print "regular results for query %d:" % query_imid, res
# Rerank by trying to fit a homography.
q_locs, q_descr = sift.read_features_from_file(featlist[query_imid])
fp = homography.make_homog(q_locs[:, :2].T)
model = homography.RansacModel()
rank = {}
for ndx in res[1:]:
locs, descr = sift.read_features_from_file(featlist[ndx - 1]) # res is 1-based
matches = sift.match(q_descr, descr)
ind = matches.nonzero()[0]
ind2 = [int(matches[i]) for i in ind]
tp = homography.make_homog(locs[:, :2].T)
try:
H, inliers = homography.H_from_ransac(fp[:, ind], tp[:, ind2], model, match_threshold=4)
except:
inliers = []
rank[ndx] = len(inliers)
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res[0]] + [s[0] for s in sorted_rank]
print "homography results for query %d" % query_imid, res_geom
imagesearch.plot_results(searcher, res[:8])
imagesearch.plot_results(searcher, res_geom[:8])
fp = homography.make_homog(q_locs[:, :2].T)
model = homography.RansacModel()
rank = {}
for ndx in res[1:]:
locs, descr = sift.read_features_from_file(featlist[ndx -
1]) # res is 1-based
matches = sift.match(q_descr, descr)
ind = matches.nonzero()[0]
ind2 = [int(matches[i]) for i in ind]
tp = homography.make_homog(locs[:, :2].T)
try:
H, inliers = homography.H_from_ransac(fp[:, ind],
tp[:, ind2],
model,
match_threshold=4)
except:
inliers = []
rank[ndx] = len(inliers)
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res[0]] + [s[0] for s in sorted_rank]
print 'homography results for query %d' % query_imid, res_geom
imagesearch.plot_results(searcher, res[:8])
imagesearch.plot_results(searcher, res_geom[:8])
def euclid():
nbr_results = 6
for n_im in xrange(len(imlist)):
print "n_im=", n_im
res = [w[1] for w in src.query(imlist[n_im])[:nbr_results]]
imagesearch.plot_results(src, res)
import imagesearch
import pickle
import sift
import imtools
import vocabulary as voc
# import PIL and pylab for plotting
from PIL import Image
from scipy.cluster.vq import *
from numpy import *
from pylab import *
#Teste de apresentacao de varios clusters
# get list of images
imlist = imtools.get_imlist('../img/sunsets/treino/')
nbr_images = len(imlist)
featurefiles = [ imlist[i][:-3]+'sift' for i in range(nbr_images) ]
nbr_results = 6
for i in range(2):
image = imlist[i]
res = [w[1] for w in src.query(image)[:nbr_results]]
imagesearch.plot_results(src,res)
"""After ch07_buildindex.py has built an index in test.db, this program can
query it.
"""
imlist = imtools.get_imlist('/Users/thakis/Downloads/ukbench/first1000')[:100]
imcount = len(imlist)
featlist = [imlist[i][:-3] + 'sift' for i in range(imcount)]
with open('vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
searcher = imagesearch.Searcher('test.db', voc)
locs, descr = sift.read_features_from_file(featlist[0])
imwords = voc.project(descr)
print 'ask using a histogram...'
print searcher.candidates_from_histogram(imwords)[:10]
print 'try a query...'
res = searcher.query(imlist[0])[:10]
print res
print 'score:'
# Score a small subset, so this runs fast.
print imagesearch.compute_ukbench_score(searcher, imlist[:10])
# Plot images most similar to imlist[0].
imagesearch.plot_results(searcher, [r[1] for r in res[:6]])
print 'top matches (regular):', res_reg
# load image features for query image
q_locs,q_descr = sift.read_features_from_file(featlist[q_ind])
fp = homography.make_homog(q_locs[:,:2].T)
# RANSAC model for homography fitting
model = homography.RansacModel()
rank = {}
# load image features for result
for ndx in res_reg[1:]:
locs,descr = sift.read_features_from_file(featlist[ndx])
# get matches
matches = sift.match(q_descr,descr)
ind = matches.nonzero()[0]
ind2 = matches[ind]
tp = homography.make_homog(locs[:,:2].T)
# compute homography, count inliers. if not enough matches return empty list
try:
H,inliers = homography.H_from_ransac(fp[:,ind],tp[:,ind2],model,match_theshold=4)
except:
inliers = []
# store inlier count
rank[ndx] = len(inliers)
# sort dictionary to get the most inliers first
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]]+[s[0] for s in sorted_rank]
print 'top matches (homography):', res_geom
# plot the top results
imagesearch.plot_results(src,res_reg[:8])
imagesearch.plot_results(src,res_geom[:8])
# 検索結果の画像特徴量を読み込む
for ndx in res_reg[1:]:
locs,descr = sift.read_features_from_file(featlist[ndx-1])
locs[:,[0,1]] = locs[:,[1,0]]
# 一致度を調べる
matches = sift.match(q_descr,descr)
ind = matches.nonzero()[0]
ind2 = matches[ind]
tp = homography.make_homog(locs[:,:2].T)
# ホモグラフィーを計算し、インライアを数える。
# 一致度が足りなければ空リストを返す。
try:
H,inliers = homography.H_from_ransac(fp[:,ind],tp[:,ind2],
model,match_theshold=4)
except:
inliers = []
# インライアの数を格納する
rank[ndx] = len(inliers)
# 最もモデルに当てはまるものが先頭になるよう辞書をソートする
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]]+[s[0] for s in sorted_rank]
print 'top matches (homography):', res_geom
# 検索結果を描画する
imagesearch.plot_results(src,res_reg[:8])
imagesearch.plot_results(src,res_geom[:8])
q_locs, q_descr = sift.read_feature_from_file(feature[q_ind])
fp = homography.make_homog(q_locs[:, :2].T)
model = homography.RansacModel()
rank = {}
# load image features for result
#载入候选图像的特征
for ndx in res_reg[1:]:
locs,descr = sift.read_feature_from_file(feature[ndx]) # because 'ndx' is a rowid of the DB that starts at 1
# get matches
matches = sift.match(q_descr,descr)
ind = matches.nonzero()[0]
ind2 = matches[ind]
tp = homography.make_homog(locs[:,:2].T)
# compute homography, count inliers. if not enough matches return empty list
try:
H,inliers = homography.H_from_ransac(fp[:,ind],tp[:,ind2],model,match_theshold=4)
except:
inliers = []
# store inlier count
rank[ndx] = len(inliers)
# sort dictionary to get the most inliers first
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]]+[s[0] for s in sorted_rank]
print 'top matches (homography):', res_geom
# 显示查询结果
imagesearch.plot_results(src,res_reg[:8]) #常规查询
imagesearch.plot_results(src,res_geom[:8]) #重排后的结果
import imagesearch
"""After ch07_buildindex.py has built an index in test.db, this program can
query it.
"""
imlist = imtools.get_imlist('/Users/thakis/Downloads/ukbench/first1000')[:100]
imcount = len(imlist)
featlist = [imlist[i][:-3] + 'sift' for i in range(imcount)]
with open('vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
searcher = imagesearch.Searcher('test.db', voc)
locs, descr = sift.read_features_from_file(featlist[0])
imwords = voc.project(descr)
print 'ask using a histogram...'
print searcher.candidates_from_histogram(imwords)[:10]
print 'try a query...'
res = searcher.query(imlist[0])[:10]
print res
print 'score:'
# Score a small subset, so this runs fast.
print imagesearch.compute_ukbench_score(searcher, imlist[:10])
# Plot images most similar to imlist[0].
imagesearch.plot_results(searcher, [r[1] for r in res[:6]])
for ndx in res_reg[1:]:
locs, descr = sift.read_features_from_file(featlist[ndx])
# 获取匹配数
matches = sift.match(q_descr, descr)
ind = matches.nonzero()[0]
ind2 = matches[ind]
tp = homography.make_homog(locs[:, :2].T)
# 计算单应性，对内点计数。如果没有足够的匹配数则返回空列表
try:
H, inliers = homography.H_from_ransac(fp[:, ind],
tp[:, ind2],
model,
match_theshold=4)
except:
inliers = []
# 存储内点数
rank[ndx] = len(inliers)
# 将字典排序，以首先获取最内层的内点数
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]] + [s[0] for s in sorted_rank]
print 'top matches (homography):', res_geom
# 显示靠前的搜索结果
for i in range(8):
imagesearch.plot_results(src, res_reg[i:i + 1])
imagesearch.plot_results(src, res_geom[i:i + 1])
def euclid():
nbr_results = 6
for n_im in xrange(len(imlist)):
print "n_im=",n_im
res = [w[1] for w in src.query(imlist[n_im])[:nbr_results]]
imagesearch.plot_results(src,res)
import pickle
imlist = imtools.get_imlist('first500')
nbr_images = len(imlist)
featlist = [imlist[i][:-3] + 'sift' for i in range(nbr_images)]
'''
for i in range(nbr_images):
sift.process_image(imlist[i],featlist[i])
'''
f = open('vocabulary.pkl', 'rb')
voc = pickle.load(f)
f.close()
src = imagesearch.Searcher('test.db', voc)
locs, descr = sift.read_features_from_file(featlist[0])
iw = voc.project(descr)
print 'ask using a histogram...'
print src.candidates_from_histogram(iw)[:10]
print '\n==================\n'
print 'try a query...'
print src.query(imlist[0])[:10]
print 'score = ', imagesearch.compute_ukbench_score(src, imlist[:4])
nbr_results = 6
res = [w[1] for w in src.query(imlist[0])[:nbr_results]]
imagesearch.plot_results(src, res)
