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()
l = {}
d = {}
for i in range(5):
sift.process_image(root + imname[i], root + featname[i])
l[i], d[i] = sift.read_features_from_file(featname[i])
matches = {}
for i in range(4):
matches[i] = sift.match(d[i + 1], d[i])
# visualize the matches (Figure 3-11 in the book)
for i in range(4):
im1 = array(Image.open(imname[i]))
im2 = array(Image.open(imname[i + 1]))
figure()
sift.plot_matches(im2, im1, l[i + 1], l[i], matches[i], show_below=True)
# function to convert the matches to hom. points
def convert_points(j):
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(l[j + 1][ndx, :2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(l[j][ndx2, :2].T)
# switch x and y - TODO this should move elsewhere
fp = vstack([fp[1], fp[0], fp[2]])
tp = vstack([tp[1], tp[0], tp[2]])
return fp, tp
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()
l = {}
d = {}
for i in range(5):
# sift.process_image(imname[i],featname[i])
l[i],d[i] = sift.read_features_from_file(featname[i])
matches = {}
for i in range(4):
matches[i] = sift.match(d[i+1],d[i])
# visualize the matches (Figure 3-11 in the book)
for i in range(4):
im1 = array(Image.open(imname[i]))
im2 = array(Image.open(imname[i+1]))
figure()
sift.plot_matches(im2,im1,l[i+1],l[i],matches[i],show_below=True)
# function to convert the matches to hom. points
def convert_points(j):
ndx = matches[j].nonzero()[0]
fp = homography.make_homog(l[j+1][ndx,:2].T)
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(l[j][ndx2,:2].T)
# switch x and y - TODO this should move elsewhere
fp = vstack([fp[1],fp[0],fp[2]])
tp = vstack([tp[1],tp[0],tp[2]])
return fp,tp
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()
l2, d2 = sift.read_features_from_file('im2.sift')
matches = sift.match_twosided(d1, d2)
ndx = matches.nonzero()[0]
x1 = homography.make_homog(l1[ndx, :2].T)#将点集转化为齐次坐标表示
ndx2 = [int(matches[i]) for i in ndx]
x2 = homography.make_homog(l2[ndx2, :2].T)#将点集转化为齐次坐标表示
d1n = d1[ndx]
d2n = d2[ndx2]
x1n = x1.copy()
x2n = x2.copy()
figure(figsize=(16,16))
sift.plot_matches(im1, im2, l1, l2, matches, True)#可视化
show()
def F_from_ransac(x1, x2, model, maxiter=5000, match_threshold=1e-6):
"""
使用RANSAC从点对应中稳健估计基本矩阵F.
（来自http://www.scipy.org/Cookbook/RANSAC的ransac.py）。
input: x1, x2 (3*n arrays) points in hom. coordinates. """
from PCV.tools import ransac
data = np.vstack((x1, x2))
d = 10 # 20 is the original
# 计算F并返回inlier索引
F, ransac_data = ransac.ransac(data.T, model,
8, maxiter, match_threshold, d, return_all=True)
return F, ransac_data['inliers']
