image-js/lib/featureMatching/keypoints/getEigenvaluesForScore.js

Image processing and manipulation in JavaScript

import { EigenvalueDecomposition, Matrix, WrapperMatrix1D } from 'ml-matrix';
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 windowSize - Size of the window, where data should be scanned.
 * @returns Array of two eigenvalues.
 */
export function getEigenvaluesForScore(image, origin, windowSize = 7) {
    if (!(windowSize % 2)) {
        throw new TypeError('windowSize must be an odd integer');
    }
    const cropOrigin = {
        row: origin.row - (windowSize - 1) / 2,
        column: origin.column - (windowSize - 1) / 2,
    };
    const window = image.crop({
        origin: cropOrigin,
        width: windowSize,
        height: windowSize,
    });
    const xDerivative = window.gradientFilter({ kernelX: SOBEL_X });
    const yDerivative = window.gradientFilter({ kernelY: SOBEL_Y });
    const xMatrix = new WrapperMatrix1D(xDerivative.getRawImage().data, {
        rows: xDerivative.height,
    });
    const yMatrix = new WrapperMatrix1D(yDerivative.getRawImage().data, {
        rows: yDerivative.height,
    });
    const xx = xMatrix.mmul(xMatrix);
    const xy = yMatrix.mmul(xMatrix);
    const yy = yMatrix.mmul(yMatrix);
    const xxSum = xx.sum();
    const xySum = xy.sum();
    const yySum = yy.sum();
    const structureTensor = new Matrix([
        [xxSum, xySum],
        [xySum, yySum],
    ]);
    return new EigenvalueDecomposition(structureTensor).realEigenvalues;
}
//# sourceMappingURL=getEigenvaluesForScore.js.map