python数字图像处理实现直方图与均衡化

import numpy as np
from skimage import exposure,data
image =data.camera()*1.0
hist1=np.histogram(image, bins=2)  #用numpy包计算直方图
hist2=exposure.histogram(image, nbins=2) #用skimage计算直方图
print(hist1)
print(hist2)

from skimage import data
import matplotlib.pyplot as plt
img=data.camera()
plt.figure("hist")
arr=img.flatten()
n, bins, patches = plt.hist(arr, bins=256, normed=1,edgecolor='None',facecolor='red') 
plt.show()

from skimage import data
import matplotlib.pyplot as plt
img=data.lena()
ar=img[:,:,0].flatten()
plt.hist(ar, bins=256, normed=1,facecolor='r',edgecolor='r',hold=1)
ag=img[:,:,1].flatten()
plt.hist(ag, bins=256, normed=1, facecolor='g',edgecolor='g',hold=1)
ab=img[:,:,2].flatten()
plt.hist(ab, bins=256, normed=1, facecolor='b',edgecolor='b')
plt.show()

from skimage import data,exposure
import matplotlib.pyplot as plt
img=data.moon()
plt.figure("hist",figsize=(8,8))

arr=img.flatten()
plt.subplot(221)
plt.imshow(img,plt.cm.gray) #原始图像
plt.subplot(222)
plt.hist(arr, bins=256, normed=1,edgecolor='None',facecolor='red') #原始图像直方图

img1=exposure.equalize_hist(img)
arr1=img1.flatten()
plt.subplot(223)
plt.imshow(img1,plt.cm.gray) #均衡化图像
plt.subplot(224)
plt.hist(arr1, bins=256, normed=1,edgecolor='None',facecolor='red') #均衡化直方图

plt.show()