curve.py 文件源码

def discretize_bspline(control, knots, count=None, scale=1.0):
    '''
    Given a B-Splines control points and knot vector, return
    a sampled version of the curve.

    Arguments
    ----------
    control:  (o,d) list of control points of the b- spline. 
    knots:    (j) list of knots 
    count:    int, number of sections to discretize the spline in to.
              If not specified, RES_LENGTH will be used to inform this. 

    Returns
    ----------
    discrete: (count,d) list of points, a polyline of the B-spline.
    '''

    # evaluate the b-spline using scipy/fitpack
    from scipy.interpolate import splev
    # (n, d) control points where d is the dimension of vertices
    control = np.array(control)
    degree  = len(knots) - len(control) - 1
    if count is None:
        norm  = np.linalg.norm(np.diff(control, axis=0), axis=1).sum()
        count = int(np.clip(norm / (res.seg_frac*scale),
                            res.min_sections*len(control), 
                            res.max_sections*len(control)))

    ipl      = np.linspace(knots[0], knots[-1], count)
    discrete = splev(ipl, [knots, control.T, degree])
    discrete = np.column_stack(discrete)
    return discrete