* 图像局部特征描述子

一.图像匹配

二.Harris角点

1.如何检测出Harris角点?

2.图片匹配实例

#-*- 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/sf_view1.jpg").convert("L"))
im2 = array(Image.open("../data/sf_view2.jpg").convert("L"))
#resize to make matching faster
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()

三.SIFT(尺度不变特征变换)

1.介绍:

2.SIFT算法的特点:

3.原理简述:

尺度空间:

DoG极值检测

关键点描述子

4.SIFT算法步骤:

5.检测感兴趣点

# -*- coding: utf-8 -*-
from PIL import Image
from pylab import *
from PCV.localdescriptors import sift
from PCV.localdescriptors import harris
# 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)
imname = '../data/empire.jpg'
im = array(Image.open(imname).convert('L'))
sift.process_image(imname, 'empire.sift')
l1, d1 = sift.read_features_from_file('empire.sift')
figure()
gray()
subplot(131)
sift.plot_features(im, l1, circle=False)
title(u'SIFT特征',fontproperties=font)
subplot(132)
sift.plot_features(im, l1, circle=True)
title(u'用圆圈表示SIFT特征尺度',fontproperties=font)
# 检测harris角点
harrisim = harris.compute_harris_response(im)
subplot(133)
filtered_coords = harris.get_harris_points(harrisim, 6, 0.1)
imshow(im)
plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], '*')
axis('off')
title(u'Harris角点',fontproperties=font)
show()

6.匹配描述子

SIFT

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/crans_1_small.jpg'
 im2f = '../data/crans_2_small.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()

Harris

# -*- 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/sf_view1.jpg").convert("L"))
im2 = array(Image.open("../data/sf_view2.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()

7.匹配地理标记图像

下面我们通过计算机来实现:

# -*- 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 = "F:/dropbox/Dropbox/translation/pcv-notebook/data/panoimages" # set this to the path where you downloaded the panoramio images
path = "F:/dropbox/Dropbox/translation/pcv-notebook/data/panoimages/" # 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('jmu.png')