| ব্যবহার | রিটার্নস |
|---|---|
Kernel. add (kernel2, normalize ) | কার্নেল |
| যুক্তি | টাইপ | বিস্তারিত |
|---|---|---|
এই: kernel1 | কার্নেল | প্রথম কার্নেল। |
kernel2 | কার্নেল | দ্বিতীয় কার্নেল। |
normalize | বুলিয়ান, ডিফল্ট: মিথ্যা | কার্নেলকে স্বাভাবিক করুন। |
উদাহরণ
কোড এডিটর (জাভাস্ক্রিপ্ট)
// 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 (পাইথন)
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]
| ব্যবহার | রিটার্নস |
|---|---|
Kernel. add (kernel2, normalize ) | কার্নেল |
| যুক্তি | টাইপ | বিস্তারিত |
|---|---|---|
এই: kernel1 | কার্নেল | প্রথম কার্নেল। |
kernel2 | কার্নেল | দ্বিতীয় কার্নেল। |
normalize | বুলিয়ান, ডিফল্ট: মিথ্যা | কার্নেলকে স্বাভাবিক করুন। |
উদাহরণ
কোড এডিটর (জাভাস্ক্রিপ্ট)
// 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 (পাইথন)
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]