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Neural doodle with Keras
# Script Usage
## Arguments
```
--nlabels: # of regions (colors) in mask images
--style-image: image to learn style from
--style-mask: semantic labels for style image
--target-mask: semantic labels for target image (your doodle)
--content-image: optional image to learn content from
--target-image-prefix: path prefix for generated target images
```
## Example 1: doodle using a style image, style mask
and target mask.
```
python neural_doodle.py --nlabels 4 --style-image Monet/style.png --style-mask Monet/style_mask.png --target-mask Monet/target_mask.png --target-image-prefix generated/monet
```
## Example 2: doodle using a style image, style mask,
target mask and an optional content image.
```
python neural_doodle.py --nlabels 4 --style-image Renoir/style.png --style-mask Renoir/style_mask.png --target-mask Renoir/target_mask.png --content-image Renoir/creek.jpg --target-image-prefix generated/renoir
```
# References
- [Dmitry Ulyanov's blog on fast-neural-doodle]
(http://dmitryulyanov.github.io/feed-forward-neural-doodle/)
- [Torch code for fast-neural-doodle]
(https://github.com/DmitryUlyanov/fast-neural-doodle)
- [Torch code for online-neural-doodle]
(https://github.com/DmitryUlyanov/online-neural-doodle)
- [Paper Texture Networks: Feed-forward Synthesis of Textures and Stylized Images]
(http://arxiv.org/abs/1603.03417)
- [Discussion on parameter tuning]
(https://github.com/keras-team/keras/issues/3705)
# Resources
Example images can be downloaded from
https://github.com/DmitryUlyanov/fast-neural-doodle/tree/master/data
```python
from __future__ import print_function
import time
import argparse
import numpy as np
from scipy.optimize import fmin_l_bfgs_b
from keras import backend as K
from keras.layers import Input, AveragePooling2D
from keras.models import Model
from keras.preprocessing.image import load_img, save_img, img_to_array
from keras.applications import vgg19
# Command line arguments
parser = argparse.ArgumentParser(description='Keras neural doodle example')
parser.add_argument('--nlabels', type=int,
help='number of semantic labels'
' (regions in differnet colors)'
' in style_mask/target_mask')
parser.add_argument('--style-image', type=str,
help='path to image to learn style from')
parser.add_argument('--style-mask', type=str,
help='path to semantic mask of style image')
parser.add_argument('--target-mask', type=str,
help='path to semantic mask of target image')
parser.add_argument('--content-image', type=str, default=None,
help='path to optional content image')
parser.add_argument('--target-image-prefix', type=str,
help='path prefix for generated results')
args = parser.parse_args()
style_img_path = args.style_image
style_mask_path = args.style_mask
target_mask_path = args.target_mask
content_img_path = args.content_image
target_img_prefix = args.target_image_prefix
use_content_img = content_img_path is not None
num_labels = args.nlabels
num_colors = 3 # RGB
# determine image sizes based on target_mask
ref_img = img_to_array(load_img(target_mask_path))
img_nrows, img_ncols = ref_img.shape[:2]
num_iterations = 50
total_variation_weight = 50.
style_weight = 1.
content_weight = 0.1 if use_content_img else 0
content_feature_layers = ['block5_conv2']
# To get better generation qualities, use more conv layers for style features
style_feature_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1',
'block4_conv1', 'block5_conv1']
# helper functions for reading/processing images
def preprocess_image(image_path):
img = load_img(image_path, target_size=(img_nrows, img_ncols))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
img = vgg19.preprocess_input(img)
return img
def deprocess_image(x):
if K.image_data_format() == 'channels_first':
x = x.reshape((3, img_nrows, img_ncols))
x = x.transpose((1, 2, 0))
else:
x = x.reshape((img_nrows, img_ncols, 3))
# Remove zero-center by mean pixel
x[:, :, 0] += 103.939
x[:, :, 1] += 116.779
x[:, :, 2] += 123.68
# 'BGR'->'RGB'
x = x[:, :, ::-1]
x = np.clip(x, 0, 255).astype('uint8')
return x
def kmeans(xs, k):
assert xs.ndim == 2
try:
from sklearn.cluster import k_means
_, labels, _ = k_means(xs.astype('float64'), k)
except ImportError:
from scipy.cluster.vq import kmeans2
_, labels = kmeans2(xs, k, missing='raise')
return labels
def load_mask_labels():
'''Load both target and style masks.
A mask image (nr x nc) with m labels/colors will be loaded
as a 4D boolean tensor:
(1, m, nr, nc) for 'channels_first' or (1, nr, nc, m) for 'channels_last'
'''
target_mask_img = load_img(target_mask_path,
target_size=(img_nrows, img_ncols))
target_mask_img = img_to_array(target_mask_img)
style_mask_img = load_img(style_mask_path,
target_size=(img_nrows, img_ncols))
style_mask_img = img_to_array(style_mask_img)
if K.image_data_format() == 'channels_first':
mask_vecs = np.vstack([style_mask_img.reshape((3, -1)).T,
target_mask_img.reshape((3, -1)).T])
else:
mask_vecs = np.vstack([style_mask_img.reshape((-1, 3)),
target_mask_img.reshape((-1, 3))])
labels = kmeans(mask_vecs, num_labels)
style_mask_label = labels[:img_nrows *
img_ncols].reshape((img_nrows, img_ncols))
target_mask_label = labels[img_nrows *
img_ncols:].reshape((img_nrows, img_ncols))
stack_axis = 0 if K.image_data_format() == 'channels_first' else -1
style_mask = np.stack([style_mask_label == r for r in range(num_labels)],
axis=stack_axis)
target_mask = np.stack([target_mask_label == r for r in range(num_labels)],
axis=stack_axis)
return (np.expand_dims(style_mask, axis=0),
np.expand_dims(target_mask, axis=0))
# Create tensor variables for images
if K.image_data_format() == 'channels_first':
shape = (1, num_colors, img_nrows, img_ncols)
else:
shape = (1, img_nrows, img_ncols, num_colors)
style_image = K.variable(preprocess_image(style_img_path))
target_image = K.placeholder(shape=shape)
if use_content_img:
content_image = K.variable(preprocess_image(content_img_path))
else:
content_image = K.zeros(shape=shape)
images = K.concatenate([style_image, target_image, content_image], axis=0)
# Create tensor variables for masks
raw_style_mask, raw_target_mask = load_mask_labels()
style_mask = K.variable(raw_style_mask.astype('float32'))
target_mask = K.variable(raw_target_mask.astype('float32'))
masks = K.concatenate([style_mask, target_mask], axis=0)
# index constants for images and tasks variables
STYLE, TARGET, CONTENT = 0, 1, 2
# Build image model, mask model and use layer outputs as features
# image model as VGG19
image_model = vgg19.VGG19(include_top=False, input_tensor=images)
# mask model as a series of pooling
mask_input = Input(tensor=masks, shape=(None, None, None), name='mask_input')
x = mask_input
for layer in image_model.layers[1:]:
name = 'mask_%s' % layer.name
if 'conv' in layer.name:
x = AveragePooling2D((3, 3), padding='same', strides=(
1, 1), name=name)(x)
elif 'pool' in layer.name:
x = AveragePooling2D((2, 2), name=name)(x)
mask_model = Model(mask_input, x)
# Collect features from image_model and task_model
image_features = {}
mask_features = {}
for img_layer, mask_layer in zip(image_model.layers, mask_model.layers):
if 'conv' in img_layer.name:
assert 'mask_' + img_layer.name == mask_layer.name
layer_name = img_layer.name
img_feat, mask_feat = img_layer.output, mask_layer.output
image_features[layer_name] = img_feat
mask_features[layer_name] = mask_feat
# Define loss functions
def gram_matrix(x):
assert K.ndim(x) == 3
features = K.batch_flatten(x)
gram = K.dot(features, K.transpose(features))
return gram
def region_style_loss(style_image, target_image, style_mask, target_mask):
'''Calculate style loss between style_image and target_image,
for one common region specified by their (boolean) masks
'''
assert 3 == K.ndim(style_image) == K.ndim(target_image)
assert 2 == K.ndim(style_mask) == K.ndim(target_mask)
if K.image_data_format() == 'channels_first':
masked_style = style_image * style_mask
masked_target = target_image * target_mask
num_channels = K.shape(style_image)[0]
else:
masked_style = K.permute_dimensions(
style_image, (2, 0, 1)) * style_mask
masked_target = K.permute_dimensions(
target_image, (2, 0, 1)) * target_mask
num_channels = K.shape(style_image)[-1]
num_channels = K.cast(num_channels, dtype='float32')
s = gram_matrix(masked_style) / K.mean(style_mask) / num_channels
c = gram_matrix(masked_target) / K.mean(target_mask) / num_channels
return K.mean(K.square(s - c))
def style_loss(style_image, target_image, style_masks, target_masks):
'''Calculate style loss between style_image and target_image,
in all regions.
'''
assert 3 == K.ndim(style_image) == K.ndim(target_image)
assert 3 == K.ndim(style_masks) == K.ndim(target_masks)
loss = K.variable(0)
for i in range(num_labels):
if K.image_data_format() == 'channels_first':
style_mask = style_masks[i, :, :]
target_mask = target_masks[i, :, :]
else:
style_mask = style_masks[:, :, i]
target_mask = target_masks[:, :, i]
loss = loss + region_style_loss(style_image,
target_image,
style_mask,
target_mask)
return loss
def content_loss(content_image, target_image):
return K.sum(K.square(target_image - content_image))
def total_variation_loss(x):
assert 4 == K.ndim(x)
if K.image_data_format() == 'channels_first':
a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] -
x[:, :, 1:, :img_ncols - 1])
b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] -
x[:, :, :img_nrows - 1, 1:])
else:
a = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] -
x[:, 1:, :img_ncols - 1, :])
b = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] -
x[:, :img_nrows - 1, 1:, :])
return K.sum(K.pow(a + b, 1.25))
# Overall loss is the weighted sum of content_loss, style_loss and tv_loss
# Each individual loss uses features from image/mask models.
loss = K.variable(0)
for layer in content_feature_layers:
content_feat = image_features[layer][CONTENT, :, :, :]
target_feat = image_features[layer][TARGET, :, :, :]
loss = loss + content_weight * content_loss(content_feat, target_feat)
for layer in style_feature_layers:
style_feat = image_features[layer][STYLE, :, :, :]
target_feat = image_features[layer][TARGET, :, :, :]
style_masks = mask_features[layer][STYLE, :, :, :]
target_masks = mask_features[layer][TARGET, :, :, :]
sl = style_loss(style_feat, target_feat, style_masks, target_masks)
loss = loss + (style_weight / len(style_feature_layers)) * sl
loss = loss + total_variation_weight * total_variation_loss(target_image)
loss_grads = K.gradients(loss, target_image)
# Evaluator class for computing efficiency
outputs = [loss]
if isinstance(loss_grads, (list, tuple)):
outputs += loss_grads
else:
outputs.append(loss_grads)
f_outputs = K.function([target_image], outputs)
def eval_loss_and_grads(x):
if K.image_data_format() == 'channels_first':
x = x.reshape((1, 3, img_nrows, img_ncols))
else:
x = x.reshape((1, img_nrows, img_ncols, 3))
outs = f_outputs([x])
loss_value = outs[0]
if len(outs[1:]) == 1:
grad_values = outs[1].flatten().astype('float64')
else:
grad_values = np.array(outs[1:]).flatten().astype('float64')
return loss_value, grad_values
class Evaluator(object):
def __init__(self):
self.loss_value = None
self.grads_values = None
def loss(self, x):
assert self.loss_value is None
loss_value, grad_values = eval_loss_and_grads(x)
self.loss_value = loss_value
self.grad_values = grad_values
return self.loss_value
def grads(self, x):
assert self.loss_value is not None
grad_values = np.copy(self.grad_values)
self.loss_value = None
self.grad_values = None
return grad_values
evaluator = Evaluator()
# Generate images by iterative optimization
if K.image_data_format() == 'channels_first':
x = np.random.uniform(0, 255, (1, 3, img_nrows, img_ncols)) - 128.
else:
x = np.random.uniform(0, 255, (1, img_nrows, img_ncols, 3)) - 128.
for i in range(num_iterations):
print('Start of iteration', i, '/', num_iterations)
start_time = time.time()
x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
fprime=evaluator.grads, maxfun=20)
print('Current loss value:', min_val)
# save current generated image
img = deprocess_image(x.copy())
fname = target_img_prefix + '_at_iteration_%d.png' % i
save_img(fname, img)
end_time = time.time()
print('Image saved as', fname)
print('Iteration %d completed in %ds' % (i, end_time - start_time))
```
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