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https://github.com/wassname/scikit-image.git
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martin
9fa9c0387c
for better interaction sharing achieved by setting sharex and sharey, and setting the axes 'adjustable' parameter to 'box-forced'
85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
"""
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=================================
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Gamma and log contrast adjustment
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=================================
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This example adjusts image contrast by performing a Gamma and a Logarithmic
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correction on the input image.
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"""
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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from skimage import data, img_as_float
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from skimage import exposure
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matplotlib.rcParams['font.size'] = 8
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def plot_img_and_hist(img, axes, bins=256):
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"""Plot an image along with its histogram and cumulative histogram.
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"""
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img = img_as_float(img)
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ax_img, ax_hist = axes
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ax_cdf = ax_hist.twinx()
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# Display image
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ax_img.imshow(img, cmap=plt.cm.gray)
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ax_img.set_axis_off()
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# Display histogram
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ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black')
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ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))
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ax_hist.set_xlabel('Pixel intensity')
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ax_hist.set_xlim(0, 1)
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ax_hist.set_yticks([])
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# Display cumulative distribution
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img_cdf, bins = exposure.cumulative_distribution(img, bins)
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ax_cdf.plot(bins, img_cdf, 'r')
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ax_cdf.set_yticks([])
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return ax_img, ax_hist, ax_cdf
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# Load an example image
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img = data.moon()
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# Gamma
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gamma_corrected = exposure.adjust_gamma(img, 2)
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# Logarithmic
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logarithmic_corrected = exposure.adjust_log(img, 1)
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# Display results
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fig = plt.figure(figsize=(8, 5))
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axes = np.zeros((2,3), dtype=np.object)
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axes[0, 0] = plt.subplot(2, 3, 1, adjustable='box-forced')
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axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0], adjustable='box-forced')
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axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0], adjustable='box-forced')
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axes[1, 0] = plt.subplot(2, 3, 4)
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axes[1, 1] = plt.subplot(2, 3, 5)
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axes[1, 2] = plt.subplot(2, 3, 6)
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ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
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ax_img.set_title('Low contrast image')
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y_min, y_max = ax_hist.get_ylim()
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ax_hist.set_ylabel('Number of pixels')
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ax_hist.set_yticks(np.linspace(0, y_max, 5))
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ax_img, ax_hist, ax_cdf = plot_img_and_hist(gamma_corrected, axes[:, 1])
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ax_img.set_title('Gamma correction')
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ax_img, ax_hist, ax_cdf = plot_img_and_hist(logarithmic_corrected, axes[:, 2])
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ax_img.set_title('Logarithmic correction')
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ax_cdf.set_ylabel('Fraction of total intensity')
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ax_cdf.set_yticks(np.linspace(0, 1, 5))
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# prevent overlap of y-axis labels
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fig.subplots_adjust(wspace=0.4)
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plt.show()
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