diff options
Diffstat (limited to 'converter/other/pamtosvg/fit.c')
| -rw-r--r-- | converter/other/pamtosvg/fit.c | 60 |
1 files changed, 29 insertions, 31 deletions
diff --git a/converter/other/pamtosvg/fit.c b/converter/other/pamtosvg/fit.c index 9465ff75..627435cf 100644 --- a/converter/other/pamtosvg/fit.c +++ b/converter/other/pamtosvg/fit.c @@ -38,7 +38,7 @@ #include "vector.h" #include "curve.h" #include "pxl-outline.h" -#include "epsilon-equal.h" +#include "epsilon.h" #define CUBE(x) ((x) * (x) * (x)) @@ -1157,17 +1157,17 @@ splineLinearEnough(spline_type * const splineP, LOG ("Checking linearity:\n"); - A = END_POINT(*splineP).x - START_POINT(*splineP).x; - B = END_POINT(*splineP).y - START_POINT(*splineP).y; - C = END_POINT(*splineP).z - START_POINT(*splineP).z; + A = END_POINT(*splineP).x - BEG_POINT(*splineP).x; + B = END_POINT(*splineP).y - BEG_POINT(*splineP).y; + C = END_POINT(*splineP).z - BEG_POINT(*splineP).z; startEndDist = (float) (SQR(A) + SQR(B) + SQR(C)); LOG1("start_end_distance is %.3f.\n", sqrt(startEndDist)); LOG3(" Line endpoints are (%.3f, %.3f, %.3f) and ", - START_POINT(*splineP).x, - START_POINT(*splineP).y, - START_POINT(*splineP).z); + BEG_POINT(*splineP).x, + BEG_POINT(*splineP).y, + BEG_POINT(*splineP).z); LOG3("(%.3f, %.3f, %.3f)\n", END_POINT(*splineP).x, END_POINT(*splineP).y, END_POINT(*splineP).z); @@ -1180,9 +1180,9 @@ splineLinearEnough(spline_type * const splineP, float const t = CURVE_T(curve, thisPoint); Point const splinePoint = evaluate_spline(*splineP, t); - float const a = splinePoint.x - START_POINT(*splineP).x; - float const b = splinePoint.y - START_POINT(*splineP).y; - float const c = splinePoint.z - START_POINT(*splineP).z; + float const a = splinePoint.x - BEG_POINT(*splineP).x; + float const b = splinePoint.y - BEG_POINT(*splineP).y; + float const c = splinePoint.z - BEG_POINT(*splineP).z; float const w = (A*a + B*b + C*c) / startEndDist; @@ -1240,7 +1240,7 @@ fitWithLine(curve * const curveP) { LOG("Fitting with straight line:\n"); SPLINE_DEGREE(line) = LINEARTYPE; - START_POINT(line) = CONTROL1(line) = CURVE_POINT(curveP, 0); + BEG_POINT(line) = CONTROL1(line) = CURVE_POINT(curveP, 0); END_POINT(line) = CONTROL2(line) = LAST_CURVE_POINT(curveP); /* Make sure that this line is never changed to a cubic. */ @@ -1272,31 +1272,29 @@ fitOneSpline(curve * const curveP, Make it a cubic spline. -----------------------------------------------------------------------------*/ - /* We already have the start and end points of the spline, so all - we need are the control points. And we know in what direction - each control point is from its respective end point, so all we - need to figure out is its distance. (The control point's - distance from the end point is an indication of how long the - curve goes in its direction). + /* We already have the start and end points of the spline, so all we need + are the control points. And we know in what direction each control + point is from its respective end point, so all we need to figure out is + its distance. (The control point's distance from the end point is an + indication of how long the curve goes in its direction). - We call the distance from an end point to the associated control - point "alpha". + We call the distance from an end point to the associated control point + "alpha". - We want to find starting and ending alpha that minimize the - least-square error in approximating *curveP with the spline. + We want to find starting and ending alpha that minimize the + least-square error in approximating *curveP with the spline. - How we do that is a complete mystery to me, but the original author - said to see pp.57--59 of the Phoenix thesis. I haven't seen that. + How we do that is a complete mystery to me, but the original author + said to see pp. 57-59 of the Phoenix thesis. I haven't seen that. - In our expression of the math here, we use a struct with "beg" and - "end" members where the paper uses a matrix with "1" and "2" - subscripts, respectively. A C array is a closer match to a math - matrix, but we think the struct is easier to read. - - The B?(t) here corresponds to B_i^3(U_i) there. - The Bernstein polynomials of degree n are defined by - B_i^n(t) = { n \choose i } t^i (1-t)^{n-i}, i = 0..n + In our expression of the math here, we use a struct with "beg" and + "end" members where the paper uses a matrix with "1" and "2" + subscripts, respectively. A C array is a closer match to a math + matrix, but we think the struct is easier to read. + The B?(t) here corresponds to B_i^3(U_i) there. + The Bernstein polynomials of degree n are defined by + B_i^n(t) = { n \choose i } t^i (1-t)^{n-i}, i = 0..n */ struct VectorPair { Vector beg; |