-
Notifications
You must be signed in to change notification settings - Fork 0
/
MultiPiePascal128ICA.py
181 lines (134 loc) · 5.44 KB
/
MultiPiePascal128ICA.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 4 16:10:16 2013
@author: attale00
"""
# -*- 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+'/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 _saveRF(testSet,rf):
rf.fit(testSet.data,testSet.targetNum)
pickle.dump(rf,open('/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_nTSmallHOGColor','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 _classifyWithOld(path,testSet,mode):
#f=file('/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_1','r')
#np.random.shuffle(testSet.data)
cp='/home/attale00/Desktop/classifiers/RandomForestMouthclassifier_nTHOGColor128'
print 'using classifier: '+cp
f=file(cp,'r')
clf = pickle.load(f)
testSet.classifiedAs=clf.predict(testSet.data)
testSet.hasBeenClassified = True
testSet.classifiedAs=clf.predict(testSet.data)
testSet.probabilities=clf.predict_proba(testSet.data)
for i in range(len(testSet.data)):
if testSet.probabilities[i][1]>=.35:
testSet.classifiedAs[i]=1
else:
testSet.classifiedAs[i]=0
plottingUtils.plotPoses(classifierUtils.splitByPose(testSet))
if mode =='s':
_score(clf,testSet)
else:
_view(clf,testSet,path+'Multi-PIE/extracted/')
_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)
def _cvDissect(testSet,rf,mapping):
for i in range(3):
train,test = testSet.splitInTestAndTraining(frac=.7)
rf.fit(train.data,train.targetNum)
test.classifiedAs=rf.predict(test.data)
test.hasBeenClassified = True
classifierUtils.evaluateClassification(test,{0:'closed or narrow',1:'open or wide open'})
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)