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pythonnumpyscipyipythonndimage
João Mamede
João Mamede

Reputation: 147

Scipy labels and measuring max pixel in each label

So I want to measure the max pixel and average of pixels in each label(in multiple array) using scipy. for example

(img , other is a numpy array of an opened tif)

import numpy as np
import scipy.ndimage as ndi

a = img > 200
labels,nb = ndi.labels(a)

merge = np.zeros(img.shape)
merge = other * a

So for each label I want to find the min value of the pixel, the max value of the pixel and the average of the intensities(the area of each label I can count) for img and merge. And I want to be able to make an histogram with these counts per label(connected area in img). (I know how to make the histograms once I have the np array or list)

I was thinking in making a loop for each label and then make a binary structure only with that label and measure the values. Is there any fast scipy/numpy method to do it without going through a loop?

Thanks!

Upvotes: 2

Views: 1796

Answers (1)

Taro Sato
Taro Sato

Reputation: 1452

You can use labeled_comprehension to do this all in one shot:

#!/usr/bin/env python2.7
import numpy as np
from scipy import ndimage as nd


hist = []

def analyze(x):
    xmin = x.min()
    xmax = x.max()
    xmean = x.mean()
    xhist = np.histogram(x, range=(xmin, xmax))
    hist.append({'min': xmin,
                 'max': xmax,
                 'mean': xmean,
                 'hist': xhist})
    return 1

a = np.array([[1, 2, 0, 0],
              [5, 3, 0, 4],
              [0, 0, 0, 7],
              [9, 3, 0, 0]])

lbl, nlbl = nd.label(a)

lbls = np.arange(1, nlbl + 1)

nd.labeled_comprehension(a, lbl, lbls, analyze, float, -1)

print a
print
print lbl
print
print hist

Upvotes: 1

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