/
MultiPiePascal.py
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MultiPiePascal.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jun 4 11:05:28 2013
@author: attale00
"""
# -*- coding: utf-8 -*-
"""
Created on Tue May 7 17:29:01 2013
@author: attale00
"""
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 12 15:24:39 2013
This script classifies the multipie pictures with the random forest classifer learned on the aflw database pics
. It was trained on hog features only on the down scaled images. see the accompanying info file to the classfier for details
@author: attale00
"""
import utils
import featureGeneration as fg
import cv2
import numpy as np
import pickle
import sys
import plottingUtils
import classifierUtils
from sklearn import svm
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 _saveRF(testSet):
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40)
rf.fit(testSet.data,testSet.targetNum)
pickle.dump(rf,open('/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_nTHOGColor','w'))
f=open('/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_nTHOGColor.txt','w')
f.write('Source Images: Multi-Pie')
f.write('attribute: Glasses')
f.write('Features: Hog\n')
f.write('Features: getHogFeature(orientations = 4, cells_per_block=(26,9),maskFromAlpha=True \n')
f.write('ROI:(88,165,150,362)\n')
f.write('labels: none: 0, light,thick: 1\n')
f.close()
def _cross_validate(testSet):
rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40)
#rf=svm.SVC(class_weight='auto')
print 'Scores'
#print classifierUtils.standardCrossvalidation(rf,testSet,n_jobs=5)
classifierUtils.dissectedCV(rf,testSet)
print '----'
# print 'shuffeling the labels'
# np.random.shuffle(testSet.targetNum)
#
# rf=classifierUtils.standardRF(max_features = np.sqrt(len(testSet.data[0])),min_split=5,max_depth=40)
# #rf=svm.SVC(class_weight='auto')
# print 'Scores'
# print classifierUtils.standardCrossvalidation(rf,testSet,n_jobs=5)
# print '----'
return
def _classifyWithOld(path,testSet,mode):
#f=file('/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_1','r')
#np.random.shuffle(testSet.data)
cp='/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_nTHogColorNoMask'
print 'using classifier: '+cp
f=file(cp,'r')
clf = pickle.load(f)
testSet.classifiedAs=clf.predict(testSet.data)
testSet.hasBeenClassified = True
if mode =='s':
_score(clf,testSet)
else:
p='/export/comoros/egger/facedb/attale01_multipie/new_originals/'
_view(clf,testSet,p)
_score(clf,testSet)
def _score(clf,testSet):
score = clf.score(testSet.data,testSet.targetNum)
testSet.hasBeenClassified = True
classifierUtils.evaluateClassification(testSet,{0:'closed or narrow',1:'open or wide open'})
print 'Overall Score: {:.3f}'.format(score)
return
def _view(clf,testSet,path):
viewer = plottingUtils.ClassifiedImViewer(path,testSet)
viewer.view(comparer=plottingUtils.MouthTwo2FourComparer)
if __name__=='__main__':
if len(sys.argv)==2:
if sys.argv[1] in ['s','Score']:
m = 's'
elif sys.argv[1] in ['v','View']:
m='v'
elif sys.argv[1] in ['c']:
m='c'
elif sys.argv[1] in ['save']:
m='save'
else:
print 'Option not supported, valid options are s,v. Now just scoring'
m='s'
else:
m='s'
main(m)