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neural_style.py
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neural_style.py
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import tensorflow as tf
import numpy as np
import scipy.io
import argparse
import struct
import errno
import time
import cv2
import os
'''
parsing and configuration
'''
def parse_args():
desc = "TensorFlow implementation of 'A Neural Algorithm for Artistic Style'"
parser = argparse.ArgumentParser(description=desc)
# options for single image
parser.add_argument('--verbose', action='store_true',
help='Boolean flag indicating if statements should be printed to the console.')
parser.add_argument('--img_name', type=str,
default='result',
help='Filename of the output image.')
parser.add_argument('--style_imgs', nargs='+', type=str,
help='Filenames of the style images (example: starry-night.jpg)',
required=True)
parser.add_argument('--style_imgs_weights', nargs='+', type=float,
default=[1.0],
help='Interpolation weights of each of the style images. (example: 0.5 0.5)')
parser.add_argument('--content_img', type=str,
help='Filename of the content image (example: lion.jpg)')
parser.add_argument('--style_imgs_dir', type=str,
default='./styles',
help='Directory path to the style images. (default: %(default)s)')
parser.add_argument('--content_img_dir', type=str,
default='./image_input',
help='Directory path to the content image. (default: %(default)s)')
parser.add_argument('--init_img_type', type=str,
default='content',
choices=['random', 'content', 'style'],
help='Image used to initialize the network. (default: %(default)s)')
parser.add_argument('--max_size', type=int,
default=512,
help='Maximum width or height of the input images. (default: %(default)s)')
parser.add_argument('--content_weight', type=float,
default=5e0,
help='Weight for the content loss function. (default: %(default)s)')
parser.add_argument('--style_weight', type=float,
default=1e4,
help='Weight for the style loss function. (default: %(default)s)')
parser.add_argument('--tv_weight', type=float,
default=1e-3,
help='Weight for the total variational loss function. Set small (e.g. 1e-3). (default: %(default)s)')
parser.add_argument('--temporal_weight', type=float,
default=2e2,
help='Weight for the temporal loss function. (default: %(default)s)')
parser.add_argument('--content_loss_function', type=int,
default=1,
choices=[1, 2, 3],
help='Different constants for the content layer loss function. (default: %(default)s)')
parser.add_argument('--content_layers', nargs='+', type=str,
default=['conv4_2'],
help='VGG19 layers used for the content image. (default: %(default)s)')
parser.add_argument('--style_layers', nargs='+', type=str,
default=['relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1'],
help='VGG19 layers used for the style image. (default: %(default)s)')
parser.add_argument('--content_layer_weights', nargs='+', type=float,
default=[1.0],
help='Contributions (weights) of each content layer to loss. (default: %(default)s)')
parser.add_argument('--style_layer_weights', nargs='+', type=float,
default=[0.2, 0.2, 0.2, 0.2, 0.2],
help='Contributions (weights) of each style layer to loss. (default: %(default)s)')
parser.add_argument('--original_colors', action='store_true',
help='Transfer the style but not the colors.')
parser.add_argument('--color_convert_type', type=str,
default='yuv',
choices=['yuv', 'ycrcb', 'luv', 'lab'],
help='Color space for conversion to original colors (default: %(default)s)')
parser.add_argument('--color_convert_time', type=str,
default='after',
choices=['after', 'before'],
help='Time (before or after) to convert to original colors (default: %(default)s)')
parser.add_argument('--style_mask', action='store_true',
help='Transfer the style to masked regions.')
parser.add_argument('--style_mask_imgs', nargs='+', type=str,
default=None,
help='Filenames of the style mask images (example: face_mask.png) (default: %(default)s)')
parser.add_argument('--noise_ratio', type=float,
default=1.0,
help="Interpolation value between the content image and noise image if the network is initialized with 'random'.")
parser.add_argument('--seed', type=int,
default=0,
help='Seed for the random number generator. (default: %(default)s)')
parser.add_argument('--model_weights', type=str,
default='imagenet-vgg-verydeep-19.mat',
help='Weights and biases of the VGG-19 network.')
parser.add_argument('--pooling_type', type=str,
default='avg',
choices=['avg', 'max'],
help='Type of pooling in convolutional neural network. (default: %(default)s)')
parser.add_argument('--device', type=str,
default='/gpu:0',
choices=['/gpu:0', '/cpu:0'],
help='GPU or CPU mode. GPU mode requires NVIDIA CUDA. (default|recommended: %(default)s)')
parser.add_argument('--img_output_dir', type=str,
default='./image_output',
help='Relative or absolute directory path to output image and data.')
# optimizations
parser.add_argument('--optimizer', type=str,
default='lbfgs',
choices=['lbfgs', 'adam'],
help='Loss minimization optimizer. L-BFGS gives better results. Adam uses less memory. (default|recommended: %(default)s)')
parser.add_argument('--learning_rate', type=float,
default=1e0,
help='Learning rate parameter for the Adam optimizer. (default: %(default)s)')
parser.add_argument('--max_iterations', type=int,
default=1000,
help='Max number of iterations for the Adam or L-BFGS optimizer. (default: %(default)s)')
parser.add_argument('--print_iterations', type=int,
default=50,
help='Number of iterations between optimizer print statements. (default: %(default)s)')
# options for video frames
parser.add_argument('--video', action='store_true',
help='Boolean flag indicating if the user is generating a video.')
parser.add_argument('--start_frame', type=int,
default=1,
help='First frame number.')
parser.add_argument('--end_frame', type=int,
default=1,
help='Last frame number.')
parser.add_argument('--first_frame_type', type=str,
choices=['random', 'content', 'style'],
default='content',
help='Image used to initialize the network during the rendering of the first frame.')
parser.add_argument('--init_frame_type', type=str,
choices=['prev_warped', 'prev', 'random', 'content', 'style'],
default='prev_warped',
help='Image used to initialize the network during the every rendering after the first frame.')
parser.add_argument('--video_input_dir', type=str,
default='./video_input',
help='Relative or absolute directory path to input frames.')
parser.add_argument('--video_output_dir', type=str,
default='./video_output',
help='Relative or absolute directory path to output frames.')
parser.add_argument('--content_frame_frmt', type=str,
default='frame_{}.ppm',
help='Filename format of the input content frames.')
parser.add_argument('--backward_optical_flow_frmt', type=str,
default='backward_{}_{}.flo',
help='Filename format of the backward optical flow files.')
parser.add_argument('--forward_optical_flow_frmt', type=str,
default='forward_{}_{}.flo',
help='Filename format of the forward optical flow files')
parser.add_argument('--content_weights_frmt', type=str,
default='reliable_{}_{}.txt',
help='Filename format of the optical flow consistency files.')
parser.add_argument('--prev_frame_indices', nargs='+', type=int,
default=[1],
help='Previous frames to consider for longterm temporal consistency.')
parser.add_argument('--first_frame_iterations', type=int,
default=2000,
help='Maximum number of optimizer iterations of the first frame. (default: %(default)s)')
parser.add_argument('--frame_iterations', type=int,
default=800,
help='Maximum number of optimizer iterations for each frame after the first frame. (default: %(default)s)')
args = parser.parse_args()
# normalize weights
args.style_layer_weights = normalize(args.style_layer_weights)
args.content_layer_weights = normalize(args.content_layer_weights)
args.style_imgs_weights = normalize(args.style_imgs_weights)
# create directories for output
if args.video:
maybe_make_directory(args.video_output_dir)
else:
maybe_make_directory(args.img_output_dir)
return args
'''
pre-trained vgg19 convolutional neural network
remark: layers are manually initialized for clarity.
'''
def build_model(input_img):
if args.verbose: print('\nBUILDING VGG-19 NETWORK')
net = {}
_, h, w, d = input_img.shape
if args.verbose: print('loading model weights...')
vgg_rawnet = scipy.io.loadmat(args.model_weights)
vgg_layers = vgg_rawnet['layers'][0]
if args.verbose: print('constructing layers...')
net['input'] = tf.Variable(np.zeros((1, h, w, d), dtype=np.float32))
if args.verbose: print('LAYER GROUP 1')
net['conv1_1'] = conv_layer('conv1_1', net['input'], W=get_weights(vgg_layers, 0))
net['relu1_1'] = relu_layer('relu1_1', net['conv1_1'], b=get_bias(vgg_layers, 0))
net['conv1_2'] = conv_layer('conv1_2', net['relu1_1'], W=get_weights(vgg_layers, 2))
net['relu1_2'] = relu_layer('relu1_2', net['conv1_2'], b=get_bias(vgg_layers, 2))
net['pool1'] = pool_layer('pool1', net['relu1_2'])
if args.verbose: print('LAYER GROUP 2')
net['conv2_1'] = conv_layer('conv2_1', net['pool1'], W=get_weights(vgg_layers, 5))
net['relu2_1'] = relu_layer('relu2_1', net['conv2_1'], b=get_bias(vgg_layers, 5))
net['conv2_2'] = conv_layer('conv2_2', net['relu2_1'], W=get_weights(vgg_layers, 7))
net['relu2_2'] = relu_layer('relu2_2', net['conv2_2'], b=get_bias(vgg_layers, 7))
net['pool2'] = pool_layer('pool2', net['relu2_2'])
if args.verbose: print('LAYER GROUP 3')
net['conv3_1'] = conv_layer('conv3_1', net['pool2'], W=get_weights(vgg_layers, 10))
net['relu3_1'] = relu_layer('relu3_1', net['conv3_1'], b=get_bias(vgg_layers, 10))
net['conv3_2'] = conv_layer('conv3_2', net['relu3_1'], W=get_weights(vgg_layers, 12))
net['relu3_2'] = relu_layer('relu3_2', net['conv3_2'], b=get_bias(vgg_layers, 12))
net['conv3_3'] = conv_layer('conv3_3', net['relu3_2'], W=get_weights(vgg_layers, 14))
net['relu3_3'] = relu_layer('relu3_3', net['conv3_3'], b=get_bias(vgg_layers, 14))
net['conv3_4'] = conv_layer('conv3_4', net['relu3_3'], W=get_weights(vgg_layers, 16))
net['relu3_4'] = relu_layer('relu3_4', net['conv3_4'], b=get_bias(vgg_layers, 16))
net['pool3'] = pool_layer('pool3', net['relu3_4'])
if args.verbose: print('LAYER GROUP 4')
net['conv4_1'] = conv_layer('conv4_1', net['pool3'], W=get_weights(vgg_layers, 19))
net['relu4_1'] = relu_layer('relu4_1', net['conv4_1'], b=get_bias(vgg_layers, 19))
net['conv4_2'] = conv_layer('conv4_2', net['relu4_1'], W=get_weights(vgg_layers, 21))
net['relu4_2'] = relu_layer('relu4_2', net['conv4_2'], b=get_bias(vgg_layers, 21))
net['conv4_3'] = conv_layer('conv4_3', net['relu4_2'], W=get_weights(vgg_layers, 23))
net['relu4_3'] = relu_layer('relu4_3', net['conv4_3'], b=get_bias(vgg_layers, 23))
net['conv4_4'] = conv_layer('conv4_4', net['relu4_3'], W=get_weights(vgg_layers, 25))
net['relu4_4'] = relu_layer('relu4_4', net['conv4_4'], b=get_bias(vgg_layers, 25))
net['pool4'] = pool_layer('pool4', net['relu4_4'])
if args.verbose: print('LAYER GROUP 5')
net['conv5_1'] = conv_layer('conv5_1', net['pool4'], W=get_weights(vgg_layers, 28))
net['relu5_1'] = relu_layer('relu5_1', net['conv5_1'], b=get_bias(vgg_layers, 28))
net['conv5_2'] = conv_layer('conv5_2', net['relu5_1'], W=get_weights(vgg_layers, 30))
net['relu5_2'] = relu_layer('relu5_2', net['conv5_2'], b=get_bias(vgg_layers, 30))
net['conv5_3'] = conv_layer('conv5_3', net['relu5_2'], W=get_weights(vgg_layers, 32))
net['relu5_3'] = relu_layer('relu5_3', net['conv5_3'], b=get_bias(vgg_layers, 32))
net['conv5_4'] = conv_layer('conv5_4', net['relu5_3'], W=get_weights(vgg_layers, 34))
net['relu5_4'] = relu_layer('relu5_4', net['conv5_4'], b=get_bias(vgg_layers, 34))
net['pool5'] = pool_layer('pool5', net['relu5_4'])
return net
def conv_layer(layer_name, layer_input, W):
conv = tf.nn.conv2d(layer_input, W, strides=[1, 1, 1, 1], padding='SAME')
if args.verbose: print('--{} | shape={} | weights_shape={}'.format(layer_name,
conv.get_shape(), W.get_shape()))
return conv
def relu_layer(layer_name, layer_input, b):
relu = tf.nn.relu(layer_input + b)
if args.verbose:
print('--{} | shape={} | bias_shape={}'.format(layer_name, relu.get_shape(),
b.get_shape()))
return relu
def pool_layer(layer_name, layer_input):
if args.pooling_type == 'avg':
pool = tf.nn.avg_pool(layer_input, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
elif args.pooling_type == 'max':
pool = tf.nn.max_pool(layer_input, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
if args.verbose:
print('--{} | shape={}'.format(layer_name, pool.get_shape()))
return pool
def get_weights(vgg_layers, i):
weights = vgg_layers[i][0][0][2][0][0]
W = tf.constant(weights)
return W
def get_bias(vgg_layers, i):
bias = vgg_layers[i][0][0][2][0][1]
b = tf.constant(np.reshape(bias, (bias.size)))
return b
'''
'a neural algorithm for artistic style' loss functions
'''
def content_layer_loss(p, x):
_, h, w, d = p.get_shape()
M = h.value * w.value
N = d.value
if args.content_loss_function == 1:
K = 1. / (2. * N**0.5 * M**0.5)
elif args.content_loss_function == 2:
K = 1. / (N * M)
elif args.content_loss_function == 3:
K = 1. / 2.
loss = K * tf.reduce_sum(tf.pow((x - p), 2))
return loss
def style_layer_loss(a, x):
_, h, w, d = a.get_shape()
M = h.value * w.value
N = d.value
A = gram_matrix(a, M, N)
G = gram_matrix(x, M, N)
loss = (1./(4 * N**2 * M**2)) * tf.reduce_sum(tf.pow((G - A), 2))
return loss
def gram_matrix(x, area, depth):
F = tf.reshape(x, (area, depth))
G = tf.matmul(tf.transpose(F), F)
return G
def mask_style_layer(a, x, mask_img):
_, h, w, d = a.get_shape()
mask = get_mask_image(mask_img, w.value, h.value)
mask = tf.convert_to_tensor(mask)
tensors = []
for _ in range(d.value):
tensors.append(mask)
mask = tf.stack(tensors, axis=2)
mask = tf.stack(mask, axis=0)
mask = tf.expand_dims(mask, 0)
a = tf.multiply(a, mask)
x = tf.multiply(x, mask)
return a, x
def sum_masked_style_losses(sess, net, style_imgs):
total_style_loss = 0.
weights = args.style_imgs_weights
masks = args.style_mask_imgs
for img, img_weight, img_mask in zip(style_imgs, weights, masks):
sess.run(net['input'].assign(img))
style_loss = 0.
for layer, weight in zip(args.style_layers, args.style_layer_weights):
a = sess.run(net[layer])
x = net[layer]
a = tf.convert_to_tensor(a)
a, x = mask_style_layer(a, x, img_mask)
style_loss += style_layer_loss(a, x) * weight
style_loss /= float(len(args.style_layers))
total_style_loss += (style_loss * img_weight)
total_style_loss /= float(len(style_imgs))
return total_style_loss
def sum_style_losses(sess, net, style_imgs):
total_style_loss = 0.
weights = args.style_imgs_weights
for img, img_weight in zip(style_imgs, weights):
sess.run(net['input'].assign(img))
style_loss = 0.
for layer, weight in zip(args.style_layers, args.style_layer_weights):
a = sess.run(net[layer])
x = net[layer]
a = tf.convert_to_tensor(a)
style_loss += style_layer_loss(a, x) * weight
style_loss /= float(len(args.style_layers))
total_style_loss += (style_loss * img_weight)
total_style_loss /= float(len(style_imgs))
return total_style_loss
def sum_content_losses(sess, net, content_img):
sess.run(net['input'].assign(content_img))
content_loss = 0.
for layer, weight in zip(args.content_layers, args.content_layer_weights):
p = sess.run(net[layer])
x = net[layer]
p = tf.convert_to_tensor(p)
content_loss += content_layer_loss(p, x) * weight
content_loss /= float(len(args.content_layers))
return content_loss
'''
'artistic style transfer for videos' loss functions
'''
def temporal_loss(x, w, c):
c = c[np.newaxis,:,:,:]
D = float(x.size)
loss = (1. / D) * tf.reduce_sum(c * tf.nn.l2_loss(x - w))
loss = tf.cast(loss, tf.float32)
return loss
def get_longterm_weights(i, j):
c_sum = 0.
for k in range(args.prev_frame_indices):
if i - k > i - j:
c_sum += get_content_weights(i, i - k)
c = get_content_weights(i, i - j)
c_max = tf.maximum(c - c_sum, 0.)
return c_max
def sum_longterm_temporal_losses(sess, net, frame, input_img):
x = sess.run(net['input'].assign(input_img))
loss = 0.
for j in range(args.prev_frame_indices):
prev_frame = frame - j
w = get_prev_warped_frame(frame)
c = get_longterm_weights(frame, prev_frame)
loss += temporal_loss(x, w, c)
return loss
def sum_shortterm_temporal_losses(sess, net, frame, input_img):
x = sess.run(net['input'].assign(input_img))
prev_frame = frame - 1
w = get_prev_warped_frame(frame)
c = get_content_weights(frame, prev_frame)
loss = temporal_loss(x, w, c)
return loss
'''
utilities and i/o
'''
def read_image(path):
# bgr image
img = cv2.imread(path, cv2.IMREAD_COLOR)
check_image(img, path)
img = img.astype(np.float32)
img = preprocess(img)
return img
def write_image(path, img):
img = postprocess(img)
cv2.imwrite(path, img)
def preprocess(img):
imgpre = np.copy(img)
# bgr to rgb
imgpre = imgpre[...,::-1]
# shape (h, w, d) to (1, h, w, d)
imgpre = imgpre[np.newaxis,:,:,:]
imgpre -= np.array([123.68, 116.779, 103.939]).reshape((1,1,1,3))
return imgpre
def postprocess(img):
imgpost = np.copy(img)
imgpost += np.array([123.68, 116.779, 103.939]).reshape((1,1,1,3))
# shape (1, h, w, d) to (h, w, d)
imgpost = imgpost[0]
imgpost = np.clip(imgpost, 0, 255).astype('uint8')
# rgb to bgr
imgpost = imgpost[...,::-1]
return imgpost
def read_flow_file(path):
with open(path, 'rb') as f:
# 4 bytes header
header = struct.unpack('4s', f.read(4))[0]
# 4 bytes width, height
w = struct.unpack('i', f.read(4))[0]
h = struct.unpack('i', f.read(4))[0]
flow = np.ndarray((2, h, w), dtype=np.float32)
for y in range(h):
for x in range(w):
flow[0,y,x] = struct.unpack('f', f.read(4))[0]
flow[1,y,x] = struct.unpack('f', f.read(4))[0]
return flow
def read_weights_file(path):
lines = open(path).readlines()
header = list(map(int, lines[0].split(' ')))
w = header[0]
h = header[1]
vals = np.zeros((h, w), dtype=np.float32)
for i in range(1, len(lines)):
line = lines[i].rstrip().split(' ')
vals[i-1] = np.array(list(map(np.float32, line)))
vals[i-1] = list(map(lambda x: 0. if x < 255. else 1., vals[i-1]))
# expand to 3 channels
weights = np.dstack([vals.astype(np.float32)] * 3)
return weights
def normalize(weights):
denom = sum(weights)
if denom > 0.:
return [float(i) / denom for i in weights]
else: return [0.] * len(weights)
def maybe_make_directory(dir_path):
if not os.path.exists(dir_path):
os.makedirs(dir_path)
def check_image(img, path):
if img is None:
raise OSError(errno.ENOENT, "No such file", path)
'''
rendering -- where the magic happens
'''
def stylize(content_img, style_imgs, init_img, frame=None):
with tf.device(args.device), tf.Session() as sess:
# setup network
net = build_model(content_img)
# style loss
if args.style_mask:
L_style = sum_masked_style_losses(sess, net, style_imgs)
else:
L_style = sum_style_losses(sess, net, style_imgs)
# content loss
L_content = sum_content_losses(sess, net, content_img)
# denoising loss
L_tv = tf.image.total_variation(net['input'])
# loss weights
alpha = args.content_weight
beta = args.style_weight
theta = args.tv_weight
# total loss
L_total = alpha * L_content
L_total += beta * L_style
L_total += theta * L_tv
# video temporal loss
if args.video and frame > 1:
gamma = args.temporal_weight
L_temporal = sum_shortterm_temporal_losses(sess, net, frame, init_img)
L_total += gamma * L_temporal
# optimization algorithm
optimizer = get_optimizer(L_total)
if args.optimizer == 'adam':
minimize_with_adam(sess, net, optimizer, init_img, L_total)
elif args.optimizer == 'lbfgs':
minimize_with_lbfgs(sess, net, optimizer, init_img)
output_img = sess.run(net['input'])
if args.original_colors:
output_img = convert_to_original_colors(np.copy(content_img), output_img)
if args.video:
write_video_output(frame, output_img)
else:
write_image_output(output_img, content_img, style_imgs, init_img)
def minimize_with_lbfgs(sess, net, optimizer, init_img):
if args.verbose: print('\nMINIMIZING LOSS USING: L-BFGS OPTIMIZER')
init_op = tf.global_variables_initializer()
sess.run(init_op)
sess.run(net['input'].assign(init_img))
optimizer.minimize(sess)
def minimize_with_adam(sess, net, optimizer, init_img, loss):
if args.verbose: print('\nMINIMIZING LOSS USING: ADAM OPTIMIZER')
train_op = optimizer.minimize(loss)
init_op = tf.global_variables_initializer()
sess.run(init_op)
sess.run(net['input'].assign(init_img))
iterations = 0
while (iterations < args.max_iterations):
sess.run(train_op)
if iterations % args.print_iterations == 0 and args.verbose:
curr_loss = loss.eval()
print("At iterate {}\tf= {}".format(iterations, curr_loss))
iterations += 1
def get_optimizer(loss):
print_iterations = args.print_iterations if args.verbose else 0
if args.optimizer == 'lbfgs':
optimizer = tf.contrib.opt.ScipyOptimizerInterface(
loss, method='L-BFGS-B',
options={'maxiter': args.max_iterations,
'disp': print_iterations})
elif args.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(args.learning_rate)
return optimizer
def write_video_output(frame, output_img):
fn = args.content_frame_frmt.format(str(frame).zfill(4))
path = os.path.join(args.video_output_dir, fn)
write_image(path, output_img)
def write_image_output(output_img, content_img, style_imgs, init_img):
out_dir = os.path.join(args.img_output_dir, args.img_name)
maybe_make_directory(out_dir)
img_path = os.path.join(out_dir, args.img_name+'.png')
content_path = os.path.join(out_dir, 'content.png')
init_path = os.path.join(out_dir, 'init.png')
write_image(img_path, output_img)
write_image(content_path, content_img)
write_image(init_path, init_img)
index = 0
for style_img in style_imgs:
path = os.path.join(out_dir, 'style_'+str(index)+'.png')
write_image(path, style_img)
index += 1
# save the configuration settings
out_file = os.path.join(out_dir, 'meta_data.txt')
f = open(out_file, 'w')
f.write('image_name: {}\n'.format(args.img_name))
f.write('content: {}\n'.format(args.content_img))
index = 0
for style_img, weight in zip(args.style_imgs, args.style_imgs_weights):
f.write('styles['+str(index)+']: {} * {}\n'.format(weight, style_img))
index += 1
index = 0
if args.style_mask_imgs is not None:
for mask in args.style_mask_imgs:
f.write('style_masks['+str(index)+']: {}\n'.format(mask))
index += 1
f.write('init_type: {}\n'.format(args.init_img_type))
f.write('content_weight: {}\n'.format(args.content_weight))
f.write('style_weight: {}\n'.format(args.style_weight))
f.write('tv_weight: {}\n'.format(args.tv_weight))
f.write('content_layers: {}\n'.format(args.content_layers))
f.write('style_layers: {}\n'.format(args.style_layers))
f.write('optimizer_type: {}\n'.format(args.optimizer))
f.write('max_iterations: {}\n'.format(args.max_iterations))
f.write('max_image_size: {}\n'.format(args.max_size))
f.close()
'''
image loading and processing
'''
def get_init_image(init_type, content_img, style_imgs, frame=None):
if init_type == 'content':
return content_img
elif init_type == 'style':
return style_imgs[0]
elif init_type == 'random':
init_img = get_noise_image(args.noise_ratio, content_img)
return init_img
# only for video frames
elif init_type == 'prev':
init_img = get_prev_frame(frame)
return init_img
elif init_type == 'prev_warped':
init_img = get_prev_warped_frame(frame)
return init_img
def get_content_frame(frame):
fn = args.content_frame_frmt.format(str(frame).zfill(4))
path = os.path.join(args.video_input_dir, fn)
img = read_image(path)
return img
def get_content_image(content_img):
path = os.path.join(args.content_img_dir, content_img)
# bgr image
img = cv2.imread(path, cv2.IMREAD_COLOR)
check_image(img, path)
img = img.astype(np.float32)
h, w, d = img.shape
mx = args.max_size
# resize if > max size
if h > w and h > mx:
w = (float(mx) / float(h)) * w
img = cv2.resize(img, dsize=(int(w), mx), interpolation=cv2.INTER_AREA)
if w > mx:
h = (float(mx) / float(w)) * h
img = cv2.resize(img, dsize=(mx, int(h)), interpolation=cv2.INTER_AREA)
img = preprocess(img)
return img
def get_style_images(content_img):
_, ch, cw, cd = content_img.shape
style_imgs = []
for style_fn in args.style_imgs:
path = os.path.join(args.style_imgs_dir, style_fn)
# bgr image
img = cv2.imread(path, cv2.IMREAD_COLOR)
check_image(img, path)
img = img.astype(np.float32)
img = cv2.resize(img, dsize=(cw, ch), interpolation=cv2.INTER_AREA)
img = preprocess(img)
style_imgs.append(img)
return style_imgs
def get_noise_image(noise_ratio, content_img):
np.random.seed(args.seed)
noise_img = np.random.uniform(-20., 20., content_img.shape).astype(np.float32)
img = noise_ratio * noise_img + (1.-noise_ratio) * content_img
return img
def get_mask_image(mask_img, width, height):
path = os.path.join(args.content_img_dir, mask_img)
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
check_image(img, path)
img = cv2.resize(img, dsize=(width, height), interpolation=cv2.INTER_AREA)
img = img.astype(np.float32)
mx = np.amax(img)
img /= mx
return img
def get_prev_frame(frame):
# previously stylized frame
prev_frame = frame - 1
fn = args.content_frame_frmt.format(str(prev_frame).zfill(4))
path = os.path.join(args.video_output_dir, fn)
img = cv2.imread(path, cv2.IMREAD_COLOR)
check_image(img, path)
return img
def get_prev_warped_frame(frame):
prev_img = get_prev_frame(frame)
prev_frame = frame - 1
# backwards flow: current frame -> previous frame
fn = args.backward_optical_flow_frmt.format(str(frame), str(prev_frame))
path = os.path.join(args.video_input_dir, fn)
flow = read_flow_file(path)
warped_img = warp_image(prev_img, flow).astype(np.float32)
img = preprocess(warped_img)
return img
def get_content_weights(frame, prev_frame):
forward_fn = args.content_weights_frmt.format(str(prev_frame), str(frame))
backward_fn = args.content_weights_frmt.format(str(frame), str(prev_frame))
forward_path = os.path.join(args.video_input_dir, forward_fn)
backward_path = os.path.join(args.video_input_dir, backward_fn)
forward_weights = read_weights_file(forward_path)
backward_weights = read_weights_file(backward_path)
return forward_weights #, backward_weights
def warp_image(src, flow):
_, h, w = flow.shape
flow_map = np.zeros(flow.shape, dtype=np.float32)
for y in range(h):
flow_map[1,y,:] = float(y) + flow[1,y,:]
for x in range(w):
flow_map[0,:,x] = float(x) + flow[0,:,x]
# remap pixels to optical flow
dst = cv2.remap(
src, flow_map[0], flow_map[1],
interpolation=cv2.INTER_CUBIC, borderMode=cv2.BORDER_TRANSPARENT)
return dst
def convert_to_original_colors(content_img, stylized_img):
content_img = postprocess(content_img)
stylized_img = postprocess(stylized_img)
if args.color_convert_type == 'yuv':
cvt_type = cv2.COLOR_BGR2YUV
inv_cvt_type = cv2.COLOR_YUV2BGR
elif args.color_convert_type == 'ycrcb':
cvt_type = cv2.COLOR_BGR2YCR_CB
inv_cvt_type = cv2.COLOR_YCR_CB2BGR
elif args.color_convert_type == 'luv':
cvt_type = cv2.COLOR_BGR2LUV
inv_cvt_type = cv2.COLOR_LUV2BGR
elif args.color_convert_type == 'lab':
cvt_type = cv2.COLOR_BGR2LAB
inv_cvt_type = cv2.COLOR_LAB2BGR
content_cvt = cv2.cvtColor(content_img, cvt_type)
stylized_cvt = cv2.cvtColor(stylized_img, cvt_type)
c1, _, _ = cv2.split(stylized_cvt)
_, c2, c3 = cv2.split(content_cvt)
merged = cv2.merge((c1, c2, c3))
dst = cv2.cvtColor(merged, inv_cvt_type).astype(np.float32)
dst = preprocess(dst)
return dst
def render_single_image():
content_img = get_content_image(args.content_img)
style_imgs = get_style_images(content_img)
with tf.Graph().as_default():
print('\n---- RENDERING SINGLE IMAGE ----\n')
init_img = get_init_image(args.init_img_type, content_img, style_imgs)
tick = time.time()
stylize(content_img, style_imgs, init_img)
tock = time.time()
print('Single image elapsed time: {}'.format(tock - tick))
def render_video():
for frame in range(args.start_frame, args.end_frame+1):
with tf.Graph().as_default():
print('\n---- RENDERING VIDEO FRAME: {}/{} ----\n'.format(frame, args.end_frame))
if frame == 1:
content_frame = get_content_frame(frame)
style_imgs = get_style_images(content_frame)
init_img = get_init_image(args.first_frame_type, content_frame, style_imgs, frame)
args.max_iterations = args.first_frame_iterations
tick = time.time()
stylize(content_frame, style_imgs, init_img, frame)
tock = time.time()
print('Frame {} elapsed time: {}'.format(frame, tock - tick))
else:
content_frame = get_content_frame(frame)
style_imgs = get_style_images(content_frame)
init_img = get_init_image(args.init_frame_type, content_frame, style_imgs, frame)
args.max_iterations = args.frame_iterations
tick = time.time()
stylize(content_frame, style_imgs, init_img, frame)
tock = time.time()
print('Frame {} elapsed time: {}'.format(frame, tock - tick))
def main():
global args
args = parse_args()
if args.video: render_video()
else: render_single_image()
if __name__ == '__main__':
main()