/**
 * @typedef {{
 *id?: string;
 *x: number;
 *y: number;
 *z: number;
 *maxX: number;
 *minX: number;
 *maxY: number;
 *minY: number;
 }} Peak2D */
/**
 * Detects all the 2D-peaks in the given spectrum based on center of mass logic.
 * @param {number[][] | Float64Array[] | number[] | Float64Array} input - matrix to get the local maxima
 * @param {object} [options = {}] - options of the method.
 * @param {number} [options.nStdDev = 3] - number of times of the standard deviations for the noise level.Float64Array
 * @param {number[][] | Float64Array[]} [options.kernel] - kernel to the convolution step.
 * @param {'drain' | 'floodfill'} [options.labelling = 'drain'] - select the labelling algorithm to assign pixels.
 * @param {number[] | Float64Array} [options.originalData] - original data useful when the original matrix has values and the input matrix has absolute ones
 * @param {number[] | Float64Array} [options.filteredData] - convoluted data, if it is defined the convolution step is skipped
 * @param {number} [options.rows] - number of rows
 * @param {number} [options.cols] - number of columns
 * @returns {Peak2D[]} - Array of found peaks.
 */
export function findPeaks2DRegion(input: number[][] | Float64Array[] | number[] | Float64Array, options?: {
    nStdDev?: number | undefined;
    kernel?: number[][] | Float64Array<ArrayBufferLike>[] | undefined;
    labelling?: "drain" | "floodfill" | undefined;
    originalData?: number[] | Float64Array<ArrayBufferLike> | undefined;
    filteredData?: number[] | Float64Array<ArrayBufferLike> | undefined;
    rows?: number | undefined;
    cols?: number | undefined;
}): Peak2D[];
/**
 * Detects all the 2D-peaks in the given spectrum based on the Max logic.
 * amc
 * @param input
 * @param options
 */
export function findPeaks2DMax(input: any, options: any): {
    x: number;
    y: number;
    z: any;
}[];
export type Peak2D = {
    id?: string;
    x: number;
    y: number;
    z: number;
    maxX: number;
    minX: number;
    maxY: number;
    minY: number;
};
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