I am trying to apply Haralick textures to a SAR image (float32). As far as I know, the image first needs to be quantized to a reasonable bit depth prior to calculate the co-ocurrence matrix. In the original paper, Haralick proposes using a "equal probability quantizer". As far as I know is the algorithm employed by Mathlab "imquantize". I have been looking for a pre-existing python/numpy implementation (no success).

I also have reading pre-existing java code from ESA SNAP. As far I know histogram calculations would be straightforward with numpy, but if I understand correctly, every pixel is assigned a "level/histogram bin" using bisection search, so I do not see any straightforward way to properly vectorize the code.

EDITED after this point ===================

I have made simple implementation in Python + Numpy based on the pre-existing java code from ESA SNAP, and the kind advice of Marcus Müller.

import numpy as np

def eqProbQuant(image, levels=32):
    # Sort the pixels by value
    sorted_image = np.sort(image)
    # Get the pixel count
    pixel_count = sorted_image.shape[0]
    # Get the number of pixels per bin
    samples_per_bin = int(pixel_count/levels)
    # Get locations where the bin would change
    edge_samples = np.arange(levels+1) * samples_per_bin
    # Get the values at those locations (bin edges)
    bin_edges = sorted_image[edge_samples]
    # Use the values to apply quantization
    quantized = np.digitize(image, bin_edges)
    return quantized

If possible, I would like to know if this code would apply the algorithm correctly (I have very little experience with signal processing)

  • $\begingroup$ Hi! Welcome here. Asking for code written to your specification is off-topic here, but I'm 100% certain you can rephrase your question to describe the signal processing problem you're having implementing that quantizer yourself (it really shouldn't be hard at all). $\endgroup$ Aug 1, 2020 at 9:48
  • $\begingroup$ Thanks a lot for your kind advice, I tried to rephrase the question in a more polite way. I want to apologise for the original phrasing. I come from biological sciences, and the topic escapes me a bit. Do you see any way it could be further improved? $\endgroup$ Aug 1, 2020 at 10:33
  • $\begingroup$ I shouldn't have used "rephrasing". I meant to say "you can pose a different problem than asking for code that someone else wrote" :) Anyway, the question is fine, let me try to answer it as is $\endgroup$ Aug 1, 2020 at 11:02
  • $\begingroup$ Re-focused again (this time without nagging for code!), just asking if the code would properly apply the algorithm. I would greatly appereciate it if you could take a look to know if this would work as intended. If not, I understand, you already helped enough $\endgroup$ Aug 2, 2020 at 10:22

1 Answer 1


There is any way to assign a a "level/histogram bin" to each pixel without iterating through the entire image?

(emphasis mine)

No, because what you describe is an operation that needs to be applied to each pixel.

Also, a histogram needs to be calculated taking all pixels into account (that's the definition of a histogram!).

Anyway, that's not a problem at all – in SAR processing, this operation is definitely of "a lesser concern" when it comes to computational intensity.

  1. Calculate a list of pixel values sorted by intensity
  2. assign value ranges based on that

Sort the pixels

That's inherently an operation that can't be fully parallelized – in the end, there is one list for the whole image. However, you can parallelize it for the most part: Let your multiple processors (GPU threads, …) sort disjunct parts of the image, then merge the sorted lists in order (and that can be done in cascaded ways, too). Pseudocode:

N := numbers of pixels
I := image considered as linear array of pixels
O := output list of sorted pixel values, same length as I
T := temporary list of list of pixels
P := numbers of processing cores

   T[p][:] := sort(I[p*(N/P) … (p+1)*(N/P)])



Then, just take the sorted list of values, divide into the desired number of bins of equal size, note down the "starting" value of each bin (i.e. the values at O[0], O[N/n_bins], O[2*N/n_bins]...).

Then, (again, in parallel if desired – there's no data interdependence between pixels) move through your image and replace the pixel values by the bin number.


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