def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
misc.save_image_grid(
images,
os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)),
[0, 255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images_cond2(run_id, x, y, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=1):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
training_set = dataset.load_dataset(data_dir=config.data_dir, shuffle_mb=0, verbose=True, **config.dataset)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
real, label = training_set.get_minibatch_np(num_pngs)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
size=128
grid= np.zeros((3, x*256, y* 256))
image=[]
for i in range(1, 18):
filename = 'cond4/'+str(i)+'.png'
if os.path.isfile(filename):
im=Image.open(filename)
im.load()
im = np.asarray(im, dtype=np.float32 )
im=np.transpose(im, (2, 0, 1))
image.append(im)
print(image[i-1].shape)
print(len(image))
for i in range (1, x):
grid[:, (i)*256:(i)*256+256, 0:256]= image[i]
print(i)
for j in range (i, 16):
grid[:, 0:256, (j-i)*256:(j-i)*256+256]= image[j]
for j in range (128, y*256-128, 128):
for i in range (128, x*256-128, 128):
real = grid[:,i:i+256, j:j+256]
real1= real[:, :(size),:(size)]
real2= real[:, (size):,:(size)]
real3= real[:, :(size),(size):]
real1=(real1.astype(np.float32)-127.5)/127.5
real2=(real2.astype(np.float32)-127.5)/127.5
real3=(real3.astype(np.float32)-127.5)/127.5
latents = np.random.randn(3, 128, 128)
left = np.concatenate((real1, real2), axis=1)
print('left:'+str(left.shape))
right = np.concatenate((real3, latents), axis=1)
lat_and_cond = np.concatenate((left, right), axis=2)
lat_and_cond = lat_and_cond[np.newaxis]
fake_images_out_small = Gs.get_output_for(lat_and_cond, is_training=False)
fake_images_out_small = (fake_images_out_small.eval()*127.5)+127.5
print(fake_images_out_small.shape)
fake_images_out_small=fake_images_out_small[0, :,:,:]
grid[:,i+128:i+256, j+128:j+256]=fake_images_out_small
images = grid[np.newaxis]
misc.save_image_grid(images, os.path.join(result_subdir, 'grid.png'), [0,255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_interpolate_midle_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8,
middle_img=10):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
latents = misc.random_latents(middle_img + 2,
Gs,
random_state=random_state)
from_to_tensor = latents[middle_img + 1] - latents[0]
from_z = latents[0]
#between_x_list = [from_x]
counter = 0
for alpha in np.linspace(-0.5, 0.5, middle_img +
2): #np.linspace(0, 1, middle_img + 1):
print('alpha: ', alpha, 'counter= ', counter)
between_z = from_z + alpha * from_to_tensor
latents[counter] = between_z
counter += 1
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
#grid_size_1=[middle_img+2,1]
grid_size_1 = [middle_img + 1, 1]
#png_prefix=0
misc.save_image_grid(
images[1:, :, :, :],
os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)),
[0, 255], grid_size_1)
'''
def find_latent_with_query_image(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=4123, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
# Create query image - tensorflow constant
query_image = cv2.imread('../../data/ACDC/training/patient001/cardiac_cycles/0/0.png')
query_image = cv2.resize(query_image, (256, 256))
print('Saving query image to "%s"...' % result_subdir)
cv2.imwrite(result_subdir+'/query_image.png', query_image)
query_image = query_image.transpose(2,0,1)
query_image = query_image[np.newaxis]
x = tf.constant(query_image, dtype=tf.float32, name='query_image')
# Create G(z) - tensorflow variable and label
latent = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
initial = tf.constant(latent, dtype=tf.float32)
z = tf.Variable(initial_value=initial, dtype=tf.float32, name='latent_space')
label = np.zeros([latent.shape[0], 5], np.float32)
label[:,4] = 1 # | 0 -> NOR | 1 -> DCM | 2 -> HCM | 3 -> MINF | 4 -> RV |
gz = Gs.run(latent, label, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.float32)
gz = tf.Variable(gz, dtype=tf.float32)
# Define a loss function
residual_loss = tf.losses.absolute_difference(x, gz)
# Define an optimizer
train_op = tf.train.AdamOptimizer(learning_rate=0.01).minimize(residual_loss)
zs, gzs, step = [], [], 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
_, loss_value = sess.run([train_op, residual_loss])
while (loss_value > 2e-04 and step <= 50000):
_, loss_value = sess.run([train_op, residual_loss])
step += 1
if step % 10000 == 0:
print('Step {}, Loss value: {}'.format(step, loss_value))
gzs.append(sess.run(gz))
zs.append(sess.run(z))
for png_idx, image in enumerate(gzs):
misc.save_image_grid(image, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
np.save(result_subdir+'/zs.npy', np.asarray(zs))
def generate_fake_images(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8)
misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images_cond(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=1):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
training_set = dataset.load_dataset(data_dir=config.data_dir, verbose=True, **config.dataset)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
real, label = training_set.get_minibatch_np(num_pngs)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
size=128
for png_idx in range(num_pngs):
real1= real[png_idx,:, :(size),:(size)]
real2= real[png_idx,:, (size):,:(size)]
real3= real[png_idx,:, :(size),(size):]
real1=(real1.astype(np.float32)-127.5)/127.5
real2=(real2.astype(np.float32)-127.5)/127.5
real3=(real3.astype(np.float32)-127.5)/127.5
latents = np.random.randn(3, 128, 128)
left = np.concatenate((real1, real2), axis=1)
print('left:'+str(left.shape))
right = np.concatenate((real3, latents), axis=1)
lat_and_cond = np.concatenate((left, right), axis=2)
lat_and_cond = lat_and_cond[np.newaxis]
fake_images_out_small = Gs.get_output_for(lat_and_cond, is_training=False)
fake_images_out_small = (fake_images_out_small.eval()*127.5)+127.5
print(fake_images_out_small.shape)
fake_images_out_small=fake_images_out_small[0, :,:,:]
real1=(real1.astype(np.float32)*127.5)+127.5
real2=(real2.astype(np.float32)*127.5)+127.5
real3=(real3.astype(np.float32)*127.5)+127.5
fake_image_out_right =np.concatenate((real3, fake_images_out_small), axis=1)
fake_image_out_left = np.concatenate((real1, real2), axis=1)
images = np.concatenate((fake_image_out_left, fake_image_out_right), axis=2)
images = images[np.newaxis]
misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
subdir=None,
random_seed=1000,
minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if subdir is None:
subdir = misc.get_id_string_for_network_pkl(network_pkl)
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = "results/images/" + subdir
if not os.path.exists(result_subdir):
os.makedirs(result_subdir)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents,
labels,
minibatch_size=minibatch_size,
num_gpus=1,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8,
randomize_noise=False)
misc.save_image_grid(
images, os.path.join(result_subdir, '%06d.png' % (png_idx)),
[0, 255], grid_size)
np.save(result_subdir + "/" + '%06d' % (png_idx), latents)
def generate_fake_images(run_id,
snapshot=None,
grid_size=[1, 1],
num_pngs=1,
image_shrink=1,
png_prefix=None,
random_seed=1000,
minibatch_size=8):
embeddings_contant = False
labels_constant = False
latents_constant = False
idx = random.randint(0, 56880)
df = pandas.read_csv('datasets/50k_sorted_tf/50k_index_sorted.csv')
print('embeddings_contant : ' + str(embeddings_contant))
print('labels_constant : ' + str(labels_constant))
print('latents_constant : ' + str(latents_constant))
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir + '/' + run_id,
config.desc)
if latents_constant:
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=None)
#embeddings = np.zeros([1, 300], dtype=np.float32)
#labels = np.zeros([1, 32], dtype=np.float32)
embeddings = np.load(
'datasets/50k_sorted_tf/sum_embedding_title.embeddings')
embeddings = embeddings.astype('float32')
labels = np.load(
'datasets/50k_sorted_tf/sum_embedding_category_average.labels')
labels = labels.astype('float32')
name1 = ''
if labels_constant:
label = labels[idx]
name1 = name1 + ' ' + df.at[idx, 'category1']
label = label.reshape(1, label.shape[0])
if embeddings_contant:
embedding = embeddings[idx]
title = df.at[idx, 'title']
name1 = name1 + ' ' + title[:10]
embedding = embedding.reshape(1, embedding.shape[0])
#print(latents.shape)
for png_idx in range(num_pngs):
name = ''
name = name + name1
print('Generating png %d / %d...' % (png_idx, num_pngs))
rand = random.randint(0, 56880)
#rand = png_idx * 1810
#labels = sess.run(classes[0])
if not latents_constant:
latents = misc.random_latents(np.prod(grid_size),
Gs,
random_state=random_state)
if not labels_constant:
label = labels[rand]
label = label.reshape(1, label.shape[0])
name = name + ' ' + df.at[rand, 'category1']
if not embeddings_contant:
embedding = embeddings[rand]
title = df.at[rand, 'title']
name = name + ' ' + title[:10]
embedding = embedding.reshape(1, embedding.shape[0])
#print(labels.shape)
images = Gs.run(latents,
label,
embedding,
minibatch_size=minibatch_size,
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_shrink=image_shrink,
out_dtype=np.uint8)
misc.save_image_grid(
images, os.path.join(result_subdir,
'%s%06d.png' % (name, png_idx)), [0, 255],
grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def find_dir_latent_with_query_image(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=4123, minibatch_size=8, dir_path='../../data/ACDC/latents/cleaned_testing/'):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
replicate_folder_structure(dir_path, result_subdir+'/')
train_patients = sorted_nicely(glob.glob(dir_path+'*'))
for patient in train_patients:
cardiac_cycles = sorted_nicely(glob.glob(patient+'/*/*/*.png'))
cfg = open(patient+'/Info.cfg')
label = condition_to_onehot(cfg.readlines()[2][7:])
cont = 0
for cycle in cardiac_cycles:
# Get folder containing the image
supfolder = sup_folder(cycle)
latent_subir = result_subdir + '/' + supfolder
# Create query image - tensorflow constant
query_image = cv2.imread(cycle) # read frame
query_image = cv2.resize(query_image, (256, 256))
query_image = query_image.transpose(2,0,1)
query_image = query_image[np.newaxis]
x = tf.constant(query_image, dtype=tf.float32, name='query_image')
# Create G(z) - tensorflow variable and label
latent = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
initial = tf.constant(latent, dtype=tf.float32)
z = tf.Variable(initial_value=initial, dtype=tf.float32, name='latent_space')
gz = Gs.run(latent, label, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.float32)
gz = tf.Variable(gz, dtype=tf.float32)
# Define a loss function
residual_loss = tf.losses.absolute_difference(x, gz)
# Define an optimizer
train_op = tf.train.AdamOptimizer(learning_rate=0.1).minimize(residual_loss)
zs, gzs, step = [], [], 1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
_, loss_value = sess.run([train_op, residual_loss])
while (loss_value > 2e-04 and step <= 5000):
_, loss_value = sess.run([train_op, residual_loss])
step += 1
if step % 1000 == 0:
print('Step {}, Loss value: {}'.format(step, loss_value))
gzs.append(sess.run(gz))
zs.append(sess.run(z))
# save last image
print('Image saved at {}'.format(os.path.join(latent_subir, '%s.png' % (cont))))
misc.save_image_grid(gzs[-1], os.path.join(latent_subir, '%02d.png' % (cont)), [0,255], grid_size)
print('Latent vectors saved at {}'.format(os.path.join(latent_subir, 'latent_%02d.npy' % (cont))))
np.save(os.path.join(latent_subir, 'latent_%02d.npy' % (cont)), zs[-1])
print('Labels saved at {}'.format(os.path.join(latent_subir, 'label_%02d.npy' % (cont))))
np.save(os.path.join(latent_subir, 'label_%02d.npy' % (cont)), label)
cont+=1
cfg.close()
cont = 0
