可以使用Ivan的detect_peaks函数对任意维度的数组找到局部极小值的位置，并进行少量修改：
import numpy as npimport scipy.ndimage.filters as filtersimport scipy.ndimage.morphology as morphologydef detect_local_minima(arr): # https://stackoverflow.com/questions/3684484/peak-detection-in-a-2d-array/3689710#3689710 """ Takes an array and detects the troughs using the local maximum filter. Returns a boolean mask of the troughs (i.e. 1 when the pixel's value is the neighborhood maximum, 0 otherwise) """ # define an connected neighborhood # http://www.scipy.org/doc/api_docs/SciPy.ndimage.morphology.html#generate_binary_structure neighborhood = morphology.generate_binary_structure(len(arr.shape),2) # apply the local minimum filter; all locations of minimum value # in their neighborhood are set to 1 # http://www.scipy.org/doc/api_docs/SciPy.ndimage.filters.html#minimum_filter local_min = (filters.minimum_filter(arr, footprint=neighborhood)==arr) # local_min 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 = (arr==0) # # a little technicality: we must erode the background in order to # successfully subtract it from local_min, otherwise a line will # appear along the background border (artifact of the local minimum filter) # http://www.scipy.org/doc/api_docs/SciPy.ndimage.morphology.html#binary_erosion eroded_background = morphology.binary_erosion( background, structure=neighborhood, border_value=1) # # we obtain the final mask, containing only peaks, # by removing the background from the local_min mask detected_minima = local_min ^ eroded_background return np.where(detected_minima)
您可以这样使用：
arr=np.array([[[0,0,0,-1],[0,0,0,0],[0,0,0,0],[0,0,0,0],[-1,0,0,0]], [[0,0,0,0],[0,-1,0,0],[0,0,0,0],[0,0,0,-1],[0,0,0,0]]])local_minima_locations = detect_local_minima(arr)print(arr)# [[[ 0 0 0 -1]# [ 0 0 0 0]# [ 0 0 0 0]# [ 0 0 0 0]# [-1 0 0 0]]# [[ 0 0 0 0]# [ 0 -1 0 0]# [ 0 0 0 0]# [ 0 0 0 -1]# [ 0 0 0 0]]]
这表示最小值出现在索引[0,0,3]，[0,4,0]，[1,1,1]和[1,3,3]处：
print(local_minima_locations)# (array([0, 0, 1, 1]), array([0, 4, 1, 3]), array([3, 0, 1, 3]))print(arr[local_minima_locations])# [-1 -1 -1 -1]
