1

index.js

@@ -1,79 +1,46 @@
-import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.10.1';
-
-// Since we will download the model from the Hugging Face Hub, we can skip the local model check
-env.allowLocalModels = false;
-
-// Reference the elements that we will need
-const status = document.getElementById('status');
-const fileUpload = document.getElementById('upload');
-const imageContainer = document.getElementById('container');
-const example = document.getElementById('example');
-
-const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
-
-// Create a new object detection pipeline
-status.textContent = 'Loading model...';
-const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
-status.textContent = 'Ready';
-
-example.addEventListener('click', (e) => {
-    e.preventDefault();
-    detect(EXAMPLE_URL);
-});
-
-fileUpload.addEventListener('change', function (e) {
-    const file = e.target.files[0];
-    if (!file) {
-        return;
-    }
-
-    const reader = new FileReader();
-
-    // Set up a callback when the file is loaded
-    reader.onload = e2 => detect(e2.target.result);
-
-    reader.readAsDataURL(file);
-});
-
-
-// Detect objects in the image
-async function detect(img) {
-    imageContainer.innerHTML = '';
-    imageContainer.style.backgroundImage = `url(${img})`;
-
-    status.textContent = 'Analysing...';
-    const output = await detector(img, {
-        threshold: 0.5,
-        percentage: true,
-    });
-    status.textContent = '';
-    output.forEach(renderBox);
-}
-
-// Render a bounding box and label on the image
-function renderBox({ box, label }) {
-    const { xmax, xmin, ymax, ymin } = box;
-
-    // Generate a random color for the box
-    const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);
-
-    // Draw the box
-    const boxElement = document.createElement('div');
-    boxElement.className = 'bounding-box';
-    Object.assign(boxElement.style, {
-        borderColor: color,
-        left: 100 * xmin + '%',
-        top: 100 * ymin + '%',
-        width: 100 * (xmax - xmin) + '%',
-        height: 100 * (ymax - ymin) + '%',
-    })
-
-    // Draw label
-    const labelElement = document.createElement('span');
-    labelElement.textContent = label;
-    labelElement.className = 'bounding-box-label';
-    labelElement.style.backgroundColor = color;
-
-    boxElement.appendChild(labelElement);
-    imageContainer.appendChild(boxElement);
-}
+import { AutoProcessor, VitMatteForImageMatting, RawImage,  Tensor, cat  } from '@xenova/transformers';
+
+// Load processor and model
+const processor = await AutoProcessor.from_pretrained('Xenova/vitmatte-small-composition-1k');
+const model = await VitMatteForImageMatting.from_pretrained('Xenova/vitmatte-small-composition-1k');
+
+// Load image and trimap
+const image = await RawImage.fromURL('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_image.png');
+const trimap = await RawImage.fromURL('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/vitmatte_trimap.png');
+
+// Prepare image + trimap for the model
+const inputs = await processor(image, trimap);
+
+// Predict alpha matte
+const { alphas } = await model(inputs);
+console.log('alpha', alphas)
+// Tensor {
+//   dims: [ 1, 1, 640, 960 ],
+//   type: 'float32',
+//   size: 614400,
+//   data: Float32Array(614400) [ 0.9894027709960938, 0.9970508813858032, ... ]
+// }
+//
+// import { Tensor, cat } from '@xenova/transformers';
+
+// Visualize predicted alpha matte
+const imageTensor = new Tensor(
+  'uint8',
+  new Uint8Array(image.data),
+  [image.height, image.width, image.channels]
+).transpose(2, 0, 1);
+
+// Convert float (0-1) alpha matte to uint8 (0-255)
+const alphaChannel = alphas
+  .squeeze(0)
+  .mul_(255)
+  .clamp_(0, 255)
+  .round_()
+  .to('uint8');
+
+// Concatenate original image with predicted alpha
+const imageData = cat([imageTensor, alphaChannel], 0);
+
+// Save output image
+const outputImage = RawImage.fromTensor(imageData);
+outputImage.save('output.png');