66 lines
2.1 KiB
JavaScript
66 lines
2.1 KiB
JavaScript
//load the candle yolo wasm module
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import init, { Model, ModelPose } from "./build/m.js";
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async function fetchArrayBuffer(url) {
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const cacheName = "yolo-candle-cache";
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const cache = await caches.open(cacheName);
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const cachedResponse = await cache.match(url);
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if (cachedResponse) {
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const data = await cachedResponse.arrayBuffer();
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return new Uint8Array(data);
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}
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const res = await fetch(url, { cache: "force-cache" });
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cache.put(url, res.clone());
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return new Uint8Array(await res.arrayBuffer());
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}
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class Yolo {
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static instance = {};
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// Retrieve the YOLO model. When called for the first time,
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// this will load the model and save it for future use.
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static async getInstance(modelID, modelURL, modelSize) {
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// load individual modelID only once
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if (!this.instance[modelID]) {
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await init();
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self.postMessage({ status: `loading model ${modelID}:${modelSize}` });
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const weightsArrayU8 = await fetchArrayBuffer(modelURL);
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if (/pose/.test(modelID)) {
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// if pose model, use ModelPose
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this.instance[modelID] = new ModelPose(weightsArrayU8, modelSize);
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} else {
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this.instance[modelID] = new Model(weightsArrayU8, modelSize);
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}
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} else {
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self.postMessage({ status: "model already loaded" });
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}
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return this.instance[modelID];
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}
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}
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self.addEventListener("message", async (event) => {
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const { imageURL, modelID, modelURL, modelSize, confidence, iou_threshold } =
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event.data;
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try {
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self.postMessage({ status: "detecting" });
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const yolo = await Yolo.getInstance(modelID, modelURL, modelSize);
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self.postMessage({ status: "loading image" });
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const imgRes = await fetch(imageURL);
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const imgData = await imgRes.arrayBuffer();
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const imageArrayU8 = new Uint8Array(imgData);
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self.postMessage({ status: `running inference ${modelID}:${modelSize}` });
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const bboxes = yolo.run(imageArrayU8, confidence, iou_threshold);
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// Send the output back to the main thread as JSON
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self.postMessage({
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status: "complete",
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output: JSON.parse(bboxes),
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});
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} catch (e) {
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self.postMessage({ error: e });
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}
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});
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