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Update index.js
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index.js
CHANGED
@@ -10,6 +10,7 @@ const imageContainer = document.getElementById('container');
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const example = document.getElementById('example');
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const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
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// Create a new object detection pipeline
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status.textContent = 'Loading model...';
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@@ -50,7 +51,6 @@ async function detect(img) {
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const { pixel_values } = await processor(image);
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const { output0 } = await model({ images: pixel_values });
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const predictions = output0.tolist()[0];
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console.log(predictions);
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const threshold = 0.5;
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for (const [xmin, ymin, xmax, ymax, score, id] of predictions) {
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if (score < threshold) continue;
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@@ -69,10 +69,10 @@ function renderBox(xmin, ymin, xmax, ymax, score, label) {
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boxElement.className = 'bounding-box';
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Object.assign(boxElement.style, {
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borderColor: color,
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left: 100 * xmin + '%',
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top: 100 * ymin + '%',
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width: 100 * (xmax - xmin) + '%',
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height: 100 * (ymax - ymin) + '%',
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})
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// Draw label
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const example = document.getElementById('example');
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const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
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const IMAGE_SIZE = 640;
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// Create a new object detection pipeline
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status.textContent = 'Loading model...';
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const { pixel_values } = await processor(image);
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const { output0 } = await model({ images: pixel_values });
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const predictions = output0.tolist()[0];
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const threshold = 0.5;
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for (const [xmin, ymin, xmax, ymax, score, id] of predictions) {
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if (score < threshold) continue;
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boxElement.className = 'bounding-box';
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Object.assign(boxElement.style, {
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borderColor: color,
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left: 100 * xmin / IMAGE_SIZE + '%',
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top: 100 * ymin / IMAGE_SIZE + '%',
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width: 100 * (xmax - xmin) / IMAGE_SIZE + '%',
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height: 100 * (ymax - ymin) / IMAGE_SIZE + '%',
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})
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// Draw label
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