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Created September 2, 2017 18:17
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"from __future__ import print_function\n",
"from keras.preprocessing.image import load_img, img_to_array\n",
"from scipy.misc import imsave\n",
"import numpy as np\n",
"from scipy.optimize import fmin_l_bfgs_b\n",
"import time\n",
"\n",
"import vgg16\n",
"from keras import backend as K\n",
"from keras.applications.imagenet_utils import preprocess_input"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"base_image_path = 'tubingen.jpg'\n",
"style_reference_image_path = 'starry_night_google.jpg'\n",
"result_prefix = 'art'\n",
"iterations = 5\n",
"total_variation_weight = 1.0\n",
"style_weight = 1.0\n",
"content_weight = 0.075"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"width, height = load_img(base_image_path).size\n",
"img_nrows = 400\n",
"img_ncols = int(width * img_nrows / height)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def preprocess_image(image_path):\n",
" img = load_img(image_path, target_size=(img_nrows, img_ncols))\n",
" img = img_to_array(img)\n",
" img = np.expand_dims(img, axis=0)\n",
" img = vgg16.preprocess_input(img)\n",
" return img\n",
"\n",
"def deprocess_image(x):\n",
" x = x.reshape((3, img_nrows, img_ncols))\n",
" x = x.transpose((1, 2, 0))\n",
" # Remove zero-center by mean pixel\n",
" x[:, :, 0] += 103.939\n",
" x[:, :, 1] += 116.779\n",
" x[:, :, 2] += 123.68\n",
" # 'BGR'->'RGB'\n",
" x = x[:, :, ::-1]\n",
" x = np.clip(x, 0, 255).astype('uint8')\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"base_image = K.variable(preprocess_image(base_image_path))\n",
"style_reference_image = K.variable(preprocess_image(style_reference_image_path))\n",
"combination_image = K.placeholder((1, 3, img_nrows, img_ncols))\n",
"input_tensor = K.concatenate([base_image,\n",
" style_reference_image,\n",
" combination_image], axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model loaded.\n"
]
}
],
"source": [
"model = vgg16.VGG16(input_tensor=input_tensor,\n",
" weights='imagenet', include_top=False)\n",
"print('Model loaded.')\n",
"outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def gram_matrix(x):\n",
" assert K.ndim(x) == 3\n",
" features = K.batch_flatten(x)\n",
" gram = K.dot(features, K.transpose(features))\n",
" return gram\n",
"\n",
"def style_loss(style, combination):\n",
" assert K.ndim(style) == 3\n",
" assert K.ndim(combination) == 3\n",
" S = gram_matrix(style)\n",
" C = gram_matrix(combination)\n",
" channels = 3\n",
" size = img_nrows * img_ncols\n",
" return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))\n",
"\n",
"def content_loss(base, combination):\n",
" return K.sum(K.square(combination - base))\n",
"\n",
"def total_variation_loss(x):\n",
" assert K.ndim(x) == 4\n",
" a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, 1:, :img_ncols - 1])\n",
" b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, :img_nrows - 1, 1:])\n",
" return K.sum(K.pow(a + b, 1.25))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"loss = K.variable(0.)\n",
"layer_features = outputs_dict['block5_conv2']\n",
"base_image_features = layer_features[0, :, :, :]\n",
"combination_features = layer_features[2, :, :, :]\n",
"loss += content_weight * content_loss(base_image_features,\n",
" combination_features)\n",
"\n",
"feature_layers = ['block1_conv1', 'block2_conv1',\n",
" 'block3_conv1', 'block4_conv1',\n",
" 'block5_conv1']\n",
"for layer_name in feature_layers:\n",
" layer_features = outputs_dict[layer_name]\n",
" style_reference_features = layer_features[1, :, :, :]\n",
" combination_features = layer_features[2, :, :, :]\n",
" sl = style_loss(style_reference_features, combination_features)\n",
" loss += (style_weight / len(feature_layers)) * sl\n",
"loss += total_variation_weight * total_variation_loss(combination_image)\n",
"grads = K.gradients(loss, combination_image)\n",
"outputs = [loss]\n",
"if isinstance(grads, (list, tuple)):\n",
" outputs += grads\n",
"else:\n",
" outputs.append(grads)\n",
"\n",
"f_outputs = K.function([combination_image], outputs)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def eval_loss_and_grads(x):\n",
" x = x.reshape((1, 3, img_nrows, img_ncols))\n",
" print('.', end='')\n",
" outs = f_outputs([x])\n",
" loss_value = outs[0]\n",
" if len(outs[1:]) == 1:\n",
" grad_values = outs[1].flatten().astype('float64')\n",
" else:\n",
" grad_values = np.array(outs[1:]).flatten().astype('float64')\n",
" return loss_value, grad_values\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Evaluator(object):\n",
"\n",
" def __init__(self):\n",
" self.loss_value = None\n",
" self.grads_values = None\n",
"\n",
" def loss(self, x):\n",
" assert self.loss_value is None\n",
" loss_value, grad_values = eval_loss_and_grads(x)\n",
" self.loss_value = loss_value\n",
" self.grad_values = grad_values\n",
" return self.loss_value\n",
"\n",
" def grads(self, x):\n",
" assert self.loss_value is not None\n",
" grad_values = np.copy(self.grad_values)\n",
" self.loss_value = None\n",
" self.grad_values = None\n",
" return grad_values\n",
"\n",
"evaluator = Evaluator()\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1L, 3L, 400L, 533L)\n",
"Start of iteration 0\n",
".....................\n",
"Current loss value: 2857231872.0\n",
"Image saved as art_at_iteration_0.png\n",
"Iteration 0 completed in 223s\n",
"Start of iteration 1\n",
".....................\n",
"Current loss value: 2399473664.0\n",
"Image saved as art_at_iteration_1.png\n",
"Iteration 1 completed in 223s\n",
"Start of iteration 2\n",
".....................\n",
"Current loss value: 2273068544.0\n",
"Image saved as art_at_iteration_2.png\n",
"Iteration 2 completed in 243s\n",
"Start of iteration 3\n",
".....................\n",
"Current loss value: 2208670208.0\n",
"Image saved as art_at_iteration_3.png\n",
"Iteration 3 completed in 251s\n",
"Start of iteration 4\n",
".....................\n",
"Current loss value: 2167820032.0\n",
"Image saved as art_at_iteration_4.png\n",
"Iteration 4 completed in 260s\n"
]
}
],
"source": [
"x = preprocess_image(base_image_path)\n",
"print(x.shape)\n",
"for i in range(iterations):\n",
" print('Start of iteration', i)\n",
" start_time = time.time()\n",
" x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(), fprime=evaluator.grads, maxfun=20)\n",
" print()\n",
" print('Current loss value:', min_val)\n",
" # save current generated image\n",
" img = deprocess_image(x.copy())\n",
" fname = result_prefix + '_at_iteration_%d.png' % i\n",
" imsave(fname, img)\n",
" end_time = time.time()\n",
" print('Image saved as', fname)\n",
" print('Iteration %d completed in %ds' % (i, end_time - start_time))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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