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)
fg_mode = 0
size=(4,12)
overlap=2
#size=(40,120)
fg.getImagePatchStat(testSet,path=path_ea,patchSize=size,overlap = overlap,mode=fg_mode)
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_size_{}'.format(size[0]),'w'))
elif mode in ['save']:
print 'saving new classifier'
_saveRF(testSet,rf)
else:
print 'not doing anything'
def errorPatch():
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('/local/attale00/a_labels','mouth',labeledImages,cutoffSeq='.png',suffix='_face0.labels')
#
fileNames = labeledImages;
testSet = fg.dataContainer(labs)
fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12),overlap = 2)
testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)
clfPath = '/home/attale00/Desktop/classifiers/errorpatches/rferror'
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);
# 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
#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)
# data=X
#
## 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,:])
#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)
fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12),overlap = 3)
#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'
_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/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'