-
Notifications
You must be signed in to change notification settings - Fork 50
/
demo2.py
executable file
·175 lines (161 loc) · 7.21 KB
/
demo2.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
#!/usr/bin/env python2
from __future__ import division
from __future__ import with_statement
from __future__ import print_function
import time
timestamp=int(round(time.time()))
import numpy
import deepmodels
import json
import os.path
import argparse
import alignface
import imageutils
import utils
def fit_submanifold_landmarks_to_image(template,original,Xlm,face_d,face_p,landmarks=list(range(68))):
'''
Fit the submanifold to the template and take the top-K.
Xlm is a N x 68 x 2 list of landmarks.
'''
lossX=numpy.empty((len(Xlm),),dtype=numpy.float64)
MX=numpy.empty((len(Xlm),2,3),dtype=numpy.float64)
nfail=0
for i in range(len(Xlm)):
lm=Xlm[i]
try:
M,loss=alignface.fit_face_landmarks(Xlm[i],template,landmarks=landmarks,image_dims=original.shape[:2])
lossX[i]=loss
MX[i]=M
except alignface.FitError:
lossX[i]=float('inf')
MX[i]=0
nfail+=1
if nfail>1:
print('fit submanifold, {} errors.'.format(nfail))
a=numpy.argsort(lossX)
return a,lossX,MX
if __name__=='__main__':
# configure by command-line arguments
parser=argparse.ArgumentParser(description='Generate high resolution face transformations.',formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('method',type=str,choices=['older','younger','facehair'],help='desired transformation')
parser.add_argument('input',type=str,nargs='+',help='input color image')
parser.add_argument('--backend',type=str,default='torch',choices=['torch','caffe+scipy'],help='reconstruction implementation')
parser.add_argument('--device_id',type=int,default=0,help='zero-indexed CUDA device')
parser.add_argument('--K',type=int,default=100,help='number of nearest neighbors')
parser.add_argument('--scaling',type=str,default='none',choices=['none','beta'],help='type of step scaling')
parser.add_argument('--iter',type=int,default=500,help='number of reconstruction iterations')
parser.add_argument('--postprocess',type=str,default='mask',help='comma-separated list of postprocessing operations')
parser.add_argument('--delta',type=str,default='3.5',help='comma-separated list of interpolation steps')
parser.add_argument('--output_format',type=str,default='png',choices=['png','jpg'],help='output image format')
parser.add_argument('--comment',type=str,default='',help='the comment is appended to the output filename')
parser.add_argument('--extradata',action='store_true',default=False,help='extra data is saved')
parser.add_argument('--output',type=str,default='',help='output is written to this pathname')
parser.add_argument('--include_original',action='store_true',default=False,help='the first column of the output is the original image')
config=parser.parse_args()
postprocess=set(config.postprocess.split(','))
postfix_comment='_'+config.comment if config.comment else ''
print(json.dumps(config.__dict__))
# load models
minimum_resolution=200
if config.backend=='torch':
import deepmodels_torch
model=deepmodels_torch.vgg19g_torch(device_id=config.device_id)
elif config.backend=='caffe+scipy':
model=deepmodels.vgg19g(device_id=config.device_id)
else:
raise ValueError('Unknown backend')
classifier=deepmodels.facemodel_attributes()
fields=classifier.fields()
gender=fields.index('Male')
smile=fields.index('Smiling')
face_d,face_p=alignface.load_face_detector()
# Set the free parameters
K=config.K
delta_params=[float(x.strip()) for x in config.delta.split(',')]
X=config.input
t0=time.time()
opathlist=[]
# for each test image
for i in range(len(X)):
xX=X[i]
prefix_path=os.path.splitext(xX)[0]
template,original=alignface.detect_landmarks(xX,face_d,face_p)
image_dims=original.shape[:2]
if min(image_dims)<minimum_resolution:
s=float(minimum_resolution)/min(image_dims)
image_dims=(int(round(image_dims[0]*s)),int(round(image_dims[1]*s)))
original=imageutils.resize(original,image_dims)
XF=model.mean_F([original])
XA=classifier.score([xX])[0]
print(xX,', '.join(k for i,k in enumerate(fields) if XA[i]>=0))
# select positive and negative sets based on gender and mouth
if config.method=='older':
cP=[(gender,XA[gender]>=0),(smile,XA[smile]>=0),(fields.index('Young'),True)]
cQ=[(gender,XA[gender]>=0),(smile,XA[smile]>=0),(fields.index('Young'),False)]
elif config.method=='younger':
cP=[(gender,XA[gender]>=0),(smile,XA[smile]>=0),(fields.index('Young'),False)]
cQ=[(gender,XA[gender]>=0),(smile,XA[smile]>=0),(fields.index('Young'),True)]
elif config.method=='facehair':
cP=[(gender,XA[gender]>=0),(smile,XA[smile]>=0),(fields.index('No_Beard'),True),(fields.index('Mustache'),False)]
cQ=[(gender,XA[gender]>=0),(smile,XA[smile]>=0),(fields.index('No_Beard'),False),(fields.index('Mustache'),True)]
else:
raise ValueError('Unknown method')
P=classifier.select(cP,XA)
Q=classifier.select(cQ,XA)
if len(P)<4*K or len(Q)<4*K:
print('{}: Not enough images in database (|P|={}, |Q|={}).'.format(xX,len(P),len(Q)))
continue
# fit the best 4K database images to input image
Plm=classifier.lookup_landmarks(P[:4*K])
Qlm=classifier.lookup_landmarks(Q[:4*K])
idxP,lossP,MP=fit_submanifold_landmarks_to_image(template,original,Plm,face_d,face_p)
idxQ,lossQ,MQ=fit_submanifold_landmarks_to_image(template,original,Qlm,face_d,face_p)
# Use the K best fitted images
xP=[P[i] for i in idxP[:K]]
xQ=[Q[i] for i in idxQ[:K]]
PF=model.mean_F(utils.warped_image_feed(xP,MP[idxP[:K]],image_dims))
QF=model.mean_F(utils.warped_image_feed(xQ,MQ[idxQ[:K]],image_dims))
if config.scaling=='beta':
WF=(QF-PF)/((QF-PF)**2).mean()
elif config.scaling=='none':
WF=(QF-PF)
max_iter=config.iter
init=original
if config.extradata:
numpy.savez('{}_{}{}.npz'.format(prefix_path,config.method,postfix_comment),WF=WF)
# for each interpolation step
result=[]
for delta in delta_params:
print(xX,image_dims,delta,len(xP),len(xQ))
t2=time.time()
Y=model.F_inverse(XF+WF*delta,max_iter=max_iter,initial_image=init)
t3=time.time()
print('{} minutes to reconstruct'.format((t3-t2)/60.0))
result.append(Y)
max_iter=config.iter//2
init=Y
result=numpy.asarray([result])
original=numpy.asarray([original])
X_mask=prefix_path+'-mask.png'
if 'mask' in postprocess and os.path.exists(X_mask):
mask=imageutils.resize(imageutils.read(X_mask),image_dims)
result*=mask
result+=original*(1-mask)
if 'color' in postprocess:
result=utils.color_match(numpy.asarray([original]),result)
if 'mask' in postprocess and os.path.exists(X_mask):
result*=mask
result+=original*(1-mask)
if config.include_original:
m=imageutils.montage(numpy.concatenate([numpy.expand_dims(original,1),result],axis=1))
else:
m=imageutils.montage(result)
if config.output:
opath=config.output
else:
opath='{}_{}_{}{}.{}'.format(prefix_path,timestamp,config.method,postfix_comment,config.output_format)
imageutils.write(opath,m)
opathlist.append(opath)
print('Outputs are {}'.format(' '.join(opathlist)))
t1=time.time()
print('{} minutes ({} minutes per image).'.format((t1-t0)/60.0,(t1-t0)/60.0/len(X)/len(delta_params)))