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- import matplotlib
- import matplotlib.pyplot as plt
- import numpy as np
- from skimage import data, img_as_float
- from skimage import exposure
- from skimage.color import rgb2gray
- matplotlib.rcParams['font.size'] = 8
- def plot_img_and_hist(image, axes, bins=256):
- """Plot an image along with its histogram and cumulative histogram.
- """
- image = img_as_float(image)
- ax_img, ax_hist = axes
- ax_cdf = ax_hist.twinx()
- # Display image
- ax_img.imshow(image, cmap=plt.cm.gray)
- ax_img.set_axis_off()
- # Display histogram
- ax_hist.hist(image.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(image, bins)
- ax_cdf.plot(bins, img_cdf, 'r')
- ax_cdf.set_yticks([])
- return ax_img, ax_hist, ax_cdf
- # Load an example image
- img = plt.imread('rophophora.jpg')
- img = rgb2gray(img)
- # Contrast stretching
- p10, p90 = np.percentile(img, (10, 90))
- img_rescale = exposure.rescale_intensity(img, in_range=(p10, p90))
- # Equalization
- img_eq = exposure.equalize_hist(img)
- # Adaptive Equalization
- img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)
- # Display results
- fig = plt.figure(figsize=(8, 5))
- axes = np.zeros((2, 4), dtype=np.object)
- axes[0, 0] = fig.add_subplot(2, 4, 1)
- for i in range(1, 4):
- axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])
- for i in range(0, 4):
- axes[1, i] = fig.add_subplot(2, 4, 5+i)
- 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
- fig.tight_layout()
- #plt.savefig('ropho_plot_equalize.jpg',dpi=150)
- plt.show()
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