mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 01:31:21 +08:00
Tony S Yu
94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
"""
|
|
======================
|
|
Histogram Equalization
|
|
======================
|
|
|
|
This examples enhances an image with low contrast, using a method called
|
|
*histogram equalization*, which "spreads out the most frequent intensity
|
|
values" in an image [1]_. The equalized image has a roughly linear cumulative
|
|
distribution function.
|
|
|
|
While histogram equalization has the advantage that it requires no parameters,
|
|
it sometimes yields unnatural looking images. An alternative method is
|
|
*contrast stretching*, where the image is rescaled to include all intensities
|
|
that fall within the 2nd and 98th percentiles [2]_.
|
|
|
|
.. [1] http://en.wikipedia.org/wiki/Histogram_equalization
|
|
.. [2] http://homepages.inf.ed.ac.uk/rbf/HIPR2/stretch.htm
|
|
|
|
"""
|
|
|
|
from skimage import data, img_as_float
|
|
from skimage import exposure
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
|
|
def plot_img_and_hist(img, axes, bins=256):
|
|
"""Plot an image along with its histogram and cumulative histogram.
|
|
|
|
"""
|
|
img = img_as_float(img)
|
|
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, histtype='step', color='black')
|
|
ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
|
|
ax_hist.set_xlabel('Pixel intensity')
|
|
ax_hist.set_xlim(0, 1)
|
|
ax_hist.set_yticks([])
|
|
|
|
# Display cumulative distribution
|
|
img_cdf, bins = exposure.cumulative_distribution(img, bins)
|
|
ax_cdf.plot(bins, img_cdf, 'r')
|
|
ax_cdf.set_yticks([])
|
|
|
|
return ax_img, ax_hist, ax_cdf
|
|
|
|
|
|
# Load an example image
|
|
img = data.moon()
|
|
|
|
# Contrast stretching
|
|
p2 = np.percentile(img, 2)
|
|
p98 = np.percentile(img, 98)
|
|
img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
|
|
|
|
# Equalization
|
|
img_eq = exposure.equalize_hist(img)
|
|
|
|
# Adaptive Equalization
|
|
img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
|
|
|
|
# Display results
|
|
f, axes = plt.subplots(2, 4, figsize=(8, 4))
|
|
|
|
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
|
|
ax_img.set_title('Low contrast image')
|
|
|
|
y_min, y_max = ax_hist.get_ylim()
|
|
ax_hist.set_ylabel('Number of pixels')
|
|
ax_hist.set_yticks(np.linspace(0, y_max, 5))
|
|
|
|
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])
|
|
ax_img.set_title('Contrast stretching')
|
|
|
|
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])
|
|
ax_img.set_title('Histogram equalization')
|
|
|
|
ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])
|
|
ax_img.set_title('Adaptive equalization')
|
|
|
|
ax_cdf.set_ylabel('Fraction of total intensity')
|
|
ax_cdf.set_yticks(np.linspace(0, 1, 5))
|
|
|
|
# prevent overlap of y-axis labels
|
|
plt.subplots_adjust(wspace=0.4)
|
|
plt.show()
|