def main():
# Download the data for the VGG-16 model if it doesn't already exist in the directory.
vgg16.maybe_download()
run_style_transfer()
return 0
def initializeVGG16():
"""
Downloads the VGG16 model if ot does not already exist
"""
vgg16.data_dir = 'vgg16/'
if not vgg16.check_if_vgg16_exists():
vgg16.maybe_download()
def create_textures():
image_dir = '{0}'.format(IMAGE_DIRECTORY)
style_dir = '{0}'.format(STYLE_DIRECTORY)
images = sorted(
[image for image in os.listdir(image_dir) if image not in BLACKLIST])
styles = sorted(
[style for style in os.listdir(style_dir) if style not in BLACKLIST])
#print('Creating Background %s' % images[i])
content_layer_ids = [4]
style_layer_ids = list(range(13))
for i in tqdm(range(len(images))):
vgg16.maybe_download()
#print (images[i], labels[i], backgrounds[i])
style_current = random.choice(styles)
print("current style " + style_current)
content_image = Image.open('{0}/{1}'.format(IMAGE_DIRECTORY,
images[i]))
style_image = Image.open('{0}/{1}'.format(STYLE_DIRECTORY,
style_current))
style_image = resizeimage.resize_crop(style_image, [WIDTH, HEIGHT])
iterations = random.randint(15, 25)
print("ok")
#print (list(im_bk.getdata()))
content_image = np.float32(content_image)
style_image = np.float32(style_image)
#%%time
img = style_transfer(
content_image=content_image,
style_image=style_image,
content_layer_ids=content_layer_ids,
style_layer_ids=style_layer_ids,
weight_content=1.5,
weight_style=10.0,
weight_denoise=0.3,
num_iterations=iterations, #60
step_size=10.0)
print("image created")
save_image(img, 'results/' + images[i] + ".png")
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import tensorflow as tf
import numpy as np
import PIL.Image
import vgg16
from keras.preprocessing import image
vgg16.maybe_download()
#-----------------------------------------------------------------------------------------------------------------------
# Functions
def load_image(filename, max_size=None):
image = PIL.Image.open(filename)
if max_size is not None:
# Calculate the appropriate rescale-factor for
# ensuring a max height and width, while keeping
# the proportion between them.
factor = max_size / np.max(image.size)
# Scale the image's height and width.
size = np.array(image.size) * factor
# The size is now floating-point because it was scaled.
# But PIL requires the size to be integers.
size = size.astype(int)
# Resize the image.
image = image.resize(size, PIL.Image.LANCZOS)
style_image=style_image,
mixed_image=mixed_image)
print()
print("Final image:")
plot_image_big(mixed_image)
# Close the TensorFlow session to release its resources.
session.close()
# Return the mixed-image.
return mixed_image
if __name__ == "__main__":
vgg16.maybe_download() # 从网上下载训练完成的模型参数
# 若下载不成功 可以手动下载
# mkdir vgg16
# wget https://s3.amazonaws.com/cadl/models/vgg16.tfmodel
# 首先，我们载入内容图像，它有混合图像想要的大体轮廓。
content_filename = 'images/willy_wonka_old.jpg'
content_image = load_image(content_filename, max_size=None)
# 然后我们载入风格图像，它拥有混合图像想要的颜色和纹理。
style_filename = 'images/style7.jpg'
style_image = load_image(style_filename, max_size=300)
# 接着我们定义一个整数列表，它代表神经网络中我们用来匹配内容图像的层次。
# 这些是神经网络层次的索引。对于VGG16模型，第5层（索引4）似乎是唯一有效的内容层。
content_layer_ids = [4]
