def __init__(self, content_path, style_path):
"""
Default constructor
"""
self.content_image = ImageUtils.grab_image(content_path)
self.style_image = ImageUtils.grab_image(style_path)
"""
Load pre-trained VGG19 network architecture
"""
self.vgg = tf.keras.applications.VGG19(include_top=False,
weights='imagenet')
# Content layer where we will pull our feature maps
self.content_layers = ['block5_conv2']
self.style_layers = [f'block{i}_conv1' for i in range(1, 6)]
# Define a tf.Variable to contain the image to optimize.
self.updateImage()
# Anime Style Images Dataset (Absolute and relative paths)
# https://github.com/Mckinsey666/Anime-Face-Dataset
# if platform == "darwin":
# # Specifics for Vadim
# path_to_pics = "../../cropped/"
# else:
# Specifics for Victor
# path_to_pics = "../Anime-Face-Dataset/cropped/"
path_to_pics = "./anime-images/"
# Grab content image from link
# content_image = ImageUtils.grab_image('https://gradschool.cornell.edu/wp-content/uploads/2018/07/JonPark.jpg')
# content_image = ImageUtils.grab_image('http://www.mathcs.richmond.edu/~jdenny/Jory.jpg')
content_image = ImageUtils.grab_image(
'https://facultystaff.richmond.edu/~dszajda/images/doug_small_website_photo_UR_Fall_2011.jpg'
)
# Uses model from Tensor Flow Hub
print(" - Loading pre-trained model from hub")
hub_model = hub.load(
'https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/1')
print("\t> Finished")
# Make list of style images and shuffle the list
dirs = listdir(path_to_pics)
shuffle(dirs)
# Opens MatPlotLib figure
fig = plt.figure(figsize=(12, 6))
fig = plt.gcf()
# Use this to filter out original anime face dataset for detectable
# faces with OpenCV method above
# cv2.imwrite("./anime-images/out" + str(index) + ".png", image)
# For a whole directory
fails = 0
content_imgs = [
'https://gradschool.cornell.edu/wp-content/uploads/2018/07/JonPark.jpg',
'http://www.mathcs.richmond.edu/~jdenny/Jory.jpg',
'https://facultystaff.richmond.edu/~dszajda/images/doug_small_website_photo_UR_Fall_2011.jpg',
'https://pbs.twimg.com/profile_images/1008157063376199682/HQQ5PrT5_400x400.jpg'
]
content_imgs = [ImageUtils.grab_image(x) for x in content_imgs]
style_dirs = dirs[:5]
for i, filename in enumerate(style_dirs):
x, y, w, h = detectAnime(path_to_pics + filename, i)
if x is not None:
print(x, y, w, h)
style_image = ImageUtils.grab_image("out.png")
plt.subplot(len(content_imgs) + 1, len(content_imgs) + 1, i + 1)
ImageUtils.imshow(style_image)
for j, content_image in enumerate(content_imgs):
for i, filename in enumerate(style_dirs):
