AI-generated Key Takeaways
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Generates a diamond-shaped kernel for use in image processing operations.
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The kernel weights are by default normalized to sum to 1, creating a smoothing effect.
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You can control the radius of the diamond kernel and specify its units in pixels or meters.
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An optional magnitude argument allows scaling the kernel weights for custom effects.
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The diamond kernel is useful for operations that require equal consideration of pixels in all diagonal and orthogonal directions.
| Usage | Returns |
|---|---|
ee.Kernel.diamond(radius, units, normalize, magnitude) | Kernel |
| Argument | Type | Details |
|---|---|---|
radius | Float | The radius of the kernel to generate. |
units | String, default: "pixels" | The system of measurement for the kernel ('pixels' or 'meters'). If the kernel is specified in meters, it will resize when the zoom-level is changed. |
normalize | Boolean, default: true | Normalize the kernel values to sum to 1. |
magnitude | Float, default: 1 | Scale each value by this amount. |
Examples
Code Editor (JavaScript)
print('A diamond kernel', ee.Kernel.diamond({radius: 3})); /** * Output weights matrix (up to 1/100 precision for brevity) * * [0.00, 0.00, 0.00, 0.04, 0.00, 0.00, 0.00] * [0.00, 0.00, 0.04, 0.04, 0.04, 0.00, 0.00] * [0.00, 0.04, 0.04, 0.04, 0.04, 0.04, 0.00] * [0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04] * [0.00, 0.04, 0.04, 0.04, 0.04, 0.04, 0.00] * [0.00, 0.00, 0.04, 0.04, 0.04, 0.00, 0.00] * [0.00, 0.00, 0.00, 0.04, 0.00, 0.00, 0.00] */
import ee import geemap.core as geemap
Colab (Python)
from pprint import pprint print('A diamond kernel:') pprint(ee.Kernel.diamond(**{'radius': 3}).getInfo()) # Output weights matrix (up to 1/100 precision for brevity) # [0.00, 0.00, 0.00, 0.04, 0.00, 0.00, 0.00] # [0.00, 0.00, 0.04, 0.04, 0.04, 0.00, 0.00] # [0.00, 0.04, 0.04, 0.04, 0.04, 0.04, 0.00] # [0.04, 0.04, 0.04, 0.04, 0.04, 0.04, 0.04] # [0.00, 0.04, 0.04, 0.04, 0.04, 0.04, 0.00] # [0.00, 0.00, 0.04, 0.04, 0.04, 0.00, 0.00] # [0.00, 0.00, 0.00, 0.04, 0.00, 0.00, 0.00]