目录
图像差分
python
# -*- 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
python
# -*- 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 imgx
def imy(im, sigma):
imgy = zeros(im.shape)
filters.gaussian_filter(im, sigma, (1, 0), imgy)
return imgy
def 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 imgmag
im = 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
python
# -*- 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
python
# -*- 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()