def train(datasets, options, ctx):
images_path = datasets['images']
print('Reading expected image size from starting checkpoint')
dnnlib.tflib.init_tf()
_, _, Gs = pickle.load(open(options['checkpoint'], 'rb'))
width, height = Gs.output_shape[::-1][:2]
print('Resizing images')
tmp_resized = tempfile.TemporaryDirectory()
preprocess_images(images_path, tmp_resized.name, width, height, options['crop_method'])
print('Converting dataset to TFRecord')
tmp_dataset = tempfile.TemporaryDirectory()
create_from_images(tmp_dataset.name, tmp_resized.name, 1)
print('Creating training config')
result_dir = runway.utils.generate_uuid()
os.makedirs(result_dir)
kwargs = create_training_config(tmp_dataset.name, options['checkpoint'], result_dir, **options)
kwargs.update(max_steps=options['max_steps'])
gen = training_loop(**kwargs)
for (step, metrics, samples, checkpoints) in gen:
ctx.step = step
for k, v in metrics.items():
ctx.metrics[k] = v
for k, v in samples.items():
ctx.samples.add(k, v)
for k, v in checkpoints.items():
ctx.checkpoints.add(k, v)
def selfie2anime():
img_id = os.environ['id']
result_id = os.environ['result']
parser = get_parser()
args = parser.parse_args("--phase test".split())
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
#sess.reuse_variables()
gan = UGATIT(sess, args)
# build graph
gan.build_model()
# download target img
download_path = os.path.join(img_path, img_id)
download_image(images_bucket, img_id, dest=download_path)
dataset_tool.create_from_images(record_path, img_path, True)
# os.remove(del_record)
img = gan.infer(download_path)
image_url = upload_image(img, result_id)
return download_path, img
def project(model, inputs):
im = inputs['projectionImage']
if not os.path.exists('./projection'):
os.mkdir('./projection')
if not os.path.exists('./projection/imgs'):
os.mkdir ('./projection/imgs')
if not os.path.exists('./projection/records'):
os.mkdir('./projection/records')
if not os.path.exists('./projection/out'):
os.mkdir('./projection/out')
if os.path.isfile('./projection/imgs/project.png'):
os.remove('./projection/imgs/project.png')
for f in os.listdir('./projection/records/'):
if os.path.isfile(os.path.join('./projection/records/', f)):
os.remove (os.path.join('./projection/records/', f))
im.save('./projection/imgs/project.png')
dataset_tool.create_from_images("./projection/records/", "./projection/imgs/", True)
output = get_projected_real_images("records","./projection/",1,10, inputs['steps'], model)
#return the last item
return output[-1]
def project_image(proj, src_file, dst_dir, tmp_dir):
data_dir = '%s/dataset' % tmp_dir
if os.path.exists(data_dir):
shutil.rmtree(data_dir)
image_dir = '%s/images' % data_dir
tfrecord_dir = '%s/tfrecords' % data_dir
os.makedirs(image_dir, exist_ok=True)
shutil.copy(src_file, image_dir + '/')
dataset_tool.create_from_images(tfrecord_dir, image_dir, shuffle=0)
dataset_obj = dataset.load_dataset(data_dir=data_dir,
tfrecord_dir='tfrecords',
max_label_size=0,
repeat=False,
shuffle_mb=0)
print('Projecting image "%s"...' % os.path.basename(src_file))
images, _labels = dataset_obj.get_minibatch_np(1)
images = misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
proj.start(images)
while proj.get_cur_step() < proj.num_steps:
print('\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps),
end='',
flush=True)
proj.step()
print('\r%-30s\r' % '', end='', flush=True)
os.makedirs(dst_dir, exist_ok=True)
filename = os.path.join(dst_dir, os.path.basename(src_file)[:-4] + '.png')
misc.save_image_grid(proj.get_images(), filename, drange=[-1, 1])
filename = os.path.join(dst_dir, os.path.basename(src_file)[:-4] + '.npy')
np.save(filename, proj.get_dlatents()[0])
def main():
######### 潜在変数空間からそっくりさんを探す部分 ###########
# make dirctory
os.makedirs('my/pic', exist_ok=True)
# 顔画像切り出しモデルの読み込み
LANDMARKS_MODEL_URL = 'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2'
landmarks_model_path = unpack_bz2(
get_file('shape_predictor_68_face_landmarks.dat.bz2',
LANDMARKS_MODEL_URL,
cache_subdir='temp'))
# 顔画像の切り出し
RAW_IMAGES_DIR = 'sample/pic'
ALIGNED_IMAGES_DIR = 'my/pic'
landmarks_detector = LandmarksDetector(landmarks_model_path)
for img_name in os.listdir(RAW_IMAGES_DIR):
raw_img_path = os.path.join(RAW_IMAGES_DIR, img_name)
for i, face_landmarks in enumerate(
landmarks_detector.get_landmarks(raw_img_path), start=1):
face_img_name = '%s_%02d.png' % (os.path.splitext(img_name)[0], i)
aligned_face_path = os.path.join(ALIGNED_IMAGES_DIR, face_img_name)
image_align(raw_img_path, aligned_face_path, face_landmarks)
#display_pic('./my/pic/*.*')
dataset_tool.create_from_images(tfrecord_dir='./my/dataset',
image_dir='./my/pic',
shuffle=0)
vec_syn = project_real_images(1) # ()内はマルチ解像度のデータセットを作成した時の画像枚数
#display_pic('./my/pic/*.*') # ターゲット画像の表示
#display(vec_syn) # 探索した潜在変数によって生成した画像
# 探索した潜在変数の保存
#os.makedirs('my/vector', exist_ok=True)
#np.save('my/vector/vec_syn', vec_syn)
##################### ここから笑顔編集（StyleMixing） ####################
# 探索した潜在変数の読み込み
#vec_syn = np.load('my/vector/vec_syn.npy')
# あらかじめ口元の異なる人たちを7通り用意しておいて、それを読み込む
vec_hashikan = np.load('sample/vectors/vec_hashikan.npy')
# くっつけて…
vec_smile = np.vstack((vec_syn, vec_hashikan))
# ミックス！
style_mixing(vec_smile, [4, 5], 1.0)
def prepare(self,
tfrecord_dir,
shuffle=True,
with_sub_dirs=False,
ignore_labels=1):
if with_sub_dirs:
out_path = resize_dirs(self.path, 'dataset/resized', dim=self.dim)
create_from_image_folders(tfrecord_dir, out_path, shuffle,
ignore_labels)
else:
print('resizing images ...')
out_path = resize(self.path, dim=self.dim)
print('creating records ...')
create_from_images(tfrecord_dir, out_path, shuffle=True)
def project_image(proj, src_file, dst_dir, tmp_dir, video=False):
data_dir = '%s/dataset' % tmp_dir # ./stylegan2-tmp/dataset
if os.path.exists(data_dir):
shutil.rmtree(data_dir)
image_dir = '%s/images' % data_dir # ./stylegan2-tmp/dataset/images
tfrecord_dir = '%s/tfrecords' % data_dir # ./stylegan2-tmp/dataset/tfrecords
os.makedirs(image_dir, exist_ok=True)
# 将源图片文件copy到./stylegan2-tmp/dataset/images下
shutil.copy(src_file, image_dir + '/')
# 在./stylegan2-tmp/dataset/tfrecords下生成tfrecord临时文件
# tfrecord临时文件序列化存储了不同lod下的图像的shape和数据
# 举例，如果图像是1024x1024，则tfr_file命名从10--2，如：tfrecords-r10.tfrecords...tfrecords-r05.tfrecords...
dataset_tool.create_from_images(tfrecord_dir, image_dir, shuffle=0)
# TFRecordDataset类在“dataset.py”中定义，从一组.tfrecords文件中加载数据集到dataset_obj
# load_dataset是个helper函数，用于构建dataset对象（在TFRecordDataset类创建对象实例时完成）
dataset_obj = dataset.load_dataset(
data_dir=data_dir, tfrecord_dir='tfrecords',
max_label_size=0, repeat=False, shuffle_mb=0
)
# 生成用于优化迭代的目标图像（组）
print('Projecting image "%s"...' % os.path.basename(src_file))
# 取下一个minibatch=1作为Numpy数组
images, _labels = dataset_obj.get_minibatch_np(1)
# 把images的取值从[0. 255]区间调整到[-1, 1]区间
images = misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
# Projector初始化：start
proj.start(images)
if video:
video_dir = '%s/video' % tmp_dir
os.makedirs(video_dir, exist_ok=True)
while proj.get_cur_step() < proj.num_steps:
print('\r%d / %d ... ' % (proj.get_cur_step(), proj.num_steps), end='', flush=True)
# Projector优化迭代：step
proj.step()
# 如果配置了video选项，将优化过程图像存入./ stylegan2 - tmp / video
if video:
filename = '%s/%08d.png' % (video_dir, proj.get_cur_step())
misc.save_image_grid(proj.get_images(), filename, drange=[-1,1])
print('\r%-30s\r' % '', end='', flush=True)
# 在目的地目录中保存图像，保存dlatents文件
os.makedirs(dst_dir, exist_ok=True)
filename = os.path.join(dst_dir, os.path.basename(src_file)[:-4] + '.png')
misc.save_image_grid(proj.get_images(), filename, drange=[-1,1])
filename = os.path.join(dst_dir, os.path.basename(src_file)[:-4] + '.npy')
np.save(filename, proj.get_dlatents()[0])
def predict(number, img_path):
# 顔画像切り出しモデルの読み込み
LANDMARKS_MODEL_URL = 'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2'
landmarks_model_path = unpack_bz2(
get_file('shape_predictor_68_face_landmarks.dat.bz2',
LANDMARKS_MODEL_URL,
cache_subdir='temp'))
# 顔画像の切り出し
raw_img_path = img_path # 後で削除
print(raw_img_path)
img_name = raw_img_path.replace('images/', '')
DATAOUT_DIR = img_path.replace('.jpg', '') #images/2020xxxx 後で削除(=my)
ALIGNED_IMAGES_DIR = DATAOUT_DIR + '/pic'
os.makedirs(ALIGNED_IMAGES_DIR, exist_ok=True)
TFRECORD_DIR = DATAOUT_DIR + '/dataset'
landmarks_detector = LandmarksDetector(landmarks_model_path)
for i, face_landmarks in enumerate(
landmarks_detector.get_landmarks(raw_img_path), start=1):
face_img_name = '%s_%02d.png' % (os.path.splitext(img_name)[0], i)
aligned_face_path = os.path.join(ALIGNED_IMAGES_DIR, face_img_name)
image_align(raw_img_path, aligned_face_path, face_landmarks)
dataset_tool.create_from_images(tfrecord_dir=TFRECORD_DIR,
image_dir=ALIGNED_IMAGES_DIR,
shuffle=0)
vec_syn = my_project_real_images(
1, data_dir=DATAOUT_DIR) # ()内はマルチ解像度のデータセットを作成した時の画像枚数
##################### ここから笑顔編集（StyleMixing） ####################
# あらかじめ口元の異なる人たちを7通り用意しておいて、それを読み込む
vec_hashikan = np.load('sample/vectors/vec_hashikan.npy')
# くっつけて…
vec_smile = np.vstack((vec_syn, vec_hashikan))
# ミックス！
result_path = my_style_mixing(vec_smile, [4, 5],
1.0,
my_data_dir=DATAOUT_DIR,
number=number)
return result_path
def prepare(self,
tfrecord_dir,
text_dir,
shuffle=False,
with_sub_dirs=False,
ignore_labels=0,
with_text=True,
embed_dim=430):
if with_sub_dirs:
image_dir = resize_dirs(self.path, 'dataset/resized', dim=self.dim)
create_image_and_textv2(tfrecord_dir,
image_dir,
text_dir,
shuffle,
ignore_labels,
self.encoder,
model_type=self.model_type,
use_chars=self.use_chars,
embed_dim=embed_dim,
with_sub_dirs=with_sub_dirs)
elif with_text:
image_dir = resize(self.path, dim=self.dim)
create_image_and_textv2(tfrecord_dir,
image_dir,
text_dir,
shuffle,
ignore_labels,
self.encoder,
model_type=self.model_type,
use_chars=self.use_chars,
embed_dim=embed_dim,
with_sub_dirs=with_sub_dirs)
else:
print('resizing images ...')
out_path = resize(self.path, dim=self.dim)
print('creating records ...')
create_from_images(tfrecord_dir, out_path, shuffle=True)
def project_images(Gs,
images_dir,
tfrecord_dir,
data_dir,
num_snapshots,
pure_projector=False):
"""setup projector"""
print('Setting up projector')
proj = projector.Projector()
proj.set_network(Gs)
# generate tfrecords
nb_images = dataset_tool.create_from_images(str(tfrecord_dir),
str(images_dir), True)
# loading images from tfrecords
dataset_obj = training.dataset.load_dataset(tfrecord_dir=tfrecord_dir,
max_label_size=0,
verbose=True,
repeat=False,
shuffle_mb=0)
assert dataset_obj.shape == Gs.output_shape[1:]
# project all loaded images
print('=======================')
for image_idx in tqdm(range(nb_images)):
print(f'Projecting image {image_idx + 1}/{nb_images}')
images, _labels = dataset_obj.get_minibatch_np(1)
images = training.misc.adjust_dynamic_range(images, [0, 255], [-1, 1])
run_path = data_dir / f'out_{image_idx}'
run_path.mkdir()
run_projector.project_image(proj,
targets=images,
png_prefix=dnnlib.make_run_dir_path(
str(run_path / 'image_')),
num_snapshots=num_snapshots)
zs = np.random.normal(size=(32, 512))
images = classifier.gens(zs)
testresults = classifier.test_by_images(images)
for j in range(32):
if np.mean(testresults[j]) > 0.667:
cv2.imwrite(os.path.join(outputpath, '{}.png'.format(passed)),
images[j] * 255)
passed += 1
if passed == 20000:
flag = False
break
# generate tfrecord for StyleGAN finetuning
from dataset_tool import create_from_images
create_from_images(
os.path.join(outputroot,
'{}_filtered_tfrecord_{}'.format(argsk.datatype, tid)),
outputpath, 1)
# Run StyleGAN finetuning
import copy
import dnnlib
from dnnlib import EasyDict
desc = 'sgan' # Description string included in result subdir name.
train = EasyDict(run_func_name='training.training_loop.training_loop'
) # Options for training loop.
G = EasyDict(func_name='training.networks_stylegan.G_style'
) # Options for generator network.
D = EasyDict(func_name='training.networks_stylegan.D_basic'
) # Options for discriminator network.
]
# Align Faces
align_images.main('/in', '/out')
num_steps = 1000 #@param {type:"number"}
truncation_psi = 1 #@param {type:"slider", min:0, max:1, step:0.01}
# Get Filenames
files = []
for _, _, f in os.walk(r'/out'):
for file in f:
files.append(os.path.splitext(file)[0])
# Convert uploaded images to TFRecords
dataset_tool.create_from_images("/records/", "/out/", True)
# Get number of images
num_images = sum(len(files) for _, _, files in os.walk(r'/out/'))
# Run the projector
def project_real_images(dataset_name, data_dir, num_images, num_snapshots):
proj = projector.Projector(num_steps)
proj.set_network(Gs)
print('Loading images from "%s"...' % dataset_name)
dataset_obj = training.dataset.load_dataset(data_dir=data_dir,
tfrecord_dir=dataset_name,
max_label_size=0,
verbose=True,
import dataset_tool from training.dataset import load_dataset #creates tfrecord from dir tfrecord_dir = "../databases/replay-attack/blink_dataset_replay" image_dir = '../databases/replay-attack/faces/test_faces/**' shuffle = True dataset_tool.create_from_images(tfrecord_dir, image_dir, shuffle)
LANDMARKS_MODEL_URL = 'http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2'
landmarks_model_path = unpack_bz2(get_file('shape_predictor_68_face_landmarks.dat.bz2',
LANDMARKS_MODEL_URL, cache_subdir='temp'))
# 顔画像の切り出し
RAW_IMAGES_DIR = 'sample/pic'
ALIGNED_IMAGES_DIR = 'my/pic'
landmarks_detector = LandmarksDetector(landmarks_model_path)
for img_name in os.listdir(RAW_IMAGES_DIR):
raw_img_path = os.path.join(RAW_IMAGES_DIR, img_name)
for i, face_landmarks in enumerate(landmarks_detector.get_landmarks(raw_img_path), start=1):
face_img_name = '%s_%02d.png' % (os.path.splitext(img_name)[0], i)
aligned_face_path = os.path.join(ALIGNED_IMAGES_DIR, face_img_name)
image_align(raw_img_path, aligned_face_path, face_landmarks)
#display_pic('./my/pic/*.*')
dataset_tool.create_from_images(tfrecord_dir='./my/dataset',image_dir='./my/pic',shuffle=0)
vec_syn = project_real_images(5) # ()内はマルチ解像度のデータセットを作成した時の画像枚数
#display_pic('./my/pic/*.*') # ターゲット画像の表示
#display(vec_syn) # 探索した潜在変数によって生成した画像
# 探索した潜在変数の保存
os.makedirs('sample/vectors', exist_ok=True)
np.save('sample/vectors/vec_hashikan', vec_syn)
# 探索した潜在変数の読み込み
vec_syn = np.load('sample/vectors/vec_hashikan.npy')
print(vec_syn.shape)
def prepare(self, records_path):
print('resizing images ...')
out_path = resize(self.path, dim=self.dim)
print('creating records ...')
create_from_images(records_path, out_path, shuffle=True)
# Convert uploaded images to TFRecords
import dataset_tool
import pretrained_networks
import dnnlib
import dnnlib.tflib as tflib
dataset_tool.create_from_images(
"/zhome/ca/6/92701/Desktop/Master_Thesis/stylegan2/rl_images/256/image_for_latents/records/",
"/zhome/ca/6/92701/Desktop/Master_Thesis/stylegan2/rl_images/256/image_for_latents/images/",
True)
# Run the projector
import run_projector
import projector
import training.dataset
import training.misc
import os
_G, _D, Gs = pretrained_networks.load_networks(
"/zhome/ca/6/92701/Desktop/Master_Thesis/stylegan2/Old_SG2_pkl/network-snapshot-012820.pkl"
)
def project_real_images(dataset_name, data_dir, num_images, num_snapshots):
proj = projector.Projector()
proj.set_network(Gs)
print('Loading images from "%s"...' % dataset_name)
dataset_obj = training.dataset.load_dataset(data_dir=data_dir,
tfrecord_dir=dataset_name,
max_label_size=0,
# The directories only exist on the VM since images are too big to push on git
# Convert images to tfrecords and add labels(style+genre) as well
import dataset_tool as dt
import preprocess
# resize images and then save them
corvett = resize_images.Converter(image_resolution=(512, 512),
file='subset_images2')
corvett.convert()
# load images from the saved folder and convert to tfrecords
dt.create_from_images(
'datasets//subset_images_tfr',
'..//..//wikiart-master//wikiart-saved//subset_images_resized', True)
def easygen_train(model_path,
images_path,
dataset_path,
start_kimg=7000,
max_kimg=25000,
schedule='',
seed=1000):
#import stylegan
#from stylegan import config
##from stylegan import dnnlib
#from stylegan.dnnlib import EasyDict
#images_dir = '/content/raw'
#max_kimg = 25000
#start_kimg = 7000
#schedule = ''
#model_in = '/content/karras2019stylegan-cats-256x256.pkl'
#dataset_dir = '/content/stylegan_dataset' #os.path.join(cwd, 'cache', 'stylegan_dataset')
import config
config.data_dir = '/content/datasets'
config.results_dir = '/content/results'
config.cache_dir = '/contents/cache'
run_dir_ignore = [
'/contents/results', '/contents/datasets', 'contents/cache'
]
import copy
import dnnlib
from dnnlib import EasyDict
from metrics import metric_base
# Prep dataset
import dataset_tool
print("prepping dataset...")
dataset_tool.create_from_images(tfrecord_dir=dataset_path,
image_dir=images_path,
shuffle=False)
# Set up training parameters
desc = 'sgan' # Description string included in result subdir name.
train = EasyDict(run_func_name='training.training_loop.training_loop'
) # Options for training loop.
G = EasyDict(func_name='training.networks_stylegan.G_style'
) # Options for generator network.
D = EasyDict(func_name='training.networks_stylegan.D_basic'
) # Options for discriminator network.
G_opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for generator optimizer.
D_opt = EasyDict(beta1=0.0, beta2=0.99,
epsilon=1e-8) # Options for discriminator optimizer.
G_loss = EasyDict(func_name='training.loss.G_logistic_nonsaturating'
) # Options for generator loss.
D_loss = EasyDict(func_name='training.loss.D_logistic_simplegp',
r1_gamma=10.0) # Options for discriminator loss.
dataset = EasyDict() # Options for load_dataset().
sched = EasyDict() # Options for TrainingSchedule.
grid = EasyDict(
size='1080p',
layout='random') # Options for setup_snapshot_image_grid().
#metrics = [metric_base.fid50k] # Options for MetricGroup.
submit_config = dnnlib.SubmitConfig() # Options for dnnlib.submit_run().
tf_config = {'rnd.np_random_seed': seed} # Options for tflib.init_tf().
# Dataset
desc += '-custom'
dataset = EasyDict(tfrecord_dir=dataset_path)
train.mirror_augment = True
# Number of GPUs.
desc += '-1gpu'
submit_config.num_gpus = 1
sched.minibatch_base = 4
sched.minibatch_dict = {
4: 128,
8: 128,
16: 128,
32: 64,
64: 32,
128: 16,
256: 8,
512: 4
} #{4: 256, 8: 256, 16: 128, 32: 64, 64: 32, 128: 16, 256: 16}
# Default options.
train.total_kimg = max_kimg
sched.lod_initial_resolution = 8
sched.G_lrate_dict = {128: 0.0015, 256: 0.002, 512: 0.003, 1024: 0.003}
sched.D_lrate_dict = EasyDict(sched.G_lrate_dict)
# schedule
schedule_dict = {
4: 160,
8: 140,
16: 120,
32: 100,
64: 80,
128: 60,
256: 40,
512: 30,
1024: 20
} #{4: 2, 8:2, 16:2, 32:2, 64:2, 128:2, 256:2, 512:2, 1024:2} # Runs faster for small datasets
if len(schedule) >= 5 and schedule[0] == '{' and schedule[
-1] == '}' and ':' in schedule:
# is schedule a string of a dict?
try:
temp = eval(schedule)
schedule_dict = dict(temp)
# assert: it is a dict
except:
pass
elif len(schedule) > 0:
# is schedule an int?
try:
schedule_int = int(schedule)
#assert: schedule is an int
schedule_dict = {}
for i in range(1, 10):
schedule_dict[int(math.pow(2, i + 1))] = schedule_int
except:
pass
print('schedule:', str(schedule_dict))
sched.tick_kimg_dict = schedule_dict
# resume kimg
resume_kimg = start_kimg
# path to model
resume_run_id = model_path
# tick snapshots
image_snapshot_ticks = 1
network_snapshot_ticks = 1
# Submit run
kwargs = EasyDict(train)
kwargs.update(G_args=G,
D_args=D,
G_opt_args=G_opt,
D_opt_args=D_opt,
G_loss_args=G_loss,
D_loss_args=D_loss)
kwargs.update(dataset_args=dataset,
sched_args=sched,
grid_args=grid,
tf_config=tf_config)
kwargs.update(resume_kimg=resume_kimg, resume_run_id=resume_run_id)
kwargs.update(image_snapshot_ticks=image_snapshot_ticks,
network_snapshot_ticks=network_snapshot_ticks)
kwargs.submit_config = copy.deepcopy(submit_config)
kwargs.submit_config.run_dir_root = dnnlib.submission.submit.get_template_from_path(
config.result_dir)
kwargs.submit_config.run_dir_ignore += config.run_dir_ignore
kwargs.submit_config.run_desc = desc
dnnlib.submit_run(**kwargs)
for i in range(len(zs) - 1):
for index in range(steps):
fraction = index / float(steps)
out.append(zs[i + 1] * fraction + zs[i] * (1 - fraction))
return out
# Taken from https://github.com/alexanderkuk/log-progress
def log_progress(sequence, every=1, size=None, name='Items'):
print()
# Convert uploaded images to TFRecords
import dataset_tool
print(dataset_tool.__file__)
dataset_tool.create_from_images("./projection/records/", "./projection/imgs/",
True)
# Run the projector
import run_projector
import projector
import dream_projector
import importlib
importlib.reload(dream_projector)
import training.dataset
import training.misc
import os
def project_real_images(dataset_name, data_dir, num_images, num_snapshots):
