TypeError: jimp__WEBPACK_IMPORTED_MODULE_0___default(...).read is not a function

Question

import * as Jimp from "jimp";
// const Jimp = require('jimp');
import { Tensor } from 'onnxruntime-web';

export async function getImageTensorFromPath(path: string, dims: number[] =  [1, 3, 224, 224]): Promise<Tensor> {
  // 1. load the image  
  var image = await loadImageFromPath(path, dims[2], dims[3]);
  // 2. convert to tensor
  var imageTensor = imageDataToTensor(image, dims);
  // 3. return the tensor
  return imageTensor;
}

async function loadImageFromPath(path: string, width: number = 224, height: number= 224): Promise<Jimp> {
  // Use Jimp to load the image and resize it.
  var imageData = await Jimp.default.read(path).then((imageBuffer: Jimp) => {
    return imageBuffer.resize(width, height);
  });

  return imageData;
}

function imageDataToTensor(image: Jimp, dims: number[]): Tensor {
  // 1. Get buffer data from image and create R, G, and B arrays.
  var imageBufferData = image.bitmap.data;
  const [redArray, greenArray, blueArray] = new Array(new Array<number>(), new Array<number>(), new Array<number>());

  // 2. Loop through the image buffer and extract the R, G, and B channels
  for (let i = 0; i < imageBufferData.length; i += 4) {
    redArray.push(imageBufferData[i]);
    greenArray.push(imageBufferData[i + 1]);
    blueArray.push(imageBufferData[i + 2]);
    // skip data[i + 3] to filter out the alpha channel
  }

  // 3. Concatenate RGB to transpose [224, 224, 3] -> [3, 224, 224] to a number array
  const transposedData = redArray.concat(greenArray).concat(blueArray);

  // 4. convert to float32
  let i, l = transposedData.length; // length, we need this for the loop
  // create the Float32Array size 3 * 224 * 224 for these dimensions output
  const float32Data = new Float32Array(dims[1] * dims[2] * dims[3]);
  for (i = 0; i < l; i++) {
    float32Data[i] = transposedData[i] / 255.0; // convert to float
  }
  // 5. create the tensor object from onnxruntime-web.
  const inputTensor = new Tensor("float32", float32Data, dims);
  return inputTensor;
}

From what I read, I believe that this is a problem with the jimp version however I cannot seem to find a way to downgrade my existing installation of jimp. I presume that the version I have is the latest.

I am using boilerplate code from this repository: https://github.com/microsoft/onnxruntime-nextjs-template/tree/main