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)

testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)

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

names_open = []

names_closed = []

for i,f in enumerate(testSet.fileNames):

if testSet.targetNum[i] == 0:

names_closed.append(f)

elif testSet.targetNum[i] == 1:

names_open.append(f)

Xopen = fg.getAllImagesFlat(path_ea,names_open,(40,120))

XClosed = fg.getAllImagesFlat(path_ea,names_closed,(40,120))

if mode not in ['s','v']:

icaopen = FastICA(n_components=100,whiten=True)

icaopen.fit(Xopen)

meanIopen=np.mean(Xopen,axis=0)

X1open=Xopen-meanIopen

dataopen=icaopen.transform(X1open)

filtersopen=icaopen.components_

plottingUtils.showICAComponents(filtersopen,(40,120),4,4)

icaclosed = FastICA(n_components=100,whiten=True)

icaclosed.fit(XClosed)

meanIclosed=np.mean(XClosed,axis=0)

X1closed=XClosed-meanIclosed

dataclosed=icaclosed.transform(X1closed)

filtersclosed=icaclosed.components_

plottingUtils.showICAComponents(filtersclosed,(40,120),4,4)

plt.show()

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 = 5, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False)

#fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=20)

#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)

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'

_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:

rf.fit(testSet.data,testSet.targetNum)

root='/home/attale00/Desktop/classifiers/patches/'

pickle.dump(rf,open(root+'rfICAHogColor','w'))

f=open(root+'rficahogcolor.txt','w')

f.write('Source Images: AFLWALL')

f.write('attribute: Mouth')

f.write('Features: ICA HOg color')

f.write('100 comps \n')

f.write('20 color bins \n')

f.write('ppc 24,8, cpb 3,3 dir 5 \n')

f.write('ROI:(50,74,96,160)\n')

f.write('labels: none: 0, light,thick: 1\n')

f.close()

if filters is not None:

np.save(root+'filter1',filters)

#f=file('/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_1','r')

f=file('/home/attale00/Desktop/classifiers/patches/rfICAMultiPie','r')

clf = pickle.load(f)

testSet.classifiedAs=clf.predict(testSet.data)

testSet.hasBeenClassified = True

if mode =='s':

_score(clf,testSet)

else:

_view(clf,testSet,path+'Multi-PIE/extracted/')

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)