def deep_dream_video(config):
video_path = os.path.join(config['inputs_path'], config['input'])
tmp_input_dir = os.path.join(config['out_videos_path'], 'tmp_input')
tmp_output_dir = os.path.join(config['out_videos_path'], 'tmp_out')
config['dump_dir'] = tmp_output_dir
os.makedirs(tmp_input_dir, exist_ok=True)
os.makedirs(tmp_output_dir, exist_ok=True)
metadata = video_utils.dump_frames(video_path, tmp_input_dir)
last_img = None
for frame_id, frame_name in enumerate(sorted(os.listdir(tmp_input_dir))):
print(f'Processing frame {frame_id}')
frame_path = os.path.join(tmp_input_dir, frame_name)
frame = utils.load_image(frame_path, target_shape=config['img_width'])
if config['blend'] is not None and last_img is not None:
# 1.0 - get only the current frame, 0.5 - combine with last dreamed frame and stabilize the video
frame = utils.linear_blend(last_img, frame, config['blend'])
dreamed_frame = deep_dream_static_image(config, frame)
last_img = dreamed_frame
utils.save_and_maybe_display_image(
config,
dreamed_frame,
should_display=config['should_display'],
name_modifier=frame_id)
video_utils.create_video_from_intermediate_results(config, metadata)
shutil.rmtree(tmp_input_dir) # remove tmp files
print(f'Deleted tmp frame dump directory {tmp_input_dir}.')
def deep_dream_video_ouroboros(config):
img_path = os.path.join(config['inputs_path'], config['input'])
# load numpy, [0, 1], channel-last, RGB image, None will cause it to start from the uniform noise [0, 1] image
frame = None if config['use_noise'] else utils.load_image(img_path, target_shape=config['img_width'])
for frame_id in range(config['video_length']):
print(f'Dream iteration {frame_id+1}.')
frame = deep_dream_static_image(config, frame)
utils.save_and_maybe_display_image(config, frame, should_display=config['should_display'], name_modifier=frame_id)
frame = utils.transform_frame(config, frame) # transform frame e.g. central zoom, spiral, etc.
video_utils.create_video_from_intermediate_results(config)
def deep_dream_video_ouroboros(config):
"""
Feeds the output dreamed image back to the input and repeat
Name etymology for nerds: https://en.wikipedia.org/wiki/Ouroboros
"""
ts = time.time()
assert any([config['input_name'].lower().endswith(img_ext) for img_ext in SUPPORTED_IMAGE_FORMATS]), \
f'Expected an image, but got {config["input_name"]}. Supported image formats {SUPPORTED_IMAGE_FORMATS}.'
utils.print_ouroboros_video_header(config) # print some ouroboros-related metadata to the console
img_path = utils.parse_input_file(config['input'])
# load numpy, [0, 1] range, channel-last, RGB image
# use_noise and consequently None value, will cause it to initialize the frame with uniform, [0, 1] range, noise
frame = None if config['use_noise'] else utils.load_image(img_path, target_shape=config['img_width'])
for frame_id in range(config['ouroboros_length']):
print(f'Ouroboros iteration {frame_id+1}.')
# Step 1: apply DeepDream and feed the last iteration's output to the input
frame = deep_dream_static_image(config, frame)
dump_path = utils.save_and_maybe_display_image(config, frame, name_modifier=frame_id)
print(f'Saved ouroboros frame to: {os.path.relpath(dump_path)}\n')
# Step 2: transform frame e.g. central zoom, spiral, etc.
# Note: this part makes amplifies the psychodelic-like appearance
frame = utils.transform_frame(config, frame)
video_utils.create_video_from_intermediate_results(config)
print(f'time elapsed = {time.time()-ts} seconds.')
def stylize_static_image(inference_config):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
content_img_path = os.path.join(inference_config['content_images_path'], inference_config['content_img_name'])
content_image = utils.prepare_img(content_img_path, inference_config['img_width'], device)
# load the weights and set the model to evaluation mode
stylization_model = TransformerNet().to(device)
training_state = torch.load(os.path.join(inference_config["model_binaries_path"], inference_config["model_name"]))
utils.print_model_metadata(training_state)
state_dict = training_state["state_dict"]
stylization_model.load_state_dict(state_dict, strict=True)
stylization_model.eval()
with torch.no_grad():
stylized_img = stylization_model(content_image).to('cpu').numpy()[0]
utils.save_and_maybe_display_image(inference_config, stylized_img, should_display=True)
def deep_dream_video(config):
video_path = utils.parse_input_file(config['input'])
tmp_input_dir = os.path.join(OUT_VIDEOS_PATH, 'tmp_input')
tmp_output_dir = os.path.join(OUT_VIDEOS_PATH, 'tmp_out')
config['dump_dir'] = tmp_output_dir
os.makedirs(tmp_input_dir, exist_ok=True)
os.makedirs(tmp_output_dir, exist_ok=True)
metadata = video_utils.extract_frames(video_path, tmp_input_dir)
config['fps'] = metadata['fps']
utils.print_deep_dream_video_header(config)
last_img = None
for frame_id, frame_name in enumerate(sorted(os.listdir(tmp_input_dir))):
# Step 1: load the video frame
print(f'Processing frame {frame_id}')
frame_path = os.path.join(tmp_input_dir, frame_name)
frame = utils.load_image(frame_path, target_shape=config['img_width'])
# Step 2: potentially blend it with the last frame
if config['blend'] is not None and last_img is not None:
# blend: 1.0 - use the current frame, 0.0 - use the last frame, everything in between will blend the two
frame = utils.linear_blend(last_img, frame, config['blend'])
# Step 3: Send the blended frame to some good old DeepDreaming
dreamed_frame = deep_dream_static_image(config, frame)
# Step 4: save the frame and keep the reference
last_img = dreamed_frame
dump_path = utils.save_and_maybe_display_image(config, dreamed_frame, name_modifier=frame_id)
print(f'Saved DeepDream frame to: {os.path.relpath(dump_path)}\n')
video_utils.create_video_from_intermediate_results(config)
shutil.rmtree(tmp_input_dir) # remove tmp files
print(f'Deleted tmp frame dump directory {tmp_input_dir}.')
def generate_new_images(model_name,
cgan_digit=None,
generation_mode=True,
slerp=True,
a=None,
b=None,
should_display=True):
""" Generate imagery using pre-trained generator (using vanilla_generator_000000.pth by default)
Args:
model_name (str): model name you want to use (default lookup location is BINARIES_PATH).
cgan_digit (int): if specified generate that exact digit.
generation_mode (enum): generate a single image from a random vector, interpolate between the 2 chosen latent
vectors, or perform arithmetic over latent vectors (note: not every mode is supported for every model type)
slerp (bool): if True use spherical interpolation otherwise use linear interpolation.
a, b (numpy arrays): latent vectors, if set to None you'll be prompted to choose images you like,
and use corresponding latent vectors instead.
should_display (bool): Display the generated images before saving them.
"""
model_path = os.path.join(BINARIES_PATH, model_name)
assert os.path.exists(
model_path
), f'Could not find the model {model_path}. You first need to train your generator.'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Prepare the correct (vanilla, cGAN, DCGAN, ...) model, load the weights and put the model into evaluation mode
model_state = torch.load(model_path)
gan_type = model_state["gan_type"]
print(f'Found {gan_type} GAN!')
_, generator = utils.get_gan(device, gan_type)
generator.load_state_dict(model_state["state_dict"], strict=True)
generator.eval()
# Generate a single image, save it and potentially display it
if generation_mode == GenerationMode.SINGLE_IMAGE:
generated_imgs_path = os.path.join(DATA_DIR_PATH, 'generated_imagery')
os.makedirs(generated_imgs_path, exist_ok=True)
generated_img, _ = generate_from_random_latent_vector(
generator, cgan_digit if gan_type == GANType.CGAN.name else None)
utils.save_and_maybe_display_image(generated_imgs_path,
generated_img,
should_display=should_display)
# Pick 2 images you like between which you'd like to interpolate (by typing 'y' into console)
elif generation_mode == GenerationMode.INTERPOLATION:
assert gan_type == GANType.VANILLA.name or gan_type == GANType.DCGAN.name, f'Got {gan_type} but only VANILLA/DCGAN are supported for the interpolation mode.'
interpolation_name = "spherical" if slerp else "linear"
interpolation_fn = spherical_interpolation if slerp else linear_interpolation
grid_interpolated_imgs_path = os.path.join(
DATA_DIR_PATH, 'interpolated_imagery') # combined results dir
decomposed_interpolated_imgs_path = os.path.join(
grid_interpolated_imgs_path,
f'tmp_{gan_type}_{interpolation_name}_dump'
) # dump separate results
if os.path.exists(decomposed_interpolated_imgs_path):
shutil.rmtree(decomposed_interpolated_imgs_path)
os.makedirs(grid_interpolated_imgs_path, exist_ok=True)
os.makedirs(decomposed_interpolated_imgs_path, exist_ok=True)
latent_vector_a, latent_vector_b = [None, None]
# If a and b were not specified loop until the user picked the 2 images he/she likes.
found_good_vectors_flag = False
if a is None or b is None:
while not found_good_vectors_flag:
generated_img, latent_vector = generate_from_random_latent_vector(
generator)
plt.imshow(generated_img)
plt.title('Do you like this image?')
plt.show()
user_input = input(
"Do you like this generated image? [y for yes]:")
if user_input == 'y':
if latent_vector_a is None:
latent_vector_a = latent_vector
print('Saved the first latent vector.')
elif latent_vector_b is None:
latent_vector_b = latent_vector
print('Saved the second latent vector.')
found_good_vectors_flag = True
else:
print('Well lets generate a new one!')
continue
else:
print(
'Skipping latent vectors selection section and using cached ones.'
)
latent_vector_a, latent_vector_b = [a, b]
# Cache latent vectors
if a is None or b is None:
np.save(os.path.join(grid_interpolated_imgs_path, 'a.npy'),
latent_vector_a)
np.save(os.path.join(grid_interpolated_imgs_path, 'b.npy'),
latent_vector_b)
print(f'Lets do some {interpolation_name} interpolation!')
interpolation_resolution = 47 # number of images between the vectors a and b
num_interpolated_imgs = interpolation_resolution + 2 # + 2 so that we include a and b
generated_imgs = []
for i in range(num_interpolated_imgs):
t = i / (num_interpolated_imgs - 1) # goes from 0. to 1.
current_latent_vector = interpolation_fn(t, latent_vector_a,
latent_vector_b)
generated_img = generate_from_specified_numpy_latent_vector(
generator, current_latent_vector)
print(f'Generated image [{i+1}/{num_interpolated_imgs}].')
utils.save_and_maybe_display_image(
decomposed_interpolated_imgs_path,
generated_img,
should_display=should_display)
# Move from channel last to channel first (CHW->HWC), PyTorch's save_image function expects BCHW format
generated_imgs.append(
torch.tensor(np.moveaxis(generated_img, 2, 0)))
interpolated_block_img = torch.stack(generated_imgs)
interpolated_block_img = nn.Upsample(
scale_factor=2.5, mode='nearest')(interpolated_block_img)
save_image(
interpolated_block_img,
os.path.join(
grid_interpolated_imgs_path,
utils.get_available_file_name(grid_interpolated_imgs_path)),
nrow=int(np.sqrt(num_interpolated_imgs)))
elif generation_mode == GenerationMode.VECTOR_ARITHMETIC:
assert gan_type == GANType.DCGAN.name, f'Got {gan_type} but only DCGAN is supported for arithmetic mode.'
# Generate num_options face images and create a grid image from them
num_options = 100
generated_imgs = []
latent_vectors = []
padding = 2
for i in range(num_options):
generated_img, latent_vector = generate_from_random_latent_vector(
generator)
generated_imgs.append(
torch.tensor(np.moveaxis(generated_img, 2,
0))) # make_grid expects CHW format
latent_vectors.append(latent_vector)
stacked_tensor_imgs = torch.stack(generated_imgs)
final_tensor_img = make_grid(stacked_tensor_imgs,
nrow=int(np.sqrt(num_options)),
padding=padding)
display_img = np.moveaxis(final_tensor_img.numpy(), 0, 2)
# For storing latent vectors
num_of_vectors_per_category = 3
happy_woman_latent_vectors = []
neutral_woman_latent_vectors = []
neutral_man_latent_vectors = []
# Make it easy - by clicking on the plot you pick the image.
def onclick(event):
if event.dblclick:
pass
else: # single click
if event.button == 1: # left click
x_coord = event.xdata
y_coord = event.ydata
column = int(x_coord / (64 + padding))
row = int(y_coord / (64 + padding))
# Store latent vector corresponding to the image that the user clicked on.
if len(happy_woman_latent_vectors
) < num_of_vectors_per_category:
happy_woman_latent_vectors.append(
latent_vectors[10 * row + column])
print(
f'Picked image row={row}, column={column} as {len(happy_woman_latent_vectors)}. happy woman.'
)
elif len(neutral_woman_latent_vectors
) < num_of_vectors_per_category:
neutral_woman_latent_vectors.append(
latent_vectors[10 * row + column])
print(
f'Picked image row={row}, column={column} as {len(neutral_woman_latent_vectors)}. neutral woman.'
)
elif len(neutral_man_latent_vectors
) < num_of_vectors_per_category:
neutral_man_latent_vectors.append(
latent_vectors[10 * row + column])
print(
f'Picked image row={row}, column={column} as {len(neutral_man_latent_vectors)}. neutral man.'
)
else:
plt.close()
plt.figure(figsize=(10, 10))
plt.imshow(display_img)
# This is just an example you could also pick 3 neutral woman images with sunglasses, etc.
plt.title(
'Click on 3 happy women, 3 neutral women and \n 3 neutral men images (order matters!)'
)
cid = plt.gcf().canvas.mpl_connect('button_press_event', onclick)
plt.show()
plt.gcf().canvas.mpl_disconnect(cid)
print('Done choosing images.')
# Calculate the average latent vector for every category (happy woman, neutral woman, neutral man)
happy_woman_avg_latent_vector = np.mean(
np.array(happy_woman_latent_vectors), axis=0)
neutral_woman_avg_latent_vector = np.mean(
np.array(neutral_woman_latent_vectors), axis=0)
neutral_man_avg_latent_vector = np.mean(
np.array(neutral_man_latent_vectors), axis=0)
# By subtracting neutral woman from the happy woman we capture the "vector of smiling". Adding that vector
# to a neutral man we get a happy man's latent vector! Our latent space has amazingly beautiful structure!
happy_man_latent_vector = neutral_man_avg_latent_vector + (
happy_woman_avg_latent_vector - neutral_woman_avg_latent_vector)
# Generate images from these latent vectors
happy_women_imgs = np.hstack([
generate_from_specified_numpy_latent_vector(generator, v)
for v in happy_woman_latent_vectors
])
neutral_women_imgs = np.hstack([
generate_from_specified_numpy_latent_vector(generator, v)
for v in neutral_woman_latent_vectors
])
neutral_men_imgs = np.hstack([
generate_from_specified_numpy_latent_vector(generator, v)
for v in neutral_man_latent_vectors
])
happy_woman_avg_img = generate_from_specified_numpy_latent_vector(
generator, happy_woman_avg_latent_vector)
neutral_woman_avg_img = generate_from_specified_numpy_latent_vector(
generator, neutral_woman_avg_latent_vector)
neutral_man_avg_img = generate_from_specified_numpy_latent_vector(
generator, neutral_man_avg_latent_vector)
happy_man_img = generate_from_specified_numpy_latent_vector(
generator, happy_man_latent_vector)
display_vector_arithmetic_results([
happy_women_imgs, happy_woman_avg_img, neutral_women_imgs,
neutral_woman_avg_img, neutral_men_imgs, neutral_man_avg_img,
happy_man_img
])
else:
raise Exception(f'Generation mode not yet supported.')
# You usually won't need to change these as often
parser.add_argument("--should_display", action='store_true', help="Display intermediate dreaming results (default False)")
parser.add_argument("--spatial_shift_size", type=int, help='Number of pixels to randomly shift image before grad ascent', default=32)
parser.add_argument("--smoothing_coefficient", type=float, help='Directly controls standard deviation for gradient smoothing', default=0.5)
parser.add_argument("--use_noise", action='store_true', help="Use noise as a starting point instead of input image (default False)")
args = parser.parse_args()
# Wrapping configuration into a dictionary
config = dict()
for arg in vars(args):
config[arg] = getattr(args, arg)
config['dump_dir'] = OUT_VIDEOS_PATH if config['create_ouroboros'] else OUT_IMAGES_PATH
config['dump_dir'] = os.path.join(config['dump_dir'], f'{config["model_name"]}_{config["pretrained_weights"]}')
config['input_name'] = os.path.basename(config['input'])
# Create Ouroboros video (feeding neural network's output to it's input)
if config['create_ouroboros']:
deep_dream_video_ouroboros(config)
# Create a blended DeepDream video
elif any([config['input_name'].lower().endswith(video_ext) for video_ext in SUPPORTED_VIDEO_FORMATS]): # only support mp4 atm
deep_dream_video(config)
else: # Create a static DeepDream image
print('Dreaming started!')
img = deep_dream_static_image(config, img=None) # img=None -> will be loaded inside of deep_dream_static_image
dump_path = utils.save_and_maybe_display_image(config, img)
print(f'Saved DeepDream static image to: {os.path.relpath(dump_path)}\n')
default=False)
args = parser.parse_args()
# Wrapping configuration into a dictionary - keeping things clean
config = dict()
for arg in vars(args):
config[arg] = getattr(args, arg)
config['inputs_path'] = inputs_path
config['out_images_path'] = out_images_path
config['out_videos_path'] = out_videos_path
config['dump_dir'] = config['out_videos_path'] if config[
'is_video'] else config['out_images_path']
config['dump_dir'] = os.path.join(
config['dump_dir'],
f'{config["model"].name}_{config["pretrained_weights"].name}')
# DeepDream algorithm in 3 flavours: static image, video and ouroboros (feeding net output to it's input)
if any([
config['input'].endswith(video_ext)
for video_ext in SUPPORTED_VIDEO_FORMATS
]): # only support mp4 atm
deep_dream_video(config)
elif config['is_video']:
deep_dream_video_ouroboros(config)
else:
img = deep_dream_static_image(
config, img=None
) # img=None -> will be loaded inside of deep_dream_static_image
utils.save_and_maybe_display_image(
config, img, should_display=config['should_display'])
def stylize_static_image(inference_config):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Prepare the model - load the weights and put the model into evaluation mode
stylization_model = TransformerNet().to(device)
training_state = torch.load(
os.path.join(inference_config["model_binaries_path"],
inference_config["model_name"]))
state_dict = training_state["state_dict"]
stylization_model.load_state_dict(state_dict, strict=True)
stylization_model.eval()
if inference_config['verbose']:
utils.print_model_metadata(training_state)
with torch.no_grad():
if os.path.isdir(
inference_config['content_input']
): # do a batch stylization (every image in the directory)
img_dataset = utils.SimpleDataset(
inference_config['content_input'],
inference_config['img_width'])
img_loader = DataLoader(img_dataset,
batch_size=inference_config['batch_size'])
try:
processed_imgs_cnt = 0
for batch_id, img_batch in enumerate(img_loader):
processed_imgs_cnt += len(img_batch)
if inference_config['verbose']:
print(
f'Processing batch {batch_id + 1} ({processed_imgs_cnt}/{len(img_dataset)} processed images).'
)
img_batch = img_batch.to(device)
stylized_imgs = stylization_model(img_batch).to(
'cpu').numpy()
for stylized_img in stylized_imgs:
utils.save_and_maybe_display_image(
inference_config,
stylized_img,
should_display=False)
except Exception as e:
print(e)
print(
f'Consider making the batch_size (current = {inference_config["batch_size"]} images) or img_width (current = {inference_config["img_width"]} px) smaller'
)
exit(1)
else: # do stylization for a single image
content_img_path = os.path.join(
inference_config['content_images_path'],
inference_config['content_input'])
content_image = utils.prepare_img(content_img_path,
inference_config['img_width'],
device)
stylized_img = stylization_model(content_image).to(
'cpu').numpy()[0]
utils.save_and_maybe_display_image(
inference_config,
stylized_img,
should_display=inference_config['should_not_display'])