def index(self, query=None):
self.src = imagesearch.Searcher('test.db', self.voc)
html = self.header
html += """
<br />
Click an image to search. <a href='?query='>Random selection</a> of images.
<br /><br />
"""
if query:
# データベースに問い合わせ上位の画像を得る
res = self.src.query(query)[:self.maxres]
for dist, ndx in res:
imname = self.src.get_filename(ndx)
html += "<a href='?query=" + imname + "'>"
html += "<img src='" + imname + "' width='100' />"
html += "</a>"
else:
# クエリがなければランダムに選択する
random.shuffle(self.ndx)
for i in self.ndx[:self.maxres]:
imname = self.imlist[i]
html += "<a href='?query=" + imname + "'>"
html += "<img src='" + imname + "' width='100' />"
html += "</a>"
html += self.footer
return html
def index(self, query=None):
self.src = imagesearch.Searcher('test.db', self.voc)
html = self.header
html += """
<br />
Click an image to search. <a href='?query='> Random selection </a> of images.
<br /><br />
"""
if query:
# query the database and get top images
#查询数据库，并获取前面的图像
res = self.src.query(query)[:self.maxres]
for dist, ndx in res:
imname = self.src.get_filename(ndx)
html += "<a href='?query=" + imname + "'>"
html += "<img src='" + imname + "' width='200' />"
html += "</a>"
# show random selection if no query
# 如果没有查询图像则随机显示一些图像
else:
np.random.shuffle(self.ndx)
for i in self.ndx[:self.maxres]:
imname = self.imlist[i]
html += "<a href='?query=" + imname + "'>"
html += "<img src='" + imname + "' width='200' />"
html += "</a>"
html += self.footer
return html
def index(self, query=None):
database_name = 'test.db'
self.src = imagesearch.Searcher(database_name, self.voc)
html = self.header
html += """
<br />
Click an image to search. <a href='?query='>Random selection</a> of images.
<br /><br />
"""
if query:
# 查询数据库并获取靠前的图像
res = self.src.query(query)[:self.maxres]
for dist, ndx in res:
imname = self.src.get_filename(ndx)
html += "<a href='?query=" + imname + "'>"
html += "<img src='" + imname + "' width='100' />"
html += "</a>"
else:
# 如果没有查询图像,则显示随机选择的图像
random.shuffle(self.ndx)
for i in self.ndx[:self.maxres]:
imname = self.imlist[i]
html += "<a href='?query=" + imname + "'>"
html += "<img src='" + imname + "' width='100' />"
html += "</a>"
html += self.footer
return html
def query_img(img):
query_img_list = []
sift.process_image(img, './ukbench/tmp.sift')
with open('vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
index = imagesearch.Indexer('test.db', voc)
locs, descr = sift.read_features_from_file('./ukbench/tmp.sift')
index.add_to_index(img, descr)
index.db_commit()
src = imagesearch.Searcher('test.db', voc)
res = src.query(img)[:3]
for dist, ndx in res:
imname = src.get_filename(ndx)
query_img_list.append(imname)
return query_img_list
def index(self, query=None):
self.searcher = imagesearch.Searcher('test.db', self.voc)
html = self.header
html += """\
<br>
Click an image to search. <a href="?query=">Random selection</a> of images.
<br><br>"""
if query:
res = self.searcher.query(query)[:self.maxresults]
for dist, ndx in res:
imname = self.searcher.get_filename(ndx)
html += '<a href="?query=%s">' % imname
html += '<img src="/img/%s" width=100>' % os.path.basename(imname)
html += '</a>'
else:
random.shuffle(self.ndx)
for i in self.ndx[:self.maxresults]:
imname = self.imlist[i]
html += '<a href="?query=%s">' % imname
html += '<img src="/img/%s" width=100>' % os.path.basename(imname)
html += '</a>'
html += self.footer
return html
import numpy as np
import csv
import random
import math
from scipy.stats import norm
from copy import deepcopy
execfile('loaddata.py')
path = 'nail_book03'
train_csvfile = 'train_labels04.csv'
test_csvfile = 'test_labels04.csv'
csvfile = 'train_labels.csv'
HSV_fname = 'fHSV_hist.txt'
hsv_flag_fname = 'hsv_flag.csv'
src = imagesearch.Searcher('nail_image500.db', voc)
def main_boost():
train_ratio = 0.5
src = Searcher('nail_image500.db')
# get histogram
HSV_hist = np.loadtxt(HSV_fname) # fHSVのヒストグラムを読み込む
# normalize HSV hist
HSV_max = np.max(HSV_hist, axis=1)
HSV_min = np.min(HSV_hist, axis=1)
HSV_rang = (HSV_max - HSV_min).reshape((len(HSV_max), 1))
HSV_hist_norm = [(HSV_hist[i] - HSV_min[i]) / HSV_rang[i]
for i in xrange(len(HSV_rang))]
import homography
import imtools
import sift
import imagesearch
"""After ch07_buildindex.py has built an index in test.db, this program
queries it, and fits a homography to improve query results.
"""
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)
query_imid = 50
res_count = 20
res = [w[1] for w in searcher.query(imlist[query_imid])[:res_count]]
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:]:
voc.train(featlist, 1000, 10)
with open('vocabulary.pkl', 'wb') as f:
pickle.dump(voc, f)
print 'vocabulary is:', voc.name, voc.nbr_words
# load vocabulary
with open('vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
# create indexer
indx = imagesearch.Indexer('test.db', voc)
indx.create_tables()
# go through all images, project features on vocabulary and insert
for i in range(nbr_images)[:100]:
locs, descr = sift.read_features_from_file(featlist[i])
indx.add_to_index(imlist[i], descr)
# commit to database
indx.db_commit()
from pysqlite2 import dbapi2 as sqlite
con = sqlite.connect('test.db')
print con.execute('select count (filename) from imlist').fetchone()
(1000, )
print con.execute('select * from imlist').fetchone()
(u'ukbench00000.jpg', )
src = imagesearch.Searcher('test.db')
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('test.db')
print 'try a query...'
print src.query(imlist[0])[:10]