import pickle
from PCV.imagesearch import imagesearch
from PCV.localdescriptors import sift
from sqlite3 import dbapi2 as sqlite
from PCV.tools.imtools import get_imlist
# 获取图像列表
imlist = get_imlist('./first500/')
nbr_images = len(imlist)
# 获取特征列表
featlist = [imlist[i][:-3] + 'sift' for i in range(nbr_images)]
# 载入词汇
f = open('./first500/vocabulary.pkl', 'rb')
voc = pickle.load(f)
f.close()
src = imagesearch.Searcher('testImaAdd.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])
src = imagesearch.Searcher('testImaAdd.db', voc)
print('try a query...')
nbr_results = 12
res = [w[1] for w in src.query(imlist[0])[:nbr_results]]
imagesearch.plot_results(src, res)
import pickle
from PCV.imagesearch import imagesearch
from PCV.localdescriptors import sift
from sqlite3 import dbapi2 as sqlite
from PCV.tools.imtools import get_imlist
imlist = get_imlist('./first500/')
nbr_images = len(imlist)
featlist = [imlist[i][:-3]+'sift' for i in range(nbr_images)]
f = open('./first500/vocabulary.pkl', 'rb')
voc = pickle.load(f)
f.close()
src = imagesearch.Searcher('web.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]
src = imagesearch.Searcher('web.db',voc)
print 'try a query...'
nbr_results = 12
res = [w[1] for w in src.query(imlist[39])[:nbr_results]]
imagesearch.plot_results(src,res)
res_reg = [w[1] for w in src.query(imlist[q_ind])[:nbr_results]]
print 'top matches (regular) : ', res_reg
q_locs, q_descr = sift.read_features_from_file(featlist[q_ind])
fp = homography.make_homog(q_locs[:, :2].T)
model = homography.RansacModel()
rank = {}
for ndx in res_reg[1:]:
locs, descr = sift.read_features_from_file(featlist[ndx])
matches = sift.match(q_descr, descr)
ind = matches.nonzero()[0]
ind2 = matches[ind]
tp = homography.make_homog(locs[:, :2].T)
try:
H, inliners = homography.H_from_ransac(fp[:, ind],
tp[:, ind2],
model,
match_theshold=4)
except:
inliners = []
rank[ndx] = len(inliners)
sorted_rank = sorted(rank.items(), key=lambda t: t[1], reverse=True)
res_geom = [res_reg[0]] + [s[0] for s in sorted_rank]
# 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
# 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-1]) # because 'ndx' is a rowid of the DB that starts at 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])
rank = {}
# load image features for result
for ndx in res_reg[1:]:
locs, descr = sift.read_features_from_file(
featlist[ndx -
1]) # because 'ndx' is a rowid of the DB that starts at 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])
# RANSAC model for homography fitting
#用单应性进行拟合建立RANSAC模型
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_features_from_file(featlist[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
