在噪声的2维数组中进行峰值检测

我想尽可能接近地让 python 返回以下图片中最明显聚类的中心：

在我之前的问题中，我问如何获得二维数组的全局最大值和本地最大值，给出的答案工作得非常好。问题是，我通过对不同bin大小获得的全局最大值取平均得到的中心估计总是稍微偏离我会亲自设定的中心，因为我只考虑了最大的bin</ strong>而不是一组最大的bin</ strong>（就像我们通过眼睛所做的一样）。

我尝试调整对此问题的回答，但结果表明我的图像对该算法来说太嘈杂了。这是实施该答案的代码：

import numpy as np
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import generate_binary_structure, binary_erosion
import matplotlib.pyplot as pp
from os import getcwd
from os.path import join, realpath, dirname
# Save path to dir where this code exists.
mypath = realpath(join(getcwd(), dirname(__file__)))
myfile = 'data_file.dat'
x, y = np.loadtxt(join(mypath,myfile), usecols=(1, 2), unpack=True)
xmin, xmax = min(x), max(x)
ymin, ymax = min(y), max(y)
rang = [[xmin, xmax], [ymin, ymax]]
paws = []
for d_b in range(25, 110, 25):
    # Number of bins in x,y given the bin width 'd_b'
    binsxy = [int((xmax - xmin) / d_b), int((ymax - ymin) / d_b)]
    H, xedges, yedges = np.histogram2d(x, y, range=rang, bins=binsxy)
    paws.append(H)
def detect_peaks(image):
    """
    Takes an image and detect the peaks usingthe local maximum filter.
    Returns a boolean mask of the peaks (i.e. 1 when
    the pixel's value is the neighborhood maximum, 0 otherwise)
    """
    # define an 8-connected neighborhood
    neighborhood = generate_binary_structure(2,2)
    #apply the local maximum filter; all pixel of maximal value 
    #in their neighborhood are set to 1
    local_max = maximum_filter(image, footprint=neighborhood)==image
    #local_max is a mask that contains the peaks we are 
    #looking for, but also the background.
    #In order to isolate the peaks we must remove the background from the mask.
    #we create the mask of the background
    background = (image==0)
    #a little technicality: we must erode the background in order to 
    #successfully subtract it form local_max, otherwise a line will 
    #appear along the background border (artifact of the local maximum filter)
    eroded_background = binary_erosion(background, structure=neighborhood, border_value=1)
    #we obtain the final mask, containing only peaks, 
    #by removing the background from the local_max mask
    detected_peaks = local_max - eroded_background
    return detected_peaks
#applying the detection and plotting results
for i, paw in enumerate(paws):
    detected_peaks = detect_peaks(paw)
    pp.subplot(4,2,(2*i+1))
    pp.imshow(paw)
    pp.subplot(4,2,(2*i+2) )
    pp.imshow(detected_peaks)
pp.show()

这是该算法的结果情况（变化bin大小）：

显然，我的背景对于该算法来说太嘈杂了，因此问题是： 如何使该算法对噪声不那么敏感？如果存在替代解决方案，请让我知道。

编辑

按照 Bi Rico 的建议，我尝试在将其传递给本地最大值查找器之前对我的二维数组进行平滑处理，如下所示：

H, xedges, yedges = np.histogram2d(x, y, range=rang, bins=binsxy)
H1 = gaussian_filter(H, 2, mode='nearest')
paws.append(H1)

这是sigma为2、4和8时的结果：

编辑2

mode =\'constant\'比nearest要好得多。当最大箱子大小为sigma=2时，它会收敛于正确的中心位置：

那么，我如何获得出现在上述最后一幅图中的最大值的坐标？