| | ---
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| | library_name: transformers.js
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| | tags:
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| | - vision
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| | - background-removal
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| | - portrait-matting
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| | license: apache-2.0
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| | pipeline_tag: image-segmentation
|
| | ---
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| |
|
| | # wuchendi/MODNet (Matting Objective Decomposition Network)
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| |
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| | > Trimap-Free Portrait Matting in Real Time
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| |
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| | - **Repository**: <https://github.com/WuChenDi/MODNet>
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| | - **Spaces**: <https://huggingface.co/spaces/wuchendi/MODNet>
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| | - **SwanLab/MODNet**: <https://swanlab.cn/@wudi/MODNet/overview>
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| |
|
| | ### 📦 Usage with [Transformers.js](https://www.npmjs.com/package/@huggingface/transformers)
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| |
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| | First, install the `@huggingface/transformers` library from PNPM:
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| |
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| | ```bash
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| | pnpm add @huggingface/transformers
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| | ```
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| |
|
| | Then, use the following code to perform **portrait matting** with the `wuchendi/MODNet` model:
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| |
|
| | ```ts
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| | /* eslint-disable no-console */
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| | import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers'
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| |
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| | async function main() {
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| | try {
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| | console.log('🚀 Initializing MODNet...')
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| |
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| | // Load model
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| | console.log('📦 Loading model...')
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| | const model = await AutoModel.from_pretrained('wuchendi/MODNet', {
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| | dtype: 'fp32',
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| | progress_callback: (progress) => {
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| | // @ts-ignore
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| | if (progress.progress) {
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| | // @ts-ignore
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| | console.log(`Model loading progress: ${(progress.progress).toFixed(2)}%`)
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| | }
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| | }
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| | })
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| | console.log('✅ Model loaded successfully')
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| |
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| | // Load processor
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| | console.log('🔧 Loading processor...')
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| | const processor = await AutoProcessor.from_pretrained('wuchendi/MODNet', {})
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| | console.log('✅ Processor loaded successfully')
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| |
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| | // Load image from URL
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| | const url = 'https://res.cloudinary.com/dhzm2rp05/image/upload/samples/logo.jpg'
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| | console.log('🖼️ Loading image:', url)
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| | const image = await RawImage.fromURL(url)
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| | console.log('✅ Image loaded successfully', `Dimensions: ${image.width}x${image.height}`)
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| |
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| | // Pre-process image
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| | console.log('🔄 Preprocessing image...')
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| | const { pixel_values } = await processor(image)
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| | console.log('✅ Image preprocessing completed')
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| |
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| | // Generate alpha matte
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| | console.log('🎯 Generating alpha matte...')
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| | const startTime = performance.now()
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| | const { output } = await model({ input: pixel_values })
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| | const inferenceTime = performance.now() - startTime
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| | console.log('✅ Alpha matte generated', `Time: ${inferenceTime.toFixed(2)}ms`)
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| |
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| | // Save output mask
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| | console.log('💾 Saving output...')
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| | const mask = await RawImage.fromTensor(output[0].mul(255).to('uint8')).resize(image.width, image.height)
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| | await mask.save('src/assets/mask.png')
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| | console.log('✅ Output saved to assets/mask.png')
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| |
|
| | } catch (error) {
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| | console.error('❌ Error during processing:', error)
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| | throw error
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| | }
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| | }
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| |
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| | main().catch(console.error)
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| |
|
| | ```
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| |
|
| | ### 🖼️ Example Result
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| |
|
| | | Input Image | Output Mask |
|
| | | ----------------------------------- | ---------------------------------- |
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| | |  |  |
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| |
|
