import keras
from keras.preprocessing.image import load_img, img_to_array
from keras import backend as K
import numpy as np
from keras.applications import vgg19

keras.__version__

# In short, we can use a pre-trained convnet to define a loss that will:
# 
# - Preserve content by maintaining similar high-level layer activations between the target content 
#   image and the generated image. The convnet should "see" both the target image and the generated 
#   image as "containing the same things".
#
# - Preserve style by maintaining similar correlations within activations for both low-level layers 
#   and high-level layers. Indeed, feature correlations capture textures: the generated and the style 
#   reference image should share the same textures at different spatial scales.
#
# Now let's take a look at a Keras implementation of the original 2015 neural style transfer algorithm. 
# As you will see, it shares a lot of similarities with the Deep Dream implementation we developed in the 
# previous section.

image_path = '/home/daniel/github_repos/talk_dl_big_picture/dl_with_python_examples/images_neural_style_transfer'

# This is the path to the image you want to transform.
target_image_path = image_path + '/lmu.jpg'
# This is the path to the style image.
# style_reference_image_path = image_path + '/kandinsky2.jpg'
style_reference_image_path = image_path + '/kandinsky.jpg'

# Dimensions of the generated picture.
width, height = load_img(target_image_path).size
img_height = 300
img_width = int(width * img_height / height)


def preprocess_image(image_path):
    img = load_img(image_path, target_size=(img_height, img_width))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = vgg19.preprocess_input(img)
    return img


def deprocess_image(x):
    # 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


target_image = K.constant(preprocess_image(target_image_path))
style_reference_image = K.constant(preprocess_image(style_reference_image_path))

# This placeholder will contain our generated image
combination_image = K.placeholder((1, img_height, img_width, 3))

# We combine the 3 images into a single batch
input_tensor = K.concatenate([target_image,
                              style_reference_image,
                              combination_image], axis=0)

# We build the VGG19 network with our batch of 3 images as input.
# The model will be loaded with pre-trained ImageNet weights.
model = vgg19.VGG19(input_tensor=input_tensor,
                    weights='imagenet',
                    include_top=False)

def content_loss(base, combination):
    return K.sum(K.square(combination - base))

# Now, here's the style loss. It leverages an auxiliary function to compute the Gram matrix of an 
# input matrix, i.e. a map of the correlations found in the original feature matrix.

def gram_matrix(x):
    features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
    gram = K.dot(features, K.transpose(features))
    return gram


def style_loss(style, combination):
    S = gram_matrix(style)
    C = gram_matrix(combination)
    channels = 3
    size = img_height * img_width
    return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))

# To these two loss components, we add a third one, the "total variation loss". It is meant to 
# encourage spatial continuity in the generated image, thus avoiding overly pixelated results. 
# You could interpret it as a regularization loss.

def total_variation_loss(x):
    a = K.square(
        x[:, :img_height - 1, :img_width - 1, :] - x[:, 1:, :img_width - 1, :])
    b = K.square(
        x[:, :img_height - 1, :img_width - 1, :] - x[:, :img_height - 1, 1:, :])
    return K.sum(K.pow(a + b, 1.25))

# Dict mapping layer names to activation tensors
outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
# Name of layer used for content loss
content_layer = 'block5_conv2'
# Name of layers used for style loss
style_layers = ['block1_conv1',
                'block2_conv1',
                'block3_conv1',
                'block4_conv1',
                'block5_conv1']

# Weights in the weighted average of the loss components
total_variation_weight = 1e-4
style_weight = 1.
content_weight = 0.025

# Define the loss by adding all components to a `loss` variable
loss = K.variable(0.)
layer_features = outputs_dict[content_layer]
target_image_features = layer_features[0, :, :, :]
combination_features = layer_features[2, :, :, :]
loss = loss + content_weight * content_loss(target_image_features, combination_features)

for layer_name in style_layers:
    layer_features = outputs_dict[layer_name]
    style_reference_features = layer_features[1, :, :, :]
    combination_features = layer_features[2, :, :, :]
    sl = style_loss(style_reference_features, combination_features)
    loss += (style_weight / len(style_layers)) * sl

loss += total_variation_weight * total_variation_loss(combination_image)


# Get the gradients of the generated image wrt the loss
grads = K.gradients(loss, combination_image)[0]

# Function to fetch the values of the current loss and the current gradients
fetch_loss_and_grads = K.function([combination_image], [loss, grads])


class Evaluator(object):

    def __init__(self):
        self.loss_value = None
        self.grads_values = None

    def loss(self, x):
        assert self.loss_value is None
        x = x.reshape((1, img_height, img_width, 3))
        outs = fetch_loss_and_grads([x])
        loss_value = outs[0]
        grad_values = outs[1].flatten().astype('float64')
        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()


from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imsave
import time

result_prefix = 'style_transfer_result'
iterations = 20

# Run scipy-based optimization (L-BFGS) over the pixels of the generated image
# so as to minimize the neural style loss.
# This is our initial state: the target image.
# Note that `scipy.optimize.fmin_l_bfgs_b` can only process flat vectors.
x = preprocess_image(target_image_path)
x = x.flatten()
for i in range(iterations):
    print('Start of iteration', i)
    start_time = time.time()
    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x,
                                     fprime=evaluator.grads, maxfun=20)
    print('Current loss value:', min_val)
    # Save current generated image
    img = x.copy().reshape((img_height, img_width, 3))
    img = deprocess_image(img)
    fname = result_prefix + '_at_iteration_%d.png' % i
    imsave(fname, img)
    end_time = time.time()
    print('Image saved as', fname)
    print('Iteration %d completed in %ds' % (i, end_time - start_time))


from matplotlib import pyplot as plt

# Content image
plt.imshow(load_img(target_image_path, target_size=(img_height, img_width)))
plt.figure()

# Style image
plt.imshow(load_img(style_reference_image_path, target_size=(img_height, img_width)))
plt.figure()

# Generate image
plt.imshow(img)
plt.show()

imsave(image_path + '/generated_image.png', img)