def get_matching_pts_sift(im_path_1, im_path_2):
"""
Gets the matching points in the two images, using SIFT features.
"""
# Process and save features to file
params = "--edge-thresh 10 --peak-thresh 5 --verbose"
sift.process_image(im_path_1, im_path_1 + '.sift', params=params)
sift.process_image(im_path_2, im_path_2 + '.sift', params=params)
# Read features from the two images.
l1, d1 = sift.read_features_from_file(im_path_1 + '.sift')
l2, d2 = sift.read_features_from_file(im_path_2 + '.sift')
# matchscores will have an entry for each feature in im1.
# The entry will be 0 if there is not a match.
# If there is a match, the entry will be the index of the matching feature in im2.
matchscores = sift.match_twosided(d1, d2)
pts1, pts2 = get_matches(l1, l2, matchscores)
return pts1, pts2
from pylab import *
from PIL import Image
from PCV.localdescriptors import sift
"""
This is the twosided SIFT feature matching example from Section 2.2 (p 44).
"""
imname1 = '../data/climbing_1_small.jpg'
imname2 = '../data/climbing_2_small.jpg'
# process and save features to file
sift.process_image(imname1, 'climbing_1_small.sift')
sift.process_image(imname2, 'climbing_2_small.sift')
#sift.process_image(imname1, imname1+'.sift')
#sift.process_image(imname2, imname2+'.sift')
# read features and match
l1, d1 = sift.read_features_from_file('climbing_1_small.sift')
l2, d2 = sift.read_features_from_file('climbing_2_small.sift')
matchscores = sift.match_twosided(d1, d2)
# load images and plot
im1 = array(Image.open(imname1))
im2 = array(Image.open(imname2))
sift.plot_matches(im1, im2, l1, l2, matchscores, show_below=True)
show()
K[0, 2] = 0.5*col
K[1, 2] = 0.5*row
return K
# compute features
sift.process_image('../data/book_frontal.JPG', 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')
sift.process_image('../data/book_perspective.JPG', 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model)
# camera calibration
K = my_calibration((747, 1000))
# 3D points at plane z=0 with sides of length 0.2
box = cube_points([0, 0, 0.1], 0.1)
# project bottom square in first image
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
# 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]
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):
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: %d", matchscores
#np.savetxt(("../data/panoimages/panoramio_matches.txt",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]
threshold = 2 # min number of matches needed to create link
g = pydot.Dot(graph_type='graph') # don't want the default directed graph
fy = 2586 * row / 1936
K = diag([fx, fy, 1])
K[0, 2] = 0.5 * col
K[1, 2] = 0.5 * row
return K
# compute features
sift.process_image('../data/book_frontal.JPG', 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')
sift.process_image('../data/book_perspective.JPG', 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model)
# camera calibration
K = my_calibration((747, 1000))
# 3D points at plane z=0 with sides of length 0.2
box = cube_points([0, 0, 0.1], 0.1)
# project bottom square in first image
from pylab import *
from PIL import Image
from PCV.localdescriptors import sift
"""
This is the twosided SIFT feature matching example from Section 2.2 (p 44).
"""
imname1 = '../data/climbing_1_small.jpg'
imname2 = '../data/climbing_2_small.jpg'
# process and save features to file
sift.process_image(imname1, './climbing_1_small.sift')
sift.process_image(imname2, './climbing_2_small.sift')
# sift.process_image(imname1, imname1+'.sift')
# sift.process_image(imname2, imname2+'.sift')
# read features and match
l1, d1 = sift.read_features_from_file('./climbing_1_small.sift')
l2, d2 = sift.read_features_from_file('./climbing_2_small.sift')
#matchscores = sift.match(d1, d2)
matchscores = sift.match_twosided(d1, d2)
# load images and plot
im1 = array(Image.open(imname1))
im2 = array(Image.open(imname2))
sift.plot_matches(im1, im2, l1, l2, matchscores, show_below=True)
show()
tic.k('start')
l, d = {}, {}
for i in range(len(imname)):
# sift.process_image('./images/salcatraz1.jpg','./images/salcatraz1.sift')
l[i], d[i] = sift.read_features_from_file(siftname[i])
tic.k('loaded sifts')
print '{} / {} features'.format(len(d[0]), len(d[1]))
immd5 = md5.md5(''.join(imname)).hexdigest()
matchcache = 'out_ch05ex02_cache_matches_%s.pickle' % immd5
if not os.path.exists(matchcache):
#matches = sift.match(d[0], d[1])
matches = sift.match_twosided(d[0], d[1])
pickle.dump(matches, open(matchcache, 'wb'))
matches = pickle.load(open(matchcache, 'rb'))
tic.k('matched')
ndx = matches.nonzero()[0]
x1 = homography.make_homog(l[0][ndx, :2].T)
x1 = numpy.array([x1[1,:],x1[0,:],x1[2,:]])
ndx2 = [int(matches[i]) for i in ndx]
x2 = homography.make_homog(l[1][ndx2, :2].T)
x2 = numpy.array([x2[1,:],x2[0,:],x2[2,:]])
print '{} matches'.format(len(ndx))
image = [numpy.array(Image.open(name)) for name in imname]
