def add(file):
targetImgSiftPath = file[:-3] + 'sift'
sift.process_image(file, targetImgSiftPath)
voc = vocabulary.Vocabulary('target')
voc.train([targetImgSiftPath], 100, 10)
print('vocabulary is:', voc.name, voc.nbr_words)
locs, descr = sift.read_features_from_file(targetImgSiftPath)
indx = imagesearch.Indexer('testImaAdd.db', voc)
indx.add_to_index(file, descr)
indx.db_commit()
return voc
# -*- coding: utf-8 -*-
import pickle
from PCV.imagesearch import vocabulary
from PCV.tools.imtools import get_imlist
from PCV.localdescriptors import sift
#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)]
#提取文件夹下图像的sift特征
for i in range(nbr_images):
sift.process_image(imlist[i], featlist[i])
#生成词汇
voc = vocabulary.Vocabulary('ukbenchtest')
voc.train(featlist, 1000, 10)
#保存词汇
# saving vocabulary
with open('./first500/vocabulary.pkl', 'wb') as f:
pickle.dump(voc, f)
print('vocabulary is:', voc.name, voc.nbr_words)
# -*- coding: utf-8 -*-
import pickle
from PCV.imagesearch import vocabulary
from PCV.tools.imtools import get_imlist
from PCV.localdescriptors import sift
imlist = 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])
voc = vocabulary.Vocabulary('ukbench')
voc.train(featlist, 1000, 10)
# saving vocabulary
with open('./first500/vocabulary.pkl', 'wb') as f:
pickle.dump(voc, f)
print 'vocabulary is:', voc.name, voc.nbr_words
# -*- coding: utf-8 -*-
import pickle
from PCV.imagesearch import vocabulary
from PCV.tools.imtools import get_imlist
import sift
# 该文件生成训练集的词汇 并将结果保存在pkl
#获取图像列表
imlist = get_imlist('training/')
nbr_images = len(imlist)
#获取特征列表
featlist = [imlist[i][:-3] + 'sift' for i in range(nbr_images)]
#提取文件夹下图像的sift特征
# for i in range(nbr_images):
# sift.process_image(imlist[i], featlist[i])
#生成词汇
voc = vocabulary.Vocabulary('training')
voc.train(featlist, 100, 10)
# saving vocabulary
with open('training/vocabulary.pkl', 'wb') as f:
pickle.dump(voc, f)
print('vocabulary is:', voc.name, voc.nbr_words)