xP=[x.replace('lfw','lfw_aegan') for x in P]
xQ=[x.replace('lfw','lfw_aegan') for x in Q]
PF=model.mean_F(utils.image_feed(xP[:K],image_dims))
QF=model.mean_F(utils.image_feed(xQ[: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=o
# for each interpolation step
for delta in delta_params:
print(xX,b,delta)
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[-1].append(Y)
max_iter=config.iter//2
init=Y
result=numpy.asarray(result)
original=numpy.asarray(original)
if 'color' in postprocess:
result=utils.color_match(numpy.expand_dims(original,1),result)
m=imageutils.montage(numpy.concatenate([numpy.expand_dims(original,1),result],axis=1))
imageutils.write('results/demo1.png',m)
print('Output is results/demo1.png')
t1=time.time()
print('{} minutes ({} minutes per image).'.format((t1-t0)/60.0,(t1-t0)/60.0/result.shape[0]/result.shape[1]))
WF = (QF - PF) / ((QF - PF)**2).mean()
elif config.scaling == 'none':
WF = (QF - PF)
max_iter = config.iter
init = o
# for each interpolation step
for delta in delta_params:
print(xX, delta)
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[-1].append(Y)
max_iter = config.iter // 2
init = Y
result = numpy.asarray(result)
original = numpy.asarray(original)
if 'color' in postprocess:
result = utils.color_match(numpy.expand_dims(original, 1), result)
m = imageutils.montage(
numpy.concatenate([numpy.expand_dims(original, 1), result],
axis=1).transpose(1, 0, 2, 3, 4))
imageutils.write('results/demo3.png', m)
print('Output is results/demo3.png')
t1 = time.time()
print('{} minutes ({} minutes per image).'.format(
(t1 - t0) / 60.0,
(t1 - t0) / 60.0 / result.shape[0] / result.shape[1]))
caption='psnr {:.4}, ssim {:.4}'.format(psnr,ssim)
subprocess.check_call('convert {root_dir2}/{basename}_original.png {root_dir2}/{basename}_actual.png -size {w}x -font Arial-Italic -pointsize 12 caption:{caption} -append {root_dir2}/eval_{basename}.png'.format(root_dir2=pipes.quote(root_dir2),basename=pipes.quote(basename),caption=pipes.quote(caption),w=A.shape[1],h=A.shape[0]//10),shell=True)
all_psnr.append(psnr)
all_ssim.append(ssim)
print(tname,'psnr',psnr)
print(tname,'ssim',ssim)
mean_psnr=np.asarray(all_psnr).mean()
mean_ssim=np.asarray(all_ssim).mean()
with open('{}/autoencoder.txt'.format(root_dir),'a') as f:
f.write('{},{},{},{}\n'.format(model,tname,mean_psnr,mean_ssim))
t1=time.time()
print('Finished in {} minutes.'.format((t1-t0)/60.0))
return root_dir,np.asarray(all_results)
if __name__=='__main__':
filelist=['images/lfw/Aaron_Eckhart/Aaron_Eckhart_0001.jpg']
targetlist=[
#('c3c4c5',['conv3_1','conv4_1','conv5_1'],[1,1,1]),
('c3',['conv3_1'],[1]),
('c4',['conv4_1'],[1]),
('c5',['conv5_1'],[1]),
]
for model in ['vgg','vggface']:
root_dir,result=invert_model(filelist,targetlist,model,image_dims=[100,100],max_iter=3000,tv_lambda=0.001,tv_beta=2,desc='invert_{}'.format(model),device_id=0)
original=np.asarray([skimage.io.imread(x)/255.0 for x in filelist*len(targetlist)])
m=np.asarray([original,result])
skimage.io.imsave('{}/montage.png'.format(root_dir),imageutils.montage(m))
file_name = '.'.join(file_name)
numpy.savez('{}/{}_{}.npz'.format(file_save_path, file_name,
config.effect),
WF=WF)
if config.save_image: # True: make and save a image and then exit, False: just build npz files
max_iter = 500
init = original
# 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 = 500 // 2
init = Y
result = numpy.asarray([result])
original = numpy.asarray([original])
#m=imageutils.montage(numpy.concatenate([numpy.expand_dims(original,1),result],axis=1))
m = imageutils.montage(result)
opath = '{}_{}_{}{}.{}'.format(prefix_path, timestamp,
config.effect, '_', 'jpg')
print('--> opath:', opath)
imageutils.write(opath, m)
break