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 = 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=(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.getPoseLabel(testSet,pathToPoseFiles='/local/attale00/poseLabels/aflw/')
#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_hog_{}'.format(orientations),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=None,meanI=None)
else:
print 'not doing anything'
return
