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 main(mode):
path = '/local/attale00/extracted_pascal__4__Multi-PIE'
path_ea = path+'/color128/'
allLabelFiles = utils.getAllFiles('/local/attale00/a_labels')
labeledImages = [i[0:16]+'.png' for i in allLabelFiles]
#labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
#fileNames = utils.getAllFiles(path_ea);
#labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
roi=(50,74,96,160)
#roi=(44,84,88,168)
# 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;
strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 8, cells_per_block=(6,2),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=20)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,'s')
elif mode in ['c']:
print 'cross validation of data'
print 'Scores'
#print classifierUtils.standardCrossvalidation(rf,testSet,n_jobs=5)
#_cvDissect(testSet,rf)
classifierUtils.dissectedCV(rf,testSet)
print '----'
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet)
else:
print 'not doing anything'
def patches():
path = '/local/attale00/AFLW_ALL/'
path_ea = '/local/attale00/AFLW_cropped/multiPIE_cropped3/'
allLabelFiles = utils.getAllFiles('/local/attale00/a_labels')
labeledImages = [i[0:16]+'.png' for i in allLabelFiles]
labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
fileNames = labeledImages;
testSet = fg.dataContainer(labs)
roi=(0,37,0,115)
roi=None
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi)
W=np.load('/home/attale00/Desktop/classifiers/patches/filter2.npy')
m=np.load('/home/attale00/Desktop/classifiers/patches/meanI2.npy')
X1=X-m
data=np.dot(X1,W.T)
for i in range(len(fileNames)):
testSet.data[i].extend(data[i,:])
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 5, pixels_per_cell=(24,8),cells_per_block=(3,3),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=20)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
clfPath = '/home/attale00/Desktop/classifiers/patches/rfICAHogColor'
f=file(clfPath,'r')
print 'classifier used: '+ f.name
clf = pickle.load(f)
testSet.classifiedAs=clf.predict(testSet.data)
testSet.probabilities=clf.predict_proba(testSet.data)
return testSet
def main(nJobs = 1):
path = '/local/attale00/GoodPose/extracted_alpha/grayScale64'
fileNames = utils.getAllFiles(path);
labs=utils.parseLabelFiles('/local/attale00/GoodPose'+'/mouth_labels','mouth',fileNames,cutoffSeq='_0.png',suffix='_face0.labels')
print('-----computing Features-----')
roi2 = (0,32,0,64)
mouthSet = fg.dataContainer(labs)
#load the mask for the mouth room pixels and dilate it
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;
#get the features
fg.getHogFeature(mouthSet,roi2,path=path+'/',ending=None,extraMask = None)
#map the string labels to numbers (required by sklearn)
#change the mapping here for different classifiers
mouthSet.targetNum=map(utils.mapMouthLabels2Two,mouthSet.target)
n_estimators = 100
min_split = 10
max_depth = 20
max_features = np.sqrt(len(mouthSet.data[0]))
rf = classifierUtils.standardRF(max_features = max_features)
rf2=classifierUtils.standardRF(max_features=max_features)
score=classifierUtils.standardCrossvalidation(rf2,mouthSet)
rf.fit(mouthSet.data,mouthSet.targetNum)
pickle.dump(rf,open('/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_12','w'))
f=open('/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_12.txt','w')
f.write('Trained on aflw\n')
f.write('Attribute: mouth' )
f.write('Features: getHogFeature(mouthSet,roi2,path=path,ending=None,extraMask = m) on 64*64 grayScale 3 direction bins \n')
f.write('ROI:(0,32,0,64)\n')
f.write('labels: closed, narrow: 0, open, wideOpen: 1\n')
f.write('CV Score: {}\n'.format(score))
f.close()
def main(mode):
path = '/local/attale00/AFLW_ALL'
path_ea = path+'/color256/'
fileNames = utils.getAllFiles(path_ea);
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
roi=(88,165,150,362)
#roi=(44,84,88,168)
# 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;
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 4, cells_per_block=(26,9),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=20)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40)
print len(testSet.data)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
#classifierUtils.standardCrossvalidation(rf,testSet,n_jobs=5)
classifierUtils.dissectedCV(rf,testSet)
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet)
else:
print 'not doing anything'
def main(mode):
path='/local/attale00/'
allFiles = utils.getAllFiles(path+'Multi-PIE/extracted')
allLabelFiles = utils.getAllFiles(path+'Multi-PIE/labels')
#allLabelFiles = utils.getAllFiles(path+'a_labels')
labeledImages = [i[0:16]+'.png' for i in allLabelFiles]
labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
#labs=utils.parseLabelFiles(path+'a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
roi = (0,32,0,64)
#roi = (128,256,0,256)
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;
fg.getHogFeature(testSet,roi,path=path+'Multi-PIE_grayScale64/',ending=None,extraMask = None)
fg.getColorHistogram(testSet,(50,190,110,402),path = path+'/Multi-PIE/extracted/',ending=None,colorspace='lab',range=(1.,255.0),bins = 20)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
#testSet.targetNum = map(utils.mapGlassesLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
classifierUtils.dissectedCV(rf,testSet)
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf)
else:
print 'not doing anything'
def main(mode):
path = '/local/attale00/extracted_pascal__4__Multi-PIE'
path_ea = path+'/color256/'
allLabelFiles = utils.getAllFiles('/local/attale00/a_labels')
labeledImages = [i[0:16]+'.png' for i in allLabelFiles]
#labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
roi=(88,165,150,362)
#roi=(44,84,88,168)
# 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;
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 4, cells_per_block=(26,9),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=20)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,'v')
elif mode in ['c']:
print 'cross validation of data'
_cross_validate(testSet)
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet)
else:
print 'not doing anything'
def main(mode):
poses = multiPiePose()
#poses = multiPiePoseOriginal()
poseDict=splitByPose(poses,binmax=75, stepsize = 30)
for k,v in poseDict.iteritems():
fn = [i[0] for i in v]
labs=utils.parseLabelFiles(path_label,'mouth',fn,cutoffSeq='.png',suffix='_face0.labels')
labs=dict((lk,lv) for (lk,lv) in labs.iteritems() if not lv.startswith('narr'))
testSet = fg.dataContainer(labs)
fg.getHogFeature(testSet,None,path=path_ea,ending='.png',extraMask = None,orientations = 9, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = 30,min_split=12,max_depth=70)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
print '------------------- pose {}-----------------'.format(k)
plt.figure()
#obj=classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/thesis/poseSplit/pose{}'.format(k))
obj=classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/thesis/mirror/rfHogMirror'.format(k))
obj.plot(title='Mirrored,Pose: {}, ntot: {}, nOpen{}'.format(k,len(testSet.data),testSet.targetNum.count(1)))
pickle.dump(obj,open('multiPie_mirror_aggregate{}'.format(k),'w'))
elif mode in ['c']:
print 'cross validation of data'
rValues = classifierUtils.dissectedCV(rf,testSet)
plt.title('Pose: {}, n: {}'.format(k,len(v)))
#pickle.dump(rValues,open('patches_pose_hog_{}'.format(k),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,identifier = k)
def main(mode):
path = '/local/attale00/AFLW_ALL/'
path_ea = '/local/attale00/AFLW_cropped/cropped3/'
#
fileNames = utils.getAllFiles(path_ea);
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
roi=None
testSet = fg.dataContainer(labs)
testSetMirror = fg.dataContainer(labs)
for f in range(len(testSetMirror.fileNames)):
testSetMirror.fileNames[f]+='M'
orientations = 9
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = orientations, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False)
fg.getHogFeature(testSetMirror,roi,path='/local/attale00/AFLW_cropped/mirrored/', ending='.png',orientations = orientations, cells_per_block=(3,3),pixels_per_cell=(24,8))
testSet.addContainer(testSetMirror)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = 30,min_split=12,max_depth=70)
#rf = svm.NuSVC()
#rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
rValues = classifierUtils.dissectedCV(rf,testSet)
pickle.dump(rValues,open('patches_cv_hog_{}'.format(orientations),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf)
else:
print 'not doing anything'
def texture():
path = '/local/attale00/extracted_pascal__4__Multi-PIE'
path_ea = path+'/color128/'
allLabelFiles = utils.getAllFiles('/local/attale00/a_labels')
labeledImages = [i[0:16]+'.png' for i in allLabelFiles]
labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
roi=(50,74,96,160)
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(128,256),roi=roi)
W=np.load('/home/attale00/Desktop/classifiers/ica/filter1.npy')
m=np.load('/home/attale00/Desktop/classifiers/ica/meanI1.npy')
X1=X-m
data=np.dot(X1,W.T)
for i in range(len(testSet.data)):
testSet.data[i].extend(data[i,:])
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 3, cells_per_block=(6,2),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=10)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
clfPath = '/home/attale00/Desktop/classifiers/ica/rf128ICAHOGCOLOR'
f=file(clfPath,'r')
print 'classifier used: '+ f.name
clf = pickle.load(f)
testSet.classifiedAs=clf.predict(testSet.data)
testSet.probabilities=clf.predict_proba(testSet.data)
return testSet
def main(mode):
path = '/local/attale00/GoodPose'
path_ea = path+'/pascal128/'
fileNames = utils.getAllFiles(path+'/targets');
labs=utils.parseLabelFiles(path+'/mouth_labels','mouth',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
#roi=(88,165,150,362)
roi=(44,84,88,168)
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;
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 5, cells_per_block=(8,3))
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
_cross_validate(testSet)
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet)
else:
print 'not doing anything'
def main(mode):
path = '/local/attale00/AFLW_ALL/'
path_ea = '/local/attale00/AFLW_cropped/mouth_img_error/'
#
fileNames = utils.getAllFiles(path_ea);
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
fg_mode = 0
size=(4,12)
overlap=2
#size=(40,120)
orientations = 9
roi=None
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = orientations, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False)
print 'feature vector length: {}'.format(len(testSet.data[0]))
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=13,max_depth=40)
#rf = svm.NuSVC()
#rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
rValues = classifierUtils.dissectedCV(rf,testSet)
pickle.dump(rValues,open('errorpatch_hog','w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf)
else:
print 'not doing anything'
def main(mode):
path = '/local/attale00/AFLW_ALL/'
poses = aflwPose()
poseDict=splitByPose(poses,binmax=100, stepsize = 40)
for k,v in poseDict.iteritems():
fn = [i[0] for i in v]
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fn,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
fg.getHogFeature(testSet,None,path=path_ea,ending='.png',extraMask = None,orientations = 9, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = 40,min_split=12,max_depth=70)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
rValues = classifierUtils.dissectedCV(rf,testSet)
plt.title('Pose: {}, n: {}'.format(k,len(v)))
#pickle.dump(rValues,open('patches_pose_hog_{}'.format(k),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,identifier = k)
def main(mode):
path_mp = '/local/attale00/extracted_pascal__4__Multi-PIE'
path_eamp = path_mp+'/color128/'
allLabelFiles = utils.getAllFiles('/local/attale00/a_labels')
labeledImages = [i[0:16]+'.png' for i in allLabelFiles]
#labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
labsmp=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
path = '/local/attale00/AFLW_ALL'
path_ea = path+'/color128/'
fileNames = utils.getAllFiles(path_ea);
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
testSetmp = fg.dataContainer(labsmp)
roi=(50,74,96,160)
#roi=(44,84,88,168)
# 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;
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(128,256),roi=roi)
Y=fg.getAllImagesFlat(path_eamp,testSetmp.fileNames,(128,256),roi=roi)
Z=np.concatenate((X,Y),axis=0)
#
# perform ICA
ica = FastICA(n_components=100,whiten=True)
ica.fit(Z)
meanI=np.mean(Z,axis=0)
X1=X-meanI
Y1=Y-meanI
data=ica.transform(X1)
datamp = ica.transform(Y1)
filters=ica.components_
for i in range(len(testSet.fileNames)):
testSet.data[i].extend(data[i,:])
for i in range(len(testSetmp.fileNames)):
testSetmp.data[i].extend(datamp[i,:])
strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 3, cells_per_block=(6,2),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=10)
fg.getHogFeature(testSetmp,roi,path=path_eamp,ending='.png',extraMask = None,orientations = 3, cells_per_block=(6,2),maskFromAlpha=False)
fg.getColorHistogram(testSetmp,roi,path=path_eamp,ending='.png',colorspace='lab',bins=10)
#pca
# n_samples, n_features = X.shape
#
# mean_ = np.mean(X, axis=0)
# X -= mean_
# U, S, V = linalg.svd(X)
# explained_variance_ = (S ** 2) / n_samples
# explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum())
# K=V / S[:, np.newaxis] * np.sqrt(n_samples)
# filters=K[:100]
# data=np.dot(X,filters.T)
testSet.addContainer(testSetmp)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
classifierUtils.dissectedCV(rf,testSet)
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
else:
print 'not doing anything'
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
def main(mode):
labelFiles='/local/attale00/aflw_original_labels'
path_ea = '/local/attale00/AFLW_cropped/eyes/'
fileNames = utils.getAllFiles(path_ea)
goods = []
for f in fileNames:
im = cv2.imread(path_ea+f)
if im.shape[0] == 40 and im.shape[1] ==120:
goods.append(f)
fileNames = goods
labs=utils.parseLabelFiles(labelFiles,'glasses',fileNames,cutoffSeq='.png',suffix='.labels')
testSet = fg.dataContainer(labs)
#
# X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi)
#
#
# # perform ICA
# if mode not in ['s','v']:
# ica = FastICA(n_components=100,whiten=True)
# ica.fit(X)
# meanI=np.mean(X,axis=0)
# X1=X-meanI
# data=ica.transform(X1)
# filters=ica.components_
#
# elif mode in ['s','v']:
# W=np.load('/home/attale00/Desktop/classifiers/patches/filter2.npy')
# m=np.load('/home/attale00/Desktop/classifiers/patches/meanI2.npy')
# X1=X-m
# data=np.dot(X1,W.T)
#
# for i in range(len(testSet.fileNames)):
# testSet.data[i].extend(data[i,:])
#
# strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
fg.getHogFeature(testSet,None,path=path_ea,ending='.png',extraMask = None,orientations = 5, pixels_per_cell=(24,8),cells_per_block=(3,3),maskFromAlpha=False)
#testSet.targetNum=map(lambda x: 1 if x=='light' else 0,testSet.target)
#rf = svm.NuSVC()
#rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
obj=classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/thesis/color_only')
#pickle.dump(obj,open('color_only','w'))
elif mode in ['c']:
print 'cross validation of data'
testSet.targetNum=map(lambda x: 1 if x=='1' else 0,testSet.target)
rf=classifierUtils.standardRF(max_features = 27,min_split=5,max_depth=60,n_estimators = 500)
rValues = classifierUtils.dissectedCV(rf,testSet)
plt.title('No glasses against rest')
plt.show()
#pickle.dump(rValues,open('patches_mp_','w'))
elif mode in ['save']:
print 'saving new classifier'
testSet.targetNum=map(lambda x: 1 if x=='1' else 0,testSet.target)
rf=classifierUtils.standardRF(max_features = 27,min_split=5,max_depth=60,n_estimators = 500)
_saveRF(testSet,rf)
else:
print 'not doing anything'
def main(mode):
path = '/local/attale00/AFLW_ALL'
path_ea = path+'/color128/'
fileNames = utils.getAllFiles(path_ea);
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
roi=None
roi=(50,74,96,160)
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(128,256),roi=roi)
#Y=fg.getAllImagesFlat(path_mp,mpFiles,(128,256),roi=roi)
#Z=np.concatenate((X,Y),axis=0)
Z=X
#perform ICA
ica = FastICA(n_components=50,whiten=True)
ica.fit(Z)
meanI=np.mean(X,axis=0)
X1=X-meanI
data=ica.transform(X1)
filters=ica.components_
for i in range(len(fileNames)):
testSet.data[i].extend(data[i,:])
orientations = 4
bins=40
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = orientations, cells_per_block=(6,2),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=bins)
#pca
# n_samples, n_features = X.shape
#
# mean_ = np.mean(X, axis=0)
# X -= mean_
# U, S, V = linalg.svd(X)
# explained_variance_ = (S ** 2) / n_samples
# explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum())
# K=V / S[:, np.newaxis] * np.sqrt(n_samples)
# filters=K[:100]
# data=np.dot(X,filters.T)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=1,max_depth=70)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
rValues = classifierUtils.dissectedCV(rf,testSet)
pickle.dump(rValues,open('texture_combined'.format(orientations),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
else:
print 'not doing anything'
return
def main(mode):
path = '/local/attale00/extracted_pascal__4__Multi-PIE'
path_ea = path+'/color128/'
allLabelFiles = utils.getAllFiles('/local/attale00/a_labels')
labeledImages = [i[0:16]+'.png' for i in allLabelFiles]
#labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
roi=(50,74,96,160)
# X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(128,256),roi=roi)
#
#
# # perform ICA
# if mode not in ['s','v']:
# ica = FastICA(n_components=50,whiten=True)
# ica.fit(X)
# meanI=np.mean(X,axis=0)
# X1=X-meanI
# data=ica.transform(X1)
# filters=ica.components_
#
# elif mode in ['s','v']:
# W=np.load('/home/attale00/Desktop/classifiers/thesis/filter3.npy')
# m=np.load('/home/attale00/Desktop/classifiers/thesis/meanI3.npy')
# X1=X-m
# data=np.dot(X1,W.T)
#
# for i in range(len(testSet.data)):
# testSet.data[i].extend(data[i,:])
###
#
#
#
#
# strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 4, cells_per_block=(6,2),maskFromAlpha=False)
fg.getPoseLabel(testSet,pathToPoseFiles='/local/attale00/poseLabels/multipie/')
#fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=40)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
obj=classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/thesis/texture/hog_pose')
pickle.dump(obj,open('hog_pose','w'))
elif mode in ['c']:
print 'cross validation of data'
rValues = classifierUtils.dissectedCV(rf,testSet)
pickle.dump(rValues,open('texture_mp_','w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet)
else:
print 'not doing anything'
def main(mode):
path = '/local/attale00/AFLW_ALL/'
path_ea = '/local/attale00/AFLW_cropped/cropped3/'
#
fileNames = utils.getAllFiles(path_ea);
# minr = 10000;
# for f in fileNames:
# im = cv2.imread(path_ea+f,-1)
# if im.shape[0]!=40 or im.shape[1]!=120:
# print f
# print im.shape
# minr = minr if im.shape[0]>= minr else im.shape[0]
#
# print minr
#
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
testSetMirror = fg.dataContainer(labs)
for f in range(len(testSetMirror.fileNames)):
testSetMirror.fileNames[f]+='M'
roi=None
#roi=(44,84,88,168)
# 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;
#
# X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi)
# Y=fg.getAllImagesFlat('/local/attale00/AFLW_cropped/mirrored/',testSet.fileNames,(40,120),roi=roi)
# Z=np.concatenate((X,Y),axis=0)
#
# # perform ICA
# ica = FastICA(n_components=100,whiten=True)
# ica.fit(Z)
# meanI=np.mean(Z,axis=0)
# X1=X-meanI
# Y1=Y-meanI
# data=ica.transform(X1)
# datam=ica.transform(Y1)
# filters=ica.components_
# for i in range(len(fileNames)):
# testSet.data[i].extend(data[i,:])
# testSetMirror.data[i].extend(datam[i,:])
##
#
#
#
# strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
orientations = 9
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = orientations, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False)
fg.getHogFeature(testSetMirror,roi,path='/local/attale00/AFLW_cropped/mirrored/', ending='.png',orientations = orientations, cells_per_block=(3,3),pixels_per_cell=(24,8))
testSet.addContainer(testSetMirror)
#pca
# n_samples, n_features = X.shape
#
# mean_ = np.mean(X, axis=0)
# X -= mean_
# U, S, V = linalg.svd(X)
# explained_variance_ = (S ** 2) / n_samples
# explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum())
# K=V / S[:, np.newaxis] * np.sqrt(n_samples)
# filters=K[:100]
# data=np.dot(X,filters.T)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = 17,min_split=7,max_depth=40,n_estimators=200)
#rf = svm.NuSVC()
#rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
classifierUtils.dissectedCV(rf,testSet)
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
else:
print 'not doing anything'
def main(mode):
path = '/local/attale00/AFLW_ALL/'
path_ea = '/local/attale00/AFLW_cropped/cropped3/'
#
fileNames = utils.getAllFiles(path_ea);
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
roi=(0,37,0,115)
roi=None
filters = None
meanI = None
components = 35 #100 #150
bins=40
orientations = 9
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi)
# X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(120,40),roi=roi,resizeFactor = .5)
#
# perform ICA
if mode not in ['s','v']:
ica = FastICA(n_components=components,whiten=True)
ica.fit(X)
meanI=np.mean(X,axis=0)
X1=X-meanI
data=ica.transform(X1)
filters=ica.components_
elif mode in ['s','v']:
W=np.load('/home/attale00/Desktop/classifiers/patches/filterMP1.npy')
m=np.load('/home/attale00/Desktop/classifiers/patches/meanIMP1.npy')
X1=X-m
data=np.dot(X1,W.T)
for i in range(len(fileNames)):
testSet.data[i].extend(data[i,:])
#strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = orientations, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False)
fg.getColorHistogram(testSet,(0,40,40,80),path=path_ea,ending='.png',colorspace='lab',bins=bins)
#fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12))
#fg.getImagePatchStat(testSet,path='/local/attale00/AFLW_cropped/mouth_img_error/',patchSize=(4,12))
#pca
# n_samples, n_features = X.shape
#
# mean_ = np.mean(X, axis=0)
# X -= mean_
# U, S, V = linalg.svd(X)
# explained_variance_ = (S ** 2) / n_samples
# explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum())
# K=V / S[:, np.newaxis] * np.sqrt(n_samples)
# filters=K[:100]
# data=np.dot(X,filters.T)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = 40,min_split=15,max_depth=70)
#rf = svm.NuSVC()
#rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
_classifyWithOld(path,testSet,mode)
elif mode in ['c']:
print 'cross validation of data'
rValues = classifierUtils.dissectedCV(rf,testSet)
pickle.dump(rValues,open('patches_cv_combined','w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
else:
print 'not doing anything'
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(mode):
path = '/local/attale00/AFLW_ALL/'
path_ea = '/local/attale00/AFLW_cropped/multiPIE_cropped3/'
allLabelFiles = utils.getAllFiles('/local/attale00/a_labels')
labeledImages = [i[0:16]+'.png' for i in allLabelFiles]
#labs=utils.parseLabelFiles(path+'/Multi-PIE/labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
labs=utils.parseLabelFiles('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
#labs=dict((k,v) for (k,v) in labs.iteritems() if not v.startswith('narr'))
#
# minr = 10000;
# for f in fileNames:
# im = cv2.imread(path_ea+f,-1)
# if im.shape[0]!=40 or im.shape[1]!=120:
# print f
# print im.shape
# minr = minr if im.shape[0]>= minr else im.shape[0]
#
# print minr
#
testSet = fg.dataContainer(labs)
roi=(0,37,0,115)
roi=None
#roi=(44,84,88,168)
# 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;
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi)
# perform ICA
if mode not in ['s','v']:
ica = FastICA(n_components=100,whiten=True)
ica.fit(X)
meanI=np.mean(X,axis=0)
X1=X-meanI
data=ica.transform(X1)
filters=ica.components_
elif mode in ['s','v']:
W=np.load('/home/attale00/Desktop/classifiers/patches/filter2.npy')
m=np.load('/home/attale00/Desktop/classifiers/patches/meanI2.npy')
X1=X-m
data=np.dot(X1,W.T)
for i in range(len(testSet.fileNames)):
testSet.data[i].extend(data[i,:])
strel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 5, pixels_per_cell=(24,8),cells_per_block=(3,3),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=20)
#fg.getImagePatchStat(testSet,path='/local/attale00/AFLW_cropped/mouth_img_error_multiPie/',patchSize =(4,12))
#pca
# n_samples, n_features = X.shape
#
# mean_ = np.mean(X, axis=0)
# X -= mean_
# U, S, V = linalg.svd(X)
# explained_variance_ = (S ** 2) / n_samples
# explained_variance_ratio_ = (explained_variance_ /explained_variance_.sum())
# K=V / S[:, np.newaxis] * np.sqrt(n_samples)
# filters=K[:100]
# data=np.dot(X,filters.T)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = 27,min_split=13,max_depth=40)
#rf = svm.NuSVC()
#rf = linear_model.SGDClassifier(loss='perceptron', eta0=1, learning_rate='constant', penalty=None)
if mode in ['s','v']:
print 'Classifying with loaded classifier'
classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/patches/rfICAHogColor')
elif mode in ['c']:
print 'cross validation of data'
classifierUtils.dissectedCV(rf,testSet)
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
else:
print 'not doing anything'
def main(mode):
path = "/local/attale00/AFLW_ALL/"
path_mirrored = "/local/attale00/AFLW_cropped/mirrored/"
poses = aflwPose()
binmax = 100
stepsize = 40
bins = range(-binmax, binmax, stepsize)
poseDict = splitByPose(poses, bins=bins)
testSets = {}
# original Part
for k, v in poseDict.iteritems():
fn = [i[0] for i in v]
labs = utils.parseLabelFiles(
path + "/labels/labels", "mouth_opening", fn, cutoffSeq=".png", suffix="_face0.labels"
)
testSets[k] = fg.dataContainer(labs)
fg.getHogFeature(
testSets[k],
None,
path=path_ea,
ending=".png",
extraMask=None,
orientations=9,
cells_per_block=(3, 3),
pixels_per_cell=(24, 8),
maskFromAlpha=False,
)
# mirrored part
testSetsM = {}
nBins = len(bins)
for k, v in poseDict.iteritems():
binNumber = bins.index(k)
oppositeBin = bins[nBins - 1 - binNumber]
fn = [i[0] + "M" for i in poseDict[oppositeBin]]
labs = utils.parseLabelFiles(
path + "/labels/labels", "mouth_opening", fn, cutoffSeq=".pngM", suffix="_face0.labels"
)
testSetsM[k] = fg.dataContainer(labs)
fg.getHogFeature(
testSetsM[k],
None,
path=path_mirrored,
ending=".png",
orientations=9,
cells_per_block=(3, 3),
pixels_per_cell=(24, 8),
)
for k, v in poseDict.iteritems():
testSet = testSets[k]
testSet.addContainer(testSetsM[k])
testSet.targetNum = map(utils.mapMouthLabels2Two, testSet.target)
rf = classifierUtils.standardRF(max_features=40, min_split=12, max_depth=70)
if mode in ["s", "v"]:
print "Classifying with loaded classifier"
_classifyWithOld(path, testSet, mode)
elif mode in ["c"]:
print "cross validation of data"
rValues = classifierUtils.dissectedCV(rf, testSet)
plt.title("Pose: {}, n: {}".format(k, len(testSet.data)))
# pickle.dump(rValues,open('patches_pose_hog_{}'.format(k),'w'))
elif mode in ["save"]:
print "saving new classifier"
_saveRF(testSet, rf, identifier=k)