基于BOW图像检索python实现
代码地址：https://pan.baidu.com/s/1X2Ykoi8j3_6f6qTvhh95jw
准备：
- vlfeat（SIFT的开源实现）
- 安装sqlite-web：
pip install sqlite-web(好像没有用到，python3有自带的sqlite3) - 安装cherryPy（轻量级网页服务器）：
pip install cherrypy - 图片库：First1000
流程
- 用vlfeat（SIFT特征模型）提取每一张图片的特征点
- 将每一张图的特征点采样聚类成图片的视觉单词
- 即视觉单词，就是对应图片的代表
- 创建数据库，将每张图片的视觉单词入库，并建立索引
- 编写cherryPy的web应用，根据点击的图片搜索相似的图片
提取图片特征点并生成单词文件
# -*- 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('first1000/')
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('first1000/vocabulary.pkl', 'wb') as f:
pickle.dump(voc, f)
print ('vocabulary is:', voc.name, voc.nbr_words)将图片单词信息存进数据库
# -*- coding: utf-8 -*-
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('first1000/')
nbr_images = len(imlist)
#获取特征列表
featlist = [imlist[i][:-3]+'sift' for i in range(nbr_images)]
# load vocabulary
#载入词汇
with open('first1000/vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
#创建索引
indx = imagesearch.Indexer('testImaAdd.db',voc)
indx.create_tables()
# go through all images, project features on vocabulary and insert
#遍历所有的图像，并将它们的特征投影到词汇上
for i in range(nbr_images)[:1000]:
locs,descr = sift.read_features_from_file(featlist[i])
indx.add_to_index(imlist[i],descr)
# commit to database
#提交到数据库
indx.db_commit()
con = sqlite.connect('testImaAdd.db')
print (con.execute('select count (filename) from imlist').fetchone())
print (con.execute('select * from imlist').fetchone())
执行后会生成：
测试：
将数据放进数据库中之后就可以开始测试我们的图片索引。
# -*- coding: utf-8 -*-
import pickle
from PCV.localdescriptors import sift
from PCV.imagesearch import imagesearch
from PCV.geometry import homography
from PCV.tools.imtools import get_imlist
# load image list and vocabulary
#载入图像列表
imlist = get_imlist('first1000/')
nbr_images = len(imlist)
#载入特征列表
featlist = [imlist[i][:-3]+'sift' for i in range(nbr_images)]
#载入词汇
with open('first1000/vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
src = imagesearch.Searcher('testImaAdd.db',voc)
# index of query image and number of results to return
#查询图像索引和查询返回的图像数
q_ind = 0
nbr_results = 20
# regular query
# 常规查询(按欧式距离对结果排序)
res_reg = [w[1] for w in src.query(imlist[q_ind])[:nbr_results]]
print ('top matches (regular):', res_reg)
# load image features for query image
#载入查询图像特征
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
#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]) #重排后的结果配置cherryPy网页应用
- 每点击图片时随机显示10张图片
- 点击其中一张图片，搜索相似的图片显示出来
- 本质是根据点击图片的单词，寻找在数据库中与之相近的单词，并将其显示出来
- 浏览地址： http://127.0.0.1:8080
# -*- coding: utf-8 -*-
import cherrypy
import pickle
import urllib
import os
from numpy import *
#from PCV.tools.imtools import get_imlist
from PCV.imagesearch import imagesearch
import random
"""
This is the image search demo in Section 7.6.
"""
class SearchDemo:
def __init__(self):
# 载入图像列表
self.path = 'first1000/'
#self.path = 'D:/python_web/isoutu/first500/'
self.imlist = [os.path.join(self.path,f) for f in os.listdir(self.path) if f.endswith('.jpg')]
#self.imlist = get_imlist('./first500/')
#self.imlist = get_imlist('E:/python/isoutu/first500/')
self.nbr_images = len(self.imlist)
print (self.imlist)
print (self.nbr_images)
self.ndx = list(range(self.nbr_images))
print (self.ndx)
# 载入词汇
# f = open('first1000/vocabulary.pkl', 'rb')
with open('first1000/vocabulary.pkl','rb') as f:
self.voc = pickle.load(f)
#f.close()
# 显示搜索返回的图像数
self.maxres = 10
# header and footer html
self.header = """
Image search
"""
self.footer = """