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直方图均衡化.py
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直方图均衡化.py
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import cv2
import matplotlib.pyplot as plt
import numpy as np
#计算像素值出现概率
def get_probability(img):
prob = np.zeros(256)
for rv in img:
for cv in rv:
prob[cv] += 1
m,n = img.shape
prob = prob/(m * n) #求概率值
return prob
#根据像素概率将原始图像直方图均衡化
def equalization(img, prob):
prob = np.cumsum(prob) # 求累计概率
pixel_trans = [int(255 * prob[i]+0.5) for i in range(256)] # 像素值映射
m,n = img.shape
for j in range(m):
for k in range(n):
img[j,k] = pixel_trans[img[j,k]]
return img
if __name__ == '__main__':
im= cv2.imread("E:\\image processing\\tiger.jpg", 0) # 读取灰度图
plt.subplot(2, 2, 1), plt.imshow(im, cmap='gray')
plt.title('(a)initial picture'), plt.xticks([]), plt.yticks([])
prob = get_probability(im)
plt.subplot(2, 2, 2), plt.bar([i for i in range(256)], prob, width=1)
plt.title('(b)initial histogram')
im = equalization(im, prob)
prob = get_probability(im)
plt.subplot(2, 2, 4), plt.bar([i for i in range(256)], prob, width=1)
plt.title('(c)histogram after equalization')
plt.subplot(2, 2, 3), plt.imshow(im, cmap='gray')
plt.title('(d)picture after equalization'), plt.xticks([]), plt.yticks([])
plt.show()