Source code for ncempy.algo.local_max

Module to find local maxima in an image.

import numpy as np
import scipy.ndimage.filters

[docs]def local_max(img, r, thresh): """Find local maxima from comparing dilated and eroded images. Calculates images with maximum and minimum within given radius. If the difference is larger than the threshold, the original pixel position with max value is detected as local maximum. Parameters ---------- img : np.ndarray Input image. r : int Radius for locality. thresh : int or float Intensity difference threshold. Returns ------- : np.ndarray Array of points. """ try: r = int(r) thresh = int(thresh) assert(isinstance(img, np.ndarray)) except: raise TypeError('Bad input!') # prepare circular kernel y, x = np.ogrid[-r:r+1, -r:r+1] kernel = x**2 + y**2 <= r**2 # calculate max and min images img_dil = scipy.ndimage.filters.maximum_filter(img, footprint=kernel) img_ero = scipy.ndimage.filters.minimum_filter(img, footprint=kernel) # get selection of local maxima sel = (img == img_dil)*(img-img_ero > thresh) if sel.any(): # retrieve and return points points = np.argwhere(sel) return points else: # otherwise return None to avoid having an empty list return None
[docs]def points_todim(points, dims): """Convert points from px coordinates to real dim. Points are expected to be array indices for the first two dimensions in dims. Parameters ---------- points : np.ndarray Points to convert. dims : tuple Tuple of dimensions. Returns ------- : np.ndarray Converted points. """ try: # try to convert input to np.ndarray with 2 columns (necessary if only one entry provided) points = np.reshape(np.array(points), (-1,2)) # check if enough dims available assert(len(dims) >= 2) assert(len(dims[0]) == 3) except: raise TypeError('Something wrong with the input!') # do the conversion by looking up thing in dimension vectors points_d = np.array([dims[0][0][points[:, 0]], dims[1][0][points[:, 1]]]).transpose() return points_d