vlfeat主页：http://www.vlfeat.org/
使用方法：command line 使用方法
from：http://www.vlfeat.org/install-shell.html
1.下载vlfeat二进制包解压
2.在.bash_profile添加./vlfeat/bin/macui64/sift路径 详见链接
3.因为我用的virtualenv 添加path一直有问题 不生成＊＊＊.sift文件 后来直接在def process_image cmmd中用的绝对路径
vlfeat－sift代码：
from：http://www.maths.lth.se/matematiklth/personal/solem/downloads/vlfeat.py
效果：我把ratio改小了一些
from PIL import Image
import os
from numpy import *
from pylab import *
def process_image(imagename,resultname,params="--edge-thresh 10 --peak-thresh 5"):
""" process an image and save the results in a file"""
if imagename[-3:] != 'pgm':
#create a pgm file
im = Image.open(imagename).convert('L')
im.save('tmp.pgm')
imagename = 'tmp.pgm'
cmmd = str("sift "+imagename+" --output="+resultname+
" "+params)
os.system(cmmd)
print 'processed', imagename, 'to', resultname
def read_features_from_file(filename):
""" read feature properties and return in matrix form"""
f = loadtxt(filename)
return f[:,:4],f[:,4:] # feature locations, descriptors
def write_features_to_file(filename,locs,desc):
""" save feature location and descriptor to file"""
savetxt(filename,hstack((locs,desc)))
def plot_features(im,locs,circle=False):
""" show image with features. input: im (image as array),
locs (row, col, scale, orientation of each feature) """
def draw_circle(c,r):
t = arange(0,1.01,.01)*2*pi
x = r*cos(t) + c[0]
y = r*sin(t) + c[1]
plot(x,y,'b',linewidth=2)
imshow(im)
if circle:
[draw_circle([p[0],p[1]],p[2]) for p in locs]
else:
plot(locs[:,0],locs[:,1],'ob')
axis('off')
def match(desc1,desc2):
""" for each descriptor in the first image,
select its match in the second image.
input: desc1 (descriptors for the first image),
desc2 (same for second image). """
desc1 = array([d/linalg.norm(d) for d in desc1])
desc2 = array([d/linalg.norm(d) for d in desc2])
dist_ratio = 0.6
desc1_size = desc1.shape
matchscores = zeros((desc1_size[0],1))
desc2t = desc2.T #precompute matrix transpose
for i in range(desc1_size[0]):
dotprods = dot(desc1[i,:],desc2t) #vector of dot products
dotprods = 0.9999*dotprods
#inverse cosine and sort, return index for features in second image
indx = argsort(arccos(dotprods))
#check if nearest neighbor has angle less than dist_ratio times 2nd
if arccos(dotprods)[indx[0]] < dist_ratio * arccos(dotprods)[indx[1]]:
matchscores[i] = int(indx[0])
return matchscores
def appendimages(im1,im2):
""" return a new image that appends the two images side-by-side."""
#select the image with the fewest rows and fill in enough empty rows
rows1 = im1.shape[0]
rows2 = im2.shape[0]
if rows1 < rows2:
im1 = concatenate((im1,zeros((rows2-rows1,im1.shape[1]))), axis=0)
elif rows1 > rows2:
im2 = concatenate((im2,zeros((rows1-rows2,im2.shape[1]))), axis=0)
#if none of these cases they are equal, no filling needed.
return concatenate((im1,im2), axis=1)
def plot_matches(im1,im2,locs1,locs2,matchscores,show_below=True):
""" show a figure with lines joining the accepted matches
input: im1,im2 (images as arrays), locs1,locs2 (location of features),
matchscores (as output from 'match'), show_below (if images should be shown below). """
im3 = appendimages(im1,im2)
if show_below:
im3 = vstack((im3,im3))
# show image
imshow(im3)
# draw lines for matches
cols1 = im1.shape[1]
for i in range(len(matchscores)):
if matchscores[i] > 0:
plot([locs1[i,0], locs2[matchscores[i,0],0]+cols1], [locs1[i,1], locs2[matchscores[i,0],1]], 'c')
axis('off')
def match_twosided(desc1,desc2):
""" two-sided symmetric version of match(). """
matches_12 = match(desc1,desc2)
matches_21 = match(desc2,desc1)
ndx_12 = matches_12.nonzero()[0]
#remove matches that are not symmetric
for n in ndx_12:
if matches_21[int(matches_12[n])] != n:
matches_12[n] = 0
return matches_12
if __name__ == "__main__":
process_image('box.pgm','tmp.sift')
l,d = read_features_from_file('tmp.sift')
im = array(Image.open('box.pgm'))
figure()
plot_features(im,l,True)
gray()
process_image('scene.pgm','tmp2.sift')
l2,d2 = read_features_from_file('tmp2.sift')
im2 = array(Image.open('scene.pgm'))
m = match_twosided(d,d2)
figure()
plot_matches(im,im2,l,l2,m)
gray()
show()