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