07.Style-Transfer

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Style Transfer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "레퍼런스의 이미지를 타겟이미지에 적용시키는 응용. 단 원본의 content는 유지.\n",
    "스타일 = Txture, Colors, Visual Patterns\n",
    "\n",
    "- Content = The Higher-Level macrostructure of the Image\n",
    "- Loss = distance(style(ref) - style(generated)) + distance(content(orginal) - content(generated))\n",
    "- Distance = L2 norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Content Loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 신경망의 앞단 레이어의 활성화 된 값들은 이미지에 대한 국소적인 정보를 가지고 있다.\n",
    "- 상대적으로 이후의 레이어으는 전체적이며 추상적인 정보를 가지고 있다.\n",
    "- 다르게 말하면 convent의 다른 레이어의 활성화값은 다른 공간스케일의 내용에 대한 분해된 값을 제공한다.\n",
    "- 그러므로 content에 대한 정보는 convnet의 상위 레이어에 표현될 것이다.\n",
    "- 좋은 conetent loss의 후보는 타겟 이미지와 생성된 이이지의 convent의 top layer 사이의 L2 norm 값이다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Style Loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- content loss 에서는 하나의 상위 레이어만 사용하지만 style loss에서는 여러개의 convent 레이어르 사용한다.\n",
    "- convent의 모든 공간 스케일로부터 스타일 정보를 추출하기를 원한다.\n",
    "- style loss로는 활성화 값의 Gram maxrix를 사용한다. feature map 사이의 내적값이다.\n",
    "- 내적인 레이어의 피쳐들 사이의 관계에 대한 맵을 표현하는 것으로 이해할 수 있다.\n",
    "- 이러한 관계는 특정 공간에 스케일의 패턴에 대한 통계를 인식한다.\n",
    "- 그러므로 style loss는 다른 레이어의 활성화 사이의 관계를 비슷하게 유지할려고 한다.\n",
    "- 그러면 이 방법을 사용하면 다른 공간 스케일에서 발견된 텍스쳐가 ref 이미지와 generated 이밎 사이에 비슷할 것을 보장해준다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural style transfer in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- VGG19 Network를 사용\n",
    "- ref, target, generated 이미지에 대한 VGG19 레이어 활성화 값을 계산하는 네트워크를 만든다.\n",
    "- 위에서 설명한 loss function을 정의하기 위해서 레이어 활성화를 사용한다.\n",
    "- loss function을 최소화하기 위한 gradient descent 과정을 거친다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\py35\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import load_img, img_to_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "width, height :  1920 1200\n"
     ]
    }
   ],
   "source": [
    "# This is the path to the Image you want to transform.\n",
    "target_image_path = './data/image/blue-moon-lake.jpg'\n",
    "\n",
    "# This is the path to the Style Image.\n",
    "style_reference_image_path = './data/image/starry_night.jpg'\n",
    "\n",
    "# Dismensions of the generated picture.\n",
    "width, height = load_img(target_image_path).size\n",
    "print(\"width, height : \", width, height)\n",
    "\n",
    "img_height = 400\n",
    "img_width = int(width * img_height / height)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- VGG19 convert에 들어가고 나올 이미지들에 대한 로딩, 전처리, 후처리를 위한 보조 함수들이 필요하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.applications import vgg19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_image(image_path):\n",
    "    img = load_img(image_path, target_size=(img_height, img_width))\n",
    "    img = img_to_array(img)\n",
    "    img = np.expand_dims(img, axis=0)\n",
    "    img = vgg19.preprocess_input(img)\n",
    "    return img\n",
    "\n",
    "def deprocess_image(x):\n",
    "    # Remove zero-conter 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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded\n"
     ]
    }
   ],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "target_image = K.constant(preprocess_image(target_image_path))\n",
    "style_reference_image = K.constant(preprocess_image(style_reference_image_path))\n",
    "\n",
    "target_image, style_reference_image\n",
    "\n",
    "# This placeholder will contain our generated image\n",
    "combination_image = K.placeholder((1, img_height, img_width, 3))\n",
    "\n",
    "#  We conbinde the 3 images into a single batch.\n",
    "input_tensor = K.concatenate([target_image, style_reference_image, combination_image], axis=0)\n",
    "\n",
    "input_tensor\n",
    "# We build the VGG19 network with out batch of 3 iamges as input.\n",
    "# The model will be loaded whth pre-trained imgeNet weights.\n",
    "model = vgg19.VGG19(input_tensor=input_tensor, weights='imagenet', include_top=False)\n",
    "\n",
    "print('Model loaded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def content_loss(base, combination):\n",
    "    return K.sum(K.square(combination - base))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gram_matrix(x):\n",
    "    features = K.batch_flatten(K.permute_dimensions(x,(2,0,1)))\n",
    "    gram = K.dot(features, K.transpose(features))\n",
    "    return gram\n",
    "\n",
    "def style_loss(style, combination):\n",
    "    S = gram_matrix(style)\n",
    "    C = gram_matrix(combination)\n",
    "    channels = 3\n",
    "    size = img_height * img_width\n",
    "    return K.sum(K.square(S - C)) / (4 * (channels ** 2) * (size ** 2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def total_variation_loss(x):\n",
    "    a = K.square(\n",
    "        x[:, :img_height- 1, :img_width - 1, :] - x[:, 1:, :img_width -1, :])\n",
    "    b = K.square(\n",
    "        x[:, :img_height - 1, :img_width - 1, :] - x[:, :img_height -1, 1:, :])\n",
    "    return K.sum(K.pow(a + b, 1.25))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Diot mapping layer to activation Tensers\n",
    "outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])\n",
    "\n",
    "# Name of layer used for content loss\n",
    "content_layer = 'block5_conv2'\n",
    "\n",
    "# name og layer used for style loss\n",
    "style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']\n",
    "\n",
    "# Weights in the weighted average of the loss compcents\n",
    "total_variation_weight = 1e-4\n",
    "style_weight = 1\n",
    "content_weight = 0.025\n",
    "\n",
    "# Define the loss by adding all components to a 'loss' variable\n",
    "loss = K.variable(0.)\n",
    "layer_features = outputs_dict[content_layer]\n",
    "target_image_features = layer_features[1, :, :, :]\n",
    "combination_features = layer_features[2, :, :, :]\n",
    "loss += content_weight * content_loss(target_image_features, combination_features)\n",
    "\n",
    "for layer_name in style_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(style_layers)) + sl\n",
    "loss += total_variation_weight * total_variation_loss(combination_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the gradients of the generated image wrt the loss\n",
    "grads = K.gradients(loss, combination_image)[0]\n",
    "\n",
    "# Function to the fetch the value of the current loss and the current gradients\n",
    "fetch_loss_and_grads = K.function([combination_image], [loss, grads])\n",
    "\n",
    "class Evaluator(object):\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.loss_value = None\n",
    "        self.grads_value = None\n",
    "    \n",
    "    def loss(self, x):\n",
    "        assert self.loss_value is None\n",
    "        x = x.reshape((1, img_height, img_width, 3))\n",
    "        outs = fetch_loss_and_grads([x])\n",
    "        loss_value = outs[0]\n",
    "        grad_values = outs[1].flatten().astype('float64')\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start of iteration 0\n",
      "Current loss value :  9664402000.0\n",
      "Image saved as style_transfer_result_at_iteration_0.png\n",
      "Iteration 0 completed in 1025s\n",
      "Start of iteration 1\n",
      "Current loss value :  4350824400.0\n",
      "Image saved as style_transfer_result_at_iteration_1.png\n",
      "Iteration 1 completed in 1048s\n",
      "Start of iteration 2\n",
      "Current loss value :  2894161000.0\n",
      "Image saved as style_transfer_result_at_iteration_2.png\n",
      "Iteration 2 completed in 1052s\n",
      "Start of iteration 3\n",
      "Current loss value :  2291391200.0\n",
      "Image saved as style_transfer_result_at_iteration_3.png\n",
      "Iteration 3 completed in 1018s\n",
      "Start of iteration 4\n",
      "Current loss value :  1886620200.0\n",
      "Image saved as style_transfer_result_at_iteration_4.png\n",
      "Iteration 4 completed in 964s\n",
      "Start of iteration 5\n",
      "Current loss value :  1597830000.0\n",
      "Image saved as style_transfer_result_at_iteration_5.png\n",
      "Iteration 5 completed in 1072s\n",
      "Start of iteration 6\n",
      "Current loss value :  1432617900.0\n",
      "Image saved as style_transfer_result_at_iteration_6.png\n",
      "Iteration 6 completed in 1035s\n",
      "Start of iteration 7\n",
      "Current loss value :  1296190000.0\n",
      "Image saved as style_transfer_result_at_iteration_7.png\n",
      "Iteration 7 completed in 1040s\n",
      "Start of iteration 8\n",
      "Current loss value :  1194520600.0\n",
      "Image saved as style_transfer_result_at_iteration_8.png\n",
      "Iteration 8 completed in 1102s\n",
      "Start of iteration 9\n",
      "Current loss value :  1102861800.0\n",
      "Image saved as style_transfer_result_at_iteration_9.png\n",
      "Iteration 9 completed in 1052s\n",
      "Start of iteration 10\n",
      "Current loss value :  1036696600.0\n",
      "Image saved as style_transfer_result_at_iteration_10.png\n",
      "Iteration 10 completed in 1010s\n",
      "Start of iteration 11\n",
      "Current loss value :  979493440.0\n",
      "Image saved as style_transfer_result_at_iteration_11.png\n",
      "Iteration 11 completed in 1072s\n",
      "Start of iteration 12\n",
      "Current loss value :  920844800.0\n",
      "Image saved as style_transfer_result_at_iteration_12.png\n",
      "Iteration 12 completed in 957s\n",
      "Start of iteration 13\n",
      "Current loss value :  866996350.0\n",
      "Image saved as style_transfer_result_at_iteration_13.png\n",
      "Iteration 13 completed in 2850s\n",
      "Start of iteration 14\n",
      "Current loss value :  828140900.0\n",
      "Image saved as style_transfer_result_at_iteration_14.png\n",
      "Iteration 14 completed in 1581s\n",
      "Start of iteration 15\n",
      "Current loss value :  792919740.0\n",
      "Image saved as style_transfer_result_at_iteration_15.png\n",
      "Iteration 15 completed in 1156s\n",
      "Start of iteration 16\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from scipy.optimize import fmin_l_bfgs_b\n",
    "from scipy.misc import imsave\n",
    "import time\n",
    "\n",
    "result_prefix = 'style_transfer_result'\n",
    "iterations = 20\n",
    "\n",
    "# Run scipy-based optimization (L-BFGS)  over the pixels of the generated image\n",
    "# so as to minimize the neural style loss.\n",
    "# This is our initial state the target image\n",
    "# Note that 'scipy.optimize.fmion_l_bfgs_b' can only process flat vectors.\n",
    "x = preprocess_image(target_image_path)\n",
    "x =x.flatten()\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, fprime=evaluator. grads, maxfun=20)\n",
    "    x, min_val, info= fmin_l_bfgs_b(evaluator.loss, x, fprime=evaluator.grads, maxfun=20)\n",
    "    print('Current loss value : ', min_val)\n",
    "    \n",
    "    # Save current Generated Image\n",
    "    img = x.copy().reshape((img_height, img_width, 3))\n",
    "    img = deprocess_image(img)\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))\n",
    "    "
   ]
  },
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   "cell_type": "code",
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    "[!image]('style_transfer_result_at_iteration_15.png')"
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