mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 19:17:18 +08:00
Julian Taylor
d4cb154d17
percentile can compute multiple ranks in one go which in the case of the example halves the required time.
92 lines
2.3 KiB
Python
92 lines
2.3 KiB
Python
"""
|
|
============================
|
|
Local Histogram Equalization
|
|
============================
|
|
|
|
This examples enhances an image with low contrast, using a method called *local
|
|
histogram equalization*, which spreads out the most frequent intensity values in
|
|
an image.
|
|
|
|
The equalized image [1]_ has a roughly linear cumulative distribution function
|
|
for each pixel neighborhood.
|
|
|
|
The local version [2]_ of the histogram equalization emphasized every local
|
|
graylevel variations.
|
|
|
|
References
|
|
----------
|
|
.. [1] http://en.wikipedia.org/wiki/Histogram_equalization
|
|
.. [2] http://en.wikipedia.org/wiki/Adaptive_histogram_equalization
|
|
|
|
"""
|
|
import numpy as np
|
|
import matplotlib
|
|
import matplotlib.pyplot as plt
|
|
|
|
from skimage import data
|
|
from skimage.util.dtype import dtype_range
|
|
from skimage.util import img_as_ubyte
|
|
from skimage import exposure
|
|
from skimage.morphology import disk
|
|
from skimage.filter import rank
|
|
|
|
|
|
matplotlib.rcParams['font.size'] = 9
|
|
|
|
|
|
def plot_img_and_hist(img, axes, bins=256):
|
|
"""Plot an image along with its histogram and cumulative histogram.
|
|
|
|
"""
|
|
ax_img, ax_hist = axes
|
|
ax_cdf = ax_hist.twinx()
|
|
|
|
# Display image
|
|
ax_img.imshow(img, cmap=plt.cm.gray)
|
|
ax_img.set_axis_off()
|
|
|
|
# Display histogram
|
|
ax_hist.hist(img.ravel(), bins=bins)
|
|
ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
|
|
ax_hist.set_xlabel('Pixel intensity')
|
|
|
|
xmin, xmax = dtype_range[img.dtype.type]
|
|
ax_hist.set_xlim(xmin, xmax)
|
|
|
|
# Display cumulative distribution
|
|
img_cdf, bins = exposure.cumulative_distribution(img, bins)
|
|
ax_cdf.plot(bins, img_cdf, 'r')
|
|
|
|
return ax_img, ax_hist, ax_cdf
|
|
|
|
|
|
# Load an example image
|
|
img = img_as_ubyte(data.moon())
|
|
|
|
# Global equalize
|
|
img_rescale = exposure.equalize_hist(img)
|
|
|
|
# Equalization
|
|
selem = disk(30)
|
|
img_eq = rank.equalize(img, selem=selem)
|
|
|
|
|
|
# Display results
|
|
f, axes = plt.subplots(2, 3, figsize=(8, 5))
|
|
|
|
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
|
|
ax_img.set_title('Low contrast image')
|
|
ax_hist.set_ylabel('Number of pixels')
|
|
|
|
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
|
|
ax_img.set_title('Global equalise')
|
|
|
|
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
|
|
ax_img.set_title('Local equalize')
|
|
ax_cdf.set_ylabel('Fraction of total intensity')
|
|
|
|
|
|
# prevent overlap of y-axis labels
|
|
plt.subplots_adjust(wspace=0.4)
|
|
plt.show()
|