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import './style.css'; |
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import { env, AutoModel, AutoProcessor, RawImage } from '@xenova/transformers'; |
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env.backends.onnx.wasm.wasmPaths = 'https://cdn.jsdelivr.net/npm/onnxruntime-web@1.17.1/dist/'; |
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env.backends.onnx.wasm.numThreads = 1; |
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const status = document.getElementById('status'); |
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const container = document.getElementById('container'); |
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const canvas = document.getElementById('canvas'); |
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const outputCanvas = document.getElementById('output-canvas'); |
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const video = document.getElementById('video'); |
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const sizeSlider = document.getElementById('size'); |
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const sizeLabel = document.getElementById('size-value'); |
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const scaleSlider = document.getElementById('scale'); |
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const scaleLabel = document.getElementById('scale-value'); |
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function setStreamSize(width, height) { |
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video.width = outputCanvas.width = canvas.width = Math.round(width); |
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video.height = outputCanvas.height = canvas.height = Math.round(height); |
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} |
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status.textContent = 'Loading model...'; |
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const model_id = 'Xenova/modnet'; |
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let model; |
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try { |
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model = await AutoModel.from_pretrained(model_id, { |
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device: 'webgpu', |
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dtype: 'fp32', |
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}); |
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} catch (err) { |
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status.textContent = err.message; |
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alert(err.message) |
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throw err; |
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} |
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const processor = await AutoProcessor.from_pretrained(model_id); |
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let size = 256; |
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processor.feature_extractor.size = { shortest_edge: size }; |
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sizeSlider.addEventListener('input', () => { |
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size = Number(sizeSlider.value); |
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processor.feature_extractor.size = { shortest_edge: size }; |
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sizeLabel.textContent = size; |
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}); |
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sizeSlider.disabled = false; |
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let scale = 0.5; |
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scaleSlider.addEventListener('input', () => { |
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scale = Number(scaleSlider.value); |
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setStreamSize(video.videoWidth * scale, video.videoHeight * scale); |
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scaleLabel.textContent = scale; |
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}); |
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scaleSlider.disabled = false; |
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status.textContent = 'Ready'; |
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let isProcessing = false; |
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let previousTime; |
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const context = canvas.getContext('2d', { willReadFrequently: true }); |
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const outputContext = outputCanvas.getContext('2d', { willReadFrequently: true }); |
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function updateCanvas() { |
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const { width, height } = canvas; |
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if (!isProcessing) { |
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isProcessing = true; |
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(async function () { |
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context.drawImage(video, 0, 0, width, height); |
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const currentFrame = context.getImageData(0, 0, width, height); |
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const image = new RawImage(currentFrame.data, width, height, 4); |
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const inputs = await processor(image); |
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const { output } = await model({ input: inputs.pixel_values }); |
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const mask = await RawImage.fromTensor(output[0].mul(255).to('uint8')).resize(width, height); |
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const outPixelData = currentFrame; |
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for (let i = 0; i < mask.data.length; ++i) { |
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outPixelData.data[4 * i + 3] = mask.data[i]; |
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} |
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outputContext.putImageData(outPixelData, 0, 0); |
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if (previousTime !== undefined) { |
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const fps = 1000 / (performance.now() - previousTime); |
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status.textContent = `FPS: ${fps.toFixed(2)}`; |
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} |
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previousTime = performance.now(); |
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isProcessing = false; |
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})(); |
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} |
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window.requestAnimationFrame(updateCanvas); |
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} |
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navigator.mediaDevices.getUserMedia( |
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{ video: true }, |
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).then((stream) => { |
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video.srcObject = stream; |
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video.play(); |
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const videoTrack = stream.getVideoTracks()[0]; |
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const { width, height } = videoTrack.getSettings(); |
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setStreamSize(width * scale, height * scale); |
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const ar = width / height; |
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const [cw, ch] = (ar > 720 / 405) ? [720, 720 / ar] : [405 * ar, 405]; |
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container.style.width = `${cw}px`; |
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container.style.height = `${ch}px`; |
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setTimeout(updateCanvas, 50); |
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}).catch((error) => { |
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alert(error); |
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}); |
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