def __init__(self, eyetrackfolder, subjectassessmentfilename,
userfilename):
super(Eyetrack_Subject_Analysis, self).__init__()
self.subject_processor = SubjectAssessmentData(
subjectassessmentfilename)
self.curEyetrackDataFolder = eyetrackfolder
self.eyetrackDataFeature = np.array([])
self.mosForEachImage = {}
self.meanFeatureValueForEachImage = {}
self.pupilDiameterset = {}
self.individualPupilFeature = {}
self.meanvalueforpupilsize = {}
self.userinfofilename = userfilename
self.userInfo = {}
def __init__(self, eyetrackfolder,subjectassessmentfilename,userfilename):
super(Eyetrack_Subject_Analysis, self).__init__()
self.subject_processor = SubjectAssessmentData(subjectassessmentfilename)
self.curEyetrackDataFolder = eyetrackfolder
self.eyetrackDataFeature = np.array([])
self.mosForEachImage = {}
self.meanFeatureValueForEachImage = {}
self.pupilDiameterset = {}
self.individualPupilFeature = {}
self.meanvalueforpupilsize = {}
self.userinfofilename = userfilename
self.userInfo = {}
class Eyetrack_Subject_Analysis(object):
"""docstring for Eyetrack_Subject_Analysis"""
def __init__(self, eyetrackfolder, subjectassessmentfilename,
userfilename):
super(Eyetrack_Subject_Analysis, self).__init__()
self.subject_processor = SubjectAssessmentData(
subjectassessmentfilename)
self.curEyetrackDataFolder = eyetrackfolder
self.eyetrackDataFeature = np.array([])
self.mosForEachImage = {}
self.meanFeatureValueForEachImage = {}
self.pupilDiameterset = {}
self.individualPupilFeature = {}
self.meanvalueforpupilsize = {}
self.userinfofilename = userfilename
self.userInfo = {}
def getUserInfo(self):
userinfoprocessor = UserInfo(self.userinfofilename)
self.userInfo = userinfoprocessor.readUserInfoFile()
def getDisparityAngularFile(self, disparityfilesavepath):
files = os.listdir(self.curEyetrackDataFolder)
featureSet = []
index = 0
for file in files:
print index, file
index = index + 1
if os.path.splitext(file)[1] == ".csv":
curfile = self.curEyetrackDataFolder + '/' + file
curEyetrackDataProcessor = EyetrackerData(curfile)
curEyetrackDataProcessor.getDisparityAngularFile(
disparityfilesavepath)
def extrackEyetrackFeatures(self, basicEye):
self.getUserInfo()
files = os.listdir(self.curEyetrackDataFolder)
featureSet = []
index = 0
for file in files:
print index, file
index = index + 1
if os.path.splitext(file)[1] == ".csv":
curRecord = []
splitresult = file.split('_')
if len(splitresult) == 4:
imgName = splitresult[3][:-4] + '.jpg'
userName = splitresult[2]
else:
imgName = splitresult[4][:-4] + '.jpg'
userName = splitresult[2] + '_' + splitresult[3]
curRecord.append(imgName)
if basicEye == 'left' or basicEye == 'right':
curfile = self.curEyetrackDataFolder + '/' + file
elif basicEye == 'main' or basicEye == 'nomain':
print "Please Modify the folder for your eyetracker data"
if self.userInfo[
userName] == -1: #-1 means you choose the main eye and 1 means you choose the Auxiliary eye
curfile = "Your left eye folder" + '/' + file
else:
curfile = "Your right eye folder" + file
else:
print "Wrong choosed for the basic eye!"
return
curEyetrackDataProcessor = EyetrackerData(curfile)
curEyetrackFeatures = curEyetrackDataProcessor.getFixationFeatures(
)
curRecord.extend(curEyetrackFeatures)
featureSet.append(curRecord)
print curRecord
self.eyetrackDataFeature = np.array(featureSet)
def getRelationBetweenEyetrackFeaturesAndSubjectScore(self, basicEye):
"""验证总体特征与主观评分之间的相关性"""
self.extrackEyetrackFeatures(basicEye)
self.mosForEachImage = self.subject_processor.getAvgMosForEachImage()
#self.getPupilFeature()
if len(self.meanFeatureValueForEachImage) == 0:
self.getMeanFeatureValueForEachImage(basicEye)
#self.svmTrain()
# w,h = self.eyetrackDataFeature.shape
# print w,h
# percent=range(5,60,5)
# plccset=np.array([])
# for i in range(h-1):
# curPlcc = self.getRelationBetweenEyetrackFeatureAndSubjectScore(i)
# plccset = np.append(plccset,curPlcc)
# plt.xlabel('Disparity Percent in front of the sorted serial')
# plt.ylabel('LCC')
# xmajorLocator = plt.MultipleLocator(5)
# ymajorLocator = plt.MultipleLocator(0.1)
# ax = plt.subplot(111)
# plt.plot(percent,plccset,'-r*')
# ax.xaxis.set_major_locator(xmajorLocator)
# ax.yaxis.set_major_locator(ymajorLocator)
# plt.grid()
# plt.title('Relationship between the percentage we choosed and mos')
#
# plt.show()
# self.getRelationBetweenEyetrackFeatureAndSubjectScore(6)
def getRelationBetweenEyetrackFeatureAndSubjectScore(self, featureindex):
"""验证各特征与主观评分之间的相关性"""
if len(self.mosForEachImage) != len(self.meanFeatureValueForEachImage):
raise ValueError(
'the number of mos and feature is not equal,please check it')
else:
feature_score_pairlist = []
for key in self.mosForEachImage:
curList = [
key, self.mosForEachImage[key],
self.meanFeatureValueForEachImage[key][featureindex]
]
feature_score_pairlist.append(curList)
feature_score_pairlist = np.array(feature_score_pairlist)
feature_score_pairarray = np.array(feature_score_pairlist[:, 1:],
dtype=float)
x = feature_score_pairarray[:, 0]
y = feature_score_pairarray[:, 1]
plcc, pval = statstool.pearsonr(x, y)
srocc, pval = statstool.spearmanr(x, y)
print "feature %d,%f,%f" % (featureindex, plcc, srocc)
return plcc
#plt.plot(x,'b-')
#plt.plot(y,'r-')
#plt.show()
def getMeanFeatureValueForEachImage(self, basicEye):
"""获取针对每幅图像的平均特征值"""
if len(self.eyetrackDataFeature) == 0:
self.extrackEyetrackFeatures(basicEye)
classific = {}
index = 0
for item in self.eyetrackDataFeature:
imgName = item[0]
if imgName not in classific:
classific[imgName] = set([index])
else:
classific[imgName].add(index)
index += 1
# blinkfeaturesprocessor = EyetrackingDataBlinkFeaturesFactory()
# blinkfeatures = blinkfeaturesprocessor.getEyeBlinkFeatures()
for key in classific:
serial = list(classific[key])
tmp2 = self.eyetrackDataFeature[serial, 1:]
tmp = np.array(tmp2, dtype=float)
self.meanFeatureValueForEachImage[key] = np.mean(tmp, axis=0)
# tmp3 = np.mean(blinkfeatures[key],axis=0)
# self.meanFeatureValueForEachImage[key] = np.hstack((tmp3,self.meanFeatureValueForEachImage[key]))
#self.meanFeatureValueForEachImage[key] = np.append(self.meanFeatureValueForEachImage[key],self.meanvalueforpupilsize[key])
def getPupilFeature(self):
individualpupilsize = self.calAvgPupilDiameter()
averagepupilsizeforrecord = self.calAvgPupilForRecord()
meanvalueforpupilsize = {}
for key in averagepupilsizeforrecord:
splitResult = key.split('_')
imgName = splitResult[-1]
if len(splitResult) == 2:
userName = splitResult[0]
else:
userName = splitResult[0] + '_' + splitResult[1]
curpupilsize = averagepupilsizeforrecord[
key] / individualpupilsize[userName]
if imgName not in meanvalueforpupilsize:
meanvalueforpupilsize[imgName] = curpupilsize
else:
meanvalueforpupilsize[imgName] = np.append(
meanvalueforpupilsize[imgName], curpupilsize)
for key in meanvalueforpupilsize:
meanvalueforpupilsize[key] = meanvalueforpupilsize[key].reshape(
len(meanvalueforpupilsize[key]) / 2, 2)
meanvalueforpupilsize[key] = np.mean(meanvalueforpupilsize[key],
axis=0)
self.meanvalueforpupilsize = meanvalueforpupilsize
def calAvgPupilDiameter(self):
avgpupilsize = {}
for key in self.pupilDiameterset:
avgpupilsize[key] = np.mean(self.pupilDiameterset[key], axis=0)
return avgpupilsize
def calAvgPupilForRecord(self):
avgpupilsizeForRecord = {}
for key in self.individualPupilFeature:
avgpupilsizeForRecord[key] = np.mean(
self.individualPupilFeature[key], axis=0)
return avgpupilsizeForRecord
def getEyetrackerDataFeatures(self, basicEye, featuresfilesavename):
"""svm 学习训练 """
self.getRelationBetweenEyetrackFeaturesAndSubjectScore(basicEye)
feature_mos_set = np.array([])
num = 0
for key in self.mosForEachImage:
cur_set = np.array(self.mosForEachImage[key])
cur_set = np.append(cur_set,
self.meanFeatureValueForEachImage[key])
length = len(cur_set)
feature_mos_set = np.append(feature_mos_set, cur_set)
num += 1
feature_mos_set = feature_mos_set.reshape(num, length)
csvfile = file(featuresfilesavename, 'a+')
writer = csv.writer(csvfile)
writer.writerows(feature_mos_set)
csvfile.close()
class Eyetrack_Subject_Analysis(object):
"""docstring for Eyetrack_Subject_Analysis"""
def __init__(self, eyetrackfolder,subjectassessmentfilename,userfilename):
super(Eyetrack_Subject_Analysis, self).__init__()
self.subject_processor = SubjectAssessmentData(subjectassessmentfilename)
self.curEyetrackDataFolder = eyetrackfolder
self.eyetrackDataFeature = np.array([])
self.mosForEachImage = {}
self.meanFeatureValueForEachImage = {}
self.pupilDiameterset = {}
self.individualPupilFeature = {}
self.meanvalueforpupilsize = {}
self.userinfofilename = userfilename
self.userInfo = {}
def getUserInfo(self):
userinfoprocessor = UserInfo(self.userinfofilename)
self.userInfo = userinfoprocessor.readUserInfoFile()
def getDisparityAngularFile(self,disparityfilesavepath):
files = os.listdir(self.curEyetrackDataFolder)
featureSet = []
index = 0
for file in files:
print index,file
index = index+1
if os.path.splitext(file)[1] == ".csv":
curfile = self.curEyetrackDataFolder+'/'+file
curEyetrackDataProcessor = EyetrackerData(curfile)
curEyetrackDataProcessor.getDisparityAngularFile(disparityfilesavepath)
def extrackEyetrackFeatures(self,basicEye):
self.getUserInfo()
files = os.listdir(self.curEyetrackDataFolder)
featureSet = []
index = 0
for file in files:
print index,file
index = index+1
if os.path.splitext(file)[1] == ".csv":
curRecord = []
splitresult = file.split('_')
if len(splitresult) == 4:
imgName = splitresult[3][:-4]+'.jpg'
userName = splitresult[2]
else:
imgName = splitresult[4][:-4]+'.jpg'
userName = splitresult[2]+'_'+splitresult[3]
curRecord.append(imgName)
if basicEye == 'left' or basicEye == 'right':
curfile = self.curEyetrackDataFolder+'/'+file
elif basicEye == 'main' or basicEye == 'nomain':
print "Please Modify the folder for your eyetracker data"
if self.userInfo[userName] == -1:#-1 means you choose the main eye and 1 means you choose the Auxiliary eye
curfile = "Your left eye folder"+'/'+file
else:
curfile = "Your right eye folder"+file
else:
print "Wrong choosed for the basic eye!";
return
curEyetrackDataProcessor = EyetrackerData(curfile)
curEyetrackFeatures = curEyetrackDataProcessor.getFixationFeatures()
curRecord.extend(curEyetrackFeatures)
featureSet.append(curRecord)
print curRecord
self.eyetrackDataFeature = np.array(featureSet)
def getRelationBetweenEyetrackFeaturesAndSubjectScore(self,basicEye):
"""验证总体特征与主观评分之间的相关性"""
self.extrackEyetrackFeatures(basicEye)
self.mosForEachImage = self.subject_processor.getAvgMosForEachImage()
#self.getPupilFeature()
if len(self.meanFeatureValueForEachImage) == 0:
self.getMeanFeatureValueForEachImage(basicEye)
#self.svmTrain()
# w,h = self.eyetrackDataFeature.shape
# print w,h
# percent=range(5,60,5)
# plccset=np.array([])
# for i in range(h-1):
# curPlcc = self.getRelationBetweenEyetrackFeatureAndSubjectScore(i)
# plccset = np.append(plccset,curPlcc)
# plt.xlabel('Disparity Percent in front of the sorted serial')
# plt.ylabel('LCC')
# xmajorLocator = plt.MultipleLocator(5)
# ymajorLocator = plt.MultipleLocator(0.1)
# ax = plt.subplot(111)
# plt.plot(percent,plccset,'-r*')
# ax.xaxis.set_major_locator(xmajorLocator)
# ax.yaxis.set_major_locator(ymajorLocator)
# plt.grid()
# plt.title('Relationship between the percentage we choosed and mos')
#
# plt.show()
# self.getRelationBetweenEyetrackFeatureAndSubjectScore(6)
def getRelationBetweenEyetrackFeatureAndSubjectScore(self,featureindex):
"""验证各特征与主观评分之间的相关性"""
if len(self.mosForEachImage) != len(self.meanFeatureValueForEachImage):
raise ValueError('the number of mos and feature is not equal,please check it')
else:
feature_score_pairlist = []
for key in self.mosForEachImage:
curList = [key,self.mosForEachImage[key],self.meanFeatureValueForEachImage[key][featureindex]]
feature_score_pairlist.append(curList)
feature_score_pairlist = np.array(feature_score_pairlist)
feature_score_pairarray = np.array(feature_score_pairlist[:,1:], dtype=float)
x = feature_score_pairarray[:,0]
y = feature_score_pairarray[:,1]
plcc,pval = statstool.pearsonr(x,y)
srocc,pval = statstool.spearmanr(x,y)
print "feature %d,%f,%f" %(featureindex,plcc,srocc)
return plcc
#plt.plot(x,'b-')
#plt.plot(y,'r-')
#plt.show()
def getMeanFeatureValueForEachImage(self,basicEye):
"""获取针对每幅图像的平均特征值"""
if len(self.eyetrackDataFeature) == 0:
self.extrackEyetrackFeatures(basicEye)
classific = {}
index = 0
for item in self.eyetrackDataFeature:
imgName = item[0]
if imgName not in classific:
classific[imgName] = set([index])
else:
classific[imgName].add(index)
index += 1
# blinkfeaturesprocessor = EyetrackingDataBlinkFeaturesFactory()
# blinkfeatures = blinkfeaturesprocessor.getEyeBlinkFeatures()
for key in classific:
serial = list(classific[key])
tmp2 = self.eyetrackDataFeature[serial,1:]
tmp = np.array(tmp2,dtype=float)
self.meanFeatureValueForEachImage[key] = np.mean(tmp,axis = 0)
# tmp3 = np.mean(blinkfeatures[key],axis=0)
# self.meanFeatureValueForEachImage[key] = np.hstack((tmp3,self.meanFeatureValueForEachImage[key]))
#self.meanFeatureValueForEachImage[key] = np.append(self.meanFeatureValueForEachImage[key],self.meanvalueforpupilsize[key])
def getPupilFeature(self):
individualpupilsize = self.calAvgPupilDiameter()
averagepupilsizeforrecord = self.calAvgPupilForRecord()
meanvalueforpupilsize = {}
for key in averagepupilsizeforrecord:
splitResult = key.split('_')
imgName = splitResult[-1]
if len(splitResult) == 2:
userName = splitResult[0]
else:
userName = splitResult[0]+'_'+splitResult[1]
curpupilsize = averagepupilsizeforrecord[key]/individualpupilsize[userName]
if imgName not in meanvalueforpupilsize:
meanvalueforpupilsize[imgName] = curpupilsize
else:
meanvalueforpupilsize[imgName] = np.append(meanvalueforpupilsize[imgName],curpupilsize)
for key in meanvalueforpupilsize:
meanvalueforpupilsize[key] = meanvalueforpupilsize[key].reshape(len(meanvalueforpupilsize[key])/2,2)
meanvalueforpupilsize[key] = np.mean(meanvalueforpupilsize[key],axis=0)
self.meanvalueforpupilsize = meanvalueforpupilsize
def calAvgPupilDiameter(self):
avgpupilsize = {}
for key in self.pupilDiameterset:
avgpupilsize[key] = np.mean(self.pupilDiameterset[key],axis=0)
return avgpupilsize
def calAvgPupilForRecord(self):
avgpupilsizeForRecord = {}
for key in self.individualPupilFeature:
avgpupilsizeForRecord[key] = np.mean(self.individualPupilFeature[key],axis=0)
return avgpupilsizeForRecord
def getEyetrackerDataFeatures(self,basicEye,featuresfilesavename):
"""svm 学习训练 """
self.getRelationBetweenEyetrackFeaturesAndSubjectScore(basicEye)
feature_mos_set = np.array([])
num = 0
for key in self.mosForEachImage:
cur_set = np.array(self.mosForEachImage[key])
cur_set = np.append(cur_set,self.meanFeatureValueForEachImage[key])
length = len(cur_set)
feature_mos_set = np.append(feature_mos_set,cur_set)
num += 1
feature_mos_set = feature_mos_set.reshape(num,length)
csvfile=file(featuresfilesavename,'a+')
writer=csv.writer(csvfile)
writer.writerows(feature_mos_set)
csvfile.close()
