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--- |
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license: apache-2.0 |
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pipeline_tag: image-segmentation |
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tags: |
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- background-removal |
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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_landscape`. |
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```js |
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import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers'; |
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// Load model and processor |
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const model_id = 'onnx-community/mediapipe_selfie_segmentation_landscape'; |
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const model = await AutoModel.from_pretrained(model_id, { dtype: 'fp32' }); |
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const processor = await AutoProcessor.from_pretrained(model_id); |
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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_landscape.png'; |
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const image = await RawImage.read(url); |
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// Pre-process image |
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const inputs = await processor(image); |
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// Predict alpha matte |
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const { alphas } = await model(inputs); |
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// Save output mask |
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const mask = await RawImage.fromTensor(alphas[0].mul(255).to('uint8')).resize(image.width, image.height); |
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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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|  |  |  | |
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