def main(data, temp_id, size=256, delay=0, is_save=False):
save_folder = os.path.join(os.path.dirname(data), 'preprocessing')
if is_save and not os.path.exists(save_folder):
os.makedirs(save_folder)
save_folder2 = os.path.join(os.path.dirname(data), 'post')
if is_save and not os.path.exists(save_folder2):
os.makedirs(save_folder2)
# read all files paths
filenames = all_files_under(data, extension='png')
# read template image
temp_filename = filenames[temp_id]
ref_img = cv2.imread(temp_filename, cv2.IMREAD_GRAYSCALE)
ref_img = ref_img[:, -size:].copy()
_, ref_img = n4itk(ref_img) # N4 bias correction for the reference image
for idx, filename in enumerate(filenames):
print('idx: {}, filename: {}'.format(idx, filename))
img = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
ct_img = img[:, :size]
mr_img = img[:, -size:]
# N4 bias correction
ori_img, cor_img = n4itk(mr_img)
# Dynamic histogram matching between two images
his_mr = histogram_matching(cor_img, ref_img)
# Mask estimation based on Otsu auto-thresholding
mask = get_mask(his_mr, task='m2c')
# Masked out
masked_ct = ct_img & mask
masked_mr = his_mr & mask
canvas = imshow(ori_img,
cor_img,
his_mr,
masked_mr,
mask,
ct_img,
masked_ct,
size=size,
delay=delay)
canvas2 = np.hstack((masked_mr, masked_ct, mask))
if is_save:
cv2.imwrite(os.path.join(save_folder, os.path.basename(filename)),
canvas)
cv2.imwrite(os.path.join(save_folder2, os.path.basename(filename)),
canvas2)
def run_tensorflow(image,
noiseImage,
contentImage,
output_directory,
depth,
weightsLayers,
weightsLayersContent,
weightsPyramid,
weightsPyramidContent,
iter,
betaPar,
vgg_class=vgg19.Vgg19):
print('Begin execution of run_tensorflow')
print(np.shape(image))
# Variable for storing the style image
style = tf.get_variable(name="style_image",
dtype=tf.float64,
initializer=image,
trainable=False)
style = tf.cast(style, tf.float32)
noise = tf.get_variable(name="noise_image",
dtype=tf.float32,
initializer=tf.constant(noiseImage),
trainable=True)
content = tf.get_variable(name="content_image",
dtype=tf.float64,
initializer=tf.constant(contentImage),
trainable=False)
content = tf.cast(content, tf.float32)
#noise = tf.cast(noise, tf.float32)
styleList = [style]
noiseList = [noise]
contentList = [content]
fpassListContent = []
fpassListstyle = [] #list of vgg objects
fpassListNoise = []
outListstyle = [] #list of output layer of vgg objects
outListNoise = []
outListContent = []
## TODO ##
# move the pyramid code to a funciton
#it recieves the styleList and namescope name, returns the updated list
with tf.name_scope('build_pyramid_style'):
gaussKerr = tf.get_variable(initializer=np.reshape(
utils.gkern(5), (5, 5, 1, 1)),
trainable=False,
dtype='float64',
name='gauss_kernel')
gaussKerr = tf.cast(gaussKerr, tf.float32)
downsamp_filt = tf.get_variable(initializer=np.reshape(
np.array([[1., 0.], [0., 0.]]), (2, 2, 1, 1)),
trainable=False,
dtype='float64',
name='downsample_filter')
downsamp_filt = tf.cast(downsamp_filt, tf.float32)
for i in range(depth):
with tf.name_scope('cycle%d' % (i)):
[tR, tG, tB] = tf.unstack(styleList[i], num=3, axis=3)
tR = tf.expand_dims(tR, 3)
tG = tf.expand_dims(tG, 3)
tB = tf.expand_dims(tB, 3)
#convolve each input image with the gaussian filter
tR_gauss = tf.nn.conv2d(tR,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tG_gauss = tf.nn.conv2d(tG,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tB_gauss = tf.nn.conv2d(tB,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tR_downs = tf.nn.conv2d(tR_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tG_downs = tf.nn.conv2d(tG_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tB_downs = tf.nn.conv2d(tB_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tmp = tf.concat([tR_downs, tG_downs, tB_downs], axis=3)
styleList.append(tmp)
## TODO ##
## Find out what to do with the reuse
with tf.name_scope('build_pyramid_noise'):
#gaussKerr = tf.get_variable(initializer = np.reshape(utils.gkern(5), (5,5,1,1)), trainable=False, dtype='float64', name='gauss_kernel')
#gaussKerr = tf.cast(gaussKerr, tf.float32, reuse=True)
#downsamp_filt = tf.get_variable(initializer = np.reshape(np.array([[1.,0.],[0.,0.]]), (2,2,1,1)), trainable=False, dtype='float64', name='downsample_filter')
#downsamp_filt = tf.cast(downsamp_filt, tf.float32, reuse=True)
for i in range(depth):
with tf.name_scope('cycle%d' % (i)):
[tR, tG, tB] = tf.unstack(noiseList[i], num=3, axis=3)
tR = tf.expand_dims(tR, 3)
tG = tf.expand_dims(tG, 3)
tB = tf.expand_dims(tB, 3)
#convolve each input image with the gaussian filter
tR_gauss = tf.nn.conv2d(tR,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tG_gauss = tf.nn.conv2d(tG,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tB_gauss = tf.nn.conv2d(tB,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tR_downs = tf.nn.conv2d(tR_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tG_downs = tf.nn.conv2d(tG_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tB_downs = tf.nn.conv2d(tB_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tmp = tf.concat([tR_downs, tG_downs, tB_downs], axis=3)
noiseList.append(tmp)
with tf.name_scope('build_pyramid_content'):
#gaussKerr = tf.get_variable(initializer = np.reshape(utils.gkern(5), (5,5,1,1)), trainable=False, dtype='float64', name='gauss_kernel')
#gaussKerr = tf.cast(gaussKerr, tf.float32, reuse=True)
#downsamp_filt = tf.get_variable(initializer = np.reshape(np.array([[1.,0.],[0.,0.]]), (2,2,1,1)), trainable=False, dtype='float64', name='downsample_filter')
#downsamp_filt = tf.cast(downsamp_filt, tf.float32, reuse=True)
for i in range(depth):
with tf.name_scope('cycle%d' % (i)):
[tR, tG, tB] = tf.unstack(contentList[i], num=3, axis=3)
tR = tf.expand_dims(tR, 3)
tG = tf.expand_dims(tG, 3)
tB = tf.expand_dims(tB, 3)
#convolve each input image with the gaussian filter
tR_gauss = tf.nn.conv2d(tR,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tG_gauss = tf.nn.conv2d(tG,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tB_gauss = tf.nn.conv2d(tB,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tR_downs = tf.nn.conv2d(tR_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tG_downs = tf.nn.conv2d(tG_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tB_downs = tf.nn.conv2d(tB_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tmp = tf.concat([tR_downs, tG_downs, tB_downs], axis=3)
contentList.append(tmp)
# fpassList is a list fo vgg instances
# here we run the build method for each instance and
# store the output (last layer) on outList
with tf.name_scope('forward_pass_style'):
for j in range(len(styleList)):
with tf.name_scope('cycle%d' % (j)):
fpassListstyle.append(vgg_class())
out = fpassListstyle[j].build(styleList[j])
outListstyle.append(out)
with tf.name_scope('forward_pass_noise'):
for j in range(len(styleList)):
with tf.name_scope('cycle%d' % (j)):
fpassListNoise.append(vgg_class())
out = fpassListNoise[j].build(noiseList[j])
outListNoise.append(out)
with tf.name_scope('forward_pass_content'):
for j in range(len(contentList)):
with tf.name_scope('cycle%d' % (j)):
fpassListContent.append(vgg_class())
out = fpassListContent[j].build(contentList[j])
outListContent.append(out)
###################################################
## Loss function
with tf.name_scope('lossStyle'):
#Check that there are as many weigthLayers
assert len(weightsLayers) >= fpassListstyle[0].getLayersCount()
assert len(weightsPyramid) >= len(fpassListstyle)
loss_style = 0.0
#for i in range(0,5): #layers
for j in range(len(fpassListstyle)): #pyramid levels
with tf.name_scope('cyclePyramid%d' % (j)):
loss_pyra = 0.0
for i in range(0, fpassListstyle[0].getLayersCount()): #layers
with tf.name_scope('cycleLayer%d' % (i)):
origin = fpassListstyle[j].conv_list[i]
new = fpassListNoise[j].conv_list[i]
shape = origin.get_shape().as_list()
N = shape[3] #number of channels (filters)
M = shape[1] * shape[2] #width x height
F = tf.reshape(origin, (-1, N),
name='CreateF_style') #N x M
Gram_o = (
tf.matmul(tf.transpose(F, name='transpose_style'),
F,
name='Gram_style') / (N * M))
F_t = tf.reshape(new, (-1, N), name='CreateF_noise')
Gram_n = tf.matmul(tf.transpose(
F_t, name='transpose_noise'),
F_t,
name='Gram_noise') / (N * M)
loss = tf.nn.l2_loss(
(Gram_o - Gram_n), name='lossGramsubstraction') / 4
loss = tf.scalar_mul(weightsLayers[i], loss)
loss_pyra = tf.add(loss_pyra, loss)
loss_pyra = tf.scalar_mul(weightsPyramid[j], loss_pyra)
loss_style = tf.add(loss_style, loss_pyra)
tf.summary.scalar("loss_style", loss_style)
with tf.name_scope('lossContent'):
#Check that there are as many weigthLayers
assert len(
weightsLayersContent) >= fpassListContent[0].getLayersCount()
assert len(weightsPyramidContent) >= len(fpassListContent)
loss_content = 0.0
#for i in range(0,5): #layers
for j in range(len(fpassListContent)): #pyramid levels
with tf.name_scope('cyclePyramid%d' % (j)):
loss_pyra = 0.0
for i in range(0,
fpassListContent[0].getLayersCount()): #layers
with tf.name_scope('cycleLayer%d' % (i)):
con = fpassListContent[j].conv_list[i]
new = fpassListNoise[j].conv_list[i]
shape = con.get_shape().as_list()
N = shape[3] #number of channels (filters)
M = shape[1] * shape[2] #width x height
P = tf.reshape(con, (-1, N),
name='CreateF_content') #N x M
#Gram_o = (tf.matmul(tf.transpose(F, name='transpose_style'), F, name='Gram_style') / (N * M))
F = tf.reshape(new, (-1, N), name='CreateF_noise')
#Gram_n = tf.matmul(tf.transpose(F_t, name='transpose_noise'), F_t, name='Gram_noise') / (N * M)
loss = tf.nn.l2_loss(
(F - P), name='lossGramsubstraction') / 2
loss = tf.scalar_mul(weightsLayersContent[i], loss)
loss_pyra = tf.add(loss_pyra, loss)
loss_pyra = tf.scalar_mul(weightsPyramidContent[j], loss_pyra)
loss_content = tf.add(loss_content, loss_pyra)
tf.summary.scalar("loss_content", loss_content)
#betaPar = 0.5
alpha = tf.constant(1, dtype=tf.float32, name="alpha")
beta = tf.constant(betaPar, dtype=tf.float32, name="beta")
loss_sum = tf.scalar_mul(loss_content, alpha) + tf.scalar_mul(
loss_style, beta)
train_step = tf.train.AdamOptimizer(0.01).minimize(loss_sum,
var_list=[noise])
#train_step = tf.train.AdagradOptimizer(0.01).minimize(loss_sum, var_list=[noise])
restrict = tf.maximum(0., tf.minimum(1., noise), name="Restrict_noise")
r_noise = noise.assign(restrict)
tmpFile = os.path.join(output_directory, "tensor/")
if not os.path.exists(tmpFile):
os.makedirs(tmpFile)
#https://www.tensorflow.org/api_docs/python/tf/contrib/opt/ScipyOptimizerInterface
optimizer = tf.contrib.opt.ScipyOptimizerInterface(
loss_sum,
var_to_bounds={noise: (0, 1)},
method='L-BFGS-B',
options={'maxiter': iter})
#trainOP = optimizer.minimze
summary_writer = tf.summary.FileWriter(tmpFile, tf.get_default_graph())
merged_summary_op = tf.summary.merge_all()
Iterations = iter
counter = 0
temp_loss = 0
allLoss = []
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
optimizer.minimize(sess)
#tmp = fpassListContent[0].eval()
#tf.summary.image('content', tmp, 3)
answer = noise.eval()
answer = answer.reshape(np.shape(answer)[1], np.shape(answer)[2], 3)
skimage.io.imsave(
os.path.join(output_directory, "final_texture_noHistMatch.png"),
answer)
answer = (utils.histogram_matching(answer, image) *
255.).astype('uint8')
skimage.io.imsave(os.path.join(output_directory, "final_texture.png"),
answer)
#Save the pyramid
for w in range(1, len(noiseList)):
outputPyramid = noiseList[w].eval()
tmp = outputPyramid.reshape(
np.shape(outputPyramid)[1],
np.shape(outputPyramid)[2], 3)
tmp = (utils.histogram_matching(tmp, image) * 255.).astype('uint8')
skimage.io.imsave(
os.path.join(output_directory,
"final_texture_pyra%s.png" % (str(w))), tmp)
counter = 0
temp_loss = 0
for i in range(0,Iteration):
sess.run(train_step)
sess.run(r_tex)
if i == 0:
temp_loss = loss_sum.eval()
if i%10==0:
loss = loss_sum.eval()
if loss > temp_loss:
counter+=1
sys.stdout.write('\r')
sys.stdout.write("[%-50s] %d/%d ,loss=%e" % ('='*int(i*50/Iteration),i,Iteration,loss))
sys.stdout.flush()
temp_loss = loss
if i%500 == 0 and i!=0:
answer = tex.eval()
answer = answer.reshape(256,256,3)
answer = (answer*255).astype('uint8')
# print('Mean = ', np.mean(answer))
filename = "./result/%safter.jpg"%(str(i))
skimage.io.imsave(filename,answer)
if counter > 3:
print('\n','Early Stop!')
break
answer=tex.eval()
answer=answer.reshape(256,256,3)
answer = (utils.histogram_matching(answer,proc_img)*255.).astype('uint8')
skimage.io.imsave("./result/final_texture.jpg",answer)
def run_tensorflow(image,
noiseImage,
output_directory,
depth,
weightsLayers,
weightsPyramid,
iter,
vgg_class=vgg19.Vgg19):
print('Begin execution of run_tensorflow')
print(np.shape(image))
# Variable for storing the target image
target = tf.get_variable(name="target_image",
dtype=tf.float64,
initializer=image,
trainable=False)
target = tf.cast(target, tf.float32)
noise = tf.get_variable(name="noise_image",
dtype=tf.float32,
initializer=tf.constant(noiseImage),
trainable=True)
#noise = tf.cast(noise, tf.float32)
targetList = [target]
noiseList = [noise]
fpassListTarget = [] #list of vgg objects
fpassListNoise = []
outListTarget = [] #list of output layer of vgg objects
outListNoise = []
## TODO ##
# move the pyramid code to a funciton
#it recieves the targetList and namescope name, returns the updated list
with tf.name_scope('build_pyramid_target'):
gaussKerr = tf.get_variable(initializer=np.reshape(
utils.gkern(5), (5, 5, 1, 1)),
trainable=False,
dtype='float64',
name='gauss_kernel')
gaussKerr = tf.cast(gaussKerr, tf.float32)
downsamp_filt = tf.get_variable(initializer=np.reshape(
np.array([[1., 0.], [0., 0.]]), (2, 2, 1, 1)),
trainable=False,
dtype='float64',
name='downsample_filter')
downsamp_filt = tf.cast(downsamp_filt, tf.float32)
for i in range(depth):
with tf.name_scope('cycle%d' % (i)):
[tR, tG, tB] = tf.unstack(targetList[i], num=3, axis=3)
tR = tf.expand_dims(tR, 3)
tG = tf.expand_dims(tG, 3)
tB = tf.expand_dims(tB, 3)
#convolve each input image with the gaussian filter
tR_gauss = tf.nn.conv2d(tR,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tG_gauss = tf.nn.conv2d(tG,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tB_gauss = tf.nn.conv2d(tB,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tR_downs = tf.nn.conv2d(tR_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tG_downs = tf.nn.conv2d(tG_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tB_downs = tf.nn.conv2d(tB_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tmp = tf.concat([tR_downs, tG_downs, tB_downs], axis=3)
targetList.append(tmp)
## TODO ##
## Find out what to do with the reuse
with tf.name_scope('build_pyramid_noise'):
#gaussKerr = tf.get_variable(initializer = np.reshape(utils.gkern(5), (5,5,1,1)), trainable=False, dtype='float64', name='gauss_kernel')
#gaussKerr = tf.cast(gaussKerr, tf.float32, reuse=True)
#downsamp_filt = tf.get_variable(initializer = np.reshape(np.array([[1.,0.],[0.,0.]]), (2,2,1,1)), trainable=False, dtype='float64', name='downsample_filter')
#downsamp_filt = tf.cast(downsamp_filt, tf.float32, reuse=True)
for i in range(depth):
with tf.name_scope('cycle%d' % (i)):
[tR, tG, tB] = tf.unstack(noiseList[i], num=3, axis=3)
tR = tf.expand_dims(tR, 3)
tG = tf.expand_dims(tG, 3)
tB = tf.expand_dims(tB, 3)
#convolve each input image with the gaussian filter
tR_gauss = tf.nn.conv2d(tR,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tG_gauss = tf.nn.conv2d(tG,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tB_gauss = tf.nn.conv2d(tB,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tR_downs = tf.nn.conv2d(tR_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tG_downs = tf.nn.conv2d(tG_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tB_downs = tf.nn.conv2d(tB_gauss,
downsamp_filt,
strides=[1, 2, 2, 1],
padding='SAME')
tmp = tf.concat([tR_downs, tG_downs, tB_downs], axis=3)
noiseList.append(tmp)
# fpassList is a list fo vgg instances
# here we run the build method for each instance and
# store the output (last layer) on outList
with tf.name_scope('forward_pass_target'):
for j in range(len(targetList)):
with tf.name_scope('cycle%d' % (j)):
fpassListTarget.append(vgg_class())
out = fpassListTarget[j].build(targetList[j])
outListTarget.append(out)
with tf.name_scope('forward_pass_noise'):
for j in range(len(targetList)):
with tf.name_scope('cycle%d' % (j)):
fpassListNoise.append(vgg_class())
out = fpassListNoise[j].build(noiseList[j])
outListNoise.append(out)
###################################################
## Loss function
with tf.name_scope('lossFunction'):
#Check that there are as many weigthLayers
assert len(weightsLayers) >= fpassListTarget[0].getLayersCount()
assert len(weightsPyramid) >= len(fpassListTarget)
loss_sum = 0.0
#for i in range(0,5): #layers
for j in range(len(fpassListTarget)): #pyramid levels
with tf.name_scope('cyclePyramid%d' % (i)):
loss_pyra = 0.0
for i in range(0,
fpassListTarget[0].getLayersCount()): #layers
with tf.name_scope('cycleLayer%d' % (i)):
origin = fpassListTarget[j].conv_list[i]
new = fpassListNoise[j].conv_list[i]
shape = origin.get_shape().as_list()
N = shape[3] #number of channels (filters)
M = shape[1] * shape[2] #width x height
F = tf.reshape(origin, (-1, N),
name='CreateF_target') #N x M
Gram_o = (
tf.matmul(tf.transpose(F, name='transpose_target'),
F,
name='Gram_target') / (N * M))
F_t = tf.reshape(new, (-1, N), name='CreateF_noise')
Gram_n = tf.matmul(tf.transpose(
F_t, name='transpose_noise'),
F_t,
name='Gram_noise') / (N * M)
loss = tf.nn.l2_loss(
(Gram_o - Gram_n), name='lossGramsubstraction') / 2
loss = tf.scalar_mul(weightsLayers[i], loss)
loss_pyra = tf.add(loss_pyra, loss)
loss_pyra = tf.scalar_mul(weightsPyramid[j], loss_pyra)
loss_sum = tf.add(loss_sum, loss_pyra)
tf.summary.scalar("loss_sum", loss_sum)
yolo = tf.Variable(np.zeros((20, 20)), name='yolo')
yolo2 = tf.get_variable("big_matrix",
shape=(784, 10),
initializer=tf.zeros_initializer())
print(yolo)
print(yolo2)
print(noise)
dummy = tf.get_variable(name="dummy",
dtype=tf.float64,
initializer=np.zeros((5, 5)),
trainable=True)
#train_step = tf.train.AdamOptimizer(0.01).minimize(loss_sum, var_list=[noise])
train_step = tf.train.AdagradOptimizer(0.01).minimize(loss_sum,
var_list=[noise])
restrict = tf.maximum(0., tf.minimum(1., noise), name="Restrict_noise")
r_noise = noise.assign(restrict)
tmpFile = os.path.join(output_directory, "tensor/")
if not os.path.exists(tmpFile):
os.makedirs(tmpFile)
summary_writer = tf.summary.FileWriter(tmpFile, tf.get_default_graph())
merged_summary_op = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
#fpass2 = sess.run(out2)
#print(np.shape(fpass2))
#pyramid = sess.run(targetList)
#print(np.shape(targetList))
#sess.run([outListTarget, outListNoise])
Iterations = iter
counter = 0
temp_loss = 0
allLoss = []
for i in range(0, Iterations):
a = sess.run([train_step])
print(type(a))
sess.run([r_noise])
print(np.shape(r_noise))
if i == 0:
temp_loss = loss_sum.eval()
if i % 10 == 0:
loss = loss_sum.eval()
if loss > temp_loss:
counter += 1
sys.stdout.write('\r')
sys.stdout.write("[%-50s] %d/%d ,loss=%e" %
('=' * int(i * 50 / iter), i, iter, loss))
sys.stdout.flush()
temp_loss = loss
if i % 10 == 0 and i != 0 and i <= 200:
answer = noise.eval()
answer = answer.reshape(
np.shape(answer)[1],
np.shape(answer)[2], 3)
#answer = (answer*255).astype('uint8')
answer = (utils.histogram_matching(answer, image) *
255.).astype('uint8')
# print('Mean = ', np.mean(answer))
filename = os.path.join(output_directory,
"%safter.jpg" % (str(i)))
skimage.io.imsave(filename, answer)
#Save the pyramid
for w in range(1, len(noiseList)):
outputPyramid = noiseList[w].eval()
tmp = outputPyramid.reshape(
np.shape(outputPyramid)[1],
np.shape(outputPyramid)[2], 3)
tmp = (utils.histogram_matching(tmp, image) *
255.).astype('uint8')
filename = os.path.join(
output_directory,
"%safter%spyra.jpg" % (str(i), str(w)))
skimage.io.imsave(filename, tmp)
if i % 200 == 0 and i != 0 and i > 200:
answer = noise.eval()
answer = answer.reshape(
np.shape(answer)[1],
np.shape(answer)[2], 3)
#answer = (answer*255).astype('uint8')
answer = (utils.histogram_matching(answer, image) *
255.).astype('uint8')
# print('Mean = ', np.mean(answer))
filename = os.path.join(output_directory,
"%safter.jpg" % (str(i)))
skimage.io.imsave(filename, answer)
#Save the pyramid
for w in range(1, len(noiseList)):
outputPyramid = noiseList[w].eval()
tmp = outputPyramid.reshape(
np.shape(outputPyramid)[1],
np.shape(outputPyramid)[2], 3)
tmp = (utils.histogram_matching(tmp, image) *
255.).astype('uint8')
filename = os.path.join(
output_directory,
"%safter%spyra.jpg" % (str(i), str(w)))
skimage.io.imsave(filename, tmp)
#allLoss.append(loss_sum.eval())
allLoss.append(temp_loss)
if counter > 3000:
print('\n', 'Early Stop!')
break
summary_str = sess.run(merged_summary_op)
summary_writer.add_summary(summary_str, 1)
answer = noise.eval()
answer = answer.reshape(np.shape(answer)[1], np.shape(answer)[2], 3)
skimage.io.imsave(
os.path.join(output_directory, "final_texture_noHistMatch.png"),
answer)
answer = (utils.histogram_matching(answer, image) *
255.).astype('uint8')
skimage.io.imsave(os.path.join(output_directory, "final_texture.png"),
answer)
#Save the pyramid
for w in range(1, len(noiseList)):
outputPyramid = noiseList[w].eval()
tmp = outputPyramid.reshape(
np.shape(outputPyramid)[1],
np.shape(outputPyramid)[2], 3)
tmp = (utils.histogram_matching(tmp, image) * 255.).astype('uint8')
skimage.io.imsave(
os.path.join(output_directory,
"final_texture_pyra%s.png" % (str(w))), tmp)
#Some plotting
plotting(allLoss, iter, output_directory)
def run_synthesis_pyramid(tex,
images,
proc_img,
iterations,
output_directory,
weightsLayers,
weightsPyramid,
vgg_class=vgg19.Vgg19):
config = tf.ConfigProto()
gaussKerr = tf.cast(
tf.Variable(np.reshape(utils.gkern(5), (5, 5, 1, 1)),
trainable=False,
dtype='float64'), tf.float32)
#os.environ["CUDA_VISIBLE_DEVICES"]="0"
#exit(0)
with tf.Session(config=config) as sess:
vggs = [vgg_class() for i in range(len(images))]
vggs2 = [vgg_class() for i in range(len(images))]
for j in range(len(tex) - 1):
# Pyramid in TF
[tR, tG, tB] = tf.unstack(tex[j], num=3, axis=3)
tR = tf.expand_dims(tR, 3)
tG = tf.expand_dims(tG, 3)
tB = tf.expand_dims(tB, 3)
#convolve each input image with the gaussian filter
tR_gauss = tf.nn.conv2d(tR,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tG_gauss = tf.nn.conv2d(tG,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
tB_gauss = tf.nn.conv2d(tB,
gaussKerr,
strides=[1, 1, 1, 1],
padding='SAME')
#tmpR = tf.py_func(downSample, tR_gauss, tf.float32)
#tmpG = tf.py_func(downSample, tG_gauss, tf.float32)
#tmpB = tf.py_func(downSample, tB_gauss, tf.float32)
tmp = tf.stack([tR_gauss, tG_gauss, tB_gauss], axis=3)
tmp = tf.concat([tR_gauss, tG_gauss, tB_gauss], axis=3)
print("<<<<<<<<<<<<<<<HYPNOTOAD>>>>>>>>>>>>>>>>>>>>>>>>")
print(tmp)
print(tmp.get_shape())
print(tmp.get_shape().as_list()[1:])
print("<<<<<<<<<<<<<<<HYPNOTOAD>>>>>>>>>>>>>>>>>>>>>>>>")
newTmp = tf.py_func(downSample, [tmp], tf.float32)
#print("<<<<<<<<<<<<<<<HYPNOTOAD>>>>>>>>>>>>>>>>>>>>>>>>")
#print(newTmp)
#print(newTmp.get_shape())
#print(newTmp.get_shape().as_list()[1:])
#print("<<<<<<<<<<<<<<<HYPNOTOAD>>>>>>>>>>>>>>>>>>>>>>>>")
yolo = tex[j + 1].assign(newTmp)
with tf.name_scope("origin"):
for i in range(len(images)):
vggs[i].build(images[i])
with tf.name_scope("new"):
for i in range(len(images)):
vggs2[i].build(tex[i])
#Check that there are as many weigthLayers
assert len(weightsLayers) >= vggs[0].getLayersCount()
assert len(weightsPyramid) >= len(images)
loss_sum = 0.0
#for i in range(0,5): #layers
for j in range(len(images)): #pyramid levels
loss_pyra = 0.0
for i in range(0, vggs[0].getLayersCount()): #layers
origin = vggs[j].conv_list[i]
new = vggs2[j].conv_list[i]
shape = origin.get_shape().as_list()
N = shape[3] #number of channels (filters)
M = shape[1] * shape[2] #width x height
F = tf.reshape(origin, (-1, N)) #N x M
Gram_o = (tf.matmul(tf.transpose(F), F) / (N * M))
F_t = tf.reshape(new, (-1, N))
Gram_n = tf.matmul(tf.transpose(F_t), F_t) / (N * M)
loss = tf.nn.l2_loss((Gram_o - Gram_n)) / 2
loss = tf.scalar_mul(weightsLayers[i], loss)
loss_pyra = tf.add(loss_pyra, loss)
loss_pyra = tf.scalar_mul(weightsPyramid[j], loss_pyra)
loss_sum = tf.add(loss_sum, loss_pyra)
tf.summary.scalar("loss_sum", loss_sum)
train_step = tf.train.AdamOptimizer(0.01).minimize(loss_sum,
var_list=[tex])
restrict = tf.maximum(0., tf.minimum(1., tex[0]))
r_tex = tex[0].assign(restrict)
merged_summary_op = tf.summary.merge_all()
#sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())
Iteration = iterations
counter = 0
temp_loss = 0
allLoss = []
for i in range(0, Iteration):
#print('ITERATION'+str(i))
sess.run(train_step)
sess.run(r_tex)
sess.run(yolo)
tmpFile = os.path.join(output_directory, "tensor/")
if not os.path.exists(tmpFile):
os.makedirs(tmpFile)
#aa = "Users/falconr1/Documents/tmpDL/Code/result"
print(tmpFile)
summary_writer = tf.summary.FileWriter(tmpFile, sess.graph)
if i == 0:
temp_loss = loss_sum.eval()
if i % 10 == 0:
loss = loss_sum.eval()
if loss > temp_loss:
counter += 1
sys.stdout.write('\r')
sys.stdout.write(
"[%-50s] %d/%d ,loss=%e" %
('=' * int(i * 50 / Iteration), i, Iteration, loss))
sys.stdout.flush()
temp_loss = loss
if i % 100 == 0 and i != 0:
answer = tex[0].eval()
answer = answer.reshape(
np.shape(answer)[1],
np.shape(answer)[2], 3)
#answer = (answer*255).astype('uint8')
answer = (utils.histogram_matching(answer, proc_img) *
255.).astype('uint8')
# print('Mean = ', np.mean(answer))
filename = os.path.join(output_directory,
"%safter.jpg" % (str(i)))
skimage.io.imsave(filename, answer)
#Save the pyramid
for w in range(1, len(tex)):
outputPyramid = tex[w].eval()
tmp = outputPyramid.reshape(
np.shape(outputPyramid)[1],
np.shape(outputPyramid)[2], 3)
tmp = (utils.histogram_matching(tmp, proc_img) *
255.).astype('uint8')
filename = os.path.join(
output_directory,
"%safter%spyra.jpg" % (str(i), str(w)))
skimage.io.imsave(filename, tmp)
#allLoss.append(loss_sum.eval())
allLoss.append(temp_loss)
if counter > 3000:
print('\n', 'Early Stop!')
break
'''
answer = tex[0].eval()
#print(answer)
pyramid = create_pyramids((answer.reshape(np.shape(answer)[1], np.shape(answer)[2], 3)*255).astype('uint8'), levels)
im2 = [i.reshape((1, np.shape(i)[0], np.shape(i)[1], 3)) for i in pyramid]
im2 = [tf.cast(tf.convert_to_tensor(i, dtype="float64"), tf.float32) for i in im2]
#t_pyramid = tuple(tf.convert_to_tensor(np.reshape(i, (1, np.shape[0], np.shape[1], 3))) for i in pyramid)
#t_pyramid = tuple(tf.convert_to_tensor(i) for i in im2)
#print(t_pyramid[0].get_shape())
#print("**********************")
for j in range(1,len(im2)):
sess.run(tex[j].assign(im2[j]))
#print(pyramid)
'''
summary_str = sess.run(merged_summary_op)
summary_writer.add_summary(summary_str, 1)
answer = tex[0].eval()
answer = answer.reshape(np.shape(answer)[1], np.shape(answer)[2], 3)
answer = (utils.histogram_matching(answer, proc_img) *
255.).astype('uint8')
skimage.io.imsave(os.path.join(output_directory, "final_texture.png"),
answer)
#Save the pyramid
for w in range(1, len(tex)):
outputPyramid = tex[w].eval()
tmp = outputPyramid.reshape(
np.shape(outputPyramid)[1],
np.shape(outputPyramid)[2], 3)
tmp = (utils.histogram_matching(tmp, proc_img) *
255.).astype('uint8')
skimage.io.imsave(
os.path.join(output_directory,
"final_texture_pyra%s.png" % (str(w))), tmp)
#Some plotting
plotting(allLoss, iterations, output_directory)
def run_synthesis(tex,
images,
proc_img,
iterations,
output_directory,
weightsLayers,
vgg_class=vgg19.Vgg19):
config = tf.ConfigProto()
os.environ["CUDA_VISIBLE_DEVICEs"] = "0"
with tf.Session(config=config) as sess:
vgg = vgg_class()
vgg2 = vgg_class()
with tf.name_scope("origin"):
vgg.build(images)
with tf.name_scope("new"):
vgg2.build(tex)
#Check that there are as many weigthLayers
assert len(weightsLayers) == vgg.getLayersCount()
## Caculate the Loss according to the paper
loss_sum = 0.
for i in range(0, vgg.getLayersCount()):
origin = vgg.conv_list[i]
new = vgg2.conv_list[i]
shape = origin.get_shape().as_list()
N = shape[3]
M = shape[1] * shape[2]
F = tf.reshape(origin, (-1, N))
Gram_o = (tf.matmul(tf.transpose(F), F) / (N * M))
F_t = tf.reshape(new, (-1, N))
Gram_n = tf.matmul(tf.transpose(F_t), F_t) / (N * M)
loss = tf.nn.l2_loss((Gram_o - Gram_n)) / 2
loss = tf.scalar_mul(weightsLayers[i], loss)
loss_sum = tf.add(loss_sum, loss)
train_step = tf.train.AdamOptimizer(0.01).minimize(loss_sum,
var_list=[tex])
restrict = tf.maximum(0., tf.minimum(1., tex))
r_tex = tex.assign(restrict)
#sess.run(tf.initialize_all_variables())
sess.run(tf.global_variables_initializer())
Iteration = iterations
counter = 0
temp_loss = 0
allLoss = []
for i in range(0, Iteration):
sess.run(train_step)
sess.run(r_tex)
if i == 0:
temp_loss = loss_sum.eval()
if i % 100 == 0:
loss = loss_sum.eval()
if loss > temp_loss:
counter += 1
sys.stdout.write('\r')
sys.stdout.write(
"[%-50s] %d/%d ,loss=%e" %
('=' * int(i * 50 / Iteration), i, Iteration, loss))
sys.stdout.flush()
temp_loss = loss
if i % 10 == 0 and i != 0 and i < 200:
answer = tex.eval()
answer = answer.reshape(
np.shape(answer)[1],
np.shape(answer)[2], 3)
answer = (answer * 255).astype('uint8')
# print('Mean = ', np.mean(answer))
filename = os.path.join(output_directory,
"%safter.jpg" % (str(i)))
skimage.io.imsave(filename, answer)
if i % 200 == 0 and i != 0 and i > 200:
answer = tex.eval()
answer = answer.reshape(
np.shape(answer)[1],
np.shape(answer)[2], 3)
answer = (answer * 255).astype('uint8')
# print('Mean = ', np.mean(answer))
filename = os.path.join(output_directory,
"%safter.jpg" % (str(i)))
skimage.io.imsave(filename, answer)
if counter > 3000:
print('\n', 'Early Stop!')
break
#allLoss.append(loss_sum.eval())
allLoss.append(temp_loss)
answer = tex.eval()
answer = answer.reshape(np.shape(answer)[1], np.shape(answer)[2], 3)
answer = (utils.histogram_matching(answer, proc_img) *
255.).astype('uint8')
skimage.io.imsave(os.path.join(output_directory, "final_texture.png"),
answer)
#Some plotting
plotting(allLoss, iterations, output_directory)
