python - ndimage 的 center_of_mass 用于计算高斯峰的位置

Question

我尝试使用 ndimage.measurements.center_of_mass 计算高斯二维分布的峰值位置，发现质心从峰值中心偏移:

import numpy as np
from scipy import ndimage
from scipy import stats
import matplotlib.pyplot as plt

x = np.linspace(-1,1,100)
xv, yv = np.meshgrid(x, x)
r = np.sqrt((xv-0.2)**2 + (yv)**2)
norm2d = stats.norm.pdf(r)
com = ndimage.measurements.center_of_mass(norm2d)
plt.imshow(norm2d, origin="lower")
plt.scatter(*com[::-1])
plt.show()

如何在不使用最小二乘优化例程的情况下粗略计算含噪二维高斯分布的峰值位置？

Answer 1

如果使用顶部xx%的像素，可以得到正确的结果:

hist, bins = np.histogram(norm2d.ravel(), normed=True, bins=100)
threshold = bins[np.cumsum(hist) * (bins[1] - bins[0]) > 0.8][0]
mnorm2d = np.ma.masked_less(norm2d,threshold)
com = ndimage.measurements.center_of_mass(mnorm2d)
plt.imshow(norm2d, origin="lower")
plt.scatter(*com[::-1])
plt.show()

结果: