用途 | 戻り値 |
---|---|
Kernel.add(kernel2, normalize) | カーネル |
引数 | タイプ | 詳細 |
---|---|---|
これ: kernel1 | カーネル | 最初のカーネル。 |
kernel2 | カーネル | 2 番目のカーネル。 |
normalize | ブール値。デフォルト値は false です。 | カーネルを正規化します。 |
例
コードエディタ(JavaScript)
// Two kernels, they do not need to have the same dimensions. var kernelA = ee.Kernel.chebyshev({radius: 3}); var kernelB = ee.Kernel.square({radius: 1, normalize: false, magnitude: 100}); print(kernelA, kernelB); /** * Two kernel weights matrices * * [3, 3, 3, 3, 3, 3, 3] * [3, 2, 2, 2, 2, 2, 3] * [3, 2, 1, 1, 1, 2, 3] [100, 100, 100] * A [3, 2, 1, 0, 1, 2, 3] B [100, 100, 100] * [3, 2, 1, 1, 1, 2, 3] [100, 100, 100] * [3, 2, 2, 2, 2, 2, 3] * [3, 3, 3, 3, 3, 3, 3] */ print('Pointwise addition of two kernels', kernelA.add(kernelB)); /** * [3, 3, 3, 3, 3, 3, 3] * [3, 2, 2, 2, 2, 2, 3] * [3, 2, 101, 101, 101, 2, 3] * [3, 2, 101, 100, 101, 2, 3] * [3, 2, 101, 101, 101, 2, 3] * [3, 2, 2, 2, 2, 2, 3] * [3, 3, 3, 3, 3, 3, 3] */
import ee import geemap.core as geemap
Colab(Python)
from pprint import pprint # Two kernels, they do not need to have the same dimensions. kernel_a = ee.Kernel.chebyshev(**{'radius': ee.Number(3)}) kernel_b = ee.Kernel.square(**{ 'radius': 1, 'normalize': False, 'magnitude': 100 }) pprint(kernel_a.getInfo()) pprint(kernel_b.getInfo()) # Two kernel weights matrices # [3, 3, 3, 3, 3, 3, 3] # [3, 2, 2, 2, 2, 2, 3] # [3, 2, 1, 1, 1, 2, 3] [100, 100, 100] # A [3, 2, 1, 0, 1, 2, 3] B [100, 100, 100] # [3, 2, 1, 1, 1, 2, 3] [100, 100, 100] # [3, 2, 2, 2, 2, 2, 3] # [3, 3, 3, 3, 3, 3, 3] print('Pointwise addition of two kernels:') pprint(kernel_a.add(kernel_b).getInfo()) # [3, 3, 3, 3, 3, 3, 3] # [3, 2, 2, 2, 2, 2, 3] # [3, 2, 101, 101, 101, 2, 3] # [3, 2, 101, 100, 101, 2, 3] # [3, 2, 101, 101, 101, 2, 3] # [3, 2, 2, 2, 2, 2, 3] # [3, 3, 3, 3, 3, 3, 3]