def _cvDissect(testSet,rf,mapping):
for i in range(3):
train,test = testSet.splitInTestAndTraining(frac=.7)
rf.fit(train.data,train.targetNum)
test.classifiedAs=rf.predict(test.data)
test.hasBeenClassified = True
classifierUtils.evaluateClassification(test,{0:'closed or narrow',1:'open or wide open'})
def main():
path='/local/attale00/'
allFiles = utils.getAllFiles(path+'Multi-PIE/extracted')
allLabelFiles = utils.getAllFiles(path+'Multi-PIE/db_labels')
np.random.shuffle(allLabelFiles)
#get the labels from the database, for each person
sliceat=250
labelstest=utils.parseLabelFiles(path+'Multi-PIE/db_labels','sex',allLabelFiles[0:sliceat],cutoffSeq='',suffix='')
labelstraining = utils.parseLabelFiles(path+'Multi-PIE/db_labels','sex',allLabelFiles[sliceat:],cutoffSeq='',suffix='')
#now generate the label dict for each file
labsTest={}
labsTraining={}
for f in allFiles:
if labelstest.has_key(f[0:3]+'.labels'):
labsTest[f]=labelstest[f[0:3]+'.labels']
elif labelstraining.has_key(f[0:3]+'.labels'):
labsTraining[f]=labelstraining[f[0:3]+'.labels' ]
testSet = fg.dataContainer(labsTest)
trainingSet = fg.dataContainer(labsTraining)
roi=(0,64,0,64)
ppc=(8,8)
cpb=(8,8)
fg.getHogFeature(testSet,roi,path=path+'Multi-PIE_grayScale64/',ending=None,extraMask = None,pixels_per_cell=ppc,cells_per_block=cpb)
fg.getHogFeature(trainingSet,roi,path=path+'Multi-PIE_grayScale64/',ending=None,extraMask = None,pixels_per_cell=ppc, cells_per_block=cpb)
testSet.targetNum=map(utils.mapSexLabel2Two,testSet.target)
trainingSet.targetNum = map(utils.mapSexLabel2Two,trainingSet.target)
rf1=classifierUtils.standardRF(max_features=np.sqrt(len(testSet.data[0])))
rf2=classifierUtils.standardRF(max_features=np.sqrt(len(trainingSet.data[0])))
rf1.fit(testSet.data,testSet.targetNum)
s=rf1.score(trainingSet.data,trainingSet.targetNum)
trainingSet.classifiedAs=rf1.predict(trainingSet.data)
trainingSet.hasBeenClassified=True
classifierUtils.evaluateClassification(trainingSet,{0:'male',1:'female'})
print 'Score: {}'.format(s)
print '----------other way around ----\n'
rf2.fit(trainingSet.data,trainingSet.targetNum)
s=rf2.score(testSet.data,testSet.targetNum)
testSet.classifiedAs=rf2.predict(testSet.data)
testSet.hasBeenClassified=True
classifierUtils.evaluateClassification(testSet,{0:'male',1:'female'})
print 'Score: {}'.format(s)
def _score(clf,testSet):
score = clf.score(testSet.data,testSet.targetNum)
testSet.hasBeenClassified = True
classifierUtils.evaluateClassification(testSet,{0:'closed or narrow',1:'open or wide open'})
print 'Overall Score: {:.3f}'.format(score)
return
def main(nJobs = 1):
path = '/local/attale00/GoodPose'
path_ea = path+'/extracted_alpha'
path_adata = path_ea + '/a_data'
fileNames = utils.getAllFiles(path+'/targets');
attribute = 'mouth'
attribute_values = utils.parseLabelINIFile(path+'/mouth_labels/labels.ini',attribute);
print('------------Attribute: \t'+attribute+' ---------------')
for i in attribute_values:
print('Value: \t'+i)
print('----------------------------')
print('----------parsing label files------')
labs=utils.parseLabelFiles(path+'/mouth_labels','mouth',fileNames,cutoffSeq='.png',suffix='_face0.labels')
#labs=utils.parseLabelFiles(path+'/mouth_labels/labels','glasses',fileNames,cutoffSeq='.png',suffix='_face0.labels')
print('-----computing Features-----')
#make 10 bin hist for each mouth
#roi = (40,200,100,200)
roi = (50,190,110,402)
roi2=(0,128,0,256)
roi=(0,64,0,128)
#roi2=(128,256,0,256)
mouthSet = fg.dataContainer(labs)
#fg.getHistogram(20,roi,hrange=(0,255),dataC = mouthSet,path = path+'/extracted/gradients/Direction/',ending='_0.png')
eM=np.load('/home/attale00/Desktop/mouthMask.npy')
m=cv2.resize(np.uint8(eM),(256,256));
strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
dil = cv2.dilate(m,strel)
m=dil>0;
# em=m[roi[0]:roi[1],roi[2]:roi[3]]
# m= m !=True
fg.getHogFeature(mouthSet,roi2,path=path_ea+'/grayScale128/',ending='_0.png',extraMask = None)
#fg.getPixelValues(mouthSet,roi,path=path_ea+'/',ending='_0.png',mask =m, scaleFactor = 10)
#fg.getColorHistogram(mouthSet,roi,path=path_ea+'/',ending='_0.png',colorspace=None,range=(1.0,255.0),bins = 20)
mouthSet.targetNum=map(utils.mapMouthLabels2Two,mouthSet.target)
#mouthSet.targetNum=map(utils.mapGlassesLabels2Two,mouthSet.target)
score=[]
frac=np.arange(0.2,1.0,.05)
for i in frac:
trainingSet,testSet=mouthSet.splitInTestAndTraining(frac=i)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(mouthSet.data[0])))
rf.fit(trainingSet.data,trainingSet.targetNum)
score.append(rf.score(testSet.data,testSet.targetNum))
testSet.hasBeenClassified=True
testSet.classifiedAs=rf.predict(testSet.data)
print '---------------- {} -----------'.format(i)
classifierUtils.evaluateClassification(testSet,{0:'closed',1:'open'})
plt.plot(frac,score,'-*')
plt.show()
return
