use onnx model to process an image in javascript

To use an ONNX model to process an image in JavaScript, you can use the ONNX.js library. Here are the steps:

  1. Install ONNX.js using npm:
index.tsx
npm install onnxjs
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  1. Load the ONNX model:
index.tsx
const modelPath = 'path/to/your/onnx/model.onnx';
const session = new onnx.InferenceSession();
await session.loadModel(modelPath);
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  1. Prepare your input data for the model. This usually involves loading an image and preprocessing it to match the input shape of the model. In this example, let's say that the model expects an input of shape [1, 3, 224, 224], with a range of 0-1 for each pixel. We can use the Canvas API to load the image and convert it to a tensor:
index.tsx
// Load the image using the Canvas API
const canvas = document.createElement('canvas');
const ctx = canvas.getContext('2d');
const img = new Image();
img.src = 'path/to/your/image.jpg';
await new Promise(resolve => img.onload = resolve);
canvas.width = img.width;
canvas.height = img.height;
ctx.drawImage(img, 0, 0);

// Convert the image to a tensor
const imageData = ctx.getImageData(0, 0, img.width, img.height).data;
const pixels = new Float32Array(imageData).map(x => x / 255);
const input = new onnx.Tensor(pixels, 'float32', [1, 3, img.height, img.width]);
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  1. Run the inference:
index.tsx
const outputMap = await session.run([input]);
const outputData = outputMap.values().next().value.data;
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The outputData variable will contain the output of the model.

Note that this is just a simple example and the input data preparation and output data processing will vary depending on the specific model you're using.

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