Legacy ImageMagick Discussions Archive

Code: Select all

import numpy as np
import scipy.ndimage as scind

def cooccurrence(quantized_image, labels, scale=3):
     """Calculates co-occurrence matrices for all the objects in the image.

     Return an array P of shape (nobjects, nlevels, nlevels) such that
     P[o, :, :] is the cooccurence matrix for object o.

     quantized_image -- a numpy array of integer type
     labels          -- a numpy array of integer type
     scale           -- an integer

     For each object O, the cooccurrence matrix is defined as follows.
     Given a row number I in the matrix, let A be the set of pixels in
     O with gray level I, excluding pixels in the rightmost S
     columns of the image.  Let B be the set of pixels in O that are S
     pixels to the right of a pixel in A.  Row I of the cooccurence
     matrix is the gray-level histogram of the pixels in B.
     """
     nlevels = quantized_image.max() + 1
     nobjects = labels.max()
     image_a = quantized_image[:, :-scale]
     image_b = quantized_image[:, scale:]
     labels_ab = labels[:, :-scale]
     equilabel = ((labels[:, :-scale] == labels[:, scale:]) & (labels[:,:-scale] > 0))
     P, bins_P = np.histogramdd([labels_ab[equilabel]-1,image_a[equilabel], image_b[equilabel]], nobjects, nlevels, nlevels))
     pixel_count = fix(scind.sum(equilabel, labels[:,:-scale],np.arange(nobjects)+1))
     pixel_count = np.tile(pixel_count[:,np.newaxis,np.newaxis],1,nlevels,nlevels))
     return (P.astype(float) / pixel_count.astype(float), nlevels)

def fix(whatever_it_returned):
     """Convert a result from scipy.ndimage to a numpy array

     scipy.ndimage has the annoying habit of returning a single, bare
     value instead of an array if the indexes passed in are of length 1.
     For instance:
     scind.maximum(image, labels, [1]) returns a float
     but
     scind.maximum(image, labels, [1,2]) returns a list
     """
     if getattr(whatever_it_returned,"__getitem__",False):
         return np.array(whatever_it_returned)
     else:
         return np.array([whatever_it_returned])