mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-30 00:16:25 +08:00
Tony S Yu
87c2353845
`equalize_hist`: "max_intensity" parameter no longer exists---img_as_float normalizes intensity range `cumulative_distribution`: Return centers of bins instead of the edges. Move `histogram` function from filter subpackage. Add test of equalize_hist Add example of histogram equalization.
55 lines
1.4 KiB
Python
55 lines
1.4 KiB
Python
import numpy as np
|
|
|
|
from skimage.exposure import histogram
|
|
|
|
|
|
__all__ = ['threshold_otsu']
|
|
|
|
|
|
def threshold_otsu(image, nbins=256):
|
|
"""Return threshold value based on Otsu's method.
|
|
|
|
Parameters
|
|
----------
|
|
image : array
|
|
Input image.
|
|
nbins : int
|
|
Number of bins used to calculate histogram. This value is ignored for
|
|
integer arrays.
|
|
|
|
Returns
|
|
-------
|
|
threshold : float
|
|
Threshold value.
|
|
|
|
References
|
|
----------
|
|
.. [1] Wikipedia, http://en.wikipedia.org/wiki/Otsu's_Method
|
|
|
|
Examples
|
|
--------
|
|
>>> from skimage.data import camera
|
|
>>> image = camera()
|
|
>>> thresh = threshold_otsu(image)
|
|
>>> binary = image > thresh
|
|
"""
|
|
hist, bin_centers = histogram(image, nbins)
|
|
hist = hist.astype(float)
|
|
|
|
# class probabilities for all possible thresholds
|
|
weight1 = np.cumsum(hist)
|
|
weight2 = np.cumsum(hist[::-1])[::-1]
|
|
# class means for all possible thresholds
|
|
mean1 = np.cumsum(hist * bin_centers) / weight1
|
|
mean2 = (np.cumsum((hist * bin_centers)[::-1]) / weight2[::-1])[::-1]
|
|
|
|
# Clip ends to align class 1 and class 2 variables:
|
|
# The last value of `weight1`/`mean1` should pair with zero values in
|
|
# `weight2`/`mean2`, which do not exist.
|
|
variance12 = weight1[:-1] * weight2[1:] * (mean1[:-1] - mean2[1:])**2
|
|
|
|
idx = np.argmax(variance12)
|
|
threshold = bin_centers[:-1][idx]
|
|
return threshold
|
|
|