tensorflow 风格迁移

参考来源：
https://github.com/ckmarkoh/neuralart_tensorflow
https://github.com/log0/neural-style-painting/blob/master/TensorFlow%20Implementation%20of%20A%20Neural%20Algorithm%20of%20Artistic%20St

import osimport sysimport numpy as npimport scipy.ioimport scipy.miscimport tensorflow as tf# Output folder for the images.OUTPUT_DIR = 'output/'# Style image to use.STYLE_IMAGE = '/images/ocean.jpg'# Content image to use.CONTENT_IMAGE = '/images/Taipei101.jpg'# Image dimensions constants.IMAGE_WIDTH = 800IMAGE_HEIGHT = 600COLOR_CHANNELS = 3################################################################################ Algorithm constants################################################################################ 设置随机噪声图像与内容图像的比率NOISE_RATIO = 0.6# 设置迭代次数ITERATIONS = 1000# 设置内容图像与风格图像的权重alpha = 1beta = 500# 加载VGG-19 MODEL及设定均值VGG_Model = 'Downloads/imagenet-vgg-verydeep-19.mat'MEAN_VALUES = np.array([123.68, 116.779, 103.939]).reshape((1, 1, 1, 3))# 设置需要用到的卷积层CONTENT_LAYERS = [('conv4_2', 1.)]STYLE_LAYERS = [('conv1_1', 0.2), ('conv2_1', 0.2), ('conv3_1', 0.2), ('conv4_1', 0.2), ('conv5_1', 0.2)]# 生成随机噪声图，与content图以一定比率融合def generate_noise_image(content_image, noise_ratio = NOISE_RATIO):    """    Returns a noise image intermixed with the content image at a certain ratio.    """    noise_image = np.random.uniform(            -20, 20,            (1, IMAGE_HEIGHT, IMAGE_WIDTH, COLOR_CHANNELS)).astype('float32')    # White noise image from the content representation. Take a weighted average    # of the values    img = noise_image * noise_ratio + content_image * (1 - noise_ratio)    return imgdef load_image(path):    image = scipy.misc.imread(path)    # Resize the image for convnet input, there is no change but just    # add an extra dimension.    image = np.reshape(image, ((1,) + image.shape))    # Input to the VGG net expects the mean to be subtracted.    image = image - MEAN_VALUES    return imagedef save_image(path, image):    # Output should add back the mean.    image = image + MEAN_VALUES    # Get rid of the first useless dimension, what remains is the image.    image = image[0]    image = np.clip(image, 0, 255).astype('uint8')    scipy.misc.imsave(path, image)def build_net(ntype, nin, nwb=None):    if ntype == 'conv':        return tf.nn.relu(tf.nn.conv2d(nin, nwb[0], strides=[1, 1, 1, 1], padding='SAME') + nwb[1])    elif ntype == 'pool':        return tf.nn.avg_pool(nin, ksize=[1, 2, 2, 1],                              strides=[1, 2, 2, 1], padding='SAME')def get_weight_bias(vgg_layers, i):    weights = vgg_layers[i][0][0][2][0][0]    weights = tf.constant(weights)    bias = vgg_layers[i][0][0][2][0][1]    bias = tf.constant(np.reshape(bias, (bias.size)))    return weights, biasdef build_vgg19(path):    net = {}    vgg_rawnet = scipy.io.loadmat(path)    vgg_layers = vgg_rawnet['layers'][0]    net['input'] = tf.Variable(np.zeros((1, IMAGE_HEIGHT, IMAGE_WIDTH, 3)).astype('float32'))    net['conv1_1'] = build_net('conv', net['input'], get_weight_bias(vgg_layers, 0))    net['conv1_2'] = build_net('conv', net['conv1_1'], get_weight_bias(vgg_layers, 2))    net['pool1'] = build_net('pool', net['conv1_2'])    net['conv2_1'] = build_net('conv', net['pool1'], get_weight_bias(vgg_layers, 5))    net['conv2_2'] = build_net('conv', net['conv2_1'], get_weight_bias(vgg_layers, 7))    net['pool2'] = build_net('pool', net['conv2_2'])    net['conv3_1'] = build_net('conv', net['pool2'], get_weight_bias(vgg_layers, 10))    net['conv3_2'] = build_net('conv', net['conv3_1'], get_weight_bias(vgg_layers, 12))    net['conv3_3'] = build_net('conv', net['conv3_2'], get_weight_bias(vgg_layers, 14))    net['conv3_4'] = build_net('conv', net['conv3_3'], get_weight_bias(vgg_layers, 16))    net['pool3'] = build_net('pool', net['conv3_4'])    net['conv4_1'] = build_net('conv', net['pool3'], get_weight_bias(vgg_layers, 19))    net['conv4_2'] = build_net('conv', net['conv4_1'], get_weight_bias(vgg_layers, 21))    net['conv4_3'] = build_net('conv', net['conv4_2'], get_weight_bias(vgg_layers, 23))    net['conv4_4'] = build_net('conv', net['conv4_3'], get_weight_bias(vgg_layers, 25))    net['pool4'] = build_net('pool', net['conv4_4'])    net['conv5_1'] = build_net('conv', net['pool4'], get_weight_bias(vgg_layers, 28))    net['conv5_2'] = build_net('conv', net['conv5_1'], get_weight_bias(vgg_layers, 30))    net['conv5_3'] = build_net('conv', net['conv5_2'], get_weight_bias(vgg_layers, 32))    net['conv5_4'] = build_net('conv', net['conv5_3'], get_weight_bias(vgg_layers, 34))    net['pool5'] = build_net('pool', net['conv5_4'])    return netdef content_layer_loss(p, x):    M = p.shape[1] * p.shape[2]    N = p.shape[3]    loss = (1. / (2 * N * M)) * tf.reduce_sum(tf.pow((x - p), 2))    return lossdef content_loss_func(sess, net):    layers = CONTENT_LAYERS    total_content_loss = 0.0    for layer_name, weight in layers:        p = sess.run(net[layer_name])        x = net[layer_name]        total_content_loss += content_layer_loss(p, x)*weight    total_content_loss /= float(len(layers))    return total_content_lossdef gram_matrix(x, area, depth):    x1 = tf.reshape(x, (area, depth))    g = tf.matmul(tf.transpose(x1), x1)    return gdef style_layer_loss(a, x):    M = a.shape[1] * a.shape[2]    N = a.shape[3]    A = gram_matrix(a, M, N)    G = gram_matrix(x, M, N)    loss = (1. / (4 * N ** 2 * M ** 2)) * tf.reduce_sum(tf.pow((G - A), 2))    return lossdef style_loss_func(sess, net):    layers = STYLE_LAYERS    total_style_loss = 0.0    for layer_name, weight in layers:        a = sess.run(net[layer_name])        x = net[layer_name]        total_style_loss += style_layer_loss(a, x) * weight    total_style_loss /= float(len(layers))    return total_style_lossdef main():    net = build_vgg19(VGG_Model)    sess = tf.Session()    sess.run(tf.initialize_all_variables())    content_img = load_image(CONTENT_IMAGE)    style_img = load_image(STYLE_IMAGE)    sess.run([net['input'].assign(content_img)])    cost_content = content_loss_func(sess, net)    sess.run([net['input'].assign(style_img)])    cost_style = style_loss_func(sess, net)    total_loss = alpha * cost_content + beta * cost_style    optimizer = tf.train.AdamOptimizer(2.0)    init_img = generate_noise_image(content_img)    train_op = optimizer.minimize(total_loss)    sess.run(tf.initialize_all_variables())    sess.run(net['input'].assign(init_img))    for it in range(ITERATIONS):        sess.run(train_op)        if it % 100 == 0:            # Print every 100 iteration.            mixed_image = sess.run(net['input'])            print('Iteration %d' % (it))            print('sum : ', sess.run(tf.reduce_sum(mixed_image)))            print('cost: ', sess.run(total_loss))            if not os.path.exists(OUTPUT_DIR):                os.mkdir(OUTPUT_DIR)            filename = 'output/%d.png' % (it)            save_image(filename, mixed_image)if __name__ == '__main__':    main()