imgaug.augmenters.Affine 中的 'order' 参数究竟是如何工作的？

Question

我一直在使用 imgaug 为我的项目扩充数据。当然，我会使用仿射变换，所以我知道我们使用 order 参数来选择插值方法。不过，选择插值的方法看起来很晦涩，至少对我来说是这样。

假设这是我的增强器（它是 Sequential() 增强器的一部分）：

iaa.Affine(scale             = {"x": (+0.8, +1.0), "y": (+0.8, +1.0)},
                translate_percent = {"x": (-0.2, +0.2), "y": (-0.2, +0.2)},
                rotate            = (-5, +5),
                shear             = ( -5,  +5),
                order             = [1, 2], #interpolation
                cval              = 255, 
)

据我所知，order = [1,2]代表二次插值，order = [0,1]代表线性插值。这是什么意思？如何获得其他插值，例如双三次或 Lanczos？

Answer 1

"Use the Source, Luke"。 directly，或来自带有 help 函数的文档字符串。

order : int or iterable of int or ia.ALL or StochasticParameter, optional(default=1)
Interpolation order to use. Same meaning as in
skimage:
        * 0: Nearest-neighbor
        * 1: Bi-linear (default)
        * 2: Bi-quadratic (not recommended by skimage)
        * 3: Bi-cubic
        * 4: Bi-quartic
        * 5: Bi-quintic
Method 0 and 1 are fast, 3 is a bit slower, 4 and 5 are very
slow.
        * If a single int, then that order will be used for all images.
        * If an iterable, then for each image a random value will be sampled
          from that iterable (i.e. list of allowed order values).
        * If ia.ALL, then equivalant to list [0, 1, 3, 4, 5].
        * If StochasticParameter, then that parameter is queried per image
          to sample the order value to use.

imgaug.augmenters.Affine 中的 'order' 参数究竟是如何工作的？

How exactly does 'order' parameter in imgaug.augmenters.Affine work?

image-manipulation

machine-learning

image-processing

computer-vision

python-3.x