voronoi_tools.py

import numpy as np



def normalize(D):
    Vmin, Vmax = D.min(), D.max()
    if Vmax - Vmin > 1e-5:
        D = (D-Vmin)/(Vmax-Vmin)
    else:
        D = np.zeros_like(D)
    return D


def normalize_threshold(D,min,max):
    Vmin, Vmax = D.min(), D.max()
    if Vmax - Vmin > 1e-5:
        D = (D-Vmin)/(Vmax-Vmin)
        D = D*(max-min)+min
    else:
        D = np.zeros_like(D)
    return D



def initialization(n, D):
    """
    Return n points distributed over [xmin, xmax] x [ymin, ymax]
    according to (normalized) density distribution.

    with xmin, xmax = 0, density.shape[1]
         ymin, ymax = 0, density.shape[0]

    The algorithm here is a simple rejection sampling.
    """

    samples = []
    while len(samples) < n:
        # X = np.random.randint(0, D.shape[1], 10*n)
        # Y = np.random.randint(0, D.shape[0], 10*n)
        X = np.random.uniform(0, D.shape[1], 10*n)
        Y = np.random.uniform(0, D.shape[0], 10*n)
        P = np.random.uniform(0, 1, 10*n)
        index = 0
        while index < len(X) and len(samples) < n:
            x, y = X[index], Y[index]
            x_, y_ = int(np.floor(x)), int(np.floor(y))
            if P[index] < D[y_, x_]:
                samples.append([x, y])
            index += 1
    return np.array(samples)