我做这个任务是为了好玩。
http://groups.csail.mit.edu/graphics/classes/6.837/F04/assignments/assignment0/
如果您想查看它的外观,站点上有示例输出。它涉及迭代函数系统,其算法根据分配是:
for "lots" of random points (x0, y0)
for k=0 to num_iters
pick a random transform fi
(xk+1, yk+1) = fi(xk, yk)
display a dot at (xk, yk)
我的实现遇到了麻烦,即:
void IFS::render(Image& img, int numPoints, int numIterations){
Vec3f color(0,1,0);
float x,y;
float u,v;
Vec2f myVector;
for(int i = 0; i < numPoints; i++){
x = (float)(rand()%img.Width())/img.Width();
y = (float)(rand()%img.Height())/img.Height();
myVector.Set(x,y);
for(int j = 0; j < numIterations;j++){
float randomPercent = (float)(rand()%100)/100;
for(int k = 0; k < num_transforms; k++){
if(randomPercent < range[k]){
matrices[k].Transform(myVector);
}
}
}
u = myVector.x()*img.Width();
v = myVector.y()*img.Height();
img.SetPixel(u,v,color);
}
}
这就是我从输入矩阵中选择随机变换的方式:
fscanf(input,"%d",&num_transforms);
matrices = new Matrix[num_transforms];
probablility = new float[num_transforms];
range = new float[num_transforms+1];
for (int i = 0; i < num_transforms; i++) {
fscanf (input,"%f",&probablility[i]);
matrices[i].Read3x3(input);
if(i == 0) range[i] = probablility[i];
else range[i] = probablility[i] + range[i-1];
}
我的输出只显示了谢尔宾斯基三角形的开始(1000 个点,1000 次迭代):
我的龙更好,但仍需要一些工作(1000 分,1000 次迭代):