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()
# -*- coding: utf-8 -*-
from PCV.localdescriptors import sift, dsift
from pylab import *
from PIL import Image
dsift.process_image_dsift('gesture/empire.jpg', 'empire.dsift', 90, 40, True)
l, d = sift.read_features_from_file('empire.dsift')
im = array(Image.open('gesture/empire.jpg'))
sift.plot_features(im, l, True)
title('dense SIFT')
show()
# -*- coding: utf-8 -*-
from PCV.localdescriptors import sift, dsift
from pylab import *
from PIL import Image
dsift.process_image_dsift('../data/empire.jpg','empire.dsift',90,40,True)
l,d = sift.read_features_from_file('empire.dsift')
im = array(Image.open('../data/empire.jpg'))
sift.plot_features(im,l,True)
title('dense SIFT')
show()
from PIL import Image
from pylab import *
from numpy import *
from PCV.localdescriptors import dsift, sift
"""
This is the dense SIFT illustration, it will reproduce the plot
in Figure 8-2.
"""
dsift.process_image_dsift('../data/empire.jpg', 'empire.sift', 90, 40, True)
l, d = sift.read_features_from_file('empire.sift')
im = array(Image.open('../data/empire.jpg'))
sift.plot_features(im, l, True)
show()
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()
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)
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()
from PIL import Image
from pylab import *
from numpy import *
from PCV.localdescriptors import dsift, sift
"""
This is the dense SIFT illustration, it will reproduce the plot
in Figure 8-2.
"""
dsift.process_image_dsift('../data/empire.jpg', 'empire.sift', 90, 40, True)
l,d = sift.read_features_from_file('empire.sift')
im = array(Image.open('../data/empire.jpg'))
sift.plot_features(im, l, True)
show()
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)
