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(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 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/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_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/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(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(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')
withColor={}
for k in labs:
f_name = path_ea+k
im = cv2.imread(f_name,-1)
c=np.shape(im)
if len(c)== 3 and c[2]>=3:
withColor[k]=labs[k]
labs = withColor
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,:])
bins = 40
#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=bins)
#fg.getPoseLabel(testSet,pathToPoseFiles='/local/attale00/poseLabels/aflw/')
#fg.getColorHistogram(testSet,(0,40,0,120),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 = 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'
rValues = classifierUtils.dissectedCV(rf,testSet)
#pickle.dump(rValues,open('patches_cv_color_{}'.format(bins),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=None,meanI=None)
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)
#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;
path_mp = '/local/attale00/extracted_pascal__4__Multi-PIE/color128/'
mpFiles = utils.getAllFiles(path_mp)
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,:])
bins = 40
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=bins)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=1,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'
rValues = classifierUtils.dissectedCV(rf,testSet)
pickle.dump(rValues,open('texture_color_{}'.format(bins),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=None,meanI=None)
else:
print 'not doing anything'
return
