def importMapFiles(BadFileList, GoodFileList, IDmap):
good_docs = []
bad_docs = []
for badFile in BadFileList:
mapDict = utility.loadObject(badFile)
bad_docs.append(importMapFile(mapDict, IDmap))
print len(IDmap)
for goodFile in GoodFileList:
mapDict = utility.loadObject(badFile)
good_docs.append(importMapFile(mapDict, IDmap))
print len(IDmap)
return good_docs, bad_docs
def importMapFiles( BadFileList, GoodFileList , IDmap ):
good_docs = []
bad_docs = []
for badFile in BadFileList:
mapDict = utility.loadObject(badFile)
bad_docs.append(importMapFile(mapDict, IDmap))
print len(IDmap)
for goodFile in GoodFileList:
mapDict = utility.loadObject(badFile)
good_docs.append(importMapFile(mapDict, IDmap))
print len(IDmap)
return good_docs, bad_docs
def importGoodReport(sGoodDirectory, dictCurrent, iBadRun):
listGoodReport = []
utility.findDesiredFiles(sGoodDirectory, listGoodReport, 'map.obj')
print 'good:', len(listGoodReport)
iGoodRun = 0
for sFile in listGoodReport:
dictTmp = utility.loadObject(sFile)
addGoodSample(dictTmp, dictCurrent)
iGoodRun = iGoodRun + 1
def importGoodReport(sGoodDirectory, dictCurrent, iBadRun):
listGoodReport = []
utility.findDesiredFiles(sGoodDirectory, listGoodReport, 'map.obj')
print 'good:', len(listGoodReport)
iGoodRun = 0
for sFile in listGoodReport:
dictTmp = utility.loadObject(sFile)
addGoodSample( dictTmp, dictCurrent )
iGoodRun = iGoodRun + 1
def importBadReport(sBadDirectory, dictCurrent, setCallSite):
listBadReport = []
utility.findDesiredFiles(sBadDirectory, listBadReport, 'map.obj')
print 'bad:', len(listBadReport)
iBadRun = 0
for sFile in listBadReport:
dictTmp = utility.loadObject(sFile)
addBadSample(dictTmp, dictCurrent, setCallSite)
iBadRun = iBadRun + 1
return iBadRun
def importBadReport(sBadDirectory, dictCurrent, setCallSite):
listBadReport = []
utility.findDesiredFiles(sBadDirectory, listBadReport, 'map.obj')
print 'bad:', len(listBadReport)
iBadRun = 0
for sFile in listBadReport:
dictTmp = utility.loadObject(sFile)
addBadSample( dictTmp, dictCurrent , setCallSite)
iBadRun = iBadRun + 1
return iBadRun
def importBadReport(sBadDirectory, dictCurrent, mapInstruction, mapFunction):
listBadReport = []
utility.findDesiredFiles(sBadDirectory, listBadReport, 'map.obj')
print 'bad:', len(listBadReport)
iBadRun = 0
for sFile in listBadReport:
#print sFile
dictTmp = utility.loadObject(sFile)
#print dictTmp[int('c57ad7', 16)]
addBadSample(dictTmp, dictCurrent, mapInstruction, mapFunction)
iBadRun = iBadRun + 1
return iBadRun
def importBadReport(sBadDirectory, dictCurrent, mapInstruction, mapFunction ):
listBadReport = []
utility.findDesiredFiles(sBadDirectory, listBadReport, 'map.obj')
print 'bad:', len(listBadReport)
iBadRun = 0
for sFile in listBadReport:
#print sFile
dictTmp = utility.loadObject(sFile)
#print dictTmp[int('c57ad7', 16)]
addBadSample( dictTmp, dictCurrent, mapInstruction, mapFunction )
iBadRun = iBadRun + 1
return iBadRun
import string
import re
import os
import sys
import commands
import pickle
import glob
import math
import utility
import gc
from sets import Set
if __name__ == '__main__':
sReportDirectory = sys.argv[1]
listReport = []
utility.findDesiredFiles(sReportDirectory, listReport, 'sample.count.obj')
iCount = 0
for report in listReport:
print report
l = utility.loadObject(report)
print l
iCount += l[0]
print len(listReport), iCount, iCount*1.0/len(listReport)
#exit(0)
def print_rank(finalResult, badDict):
rank = 0
for (key, value) in sorted(finalResult.iteritems(),
key=lambda d: d[1],
reverse=True):
strTmp = key.split('_')
print strTmp[0], strTmp[1], badDict[int(strTmp[0], 16)], value
rank = rank + 1
if rank == 100:
break
if __name__ == '__main__':
setCallSite = utility.loadObject(sys.argv[1])
sBadDirectory = sys.argv[2]
sGoodDirectory = sys.argv[3]
dictCurrent = {}
iBadRun = importBadReport(sBadDirectory, dictCurrent, setCallSite)
importGoodReport(sGoodDirectory, dictCurrent, iBadRun)
count = 0
for key in dictCurrent:
for num in range(0, 6):
if dictCurrent[key][num] > 0:
count += 1
print 'total predicate:', len(setCallSite) * 6
print len(dictCurrent) * 6
dictTmp = utility.loadObject(sFile)
addGoodSample( dictTmp, dictCurrent )
iGoodRun = iGoodRun + 1
def print_rank(finalResult, badDict):
rank = 0
for (key, value) in sorted(finalResult.iteritems(), key = lambda d:d[1], reverse = True ):
strTmp = key.split('_')
print strTmp[0], strTmp[1], badDict[int(strTmp[0], 16)], value
rank = rank + 1
if rank == 100:
break
if __name__ == '__main__':
setCallSite = utility.loadObject(sys.argv[1])
sBadDirectory = sys.argv[2]
sGoodDirectory = sys.argv[3]
dictCurrent = {}
iBadRun = importBadReport(sBadDirectory, dictCurrent, setCallSite)
importGoodReport(sGoodDirectory, dictCurrent, iBadRun)
count = 0
for key in dictCurrent:
for num in range(0,6):
if dictCurrent[key][num] > 0:
count += 1
print 'total predicate:', len(setCallSite) * 6
print len(dictCurrent) * 6
def __init__(self, videoPath):
self.videoPath = videoPath
SIFTfeatures = [] #
imageNames = [] # name of each image
# Read in video frames
for item in os.listdir(videoPath):
imagePath = videoPath +"/"+ item
locations, features = sift.siftFeature(imagePath)
SIFTfeatures.append(features)
imageNames.append(item)
# Histogramize each image
imageHistograms = []
vocabulary = util.loadObject("data/voc.pkl")
vocSize = len(vocabulary)
for imageFeature in SIFTfeatures:
imageFeature = util.normalizeSIFT(imageFeature)
histogram = self.buildHistogram(imageFeature, vocabulary)
imageHistograms.append(histogram)
imageHistograms = np.array(imageHistograms)
self.imageNames = imageNames
self.imageHistograms = imageHistograms
self.SIFTfeatures = SIFTfeatures
# Cluster frames
self.numOfFrames = len(imageNames)
self.numOfCentriods = int(self.numOfFrames / 10)
kmeans = KMeans(init="k-means++", n_clusters=self.numOfCentriods, n_init=10)
kmeans.fit(self.imageHistograms)
cluster_centroids = kmeans.cluster_centers_
# Get components of each cluter
codes, distance = vq(self.imageHistograms, cluster_centroids)
dict = {}
indice = 0
for code in codes:
keys = dict.keys()
if str(code) in keys:
dict[str(code)].append(indice)
else:
dict[str(code)] = []
dict[str(code)].append(indice)
indice += 1
# stack all SIFT features to perform PCA
stackOfSIFTfeatures = SIFTfeatures[0]
for eachFeature in SIFTfeatures[1:]:
stackOfSIFTfeatures = np.vstack((stackOfSIFTfeatures, eachFeature))
V,S, mean = pca.pca(stackOfSIFTfeatures)
self.V = V
# Perform near duplicate within each cluster
KEYFRAMES = []
keys = dict.keys()
for key in keys:
cluster = dict[key]
clusterFeatures = []
for i in cluster:
clusterFeatures.append(self.SIFTfeatures[i])
potentialKeyFrames = self.identifyKeyFrame(clusterFeatures, cluster)
KEYFRAMES += potentialKeyFrames
print str(cluster) +": "+ str(potentialKeyFrames)
self.keyFrames = KEYFRAMES
compressedHistogram = self.imageHistograms[KEYFRAMES[0]]
compressedImageName = [self.imageNames[KEYFRAMES[0]]]
for keyframe in KEYFRAMES[1:]:
compressedHistogram = np.vstack((compressedHistogram, self.imageHistograms[keyframe]))
compressedImageName.append(self.imageNames[keyframe])
self.compressedHistogram = compressedHistogram
self.compressedImageName = compressedImageName
