PS：运行环境python2.7(python3也行)
vlfeat安装参考:这里
原图：
代码如下:
from PIL import Image
from pylab import *
import sys
from PCV.localdescriptors import sift
if len(sys.argv) >= 3:
im1f, im2f = sys.argv[1], sys.argv[2]
else:
# im1f = '../data/sf_view1.jpg'
# im2f = '../data/sf_view2.jpg'
im1f = './data/P90317-103353-1.jpg'
im2f = './data/P90317-103350-1.jpg'
# im1f = '../data/climbing_1_small.jpg'
# im2f = '../data/climbing_2_small.jpg'
im1 = array(Image.open(im1f))
im2 = array(Image.open(im2f))
sift.process_image(im1f, 'out_sift_1.txt')
l1, d1 = sift.read_features_from_file('out_sift_1.txt')
figure()
gray()
subplot(121)
sift.plot_features(im1, l1, circle=False)
sift.process_image(im2f, 'out_sift_2.txt')
l2, d2 = sift.read_features_from_file('out_sift_2.txt')
subplot(122)
sift.plot_features(im2, l2, circle=False)
#matches = sift.match(d1, d2)
matches = sift.match_twosided(d1, d2)
print '{} matches'.format(len(matches.nonzero()[0]))
figure()
gray()
sift.plot_matches(im1, im2, l1, l2, matches, show_below=True)
show()遇到问题：
分析：百度无果，也许是vlfeat没有配置好
暂时解决：折腾了几个小时没弄好，只好先用cmd命令行运行生成out_sift_1.txt和out_sift_2.txt文件，然后把sift.process_image加上注释，日后再想办法
解决办法：尝试sift.py文件里vlfeat路径的\统统改为/，运行不再报错了(ps:在此环境下只能用win32的vlfeat，win64的只会生成空的.txt文件)
cmmd = str("D:/pyCharm/pycharm_workspace/vlfeat/win32/sift.exe "+imagename+" --output="+resultname+
" "+params)结果：
代码如下：
# -*- coding: utf-8 -*-
from pylab import *
from PIL import Image
from PCV.localdescriptors import harris
from PCV.tools.imtools import imresize
"""
This is the Harris point matching example in Figure 2-2.
"""
# Figure 2-2上面的图
#im1 = array(Image.open("../data/crans_1_small.jpg").convert("L"))
#im2= array(Image.open("../data/crans_2_small.jpg").convert("L"))
# Figure 2-2下面的图
im1 = array(Image.open("./data/P90317-103353-1.jpg").convert("L"))
im2 = array(Image.open("./data/P90317-103350-1.jpg").convert("L"))
# resize加快匹配速度
im1 = imresize(im1, (im1.shape[1]/2, im1.shape[0]/2))
im2 = imresize(im2, (im2.shape[1]/2, im2.shape[0]/2))
wid = 5
harrisim = harris.compute_harris_response(im1, 5)
filtered_coords1 = harris.get_harris_points(harrisim, wid+1)
d1 = harris.get_descriptors(im1, filtered_coords1, wid)
harrisim = harris.compute_harris_response(im2, 5)
filtered_coords2 = harris.get_harris_points(harrisim, wid+1)
d2 = harris.get_descriptors(im2, filtered_coords2, wid)
print 'starting matching'
matches = harris.match_twosided(d1, d2)
figure()
gray()
harris.plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
show()结果：
小结：sift运行速度比较快而且匹配更加准确
PS:
需要安装Graphviz，并且要配置环境变量)下载页面
配置环境变量：安装好后根目录下的bin文件夹路径复制到Path中
还需要安装graphviz模块和pydot-ng模块，先安装graphviz后安装pydot-ng，用conda或者pip安装
代码如下：
# -*- coding: utf-8 -*-
from pylab import *
from PIL import Image
from PCV.localdescriptors import sift
from PCV.tools import imtools
import pydot
""" This is the example graph illustration of matching images from Figure 2-10.
To download the images, see ch2_download_panoramio.py."""
#download_path = "panoimages" # set this to the path where you downloaded the panoramio images
#path = "/FULLPATH/panoimages/" # path to save thumbnails (pydot needs the full system path)
download_path = r"D:\pyCharm\pycharm_workspace\HarrisAndSift\data" # set this to the path where you downloaded the panoramio images
path = r"D:\pyCharm\pycharm_workspace\HarrisAndSift\data" # path to save thumbnails (pydot needs the full system path)
# list of downloaded filenames
imlist = imtools.get_imlist(download_path)
nbr_images = len(imlist)
# extract features
featlist = [imname[:-3] + 'sift' for imname in imlist]
for i, imname in enumerate(imlist):
sift.process_image(imname, featlist[i])
matchscores = zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images): # only compute upper triangle
print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_features_from_file(featlist[i])
l2, d2 = sift.read_features_from_file(featlist[j])
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
print "The match scores is: \n", matchscores
# copy values
for i in range(nbr_images):
for j in range(i + 1, nbr_images): # no need to copy diagonal
matchscores[j, i] = matchscores[i, j]
#可视化
threshold = 2 # min number of matches needed to create link
g = pydot.Dot(graph_type='graph') # don't want the default directed graph
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
if matchscores[i, j] > threshold:
# first image in pair
im = Image.open(imlist[i])
im.thumbnail((100, 100))
filename = path + str(i) + '.png'
im.save(filename) # need temporary files of the right size
g.add_node(pydot.Node(str(i), fontcolor='transparent', shape='rectangle', image=filename))
# second image in pair
im = Image.open(imlist[j])
im.thumbnail((100, 100))
filename = path + str(j) + '.png'
im.save(filename) # need temporary files of the right size
g.add_node(pydot.Node(str(j), fontcolor='transparent', shape='rectangle', image=filename))
g.add_edge(pydot.Edge(str(i), str(j)))
g.write_png('zhongShanJNG.png')遇到问题:
分析：也许是因为pydot和graphviz安装顺序出错
解决办法：
conda install graphviz
conda install pydot-ng
(ps:pydot-ng是兼容python2和python3的新版本)
记得在import模块时:
import pydot_ng as pydot结果：
The match scores is:
[[1.297e+03 1.800e+01 1.600e+01 0.000e+00 2.000e+00 0.000e+00 0.000e+00
0.000e+00 2.000e+00 1.000e+00 0.000e+00 0.000e+00 0.000e+00 1.000e+00
2.000e+00 0.000e+00]
[0.000e+00 1.419e+03 9.800e+01 0.000e+00 1.000e+00 1.000e+00 3.000e+00
1.000e+00 1.000e+00 1.000e+00 1.000e+00 0.000e+00 4.000e+00 2.000e+00
0.000e+00 2.000e+00]
[0.000e+00 0.000e+00 1.249e+03 1.000e+00 1.000e+00 2.000e+00 2.000e+00
1.000e+00 5.000e+00 7.000e+00 7.000e+00 6.000e+00 8.000e+00 8.000e+00
0.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 1.287e+03 4.600e+01 4.300e+01 5.900e+01
2.000e+00 1.000e+00 0.000e+00 3.000e+00 2.000e+00 5.000e+00 4.000e+00
1.000e+00 1.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 2.342e+03 1.300e+01 5.000e+00
0.000e+00 1.000e+00 0.000e+00 1.000e+00 1.000e+00 1.000e+00 3.000e+00
0.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 1.897e+03 3.100e+01
1.000e+00 4.000e+00 1.000e+00 0.000e+00 2.000e+00 0.000e+00 2.000e+00
0.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 1.329e+03
1.000e+01 9.000e+00 4.000e+00 7.000e+00 1.000e+01 1.500e+01 1.100e+01
0.000e+00 1.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
6.890e+02 4.900e+01 3.400e+01 3.900e+01 4.600e+01 4.600e+01 4.500e+01
0.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 9.900e+02 1.700e+02 8.200e+01 8.800e+01 6.400e+01 6.500e+01
0.000e+00 1.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 1.258e+03 8.600e+01 8.400e+01 6.500e+01 6.200e+01
0.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 1.402e+03 1.370e+02 1.090e+02 1.240e+02
0.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 0.000e+00 6.820e+02 1.240e+02 1.160e+02
1.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 1.246e+03 2.520e+02
0.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 1.339e+03
0.000e+00 0.000e+00]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
1.671e+03 1.910e+02]
[0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00
0.000e+00 1.484e+03]]
