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 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 = '/local/attale00/AFLW_cropped/mouth_img_error/'
#
fileNames = utils.getAllFiles(path_ea);
labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')
testSet = fg.dataContainer(labs)
components = 150
roi=None
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,:])
print 'feature vector length: {}'.format(len(testSet.data[0]))
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),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'
rValues = classifierUtils.dissectedCV(rf,testSet)
pickle.dump(rValues,open('errorpatch_ica','w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
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/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;
components = 150
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,:])
#orientations = 2
#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.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=15,max_depth=70,n_estimators=150)
#rf=classifierUtils.standardRF(max_features = 5,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_ica_{}'.format(components),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf,filters=filters,meanI=meanI)
else:
print 'not doing anything'
path_ea = '/local/attale00/AFLW_cropped/cropped2/'
#
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)
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(37,115),roi=roi)
#
# perform ICA
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_
for i in range(len(fileNames)):
testSet.data[i].extend(data[i,:])
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
#
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
#roi=(50,74,96,160)
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi)
#
# perform ICA
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_
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, 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)
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)
testSetMirror = fg.dataContainer(labs)
for f in range(len(testSetMirror.fileNames)):
testSetMirror.fileNames[f]+='M'
roi=(50,74,96,160)
X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(128,256),roi=roi)
Y=fg.getAllImagesFlat(path+'/mirror128/',testSet.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)
datam=ica.transform(Y1)
filters=ica.components_
for i in range(len(fileNames)):
testSet.data[i].extend(data[i,:])
testSetMirror.data[i].extend(datam[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)
#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(testSetMirror)
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/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'))
#
testSet = fg.dataContainer(labs)
roi=(0,37,0,115)
roi=None
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/thesis/filtercombined.npy')
# m=np.load('/home/attale00/Desktop/classifiers/thesis/meancombined.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 = 9, pixels_per_cell=(24,8),cells_per_block=(3,3),maskFromAlpha=False)
fg.getColorHistogram(testSet,roi,path=path_ea,ending='.png',colorspace='lab',bins=40)
#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'
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'
rValues = classifierUtils.dissectedCV(rf,testSet)
pickle.dump(rValues,open('patches_mp_','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 = '/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:
print 'not doing anything'
#X = image_patches.data
#
## 1000 patches a 32x32
## not so much data, reshape to 16000 patches a 8x8
#X = X.reshape(1000, 4, 8, 4, 8)
#X = np.rollaxis(X, 3, 2).reshape(-1, 8 * 8)
path = '/local/attale00/AFLW_ALL'
path_ea = path+'/color128/'
fileNames = utils.getAllFiles(path_ea);
path_mp = '/local/attale00/extracted_pascal__4__Multi-PIE/color128/'
mpFiles = utils.getAllFiles(path_mp)
roi=(50,74,96,160)
X=fg.getAllImagesFlat(path_ea,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=49,whiten=True)
ica.fit(X)
filters = ica.unmixing_matrix_
#pca
## Center data
#n_samples, n_features = X.shape
#
#mean_ = np.mean(X, axis=0)
#X -= mean_
def main():
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
###############################################################################
# data
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)
X = fg.getAllImagesFlat(path_ea, testSet.fileNames, (40, 120), roi=None)
#
# perform ICA
ica = FastICA(n_components=250, whiten=True)
ica.fit(X)
meanI = np.mean(X, axis=0)
X1 = X - meanI
data = ica.transform(X1)
for i in range(len(fileNames)):
testSet.data[i].extend(data[i, :])
DATA = np.zeros((len(testSet.data), len(testSet.data[0])))
for i in range(len(testSet.data)):
DATA[i, :] = np.array(testSet.data[i])
testSet.targetNum = map(utils.mapMouthLabels2Two, testSet.target)
classLabels = np.array(testSet.targetNum)
scores = []
parameters = {
"n_estimators": range(90, 260, 20),
"max_depth": range(50, 110, 20),
"min_samples_split": range(5, 35, 5),
"max_features": range(25, 75, 10),
"min_samples_leaf": range(1, 10, 5),
}
# parameters = {'n_estimators': range(90, 100,20),
# 'max_depth': range(50, 60,20),
# 'min_samples_split':range(5,15,5),
# 'max_features':range(25,35,10),
# 'min_samples_leaf':range(1,10,5)}
print "Performing grid search..."
print "parameters:"
pprint(parameters)
for i in range(80, 260, 15):
mf = i if i < 75 else 75
parameters["max_features"] = range(10, mf, 10)
s = GridSearchUtils.doGridSearch(data[:, 0:i], classLabels, parameters)
temp = (s[0], s[1], i)
scores.append(temp)
scores_s = sorted(scores, key=lambda x: x[0])
print scores_s
pickle.dump(scores_s, open("/local/attale00/gridsearches/ICA_3", "w"))
## fetch natural image patches
# image_patches = fetch_mldata("natural scenes data")
# X = image_patches.data
#
## 1000 patches a 32x32
## not so much data, reshape to 16000 patches a 8x8
# X = X.reshape(1000, 4, 8, 4, 8)
# X = np.rollaxis(X, 3, 2).reshape(-1, 8 * 8)
# path = '/local/attale00/AFLW_cropped/cropped3/'
path = "/local/attale00/AFLW_cropped/mouth_img_error/"
fileNames = utils.getAllFiles(path)
roi = (0, 40, 0, 120)
X = fg.getAllImagesFlat(path, fileNames, (40, 120), roi=roi)
# perform ICA
ica = FastICA(n_components=49, whiten=True, max_iter=500)
ica.fit(X)
filters = ica.unmixing_matrix_
# pca
## Center data
# 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
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
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'