04-spline_fitter.py 文件源码

def _get_spline(cls, points, pix_err=2, need_sort=True, **kwargs):
        '''
        Returns a closed spline for the points handed in.
        Input is assumed to be a (2xN) array

        =====
        input
        =====

        :param points: the points to fit the spline to
        :type points: a 2xN ndarray or a list of len =2 tuples

        :param pix_err: the error is finding the spline in pixels
        :param need_sort: if the points need to be sorted
            or should be processed as-is

        =====
        output
        =====
        tck
           The return data from the spline fitting
        '''
        if type(points) is np.ndarray:
            # make into a list
            pt_lst = zip(*points)
            # get center
            center = np.mean(points, axis=1).reshape(2, 1)
        else:
            # make a copy of the list
            pt_lst = list(points)

            # compute center
            tmp_fun = lambda x, y: (x[0] + y[0], x[1] + y[1])

            center = np.array(reduce(tmp_fun, pt_lst)).reshape(2, 1)
            center /= len(pt_lst)
        if len(pt_lst) < 5:
            raise TooFewPointsException("not enough points")

        if need_sort:
            # sort the list by angle around center
            pt_lst.sort(key=lambda x: np.arctan2(x[1] - center[1],
                                                 x[0] - center[0]))
        # add first point to end because it is periodic (makes the
        # interpolation code happy)
        pt_lst.append(pt_lst[0])
        # make array for handing in to spline fitting
        pt_array = np.vstack(pt_lst).T
        # do spline fitting

        tck, u = si.splprep(pt_array, s=len(pt_lst) * (pix_err ** 2), per=True)
        return tck