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=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/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, :])
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)
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"
classifierUtils.classifyWithOld(
path, testSet, mode, clfPath="/home/attale00/Desktop/classifiers/ica/rf128ICA_1"
)
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 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/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):
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/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/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'
