目录

图像差分

 高斯差分

形态学-物体计数

​编辑 

图片降噪

---

图像差分

# -*- coding: utf-8 -*-
from PIL import Image
from pylab import *
from scipy.ndimage import filters
import numpy# 添加中文字体支持
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\SimSun.ttc", size=14)im = array(Image.open('图片路径').convert('L'))
gray()subplot(1, 4, 1)
axis('off')
title(u'(a)原图', fontproperties=font)
imshow(im)# Sobel derivative filters
imx = zeros(im.shape)
filters.sobel(im, 1, imx)
subplot(1, 4, 2)
axis('off')
title(u'(b)x方向差分', fontproperties=font)
imshow(imx)imy = zeros(im.shape)
filters.sobel(im, 0, imy)
subplot(1, 4, 3)
axis('off')
title(u'(c)y方向差分', fontproperties=font)
imshow(imy)#mag = numpy.sqrt(imx**2 + imy**2)
mag = 255-numpy.sqrt(imx**2 + imy**2)
subplot(1, 4, 4)
title(u'(d)梯度幅度', fontproperties=font)
axis('off')
imshow(mag)show()

 高斯差分

# -*- coding: utf-8 -*-
from PIL import Image
from pylab import *
from scipy.ndimage import filters
import numpy# 添加中文字体支持
#from matplotlib.font_manager import FontProperties
#font = FontProperties(fname=r"c:\windows\fonts\SimSun.ttc", size=14)def imx(im, sigma):imgx = zeros(im.shape)filters.gaussian_filter(im, sigma, (0, 1), imgx)return imgxdef imy(im, sigma):imgy = zeros(im.shape)filters.gaussian_filter(im, sigma, (1, 0), imgy)return imgydef mag(im, sigma):# there's also gaussian_gradient_magnitude()#mag = numpy.sqrt(imgx**2 + imgy**2)imgmag = 255 - numpy.sqrt(imgx ** 2 + imgy ** 2)return imgmagim = array(Image.open('图片路径').convert('L'))
figure()
gray()sigma = [2, 5, 10]for i in  sigma:subplot(3, 4, 4*(sigma.index(i))+1)axis('off')imshow(im)imgx=imx(im, i)subplot(3, 4, 4*(sigma.index(i))+2)axis('off')imshow(imgx)imgy=imy(im, i)subplot(3, 4, 4*(sigma.index(i))+3)axis('off')imshow(imgy)imgmag=mag(im, i)subplot(3, 4, 4*(sigma.index(i))+4)axis('off')imshow(imgmag)show()

形态学-物体计数

# -*- coding: utf-8 -*-
from PIL import Image
from numpy import *
from scipy.ndimage import measurements, morphology
from scipy.ndimage import laplace
from pylab import *"""   This is the morphology counting objects example in Section 1.4.  """# 添加中文字体支持
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\SimSun.ttc", size=14)# load image and threshold to make sure it is binary
figure()
gray()
im = array(Image.open('图片路径').convert('L'))
subplot(221)
imshow(im)
axis('off')
title(u'原图', fontproperties=font)
im = (im < 128)labels, nbr_objects = measurements.label(im)
print ("Number of objects:", nbr_objects)
subplot(222)
imshow(labels)
axis('off')
title(u'标记后的图', fontproperties=font)# morphology - opening to separate objects better
im_open = morphology.binary_opening(im, ones((9, 5)), iterations=2)
subplot(223)
imshow(im_open)
axis('off')
title(u'开运算后的图像', fontproperties=font)labels_open, nbr_objects_open = measurements.label(im_open)
print ("Number of objects:", nbr_objects_open)
subplot(224)
imshow(labels_open)
axis('off')
title(u'开运算后进行标记后的图像', fontproperties=font)show()

 

 

图片降噪

# -*- coding: utf-8 -*-
from PIL import Image
from pylab import *
from numpy import *
from numpy import random
from scipy.ndimage import filters
#from scipy.misc import imsave
#import cv2 as cv
from PCV.tools import rof""" This is the de-noising example using ROF in Section 1.5. """# 添加中文字体支持
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\SimSun.ttc", size=14)im = array(Image.open('图片路径').convert('L'))U,T = rof.denoise(im,im)
G = filters.gaussian_filter(im,10)# save the result
#imsave('synth_original.pdf',im)
#imsave('synth_rof.pdf',U)
#imsave('synth_gaussian.pdf',G)# plot
figure()
gray()subplot(1,3,1)
imshow(im)
#axis('equal')
axis('off')
title(u'原噪声图像', fontproperties=font)subplot(1,3,2)
imshow(G)
#axis('equal')
axis('off')
title(u'高斯模糊后的图像', fontproperties=font)subplot(1,3,3)
imshow(U)
#axis('equal')
axis('off')
title(u'ROF降噪后的图像', fontproperties=font)show()