vlfeat-0.9.20\bin\win32 added to the path path of the system in order to use the SIFT command at the command linepip install pysqlitepip isntall matplotlibpip install cherrypyStep1.py#-*-Coding:utf-8-*-# step1.py: Extracts the feature points of a picture and generates a Word file vocabulary.pklImportPickleImportVocabularyImportImtoolsImportSift# imlist is a list of the name of the picture, the picture is placed under the static folderImlist = Imtools.get_imlist (' static/')# Total number of picturesNbr_images = Len (imlist)# Store the feature points of each picture in the corresponding. Sift signature fileFeatlist = [imlist[i][:-3]+' Sift ' forIinchRange (Nbr_images)] forIinchRange (nbr_images): Sift.process_image (Imlist[i], featlist[i])# Use K-means to train the corresponding words in the image feature file cluster# time relationship, only 46 images are used here, so only 46 words are createdVOC = vocabulary. Vocabulary (' Imagewords ') Voc.train (Featlist, $,Ten)# Save the words in Vocabulary.pkl withOpen' Vocabulary.pkl ',' WB ') asF:pickle.dump (VOC,F)# Print out the total number of wordsPrint ' vocabulary is: ', Voc.name, Voc.nbr_wordsStep2.py#-*-Coding:utf-8-*-# step2.py: According to the word file, the picture word into the SQLite databaseImportPickleImportSiftImportImagesearchImportImtools# List of picture namesImlist = Imlist = Imtools.get_imlist (' static/')# Number of picturesNbr_images = Len (imlist)# A list of image feature filesFeatlist = [imlist[i][:-3]+' Sift ' forIinchRange (Nbr_images)]# load Word file# put the word, picture name, address into the database images.db withOpen' Vocabulary.pkl ',' RB ') asF:VOC = Pickle.load (f) indx = Imagesearch. Indexer (' images.db ', VOC) indx.create_tables () forIinchRange (nbr_images): LOCS,DESCR = Sift.read_features_from_file (Featlist[i]) Indx.add_to_index (IMLIST[I],DESCR)# Submit the command to executeIndx.db_commit ()Step3.py127.0.0.1:8080#-*-Coding:utf-8-*-# step3.py: Use CherryPy to do interactive interface, display the resultImportCherryPy, OS, urllib, pickleImportImtools fromNumPyImport*ImportImagesearch# CherryPy Page# Web root directory is set in configuration file service.conf# The default port is 8080 class searchimage: def __init__(self): # Load Picture Name listSelf.imlist = Imtools.get_imlist (' static/') self.nbr_images = Len (self.imlist) self.ndx = Range (self.nbr_images)# Load the generated Word filef = open (' Vocabulary.pkl ',' RB ') SELF.VOC = Pickle.load (f) f.close ()# Set the number of pictures to start showingSelf.maxres = the # Set the structure of the pageSelf.header ="" " <!doctype html> "" "Self.footer ="" </body> "" " # Response to index page # Randomly display images when no search is found # When searching for a picture similar to that of the picture, depending on the visual word def index(self,query=none):SELF.SRC = Imagesearch. Searcher (' images.db ', SELF.VOC) HTML = self.header html + ="" " <br/> Click an image to search. <a href= '? query= ' > Random selection </a> of images. <br/><br/> "" " ifQuery# Pictures showing the results of the queryres = self.src.query (query) [: Self.maxres] forDist,ndxinchRes:imname = Self.src.get_filename (ndx) HTML + ="<a href= '? query="+imname+">"HTML + ="+imname+"' width= '/>"HTML + ="</a>" Else:# randomly displayed picturesRandom.shuffle (SELF.NDX) forIinchSelf.ndx[:self.maxres]: Imname = self.imlist[i] html + ="<a href= '? query="+imname+">"HTML + ="+imname+"' width= '/>"HTML + ="</a>"HTML + = Self.footerreturnHTML index.exposed =True# Launch AppCherrypy.quickstart (Searchimage (),'/', Os.path.join (Os.path.dirname (__file__),' service.conf '))When not searching:
When you click Search:
Search for similar images with images (visual words)
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