image-js
Version:
Image processing and manipulation in JavaScript
48 lines • 1.89 kB
JavaScript
import { EigenvalueDecomposition, Matrix } from 'ml-matrix';
import { rawDirectConvolution } from "../../filters/convolution.js";
import { SOBEL_X, SOBEL_Y } from '../../utils/constants/kernels.js';
/**
* A function that calculates eigenvalues to calculate feature score for Harris and Shi-Tomasi algorithms.
* @param image - Image take data from.
* @param origin - Center of the window, where the corner should be.
* @param cropSize - Size of the window, where data should be scanned.
* @returns Array of two eigenvalues.
*/
export function getEigenvaluesForScore(image, origin, cropSize = 5) {
if (!(cropSize % 2)) {
throw new TypeError('windowSize must be an odd integer');
}
const kernelRadius = (SOBEL_X.length - 1) / 2;
const windowRadius = (cropSize - 1) / 2;
const padded = cropSize + 2 * kernelRadius;
const cropOrigin = {
row: origin.row - windowRadius - kernelRadius,
column: origin.column - windowRadius - kernelRadius,
};
const window = image.crop({
origin: cropOrigin,
width: padded,
height: padded,
});
const xDerivative = rawDirectConvolution(window, SOBEL_X);
const yDerivative = rawDirectConvolution(window, SOBEL_Y);
let xxSum = 0;
let xySum = 0;
let yySum = 0;
for (let i = kernelRadius; i < window.height - kernelRadius; i++) {
for (let j = kernelRadius; j < window.width - kernelRadius; j++) {
const idx = i * window.width + j;
const gx = xDerivative[idx];
const gy = yDerivative[idx];
xxSum += gx * gx;
xySum += gx * gy;
yySum += gy * gy;
}
}
const structureTensor = new Matrix([
[xxSum, xySum],
[xySum, yySum],
]);
return new EigenvalueDecomposition(structureTensor).realEigenvalues;
}
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