numpy/scipy中的快速2D刚体变换

Question

我想将刚体变换应用于大量的二维图像矩阵.理想情况下,我希望能够提供一个指定平移和旋转的仿射变换矩阵,一次应用,然后在输出上进行三次样条插值.

不幸的是,似乎affine_transform在scipy.ndimage.interpolation没有做翻译.我知道我可以使用的组合shift和rotate,但是这是一种混乱和在涉及到多次插值输出.

我也试过使用这样的泛型geometric_transformation:

import numpy as np
from scipy.ndimage.interpolation import geometric_transformation

# make the affine matrix
def maketmat(xshift,yshift,rotation,dimin=(0,0)):

    # centre on the origin
    in2orig = np.identity(3)
    in2orig[:2,2] = -dimin[0]/2.,-dimin[1]/2.

    # rotate about the origin
    theta = np.deg2rad(rotation)
    rotmat = np.identity(3)
    rotmat[:2,:2] = [np.cos(theta),np.sin(theta)],[-np.sin(theta),np.cos(theta)]

    # translate to new position
    orig2out = np.identity(3)
    orig2out[:2,2] = xshift,yshift

    # the final affine matrix is just the product
    tmat = np.dot(orig2out,np.dot(rotmat,in2orig))

# function that maps output space to input space
def out2in(outcoords,affinemat):
    outcoords = np.asarray(outcoords)
    outcoords = np.concatenate((outcoords,(1.,)))
    incoords = np.dot(affinemat,outcoords)
    incoords = tuple(incoords[0:2])
    return incoords

def rbtransform(source,xshift,yshift,rotation,outdims):

    # source --> target
    forward = maketmat(xshift,yshift,rotation,source.shape)

    # target --> source
    backward = np.linalg.inv(forward)

    # now we can use geometric_transform to do the interpolation etc.
    tformed = geometric_transform(source,out2in,output_shape=outdims,extra_arguments=(backward,))

    return tformed

这可行,但它非常慢,因为它基本上是在像素坐标上循环!这样做的好方法是什么？

Answer 1

我认为affine_transform 确实做了翻译——有offset参数。