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')
print('-----computing Features-----')
#make 10 bin hist for each mouth
mouthSet = fg.getHistogram(10,(0,270,60,452),hrange=(155.,255.0),labelFileDict = labs,path = path_ea+'/grayScale/',ending='_0.png')
mouthSet.targetNum = map(utils.mapMouthLabels2Two,mouthSet.target)
fpr=[]
tpr=[]
fnr = []
for i in xrange(1,101,5):
trainingSet,testSet = mouthSet.splitInTestAndTraining(frac=.6)
(a,b,c)=_classify(trainingSet, testSet,n_estimators=i)
tpr.append(a)
fpr.append(b)
fnr.append(c)
plt.figure()
plt.plot(range(1,101,5),tpr)
plt.xlabel('number of trees')
plt.ylabel('score')
plt.title('.6 training set, max_feature 3, min_split 1' )
fpr=[]
tpr=[]
fnr = []
for i in xrange(1,20):
trainingSet,testSet = mouthSet.splitInTestAndTraining(frac=i*.05)
(a,b,c)=_classify(trainingSet, testSet,n_estimators=70)
tpr.append(a)
fpr.append(b)
fnr.append(c)
plt.figure()
plt.plot(np.arange(0.05,1,0.05),tpr)
plt.xlabel('fraction used as training set')
plt.ylabel('score')
plt.title('ntrees = 70, max_feature 3, min_split 1' )
fpr=[]
tpr=[]
fnr = []
for i in xrange(1,11):
trainingSet,testSet = mouthSet.splitInTestAndTraining(frac=.7)
(a,b,c)=_classify(trainingSet, testSet,n_estimators=70,max_features=i)
tpr.append(a)
fpr.append(b)
fnr.append(c)
plt.figure()
plt.plot(range(1,11),tpr)
plt.xlabel('number of features used')
plt.ylabel('score')
plt.title('ntrees = 70,frac=.7 min_split 1' )
plt.show()
#classifier
#linSVM = svm.SVC(kernel = 'linear',C=1)
#this takes forever: check if that can be true
#scoresLinSVM = cross_validation.cross_val_score(linSVM,data,y=targetNum,n_jobs=-1,verbose = 1)
#implement random forest classifier with verbosity level
#scoresRF = cross_validation.cross_val_score(rf,mouthSet.data,y=mouthSet.targetNum,n_jobs=nJobs,verbose = 1)
#print(scoresRF)
return
def main(nJobs = 1):
path = '/local/attale00'
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')
print('-----computing Features-----')
#make 10 bin hist for each mouth
#roi = (40,200,100,200)
roi = (50,190,110,402)
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+'/targets/grayScaleSmall/',ending=None,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)
n_estimators = range(10,180,20);
max_features = range(2,22,2)
max_depth = range(5,40,5)
max_depth.append(100)
min_split = range(1,20,2)
score=[]
var = []
for n in n_estimators:
scoresRF = _crossValidate(mouthSet, max_depth = 20,n_estimators =n ,nJobs = nJobs,max_features = np.sqrt(len(mouthSet.data[0])),min_split = 5)
score.append(scoresRF.mean())
var.append(scoresRF.std())
print scoresRF
plt.errorbar(n_estimators,score,yerr=var)
plt.xlabel('number of trees')
# plt.ylabel('cross val score')
# mouthSet2 = fg.dataContainer(labs)
# roi=(256,512,0,512)
# fg.getColorHistogram(mouthSet2,roi,path=path+'/targets/',ending=None,colorspace='lab',range=(1.,255.0),bins = 20)
# mouthSet2.targetNum=map(utils.mapMouthLabels2Two,mouthSet2.target)
# score=[]
# var = []
# for n in n_estimators:
# scoresRF = _crossValidate(mouthSet2, max_depth = 20,n_estimators =n ,nJobs = nJobs,max_features = np.sqrt(len(mouthSet2.data[0])),min_split = 5)
#
# score.append(scoresRF.mean())
# var.append(scoresRF.std())
#
# print scoresRF
# plt.errorbar(n_estimators,score,yerr=var)
# plt.xlabel('number of trees')
# plt.ylabel('cross val score')
# fg.getColorHistogram(mouthSet,roi,path=path_ea+'/',ending='_0.png',colorspace='lab',range=(100.0,255.0),bins = 20)
#
# score=[]
# var = []
# for n in n_estimators:
# scoresRF = _crossValidate(mouthSet, max_depth = 20,n_estimators =n ,nJobs = nJobs,max_features = np.sqrt(len(mouthSet.data[0])),min_split = 5)
#
# score.append(scoresRF.mean())
# var.append(scoresRF.std())
#
# print scoresRF
# plt.errorbar(n_estimators,score,yerr=var)
# plt.xlabel('number of trees')
# plt.ylabel('cross val score')
# plt.legend(['HOG','LAB','HOG+LAB'])
# plt.title('20bins')
plt.show()
#classifier
#linSVM = svm.SVC(kernel = 'linear',C=1)
#this takes forever: check if that can be true
#scoresLinSVM = cross_validation.cross_val_score(linSVM,data,y=targetNum,n_jobs=-1,verbose = 1)
#implement random forest classifier with verbosity level
# roi_narrow=(60,160,130,382)
# extraMask = np.load('/home/attale00/Desktop/emptyMouthMask.npy')
#
# fg.getMeanAndVariance(roi_narrow,mouthSet,path_ea+'/',extraMask = extraMask,ending='_0.png')
# scoresRF = _crossValidate(mouthSet,max_features = 13)
# print 'Orientation and mean and cov' +str(scoresRF)
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
