def load_mask(mask_path, shape, return_mask_img=False):
if K.image_dim_ordering() == 'th':
_, channels, width, height = shape
else:
_, width, height, channels = shape
mask = imread(mask_path, mode="L") # Grayscale mask load
mask = imresize(mask, (width, height)).astype('float32')
# Perform binarization of mask
mask[mask <= 127] = 0
mask[mask > 127] = 255
max_val = np.amax(mask)
mask /= max_val
if return_mask_img:
return mask
mask_shape = shape[1:]
mask_tensor = np.empty(mask_shape)
for i in range(channels):
if K.image_dim_ordering() == "th":
mask_tensor[i, :, :] = mask
else:
mask_tensor[:, :, i] = mask
return mask_tensor
def gram_matrix(x):
assert K.ndim(x) == 3
if K.image_dim_ordering() == 'th':
features = K.batch_flatten(x)
else:
features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1)))
gram = K.dot(features - 1, K.transpose(features - 1))
return gram
def eval_loss_and_grads(x):
if K.image_dim_ordering() == 'th':
x = x.reshape((1, 3, img_width, img_height))
else:
x = x.reshape((1, img_width, img_height, 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
def total_variation_loss(x):
assert K.ndim(x) == 4
if K.image_dim_ordering() == 'th':
a = K.square(x[:, :, :img_width - 1, :img_height - 1] -
x[:, :, 1:, :img_height - 1])
b = K.square(x[:, :, :img_width - 1, :img_height - 1] -
x[:, :, :img_width - 1, 1:])
else:
a = K.square(x[:, :img_width - 1, :img_height - 1, :] -
x[:, 1:, :img_height - 1, :])
b = K.square(x[:, :img_width - 1, :img_height - 1, :] -
x[:, :img_width - 1, 1:, :])
return K.sum(K.pow(a + b, 1.25))
def content_loss(base, combination):
channel_dim = 0 if K.image_dim_ordering() == 'th' else -1
channels = K.shape(base)[channel_dim]
size = img_width * img_height
if args.content_loss_type == 1:
multiplier = 1 / (2.0 * (channels**0.5) * (size**0.5))
elif args.content_loss_type == 2:
multiplier = 1 / (channels * size)
else:
multiplier = 1.0
return multiplier * K.sum(K.square(combination - base))
def deprocess_image(x):
"""
Convert a tensor back to a valid image.
"""
if K.image_dim_ordering() == 'th':
x = x.reshape((3, img_width, img_height))
x = x.transpose((1, 2, 0))
else:
x = x.reshape((img_width, img_height, 3))
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
def deprocess_image(self, x):
'''
Same normalization as in:
https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py
'''
if np.ndim(x) > 3:
x = np.squeeze(x)
# normalize tensor: center on 0., ensure std is 0.1
x -= x.mean()
x /= (x.std() + 1e-5)
x *= 0.1
# clip to [0, 1]
x += 0.5
x = np.clip(x, 0, 1)
# convert to RGB array
x *= 255
if KTF.image_dim_ordering() == 'th':
x = x.transpose((1, 2, 0))
# 0~255に正規化
x = np.clip(x, 0, 255).astype('uint8')
return x
def preprocess_image(image_path, load_dims=False, read_mode="color"):
global img_width, img_height, img_WIDTH, img_HEIGHT, aspect_ratio
mode = "RGB" if read_mode == 'color' else "L"
img = imread(image_path, mode=mode)
if mode == 'L':
# expand the 1 channel grayscale to 3 channel grayscale image
temp = np.zeros(img.shape + (3, ), dtype=np.uint8)
temp[:, :, 0] = img
temp[:, :, 1] = img
temp[:, :, 2] = img
img = temp
if load_dims:
img_WIDTH = img.shape[0]
img_HEIGHT = img.shape[1]
aspect_ratio = float(img_HEIGHT) / img_WIDTH
img_width = args.img_size
if maintain_aspect_ratio:
img_height = int(img_width * aspect_ratio)
else:
img_height = args.img_size
img = imresize(img, (img_width, img_height)).astype('float32')
# RGB -> BGR
img = img[:, :, ::-1]
img[:, :, 0] -= 103.939
img[:, :, 1] -= 116.779
img[:, :, 2] -= 123.68
if K.image_dim_ordering() == 'th':
img = img.transpose((2, 0, 1)).astype('float32')
img = np.expand_dims(img, axis=0)
return img
def pooling_func(x):
if pooltype == 1:
return AveragePooling2D((2, 2), strides=(2, 2))(x)
else:
return MaxPooling2D((2, 2), strides=(2, 2))(x)
# get tensor representation of base image
base_image = K.variable(
preprocess_image(base_image_path, True, read_mode=read_mode))
style_reference_images = []
for style_path in style_image_paths:
style_reference_images.append(K.variable(preprocess_image(style_path)))
# this will contain our generated image
if K.image_dim_ordering() == 'th':
combination_image = K.placeholder((1, 3, img_width, img_height))
else:
combination_image = K.placeholder((1, img_width, img_height, 3))
image_tensor = [base_image]
for style_image_tensor in style_reference_images:
image_tensor.append(style_image_tensor)
image_tensor.append(combination_image)
nb_tensors = len(image_tensor)
nb_style_images = nb_tensors - 2 # Content andoutput image not considered
# combine the various image into a single Keras tensor
input_tensor = K.concatenate(image_tensor, axis=0)