v: number) => (v > 0.2068965 ? v ** 3 : (v - 4 / 29) * (108 / 841));
const a1 = (v: number) => (v > 10.314724 ? ((v + 14.025) / 269.025) ** 2.4 : v / 3294.6);
const a2 = (v: number) => (v > 0.0088564 ? v ** (1 / 3) : v / (108 / 841) + 4 / 29);
function fromHCL(h: number, c: number, l: number): RGB {
const y = b2((l = (l + 16) / 116));
const x = b2(l + (c / 500) * Math.cos((h *= Math.PI / 180)));
const z = b2(l - (c / 200) * Math.sin(h));
return [
rgb255(b1(x * 3.021973625 - y * 1.617392459 - z * 0.404875592)),
rgb255(b1(x * -0.943766287 + y * 1.916279586 + z * 0.027607165)),
rgb255(b1(x * 0.069407491 - y * 0.22898585 + z * 1.159737864)),
];
}
function toHCL(r: number, g: number, b: number) {
const y = a2((r = a1(r)) * 0.222488403 + (g = a1(g)) * 0.716873169 + (b = a1(b)) * 0.06060791);
const l = 500 * (a2(r * 0.452247074 + g * 0.399439023 + b * 0.148375274) - y);
const q = 200 * (y - a2(r * 0.016863605 + g * 0.117638439 + b * 0.865350722));
const h = Math.atan2(q, l) * (180 / Math.PI);
return [h < 0 ? h + 360 : h, Math.sqrt(l * l + q * q), 116 * y - 16];
}
Here's a playground for the above snippet.
It includes d3's interpolateHCL and the browser native css transition for comparaison.
https://svelte.dev/repl/0a40a8348f8841d0b7007c58e4d9b54c
Here's a gist to do the conversion to and from any web color format and interpolate it in the HCL color space.
https://gist.github.com/pushkine/c8ba98294233d32ab71b7e19a0ebdbb9
I think
if (rg < 0) {
if (gb >= 0) H = 90 + H;
else { H = H - 90; }
} //works
is not really necessary because of atan2(,) instead of atan(/) from paper (but dont now anything about java atan2(,) especially