计算机视觉学习（一）——图像基本处理

直方图

代码：

 

 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()

 

 

 

 

 使用opencv：

 

 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()

 

 

 

 

直方图均衡化

代码：

 

 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()

 

 

 

 使用opencv：

 

 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)

                     原图                                           灰度图像                                        均衡化后  

                                                   

 

 

 

 

高斯滤波

代码：

 1 #高斯滤波
 2 
 3 import cv2
 4 from pylab import *
 5 
 6 def gaussian_blur_func(filename):
 7     img = cv2.imread(filename)
 8     rgbimg = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
 9     result1 = cv2.GaussianBlur(img,(5,5),0)
10     result2 = cv2.GaussianBlur(img,(5,5),1)
11     titles = ['origin image','gaussian blur image stu 0','gaussian blur image stu 1']
12     images = [rgbimg,result1,result2]
13     for i in range(3):
14         subplot(1,3,i+1),imshow(images[i])
15         title(titles[i])
16         xticks([]),yticks([])
17     show()
18 
19 
20 gaussian_blur_func('3.jpg')

原图

结果

 

 

代码参考书本代码《python计算机视觉编程》

 

 

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posted @ 2021-03-14 21:17  Easy-  阅读(94)  评论(0)    收藏  举报