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 = '/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)
roi=(0,37,0,115)
roi=None
filters = None
meanI = 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)
# 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=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/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,(0,40,40,80),path=path_ea,ending='.png',colorspace='lab',bins=20)
#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 = 23,min_split=12,max_depth=45)
#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)
s=classifierUtils.standardCrossvalidation(rf,testSet)
print s
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
else:
print 'not doing anything'
def _cross_validate(testSet):
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])))
print 'Scores'
print classifierUtils.standardCrossvalidation(rf,testSet)
print '----'
return
def main(mode):
path = '/local/attale00/AFLW_ALL/'
path_ea = '/local/attale00/AFLW_cropped/mouth_img_error_multiPie/'
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 = labeledImages;
# 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
fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12),overlap = 2)
#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/errorpatches/rferror')
elif mode in ['c']:
print 'cross validation of data'
classifierUtils.dissectedCV(rf,testSet)
print classifierUtils.standardCrossvalidation(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/mouth_img_error_multiPie/'
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'))
#
fileNames = labeledImages;
#
roi=None
testSet = fg.dataContainer(labs)
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi)
fgmode = 0
#fg.getImagePatchStat(testSet,path=path_ea,patchSize=(8,24),overlap = 2,mode=fgmode)
roi=None
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)
# 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/thesis/errorpatches/filter1.npy')
m=np.load('/home/attale00/Desktop/classifiers/thesis/errorpatches/meanI1.npy')
X1=X-m
data=np.dot(X1,W.T)
for i in range(len(testSet.fileNames)):
testSet.data[i].extend(data[i,:])
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'
#r=classifierUtils.classifyWithOld(path,testSet,mode,clfPath = '/home/attale00/Desktop/classifiers/thesis/errorpatches/mode{}'.format(fgmode))
r=classifierUtils.classifyWithOld(path,testSet,mode,clfPath='/home/attale00/Desktop/classifiers/thesis/errorpatches/errorpatch_ica')
pickle.dump(r,open('errorpatch_test_ica'.format(fgmode),'w'))
elif mode in ['c']:
print 'cross validation of data'
classifierUtils.dissectedCV(rf,testSet)
print classifierUtils.standardCrossvalidation(rf,testSet)
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
else:
print 'not doing anything'
