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Merge pull request #323 from wearepal/init-ml
Add ML output as model layer
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| PLATFORMS | ||
| arm64-darwin-21 | ||
| arm64-darwin-23 | ||
| x86_64-darwin-21 | ||
| x86_64-linux | ||
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119 changes: 119 additions & 0 deletions
119
app/javascript/projects/modelling/components/ml_tree_hedge_component.ts
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| import { createXYZ } from "ol/tilegrid" | ||
| import { Node, Output } from "rete" | ||
| import { NodeData, WorkerInputs, WorkerOutputs } from "rete/types/core/data" | ||
| import { booleanDataSocket, categoricalDataSocket } from "../socket_types" | ||
| import { BooleanTileGrid, CategoricalTileGrid } from "../tile_grid" | ||
| import { BaseComponent } from "./base_component" | ||
| import { retrieveModelDataWCS } from "../model_retrieval" | ||
| import { TypedArray } from "d3" | ||
| import { Extent } from "ol/extent" | ||
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| interface Habitat { | ||
| agg: number | ||
| AC: string | ||
| mode: number | ||
| LC: string | ||
| } | ||
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| const habitats: Habitat[] = [ | ||
| //TODO : move to a json or an alternative storage | ||
| { agg: 0, AC: "All", mode: 0, LC: "All" }, | ||
| { agg: 1, AC: "Hedge", mode: 1, LC: "Hedge" }, | ||
| { agg: 2, AC: "Tree", mode: 2, LC: "Tree" } | ||
| ] | ||
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| async function renderCategoricalData(extent: Extent, zoom: number) { | ||
| // When testing locally, disable CORS in browser settings | ||
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| const tileGrid = createXYZ() | ||
| const outputTileRange = tileGrid.getTileRangeForExtentAndZ(extent, zoom) | ||
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| const geotiff = await retrieveModelDataWCS(extent, 'ml:tree_hedge_predictions', outputTileRange) | ||
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| const rasters = await geotiff.readRasters({ bbox: extent, width: outputTileRange.getWidth(), height: outputTileRange.getHeight() }) | ||
| const image = await geotiff.getImage() | ||
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| const map: Map<number, string> = new Map() | ||
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| habitats.forEach(hab => { | ||
| if (hab.mode !== 0) map.set(hab.mode, hab.LC) | ||
| }) | ||
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| const result = new CategoricalTileGrid( | ||
| zoom, | ||
| outputTileRange.minX, | ||
| outputTileRange.minY, | ||
| outputTileRange.getWidth(), | ||
| outputTileRange.getHeight() | ||
| ) | ||
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| for (let i = 0; i < (rasters[0] as TypedArray).length; i++) { | ||
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| let x = (outputTileRange.minX + i % image.getWidth()) | ||
| let y = (outputTileRange.minY + Math.floor(i / image.getWidth())) | ||
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| result.set(x, y, rasters[0][i]) | ||
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| } | ||
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| result.setLabels(map) | ||
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| return result | ||
| } | ||
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| export class MlTreeHedgeComponent extends BaseComponent { | ||
| categoricalData: CategoricalTileGrid | null | ||
| outputCache: Map<number, BooleanTileGrid> | ||
| projectExtent: Extent | ||
| zoom: number | ||
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| constructor(projectExtent: Extent, projectZoom: number) { | ||
| super("ML Model Output") | ||
| this.category = "Inputs" | ||
| this.categoricalData = null | ||
| this.outputCache = new Map() | ||
| this.projectExtent = projectExtent | ||
| this.zoom = projectZoom | ||
| } | ||
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| async builder(node: Node) { | ||
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| node.meta.toolTip = "Custom ML outputs." | ||
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| node.meta.toolTipLink = "https://www.wearepal.ai/lmt.html" | ||
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| habitats.forEach(hab => | ||
| hab.AC === "All" ? node.addOutput(new Output(hab["mode"].toString(), hab["LC"], categoricalDataSocket)) : node.addOutput(new Output(hab["mode"].toString(), hab["LC"], booleanDataSocket)) | ||
| ) | ||
| } | ||
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| async worker(node: NodeData, inputs: WorkerInputs, outputs: WorkerOutputs, ...args: unknown[]) { | ||
| if (this.categoricalData === null) { | ||
| this.categoricalData = await renderCategoricalData(this.projectExtent, this.zoom) | ||
| } | ||
| const categoricalData = this.categoricalData! | ||
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| habitats.filter( | ||
| habitat => node.outputs[habitat.mode].connections.length > 0 | ||
| ).forEach(habitat => { | ||
| if (habitat.mode === 0) { | ||
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| outputs[habitat.mode] = this.categoricalData | ||
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| } else { | ||
| if (this.outputCache.has(habitat.mode)) { | ||
| outputs[habitat.mode] = this.outputCache.get(habitat.mode) | ||
| } | ||
| else { | ||
| const out = outputs[habitat.mode] = new BooleanTileGrid(categoricalData.zoom, categoricalData.x, categoricalData.y, categoricalData.width, categoricalData.height) | ||
| out.name = habitat.LC | ||
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| categoricalData.iterate((x, y, value) => out.set(x, y, value === habitat.mode)) | ||
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| this.outputCache.set(habitat.mode, out) | ||
| } | ||
| } | ||
| }) | ||
| } | ||
| } |