LoRaWAN-Signal-Mapping/client_src/js/LayerAI.mjs

124 lines
2.7 KiB
JavaScript

"use strict";
import path from 'path';
import L from 'leaflet';
import * as tf from '@tensorflow/tfjs';
import chroma from 'chroma-js';
import GetFromUrl from './Helpers/GetFromUrl.mjs';
import Config from './ClientConfig.mjs';
import { normalise } from '../../common/Math.mjs';
class LayerAI {
get gateway_bounds() {
let result = {
east: Infinity,
west: -Infinity,
north: Infinity,
south: -Infinity
};
for(let gateway of this.index) {
result.east = Math.min(gateway.longitude, result.east);
result.west = Math.max(gateway.longitude, result.west);
result.north = Math.min(gateway.latitude, result.north);
result.south = Math.max(gateway.latitude, result.south);
}
return result;
}
constructor(map) {
this.map = map;
this.gateways = new Map();
}
async setup() {
this.index = JSON.parse(
await GetFromUrl(Config.ai_index_file)
);
console.log(index);
for(let gateway of this.index.index) {
this.gateways.set(
gateway.id,
await tf.LayersModel.loadModel(`${window.location.href}/${path.dirname(Config.ai_index_file)}/${gateway.id}`)
);
}
this.layer = this.generate_layer();
this.layer.addTo(this.map);
}
generate_layer() {
return L.geoJSON(this.render_map(), {
style: (feature) => { return {
fillColor: feature.properties.colour,
fillOpacity: 0.4
} }
});
}
render_map() {
// FUTURE: Do this in a web worker?
let map_bounds = this.gateway_bounds;
map_bounds.north += Config.border;
map_bounds.south -= Config.border;
map_bounds.east += Config.border;
map_bounds.west -= Config.border;
let coverage = [],
colour_scale = chroma.scale(
Config.colour_scale.min,
Config.colour_scale.max
).domain(
this.index.properties.rssi_min,
this.index.properties.rssi_max
);
for(let lat = map_bounds.west; lat < map_bounds.east; lat += Config.step) {
for(let lng = map_bounds.south; lng < map_bounds.north; lng += Config.step) {
let max_predicted_rssi = -Infinity;
for(let [, ai] of this.gateways) {
max_predicted_rssi = Math.max(
max_predicted_rssi,
ai.predict(
tf.tensor1d([ lat, lng ])
)
);
}
max_predicted_rssi = normalise(max_predicted_rssi,
{ min: 0, max: 1 },
{
min: this.index.properties.rssi_min,
max: this.index.properties.rssi_max
}
);
coverage.push({
type: "Feature",
geometry: {
type: "Polygon",
coordinates: [
[lat, lng],
[lat + Config.step, lng],
[lat + Config.step, lng + Config.step],
[lat, lng + Config.step]
]
},
properties: {
colour: colour_scale(max_predicted_rssi)
}
})
}
}
return coverage;
}
}
export default LayerAI;