1 from PIL import Image
 2 from pylab import *
 3 
 4 im = array(Image.open(‘2.jpg‘).convert(‘L‘))
 5 
 6 figure()
 7 gray()
 8 contour(im,origin=‘image‘)
 9 axis(‘equal‘)
10 axis(‘off‘)
11 figure()
12 hist(im.flatten(),128)
13 show()

 1 import cv2
 2 from PIL import Image
 3 from numpy import *
 4 from pylab import *
 5 
 6 #读取图像
 7 img = cv2.imread(‘2.jpg‘)
 8 
 9 
10 #创建灰度图像
11 gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
12 
13 figure()
14 hist(gray.ravel(),256)
15 show()

 1 from PIL import Image
 2 from pylab import *
 3 from numpy import *
 4 
 5 def histeq(im,nbr_bins=256):
 6   
 7   imhist,bins = histogram(im.flatten(),nbr_bins,normed=True)
 8   cdf = imhist.cumsum() # cumulative distribution function
 9   cdf = 255 * cdf / cdf[-1]
10   im2 =interp(im.flatten(),bins[:-1],cdf)
11   return im2.reshape(im.shape),cdf
12 
13 im=array(Image.open(‘2.jpg‘).convert(‘L‘))
14 im2,cdf=histeq(im)
15 figure()
16 subplot(2, 2, 1)
17 axis(‘off‘)
18 gray()
19 title(‘(a)Original Image‘)
20 imshow(im)
21 
22 subplot(2, 2, 2)
23 axis(‘off‘)
24 title(‘(b)Image After Histogram Equalization‘)
25 imshow(im2)
26 
27 subplot(2, 2, 3)
28 axis(‘off‘)
29 title(‘(c)Histogram Of Original Image‘)
30 hist(im.flatten(), 128, density=True)
31 
32 subplot(2, 2, 4)
33 axis(‘off‘)
34 title(‘(d)Histogram After Equalization‘)
35 hist(im2.flatten(), 128, density=True)
36 
37 show()

 1 #直方图均衡化
 2 
 3 import cv2
 4 from PIL import Image
 5 
 6 from pylab import *
 7 
 8 
 9 img = cv2.imread(‘2.jpg‘)
10 
11 gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
12 cv2.imwrite(‘2gray.jpg‘,gray)
13 
14 dst = cv2.equalizeHist(gray)
15 
16 # hist_full = cv2.calcHist([dst],[0],None,[256],[0,256])
17 # cv2.imwrite(‘2hist.jpg‘,dst)
18 res = hstack((gray,dst))
19 cv2.imshow("Histogram equalizeHist",res)
20 hist(gray.ravel(),256)
21 hist(dst.ravel(),256)
22 show()
23 cv2.waitKey(0)