Neural style transfer with Keras.

Run the script with:

    python neural_style_transfer.py path_to_your_base_image.jpg     path_to_your_reference.jpg prefix_for_results

e.g.:

    python neural_style_transfer.py img/tuebingen.jpg     img/starry_night.jpg results/my_result

Optional parameters:

    --iter, To specify the number of iterations     the style transfer takes place (Default is 10)
    --content_weight, The weight given to the content loss (Default is 0.025)
    --style_weight, The weight given to the style loss (Default is 1.0)
    --tv_weight, The weight given to the total variation loss (Default is 1.0)

It is preferable to run this script on GPU, for speed.

Example result: https://twitter.com/fchollet/status/686631033085677568

# Details

Style transfer consists in generating an image with the same "content" as a base image, but with the "style" of a different picture (typically artistic).

This is achieved through the optimization of a loss function that has 3 components: "style loss", "content loss", and "total variation loss":

- The total variation loss imposes local spatial continuity between the pixels of the combination image, giving it visual coherence.

- The style loss is where the deep learning keeps in --that one is defined using a deep convolutional neural network. Precisely, it consists in a sum of L2 distances between the Gram matrices of the representations of the base image and the style reference image, extracted from different layers of a convnet (trained on ImageNet). The general idea is to capture color/texture information at different spatial scales (fairly large scales --defined by the depth of the layer considered).

- The content loss is a L2 distance between the features of the base image (extracted from a deep layer) and the features of the combination image, keeping the generated image close enough to the original one.

# References

    - [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576)

    from __future__ import print_function
    from keras.preprocessing.image import load_img, save_img, img_to_array
    import numpy as np
    from scipy.optimize import fmin_l_bfgs_b
    import time
    import argparse

    from keras.applications import vgg19
    from keras import backend as K

    parser = argparse.ArgumentParser(description='Neural style transfer with Keras.')
    parser.add_argument('base_image_path', metavar='base', type=str,
                        help='Path to the image to transform.')
    parser.add_argument('style_reference_image_path', metavar='ref', type=str,
                        help='Path to the style reference image.')
    parser.add_argument('result_prefix', metavar='res_prefix', type=str,
                        help='Prefix for the saved results.')
    parser.add_argument('--iter', type=int, default=10, required=False,
                        help='Number of iterations to run.')
    parser.add_argument('--content_weight', type=float, default=0.025, required=False,
                        help='Content weight.')
    parser.add_argument('--style_weight', type=float, default=1.0, required=False,
                        help='Style weight.')
    parser.add_argument('--tv_weight', type=float, default=1.0, required=False,
                        help='Total Variation weight.')

    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
    iterations = args.iter

    # these are the weights of the different loss components
    total_variation_weight = args.tv_weight
    style_weight = args.style_weight
    content_weight = args.content_weight

    # dimensions of the generated picture.
    width, height = load_img(base_image_path).size
    img_nrows = 400
    img_ncols = int(width * img_nrows / height)

    # util function to open, resize and format pictures into appropriate tensors

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

    # util function to convert a tensor into a valid image

    def deprocess_image(x):
        if K.image_data_format() == 'channels_first':
            x = x.reshape((3, img_nrows, img_ncols))
            x = x.transpose((1, 2, 0))
        else:
            x = x.reshape((img_nrows, img_ncols, 3))
        # 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

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

    # this will contain our generated image
    if K.image_data_format() == 'channels_first':
        combination_image = K.placeholder((1, 3, img_nrows, img_ncols))
    else:
        combination_image = K.placeholder((1, img_nrows, img_ncols, 3))

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

    # build the VGG19 network with our 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)
    print('Model loaded.')

    # 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])

    # compute the neural style loss
    # first we need to define 4 util functions

    # the gram matrix of an image tensor (feature-wise outer product)

    def gram_matrix(x):
        assert K.ndim(x) == 3
        if K.image_data_format() == 'channels_first':
            features = K.batch_flatten(x)
        else:
            features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
        gram = K.dot(features, K.transpose(features))
        return gram

    # the "style loss" is designed to maintain
    # the style of the reference image in the generated image.
    # It is based on the gram matrices (which capture style) of
    # feature maps from the style reference image
    # and from the generated image

    def style_loss(style, combination):
        assert K.ndim(style) == 3
        assert K.ndim(combination) == 3
        S = gram_matrix(style)
        C = gram_matrix(combination)
        channels = 3
        size = img_nrows * img_ncols
        return K.sum(K.square(S - C)) / (4.0 * (channels ** 2) * (size ** 2))

    # an auxiliary loss function
    # designed to maintain the "content" of the
    # base image in the generated image

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

    # the 3rd loss function, total variation loss,
    # designed to keep the generated image locally coherent

    def total_variation_loss(x):
        assert K.ndim(x) == 4
        if K.image_data_format() == 'channels_first':
            a = K.square(
                x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, 1:, :img_ncols - 1])
            b = K.square(
                x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, :img_nrows - 1, 1:])
        else:
            a = K.square(
                x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, 1:, :img_ncols - 1, :])
            b = K.square(
                x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, :img_nrows - 1, 1:, :])
        return K.sum(K.pow(a + b, 1.25))

    # combine these loss functions into a single scalar
    loss = K.variable(0.0)
    layer_features = outputs_dict['block5_conv2']
    base_image_features = layer_features[0, :, :, :]
    combination_features = layer_features[2, :, :, :]
    loss = loss + content_weight * content_loss(base_image_features,
                                                combination_features)

    feature_layers = ['block1_conv1', 'block2_conv1',
                      'block3_conv1', 'block4_conv1',
                      'block5_conv1']
    for layer_name in feature_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 = loss + (style_weight / len(feature_layers)) * sl
    loss = 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)

    outputs = [loss]
    if isinstance(grads, (list, tuple)):
        outputs += grads
    else:
        outputs.append(grads)

    f_outputs = K.function([combination_image], outputs)

    def eval_loss_and_grads(x):
        if K.image_data_format() == 'channels_first':
            x = x.reshape((1, 3, img_nrows, img_ncols))
        else:
            x = x.reshape((1, img_nrows, img_ncols, 3))
        outs = f_outputs([x])
        loss_value = outs[0]
        if len(outs[1:]) == 1:
            grad_values = outs[1].flatten().astype('float64')
        else:
            grad_values = np.array(outs[1:]).flatten().astype('float64')
        return loss_value, grad_values

    # this Evaluator class makes it possible
    # to compute loss and gradients in one pass
    # while retrieving them via two separate functions,
    # "loss" and "grads". This is done because scipy.optimize
    # requires separate functions for loss and gradients,
    # but computing them separately would be inefficient.

    class Evaluator(object):

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

        def loss(self, x):
            assert self.loss_value is None
            loss_value, grad_values = eval_loss_and_grads(x)
            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()

    # run scipy-based optimization (L-BFGS) over the pixels of the generated image
    # so as to minimize the neural style loss
    x = preprocess_image(base_image_path)

    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.flatten(),
                                         fprime=evaluator.grads, maxfun=20)
        print('Current loss value:', min_val)
        # save current generated image
        img = deprocess_image(x.copy())
        fname = result_prefix + '_at_iteration_%d.png' % i
        save_img(fname, img)
        end_time = time.time()
        print('Image saved as', fname)
        print('Iteration %d completed in %ds' % (i, end_time - start_time))

GitHub