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apply_kernel.json
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apply_kernel.json
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{
"id": "apply_kernel",
"summary": "Applies a kernel to compute pixel-wise values",
"description": "Applies a focal operation based on a weighted kernel to each value of the specified dimensions in the data cube.",
"categories": [
"cubes",
"math > image filter"
],
"parameter_order": ["data", "kernel", "factor"],
"parameters": {
"data": {
"description": "A data cube.",
"schema": {
"type": "object",
"format": "raster-cube"
},
"required": true
},
"kernel": {
"description": "The kernel to be applied on the data cube. The kernel has to be as many dimensions as the data cube has dimensions.",
"schema": {
"type": "array",
"format": "kernel",
"items": {
"description": "Usually a multi-dimensional array of numbers."
}
},
"required": true
},
"factor": {
"description": "A factor that is multiplied to each value computed by the focal operation.\n\nThis is basically a shortcut for explicitly multiplying each value by a factor afterwards, which is often required for some kernel-based algorithms such as the Gaussian blur.",
"schema": {
"type": "number",
"default": 1
}
}
},
"returns": {
"description": "A data cube with the newly computed values. The resolution, cardinality and the number of dimensions are the same as for the original data cube.",
"schema": {
"type": "object",
"format": "raster-cube"
}
}
}