def find_similar(imagenames, plot_features=False):
'''Find similar images pairs
'''
first_figure = True
images = [ MyImage(i) for i in imagenames ]
matches = defaultdict(list)
length = len(images)
if plot_features:
for image in images:
if first_figure:
first_figure = False
else:
pylab.figure()
sift.plot_features(image.im, image.locs, circle=True)
for i in range(length):
this = images[i]
print 'Find match for', this.filename, ' ',
for j in range(i+1, length):
that = images[j]
sys.stdout.write('.')
sys.stdout.flush()
matched_points = nn.match_twosided(this.desc, that.desc)
similarity = sum(matched_points > 0)
matches[i].append((similarity, j, matched_points))
matches[j].append((similarity, i, matched_points))
print ' ',
# at least 10 points matched
# and only need top 3 matches
scores = sorted([k for k in matches[i]
if k[0] > 10 ], reverse=True)[:3]
if not scores:
print 'No match :('
else:
for sim, k, matched_points in scores:
that = images[k]
print '%s(%d) ' % (that.filename, sim),
if first_figure:
first_figure = False
else:
pylab.figure()
if k < i: # only cal the top right triangle
this = images[k]
that = images[i]
sift.plot_matches(this.im, that.im,
this.locs, that.locs,
matched_points)
print
pylab.show()
def find_similar(imagenames, plot_features=False):
'''Find similar images pairs
'''
first_figure = True
images = [MyImage(i) for i in imagenames]
matches = defaultdict(list)
length = len(images)
if plot_features:
for image in images:
if first_figure:
first_figure = False
else:
pylab.figure()
sift.plot_features(image.im, image.locs, circle=True)
for i in range(length):
this = images[i]
print 'Find match for', this.filename, ' ',
for j in range(i + 1, length):
that = images[j]
sys.stdout.write('.')
sys.stdout.flush()
matched_points = nn.match_twosided(this.desc, that.desc)
similarity = sum(matched_points > 0)
matches[i].append((similarity, j, matched_points))
matches[j].append((similarity, i, matched_points))
print ' ',
# at least 10 points matched
# and only need top 3 matches
scores = sorted([k for k in matches[i] if k[0] > 10], reverse=True)[:3]
if not scores:
print 'No match :('
else:
for sim, k, matched_points in scores:
that = images[k]
print '%s(%d) ' % (that.filename, sim),
if first_figure:
first_figure = False
else:
pylab.figure()
if k < i: # only cal the top right triangle
this = images[k]
that = images[i]
sift.plot_matches(this.im, that.im, this.locs, that.locs,
matched_points)
print
pylab.show()
def plot_match(imagename1, imagename2, show=True, plot_features=True):
'''Generate SIFT features for two images, plot their
features onto the original images and link two-sided
matching points with lines
'''
pylab.gray()
# plot features on images
# plot matching points
# matchscores = sift.match_twosided(desc1, desc2)
matchscores = nn.match_twosided(desc1, desc2)
sift.plot_matches(im1, im2, locs1, locs2, matchscores)
pylab.figure()
if show:
pylab.show()
def plot_match(imagename1, imagename2, show=True, plot_features=True):
'''Generate SIFT features for two images, plot their
features onto the original images and link two-sided
matching points with lines
'''
pylab.gray()
# plot features on images
# plot matching points
# matchscores = sift.match_twosided(desc1, desc2)
matchscores = nn.match_twosided(desc1, desc2)
sift.plot_matches(im1, im2, locs1, locs2, matchscores)
pylab.figure()
if show:
pylab.show()
