Node.js ONNX Runtime Background Removal

Node.js project for removing backgrounds from images using several ONNX model like BiRefNet and u2net.

Installation

npm install @tugrul/rembg

Usage

const ort = require('onnxruntime-node');
const sharp = require('sharp');

const BackgroundRemover = require('@tugrul/rembg');

async function main(inputPath, outputPath) {
  const session = await ort.InferenceSession.create('./models/u2net_human_seg.onnx');

  const rembg = new BackgroundRemover(session, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]);
  const result = await rembg.mask(sharp(inputPath));

  await result.toFile(outputPath);
}

main('input.jpg', 'output.png');

API

Constructor

new BackgroundRemover(session, mean, std)

Initialize a new BackgroundRemover instance with a preloaded ONNX session and normalization parameters.

Parameters

• session (ort.InferenceSession, required)
  An initialized onnxruntime-node inference session for the background-removal model.

• mean (number[], required)
  Per-channel mean values used for input normalization. Expected format: [R, G, B].

• std (number[], required)
  Per-channel standard deviation values used for input normalization. Expected format: [R, G, B].

Example

const session = await ort.InferenceSession.create('u2net.onnx');
const mean = [0.485, 0.456, 0.406];
const std  = [0.229, 0.224, 0.225];

const remover = new BackgroundRemover(session, mean, std);

Methods

async normalize(image)

Normalize an image for model input.

Note: This is an internal helper in most usage scenarios. You usually call mask() directly.

Signature

normalize(image: sharp.Sharp): Promise<ort.Tensor>

Parameters

• image (sharp.Sharp, required)
  A sharp instance representing the input image.
  The method:

  • Resizes the image to the model’s expected spatial size [inputHeight, inputWidth] inferred from session.inputMetadata.
  • Forces raw RGB data.
  • Scales pixel values and applies channel-wise normalization using the mean and std passed to the constructor.
  • Reorders data from HWC to CHW.

Returns

• Promise<ort.Tensor>
  A 4D tensor of shape [1, 3, height, width] (float32), suitable as input to the ONNX model.

async mask(image)

Run the model on an input image and produce a transparent PNG where the alpha channel is derived from the model’s output mask.

Signature

mask(image: sharp.Sharp): Promise<sharp.Sharp>

Parameters

• image (sharp.Sharp, required)
  A sharp instance for the original image. The original dimensions are preserved in the final output.

Processing Steps

1. Read the original image dimensions via image.metadata().
2. Normalize the image via normalize() and create the input tensor.
3. Run inference using the configured onnxruntime-node session.
4. Assume the first output has shape [1, 1, H, W] and extract it as a single-channel mask.
5. Normalize model output to [0, 1], clip, and convert to uint8 [0, 255].
6. Construct a single-channel grayscale buffer and resize it back to the original image dimensions.
7. Combine the generated mask as the alpha channel with the original image.
8. Return a sharp pipeline configured to output a PNG with transparency.

Returns

• Promise<sharp.Sharp>
  A sharp instance representing the RGBA image with the predicted alpha mask applied.
  You can continue piping or directly write it to disk:

  const output = await remover.mask(sharp(inputPath));
  await output.toFile('output.png');

Features

• Async/await support
• Automatic image resizing
• Preserves original image dimensions
• PNG output with transparency
• Error handling

Dependencies

• onnxruntime-node: ONNX model inference
• sharp: Image processing

Notes & Assumptions

• The model input metadata must describe a 4D tensor [N, C, H, W], where C = 3 (RGB).
• The model output is assumed to be [1, 1, H, W] (single-channel mask).
• The mask is linearly normalized using the min/max values found in the raw output.
• Any errors thrown by sharp or onnxruntime-node are propagated to the caller.