Deep Learning¶
Practice Projects¶
P5 Additional: Mixed Styles¶
Style, Links and Libraries¶
Perfect and complete explanation - Artistic Style Transfer by Naoki Shibuya
In [1]:
%%html
<style>
@import url('https://fonts.googleapis.com/css?family=Orbitron|Roboto');
body {background-color: aliceblue;}
a {color: #4876ff; font-family: 'Roboto';}
h1 {color: #348ABD; font-family: 'Orbitron'; text-shadow: 4px 4px 4px #ccc;}
h2, h3 {color: slategray; font-family: 'Roboto'; text-shadow: 4px 4px 4px #ccc;}
h4 {color: #348ABD; font-family: 'Orbitron';}
span {text-shadow: 4px 4px 4px #ccc;}
div.output_prompt, div.output_area pre {color: slategray;}
div.input_prompt, div.output_subarea {color: #4876ff;}
div.output_stderr pre {background-color: aliceblue;}
div.output_stderr {background-color: slategrey;}
</style>
<script>
code_show = true;
function code_display() {
if (code_show) {
$('div.input').each(function(id) {
if (id == 0 || $(this).html().indexOf('hide_code') > -1) {$(this).hide();}
});
$('div.output_prompt').css('opacity', 0);
} else {
$('div.input').each(function(id) {$(this).show();});
$('div.output_prompt').css('opacity', 1);
};
code_show = !code_show;
}
$(document).ready(code_display);
</script>
<form action="javascript: code_display()">
<input style="color: #348ABD; background: aliceblue; opacity: 0.8;" \
type="submit" value="Click to display or hide code cells">
</form>
In [2]:
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import numpy as np
import math
import scipy.misc
import tensorflow as tf
import keras
import cv2
from tqdm import tqdm
import matplotlib.pylab as plt
from matplotlib import cm
%matplotlib inline
Display Images¶
In [3]:
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# Read from files and display images using OpenCV
def display_images(original, style):
original_img = cv2.imread("images/" + original)
style_img = cv2.imread("images/" + style)
plt.figure(1, figsize=(18,6))
plt.subplot(121)
plt.title("Original image shape: " + str(original_img .shape))
plt.imshow(cv2.cvtColor(original_img , cv2.COLOR_BGR2RGB))
plt.subplot(122)
plt.title("Style image shape: " + str(style_img.shape))
plt.imshow(cv2.cvtColor(style_img, cv2.COLOR_BGR2RGB));
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display_images('01.png', '07.png')
In [5]:
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display_images('11.png', '08.png')
Preprocess¶
In [6]:
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# Create image tensors and resize the biggest one; Pair 1
img01 = cv2.imread("images/" + "01.png").astype('float32')
img07 = cv2.imread("images/" + "07.png").astype('float32')
img01 = scipy.misc.imresize(img01, img07.shape[:2]).astype('float32')
img01.shape, img07.shape
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# Create image tensors and resize the biggest one; Pair 2
img11 = cv2.imread("images/" + "11.png").astype('float32')
img08 = cv2.imread("images/" + "08.png").astype('float32')
img11 = scipy.misc.imresize(img11, img08.shape[:2]).astype('float32')
img11.shape, img08.shape
Out[7]:
In [8]:
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# Preprocess function for VGG16
def preprocess(img):
img = img.copy()
img = np.expand_dims(img, axis=0)
return keras.applications.vgg16.preprocess_input(img)
In [9]:
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# Function for input tensors
def inputs(original_img, style_img):
original_input = tf.constant(preprocess(original_img))
style_input = tf.constant(preprocess(style_img))
generated_input = tf.placeholder(tf.float32, original_input.shape)
return original_input, style_input, generated_input
In [10]:
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# Create input tensors; Pair 1
original_input, style_input, generated_input = inputs(img01, img07)
input_tensor = tf.concat([original_input, style_input, generated_input], axis=0)
input_tensor.shape
Out[10]:
In [11]:
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# Create input tensors; Pair 2
original_input2, style_input2, generated_input2 = inputs(img11, img08)
input_tensor2 = tf.concat([original_input2, style_input2, generated_input2], axis=0)
input_tensor2.shape
Out[11]:
VGG16 Usage¶
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# Create the model using keras applications; Pair 1
vgg16_model = keras.applications.vgg16.VGG16(input_tensor=input_tensor, include_top=False)
vgg16_model.summary()
In [13]:
# Create the model using keras applications; Pair 2
vgg16_model2 = keras.applications.vgg16.VGG16(input_tensor=input_tensor2, include_top=False)
vgg16_model2.summary()
In [14]:
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# Create layer dictionaries
vgg16_layer_dict = {layer.name:layer for layer in vgg16_model.layers}
vgg16_layer_dict2 = {layer.name:layer for layer in vgg16_model2.layers}
In [15]:
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# Functions for loss calculation
def calculate_original_loss(layer_dict, original_layer_names):
loss = 0
for name in original_layer_names:
layer = layer_dict[name]
original_features = layer.output[0, :, :, :]
generated_features = layer.output[2, :, :, :]
loss += keras.backend.sum(keras.backend.square(generated_features - original_features))
return loss / len(original_layer_names)
def gram_matrix(x):
features = keras.backend.batch_flatten(keras.backend.permute_dimensions(x, (2, 0, 1)))
gram = keras.backend.dot(features, keras.backend.transpose(features))
return gram
def get_style_loss(style_features, generated_features, size):
S = gram_matrix(style_features)
G = gram_matrix(generated_features)
channels = 3
return keras.backend.sum(keras.backend.square(S - G)) / (4. * (channels**2) * (size**2))
def calculate_style_loss(layer_dict, style_layer_names, size):
loss = 0
for name in style_layer_names:
layer = layer_dict[name]
style_features = layer.output[1, :, :, :]
generated_features = layer.output[2, :, :, :]
loss += get_style_loss(style_features, generated_features, size)
return loss / len(style_layer_names)
def calculate_variation_loss(x):
row_diff = keras.backend.square(x[:, :-1, :-1, :] - x[:, 1:, :-1, :])
col_diff = keras.backend.square(x[:, :-1, :-1, :] - x[:, :-1, 1:, :])
return keras.backend.sum(keras.backend.pow(row_diff + col_diff, 1.25))
In [16]:
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# Calculate all losses; Pair 1
original_loss = calculate_original_loss(vgg16_layer_dict, ['block5_conv2'])
style_layers = ['block1_conv1','block2_conv1','block3_conv1','block4_conv1', 'block5_conv1']
style_loss = calculate_style_loss(vgg16_layer_dict, style_layers, img07.shape[0]*img07.shape[1])
variation_loss = calculate_variation_loss(generated_input)
In [17]:
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# Calculate all losses; Pair 2
original_loss2 = calculate_original_loss(vgg16_layer_dict2, ['block5_conv2'])
style_layers = ['block1_conv1','block2_conv1','block3_conv1','block4_conv1', 'block5_conv1']
style_loss2 = calculate_style_loss(vgg16_layer_dict2, style_layers, img08.shape[0]*img08.shape[1])
variation_loss2 = calculate_variation_loss(generated_input2)
In [18]:
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### Pair 1 ###
# Loss and gradients calculation
loss = 0.5 * original_loss + 1.0 * style_loss + 0.1 * variation_loss
gradients = keras.backend.gradients(loss, generated_input)[0]
calculate = keras.backend.function([generated_input], [loss, gradients])
# Preprocess the image
generated_data = preprocess(img01)
# Generate the new image
for i in tqdm(range(300)):
_, gradients_value = calculate([generated_data])
generated_data -= gradients_value * 0.001
In [25]:
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### Pair 2 ###
# Loss and gradients calculation
loss2 = 0.5 * original_loss2 + 1.0 * style_loss2 + 0.1 * variation_loss2
gradients2 = keras.backend.gradients(loss2, generated_input2)[0]
calculate2 = keras.backend.function([generated_input2], [loss2, gradients2])
# Preprocess the image
generated_data2 = preprocess(img11)
# Generate the new image
for i in tqdm(range(300)):
_, gradients_value2 = calculate2([generated_data2])
generated_data2 -= gradients_value2 * 0.001
Display Style Transfer¶
In [19]:
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# Save the result; Pair 1
np.save('generated_data_02_v3.npy', generated_data)
In [26]:
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# Save the result; Pair 2
np.save('generated_data2_02_v3.npy', generated_data2)
In [20]:
# Load the result; Pair 1
generated_data_01 = np.load('generated_data_01_v3.npy')
generated_data_02 = np.load('generated_data_02_v3.npy')
In [27]:
# Load the result; Pair 2
generated_data2_01 = np.load('generated_data2_01_v3.npy')
generated_data2_02 = np.load('generated_data2_02_v3.npy')
In [22]:
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# Reverse of preprocessing
def deprocess(img):
img = img.copy()
img = img[0]
img[:, :, 0] += 103.939
img[:, :, 1] += 116.779
img[:, :, 2] += 123.68
img = img[:, :, ::-1]
img = np.clip(img, 0, 255)
return img.astype('uint8')
Pair 1¶
In [24]:
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# Display the generated image
generated_image01 = deprocess(generated_data_01)
generated_image02 = deprocess(generated_data_02)
plt.figure(1, figsize=(18,6))
plt.subplot(121)
plt.title("Loss function: 0.5 * original_loss + 1.0 * style_loss + 0.1 * variation_loss; 10 steps")
plt.imshow(cv2.cvtColor(generated_image01, cv2.COLOR_BGR2RGB))
plt.subplot(122)
plt.title("Loss function: 0.5 * original_loss + 1.0 * style_loss + 0.1 * variation_loss; 300 steps")
plt.imshow(cv2.cvtColor(generated_image02, cv2.COLOR_BGR2RGB));
Pair 2¶
In [29]:
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# Display the generated image
generated_image201 = deprocess(generated_data2_01)
generated_image202 = deprocess(generated_data2_02)
plt.figure(1, figsize=(18,8))
plt.subplot(121)
plt.title("Loss function: 1.0 * original_loss + 1.0 * style_loss + 0.1 * variation_loss; 10 steps")
plt.imshow(cv2.cvtColor(generated_image201, cv2.COLOR_BGR2RGB))
plt.subplot(122)
plt.title("Loss function: 0.5 * original_loss + 1.0 * style_loss + 0.1 * variation_loss; 300 steps")
plt.imshow(cv2.cvtColor(generated_image202, cv2.COLOR_BGR2RGB));
