自己写个 Prisma

from scipy.misc import imread, imresize, imsave
from scipy.optimize import fmin_l_bfgs_b
from sklearn.preprocessing import normalize
import numpy as np
import time
import os
import argparse
import h5py

from keras.models import Sequential
from keras.layers.convolutional import Convolution2D, ZeroPadding2D, AveragePooling2D
from keras import backend as K

#Define base image, style image, and result image paths
args = parser.parse_args()
base_image_path = args.base_image_path
style_reference_image_path = args.style_reference_image_path
result_prefix = args.result_prefix

#Get the weights file
weights_path = r"vgg16_weights.h5"

#Init bools to decide whether or not to resize
rescale_image = strToBool(args.rescale_image)
maintain_aspect_ratio = strToBool(args.maintain_aspect_ratio)

# Init variables for style and content weights. 
total_variation_weight = args.tv_weight
style_weight = args.style_weight * args.style_scale
content_weight = args.content_weight

# Init dimensions of the generated picture.
img_width = img_height = args.img_size
assert img_height == img_width, 'Due to the use of the Gram matrix, width and height must match.'
img_WIDTH = img_HEIGHT = 0
aspect_ratio = 0

# get tensor representations of our images
base_image = K.variable(preprocess_image(base_image_path, True))
style_reference_image = K.variable(preprocess_image(style_reference_image_path))

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

# combine the 3 images into a single Keras tensor
input_tensor = K.concatenate([base_image,
                              style_reference_image,
                              combination_image], axis=0)

# build the VGG16 network with our 3 images as input
first_layer = ZeroPadding2D((1, 1))
first_layer.set_input(input_tensor, shape=(3, 3, img_width, img_height))

model = Sequential()
model.add(first_layer)
model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
model.add(ZeroPadding2D((1, 1)))
model.add(Convolution2D(64, 3, 3, activation='relu'))
model.add(AveragePooling2D((2, 2), strides=(2, 2)))

。。。

# load the weights of the VGG16 networks
load_weights(weights_path, model)

# get the symbolic outputs of each "key" layer (we gave them unique names).
outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])

# get the loss (we combine style, content, and total variation loss into a single scalar)
loss = get_total_loss(outputs_dict)

# get the gradients of the generated image wrt the loss
grads = K.gradients(loss, combination_image)

#combine loss and gradient
f_outputs = combine_loss_and_gradient(loss, grads)

# Run scipy-based optimization (L-BFGS) over the pixels of the generated image to minimize the neural style loss
# 5 Step process
x, num_iter = prepare_image()
for i in range(num_iter):

    #Step 1 - Record iterations
    print('Start of iteration', (i+1))
    start_time = time.time()

    #Step 2 - Perform l_bfgs optimization function using loss and gradient
    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
                                     fprime=evaluator.grads, maxfun=20)
    print('Current loss value:', min_val)

    #Step 3 - Get the generated image
    img = deprocess_image(x.reshape((3, img_width, img_height)))

    #Step 4 - Maintain aspect ratio
    if (maintain_aspect_ratio) & (not rescale_image):
        img_ht = int(img_width * aspect_ratio)
        print("Rescaling Image to (%d, %d)" % (img_width, img_ht))
        img = imresize(img, (img_width, img_ht), interp=args.rescale_method)
    if rescale_image:
        print("Rescaling Image to (%d, %d)" % (img_WIDTH, img_HEIGHT))
        img = imresize(img, (img_WIDTH, img_HEIGHT), interp=args.rescale_method)

    #Step 5 - Save the generated image
    fname = result_prefix + '_at_iteration_%d.png' % (i+1)
    imsave(fname, img)
    end_time = time.time()
    print('Image saved as', fname)
    print('Iteration %d completed in %ds' % (i+1, end_time - start_time))