computeHistogram

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Description: This method computes histograms of the max-tree nodes.

Constructor signature: MorphTreeAlpha.computeHistogram(self,img,wimg = [],nbins = 256,normalize = True)

Input:
  • img, 2d-array int32. Image used to compute the histograms.
  • wimg, 2d-array int32. Weight image used in the histogram computation.
  • nbins, int. Number of histogram bins. It starts in 0 and ends in nbins - 1.
  • normalize, bool. Flag indicating whether the histogram should be normalized or not.
Output
  • img, 2d-array float. Histogram array. Each line corresponds to a node histogram.

C++ Aux Function

OK

Python Wrapper

OK [C/C++ extension is up-to-date]

Python Implementation

 1 import numpy as np
 2 
 3 def computeHistogram(self,img,nbins = 256,wimg = [], normalize = True):
 4     hist = np.zeros((self.node_array.shape[1],nbins), dtype = np.int32)
 5     if wimg == []:
 6         hist[self.node_index,img] += 1
 7     else:
 8         hist[self.node_index,img] += wimg
 9         compute_hist_aux(self.node_array[0],hist)
10     if normalize:
11         hist = hist*1.0/hist.sum(axis = 1).reshape(-1,1)
12 
13     return hist.astype(float)

Example

 1 from morph_tree_alpha import MorphTreeAlpha
 2 import numpy as np
 3 
 4 
 5 
 6 img = adreadgray('lenina.pgm')
 7 Bc = np.ones((3,3), dtype = bool)
 8 morph_tree = MorphTreeAlpha(img,Bc)
 9 adshow(img)
10 morph_tree.areaOpen(800)
11 adshow(morph_tree.getImage())
12 
13 hist = morph_tree.computeHistogram(img)
14 print hist.sum(axis = 1)
[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
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  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
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  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.
  1.  1.  1.  1.  1.  1.  1.  1.  1.]