深度学习(python)-Keras-4 风格迁移

Keras实践深度学习中的风格迁移
风格迁移可以将一张图片中的风格迁移到另一张图片上。

风格迁移

用以下代码来实现风格迁移
给出要改变图像的路径，给出风格图像，设置迭代次数。

from keras.preprocessing.image import load_img,img_to_array
import numpy as np
from keras.applications import vgg19
from keras import backend as k
from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imsave
import time

target_image_path = '1011.jpg'
style_reference_image_path = '1025.jpg'

width, height = load_img(target_image_path).size
img_height = 600
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):
    x[:, :, 0] += 103.939
    x[:, :, 1] += 116.799
    x[:, :, 2] += 123.68
    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)) 
combination_image = k.placeholder((1, img_height, img_width, 3))

input_tensor = k.concatenate([target_image,style_reference_image,combination_image], axis=0)

model = vgg19.VGG19(input_tensor=input_tensor,weights='imagenet',include_top=False)

print('Model loaded')

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

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))

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))

outputs_dict = dict([(layer.name,layer.output) for layer in model.layers])
content_layer = 'block5_conv2'
style_layers = ['block1_conv1','block2_conv1',
    'block3_conv1','block4_conv1','block5_conv1']

total_variation_weight = 1e-4
style_weight = 1.
content_weight = 0.025

loss = k.variable(0.)#最终损失值
layer_features = outputs_dict[content_layer]
target_image_features = layer_features[0, :, :, :]
combination_features = layer_features[2, :, :, :]
loss+=content_weight*content_loss(target_image_features,combination_features)#加内容损失

for layer_name in style_layers:#加风格损失
    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

#加变异损失，得到最终损失函数值
loss += total_variation_weight * total_variation_loss(combination_image)

grads = k.gradients(loss, combination_image)[0]
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()

result_prefix = 'my_result'
iterations = 10

x = preprocess_image(target_image_path)#目标图片路径
x = x.flatten()#展开，应用l-bfgs

for i in range(iterations):
    print('Start of iteration', i)
    start_time = time.time()
    #在生成图片上运行L-BFGS优化；注意传递计算损失和梯度值必须为两个不同函数作为参数
    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x,
            fprime=evaluator.grads,maxfun=20)
    print('Current loss value:', min_val)
    img = x.copy().reshape((img_height, img_width, 3))
    img = deprocess_image(img)
    fname = result_prefix + '_at_iteration_%d.png' % i
    imsave(fname, img)
    print('Image saved as', fname)
    end_time = time.time()
    print('Iteration %d completed in %ds' % (i, end_time - start_time))