您可以使用简单的布尔条件找到满足截止要求的像素，然后使用
scipy.ndimage.label和
scipy.ndimage.center_of_mass查找相连的区域并计算其质心：
import numpy as npfrom scipy import ndimagefrom matplotlib import pyplot as plt# generate some lowpass-filtered noise as a test imagegen = np.random.RandomState(0)img = gen.poisson(2, size=(512, 512))img = ndimage.gaussian_filter(img.astype(np.double), (30, 30))img -= img.min()img /= img.max()# use a boolean condition to find where pixel values are > 0.75blobs = img > 0.75# label connected regions that satisfy this conditionlabels, nlabels = ndimage.label(blobs)# find their centres of mass. in this case I'm weighting by the pixel values in# `img`, but you could also pass the boolean values in `blobs` to compute the# unweighted centroids.r, c = np.vstack(ndimage.center_of_mass(img, labels, np.arange(nlabels) + 1)).T# find their distances from the top-left cornerd = np.sqrt(r*r + c*c)# plotfig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(10, 5))ax[0].imshow(img)ax[1].hold(True)ax[1].imshow(np.ma.masked_array(labels, ~blobs), cmap=plt.cm.rainbow)for ri, ci, di in zip(r, c, d): ax[1].annotate('', xy=(0, 0), xytext=(ci, ri), arrowprops={'arrowstyle':'<-', 'shrinkA':0}) ax[1].annotate('d=%.1f' % di, xy=(ci, ri), xytext=(0, -5), textcoords='offset points', ha='center', va='top', fontsize='x-large')for aa in ax.flat: aa.set_axis_off()fig.tight_layout()plt.show()
