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hist_equalization.py
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import cv2
import matplotlib
import matplotlib.pyplot as plt
import numpy
import numpy as np
from PIL import Image
from skimage import data, img_as_float
from skimage import exposure
matplotlib.rcParams['font.size'] = 8
def plot_img_and_hist(image, axes, bins=256):
"""Plot an image along with its histogram and cumulative histogram.
# """
# img_xray_hist=
# image_orgin= cv2.imread('google.jpg')
# img_gray = cv2.cvtColor(image_orgin, cv2.COLOR_BGR2GRAY)
# image = Image.fromarray(np.uint8(img_arr))
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)
plt.imsave("contrast stretching main3.png", image, cmap=plt.cm.gray)
# process_image = cv2.imread("process.png")
# # #
# laplacian = cv2.Laplacian(process_image, cv2.CV_8I, ksize=1)
# dst = cv2.convertScaleAbs(laplacian)
#
# # median_img = cv2.medianBlur(process_image, 3)
# # #
# cv2.imwrite("final.png", dst)
# plt.savefig('test1.png',ax_img)
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([])
# preprocess(numpy.array(Data))
# plt.imsave('hist_eq.png',numpy.array(ax_img))
return ax_img, ax_hist, ax_cdf
# Load an example image
# img = data.moon()
image_orgin = cv2.imread('result.png')
img = cv2.cvtColor(image_orgin, cv2.COLOR_BGR2GRAY)
# Contrast stretching
p2, p98 = np.percentile(img, (2, 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
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('contrast stretching main2.png')
# plt.show()