220 lines
7.2 KiB
Python
220 lines
7.2 KiB
Python
'''Deep Dreaming in Keras.
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Run the script with:
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```
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python deep_dream.py path_to_your_base_image.jpg prefix_for_results
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```
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e.g.:
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```
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python deep_dream.py img/mypic.jpg results/dream
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```
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It is preferable to run this script on GPU, for speed.
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If running on CPU, prefer the TensorFlow backend (much faster).
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Example results: http://i.imgur.com/FX6ROg9.jpg
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'''
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from __future__ import print_function
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from keras.preprocessing.image import load_img, img_to_array
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import numpy as np
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from scipy.misc import imsave
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from scipy.optimize import fmin_l_bfgs_b
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import time
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import argparse
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from keras.applications import vgg16
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from keras import backend as K
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from keras.layers import Input
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parser = argparse.ArgumentParser(description='Deep Dreams with Keras.')
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parser.add_argument('base_image_path', metavar='base', type=str,
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help='Path to the image to transform.')
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parser.add_argument('result_prefix', metavar='res_prefix', type=str,
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help='Prefix for the saved results.')
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args = parser.parse_args()
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base_image_path = args.base_image_path
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result_prefix = args.result_prefix
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# dimensions of the generated picture.
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img_height = 600
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img_width = 600
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# some settings we found interesting
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saved_settings = {
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'bad_trip': {'features': {'block4_conv1': 0.05,
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'block4_conv2': 0.01,
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'block4_conv3': 0.01},
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'continuity': 0.1,
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'dream_l2': 0.8,
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'jitter': 5},
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'dreamy': {'features': {'block5_conv1': 0.05,
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'block5_conv2': 0.02},
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'continuity': 0.1,
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'dream_l2': 0.02,
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'jitter': 0},
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}
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# the settings we will use in this experiment
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settings = saved_settings['dreamy']
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def preprocess_image(image_path):
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# util function to open, resize and format pictures
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# into appropriate tensors
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img = load_img(image_path, target_size=(img_height, img_width))
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img = img_to_array(img)
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img = np.expand_dims(img, axis=0)
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img = vgg16.preprocess_input(img)
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return img
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def deprocess_image(x):
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# util function to convert a tensor into a valid image
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if K.image_data_format() == 'channels_first':
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x = x.reshape((3, img_height, img_width))
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x = x.transpose((1, 2, 0))
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else:
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x = x.reshape((img_height, img_width, 3))
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# Remove zero-center by mean pixel
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x[:, :, 0] += 103.939
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x[:, :, 1] += 116.779
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x[:, :, 2] += 123.68
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# 'BGR'->'RGB'
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x = x[:, :, ::-1]
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x = np.clip(x, 0, 255).astype('uint8')
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return x
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if K.image_data_format() == 'channels_first':
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img_size = (3, img_height, img_width)
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else:
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img_size = (img_height, img_width, 3)
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# this will contain our generated image
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dream = Input(batch_shape=(1,) + img_size)
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# build the VGG16 network with our placeholder
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# the model will be loaded with pre-trained ImageNet weights
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model = vgg16.VGG16(input_tensor=dream,
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weights='imagenet', include_top=False)
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print('Model loaded.')
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# get the symbolic outputs of each "key" layer (we gave them unique names).
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layer_dict = dict([(layer.name, layer) for layer in model.layers])
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def continuity_loss(x):
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# continuity loss util function
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assert K.ndim(x) == 4
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if K.image_data_format() == 'channels_first':
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a = K.square(x[:, :, :img_height - 1, :img_width - 1] -
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x[:, :, 1:, :img_width - 1])
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b = K.square(x[:, :, :img_height - 1, :img_width - 1] -
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x[:, :, :img_height - 1, 1:])
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else:
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a = K.square(x[:, :img_height - 1, :img_width - 1, :] -
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x[:, 1:, :img_width - 1, :])
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b = K.square(x[:, :img_height - 1, :img_width - 1, :] -
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x[:, :img_height - 1, 1:, :])
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return K.sum(K.pow(a + b, 1.25))
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# define the loss
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loss = K.variable(0.)
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for layer_name in settings['features']:
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# add the L2 norm of the features of a layer to the loss
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assert layer_name in layer_dict.keys(), 'Layer ' + layer_name + ' not found in model.'
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coeff = settings['features'][layer_name]
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x = layer_dict[layer_name].output
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shape = layer_dict[layer_name].output_shape
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# we avoid border artifacts by only involving non-border pixels in the loss
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if K.image_data_format() == 'channels_first':
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loss -= coeff * K.sum(K.square(x[:, :, 2: shape[2] - 2, 2: shape[3] - 2])) / np.prod(shape[1:])
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else:
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loss -= coeff * K.sum(K.square(x[:, 2: shape[1] - 2, 2: shape[2] - 2, :])) / np.prod(shape[1:])
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# add continuity loss (gives image local coherence, can result in an artful blur)
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loss += settings['continuity'] * continuity_loss(dream) / np.prod(img_size)
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# add image L2 norm to loss (prevents pixels from taking very high values, makes image darker)
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loss += settings['dream_l2'] * K.sum(K.square(dream)) / np.prod(img_size)
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# feel free to further modify the loss as you see fit, to achieve new effects...
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# compute the gradients of the dream wrt the loss
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grads = K.gradients(loss, dream)
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outputs = [loss]
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if isinstance(grads, (list, tuple)):
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outputs += grads
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else:
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outputs.append(grads)
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f_outputs = K.function([dream], outputs)
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def eval_loss_and_grads(x):
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x = x.reshape((1,) + img_size)
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outs = f_outputs([x])
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loss_value = outs[0]
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if len(outs[1:]) == 1:
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grad_values = outs[1].flatten().astype('float64')
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else:
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grad_values = np.array(outs[1:]).flatten().astype('float64')
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return loss_value, grad_values
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class Evaluator(object):
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"""Loss and gradients evaluator.
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This Evaluator class makes it possible
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to compute loss and gradients in one pass
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while retrieving them via two separate functions,
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"loss" and "grads". This is done because scipy.optimize
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requires separate functions for loss and gradients,
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but computing them separately would be inefficient.
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"""
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def __init__(self):
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self.loss_value = None
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self.grad_values = None
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def loss(self, x):
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assert self.loss_value is None
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loss_value, grad_values = eval_loss_and_grads(x)
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self.loss_value = loss_value
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self.grad_values = grad_values
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return self.loss_value
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def grads(self, x):
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assert self.loss_value is not None
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grad_values = np.copy(self.grad_values)
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self.loss_value = None
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self.grad_values = None
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return grad_values
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evaluator = Evaluator()
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# Run scipy-based optimization (L-BFGS) over the pixels of the generated image
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# so as to minimize the loss
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x = preprocess_image(base_image_path)
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for i in range(5):
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print('Start of iteration', i)
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start_time = time.time()
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# Add a random jitter to the initial image.
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# This will be reverted at decoding time
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random_jitter = (settings['jitter'] * 2) * (np.random.random(img_size) - 0.5)
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x += random_jitter
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# Run L-BFGS for 7 steps
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x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(),
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fprime=evaluator.grads, maxfun=7)
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print('Current loss value:', min_val)
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# Decode the dream and save it
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x = x.reshape(img_size)
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x -= random_jitter
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img = deprocess_image(np.copy(x))
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fname = result_prefix + '_at_iteration_%d.png' % i
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imsave(fname, img)
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end_time = time.time()
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print('Image saved as', fname)
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print('Iteration %d completed in %ds' % (i, end_time - start_time))
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