Diff of /benchmarks/programs/lic2d/bmark-diderot.diderot

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	revision 1592, Tue Nov 1 09:41:35 2011 UTC	revision 1596, Wed Nov 2 13:33:41 2011 UTC
#	Line 2	Line 2
2	//	//
3	// demo of line integral convolution (LIC) on 2D turbulent flow	// demo of line integral convolution (LIC) on 2D turbulent flow
4	//	//
	// First, need to create padded vector dataset v.nrrd with:
	// unu pad -i ../data/turb2d.nrrd -min 0 -5 -5 -max M M+5 M+5 -b pad -v 0.0001 -o v.nrrd
	// We pad with value 0.0001; we padded with zero than normalize() would fail
	// But if you try take too many steps (increasing stepNum) then eventually
	// you will leave the vector field domain, which will crash the program,
	// so really the padding should be done with a vector field that
	// pointwards inwards, towards the center of the domain, so that anything
	// leaving the domain will get pushed back in.  OR, really, the code
	// below should be smart and use inside() or some other test to stop
	// the integration when we get too close to the domain boundary.
	//
	// Then, need to create noise texture with:
	// unu slice -i v.nrrd -a 0 -p 0 | unu resample -s 1020 561 | unu 1op nrand -o R.nrrd
	// where "1020 561" is copied from imgSizeX and imgSizeY below; we start
	// with unu slice in order to inherit the image orientation info, to
	// create a noise texture at the same world-space position as the data
	//
5	// process output with:	// process output with:
6	// unu reshape -i lic-turb2d.txt -s 3 1020 561 | unu quantize -b 8  -o lic-turb2d.ppm	// unu reshape -i lic-turb2d.txt -s 3 1020 561 | unu quantize -b 8  -o lic-turb2d.ppm
7
8	int imgSizeX = 1020;	int imgSizeX = 1020;
9	int imgSizeY = 561;	int imgSizeY = 561;
10	real h = 0.005;    // step size of integration	real h = 0.005;    // step size of integration
#	Line 46	Line 28
28	real crl = 0.0;	real crl = 0.0;
29
30	update {	update {
	// Euler integration step
	// forw = forw + h*V(forw);
	// back = back - h*V(back);
	// Midpoint method step
31	if(inside(forw, boundary)) {	if(inside(forw, boundary)) {
32	forw += h*normalize(V(forw + 0.5*h*normalize(V(forw))));	forw += h*normalize(V(forw + 0.5*h*normalize(V(forw))));
33	sum += R(forw);	sum += R(forw);
#	Line 62	Line 40
40	}	}
41	step += 1;	step += 1;
42	if((!inside(forw, boundary) && !inside(back, boundary)) || step == stepNum) {	if((!inside(forw, boundary) && !inside(back, boundary)) || step == stepNum) {
	// the pow() is a way to adjust how much velocity modulates
	// contrast; lower values increase contrast at small velocity
43	sum = pow(|V(pos0)|,0.3)*sum/real(num);	sum = pow(|V(pos0)|,0.3)*sum/real(num);
44	tensor[2,2] M = ∇⊗V(pos0);	tensor[2,2] M = ∇⊗V(pos0);
45	crl = M[0,1] - M[1,0];	crl = M[0,1] - M[1,0];
	// BUG - curl ("∇×") doesn't work
	//crl = ∇×V(pos0);
	//sum in range -1.56, 2.58
	//curl in rage -131.797974, 131.828476
	if(crl < 0.0) {
	real h = 300.0;
	}
	else {
	real h = 120.0;
	}
46	real s = lerp(0.0, 1.0, 0.0, |crl|, 132.0);	real s = lerp(0.0, 1.0, 0.0, |crl|, 132.0);
47	l = lerp(0.0, 1.0, -1.6, sum, 2.6);	l = lerp(0.0, 1.0, -1.6, sum, 2.6);
48	real c = (1.0 - |2.0 * l - 1.0|) * s;	real c = (1.0 - |2.0 * l - 1.0|) * s;
49	if(crl < 0.0) {	if(crl < 0.0) {
	int hp = 5;
50	real x = c;	real x = c;
51	real m = l - c / 2.0;	real m = l - c / 2.0;
52	RGB = [c + m, 0.0 + m, x + m];	RGB = [c + m, 0.0 + m, x + m];
53	}	}
54	else {	else {
	int hp = 2;
55	real x = 0.0;	real x = 0.0;
56	real m = l - c / 2.0;	real m = l - c / 2.0;
57	RGB = [0.0 + m, c + m, x + m];	RGB = [0.0 + m, c + m, x + m];
58	}	}
59	stabilize;	stabilize;
60	}	}
61	}	}

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