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--- |
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license: apache-2.0 |
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--- |
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## Usage (Transformers.js) |
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: |
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```bash |
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npm i @huggingface/transformers |
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``` |
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**Example:** Selfie segmentation with `onnx-community/mediapipe_selfie_segmentation-web`. |
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```js |
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import { AutoModel, RawImage, Tensor } from '@huggingface/transformers'; |
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// Load model and processor |
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const model_id = 'onnx-community/mediapipe_selfie_segmentation-web'; |
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const model = await AutoModel.from_pretrained(model_id, { dtype: 'fp32' }); |
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// Load image from URL |
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const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/selfie_segmentation.png'; |
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const image = await RawImage.read(url); |
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// Predict alpha matte |
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const { alphas } = await model({ |
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pixel_values: new Tensor( |
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'uint8', |
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image.data, |
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[1, image.height, image.width, 3], |
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), |
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}); |
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// Save output mask |
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const mask = RawImage.fromTensor(alphas[0].mul(255).to('uint8'), 'HWC') |
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mask.save('mask.png'); |
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// (Optional) Apply mask to original image |
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const result = image.clone().putAlpha(mask); |
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result.save('result.png'); |
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``` |
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| Input image | Predicted mask | Output image | |
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| :----------:|:------------:|:------------:| |
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