📑 TFHub Models & Style Transfer

🌐   style_transfer_selfies.html

Style Transfer for Paintings, Patterns, Symbols, & Selfies with TFHub Models.

✒️   Modules & Settings

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!python3 -m pip install tensorflow_hub \

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--user --quiet --no-warn-script-location --no-warn-conflicts

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#!python3 -m pip install tensorflow==2.6.0 \

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#--user --quiet --no-warn-script-location

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path='/home/sc_work/.sage/local/lib/python3.9/site-packages'

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import os,sys,warnings,urllib

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sys.path.append(path); warnings.filterwarnings('ignore')

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import tensorflow_hub as hub,tensorflow as tf

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from IPython.display import display,HTML

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from IPython.core.magic import register_line_magic

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os.environ['TFHUB_MODEL_LOAD_FORMAT']='COMPRESSED'

Language: Sage Gap GP HTML Macaulay2 Maxima Octave Python R Singular

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✒️   Data Loading

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file_path='https://raw.githubusercontent.com/'+\

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          'OlgaBelitskaya/data/main/'

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folder_path=['humans/','paintings/','decors/','symbols/']

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original,style,original_img,style_img='','',[],[]

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original_dict={'selfie 1':folder_path[0]+'00_01_001.png',

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               'selfie 2':folder_path[0]+'00_01_002.png'}

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style_sources=['Ivan Aivazovsky','Traditional Pattern','Symbol']

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style_paintings=['The Bay of Naples','Gzel','Letter']

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style_dict={style_sources[0]+' || '+\

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            style_paintings[0]:folder_path[1]+'01_00_001.png',

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            style_sources[1]+' || '+\

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            style_paintings[1]:folder_path[2]+'00_00_011.png',

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            style_sources[2]+' || '+\

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            style_paintings[2]:folder_path[3]+'00_01_00_00_001.png'}

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def load_img(img_path,rotate=True,max_dim=int(512)):

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    img=tf.io.read_file(img_path)

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    img=tf.image.decode_image(img,channels=3)

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    img=tf.image.convert_image_dtype(img,tf.float32)

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    shape=tf.cast(tf.shape(img)[:-int(1)],tf.float32)

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    new_shape=tf.cast(shape*max_dim/max(shape),tf.int32)

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    img=tf.image.resize(img,new_shape)

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    if rotate: img=tf.image.rot90(img)

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    return img[tf.newaxis,:]

Language: Sage Gap GP HTML Macaulay2 Maxima Octave Python R Singular

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@interact(layout=dict(

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    top=[['original_image','style_source','style_painting'],

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         ['max_image_size','rotate_original_image',

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         'rotate_style_image']]))

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def _get_files(original_image=selector(

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                   list(original_dict.keys()),default='selfie 1'),

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               style_source=selector(

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                   style_sources,default='Ivan Aivazovsky'),

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               style_painting=selector(

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                   style_paintings,default='The Bay of Naples'),

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               max_image_size=selector(

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                   [256,320,..,1024],default=512),

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               rotate_original_image=selector(

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                   [True,False],default=True),

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               rotate_style_image=selector(

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                   [True,False],default=False)):

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    global original,style,original_img,style_img

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    global file_path,folder_path,original_dict

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    global style_dict,style_sources,style_paintings

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    original=original_dict[original_image]

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    style_exp=style_source+' || '+style_painting

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    if style_exp not in list(style_dict.keys()): 

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        str1='please, choose another style_source & style_painting'

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        pretty_print(str1)

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    else:

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        style=style_dict[style_exp]

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        for file_name in [original,style]:

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            input_file=urllib.request.urlopen(file_path+file_name)

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            output_file=open(

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                file_name[file_name.find('/')+int(1):],'wb'); 

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            output_file.write(input_file.read())

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            output_file.close(); input_file.close()

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        original_img=load_img(

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            original[original.find('/')+int(1):],

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            rotate=rotate_original_image,max_dim=int(max_image_size))

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        style_img=load_img(

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            style[style.find('/')+int(1):],

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            rotate=rotate_style_image,max_dim=int(max_image_size))

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        sh1='shape of the original image: %s'%str(original_img.shape)

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        matrix_plot(original_img[int(0)],title=sh1,figsize=5).show()

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        sh2='shape of the style image: %s'%str(style_img.shape)

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        matrix_plot(style_img[int(0)],title=sh2,figsize=5).show()

Language: Sage Gap GP HTML Macaulay2 Maxima Octave Python R Singular

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✒️   TFHub Usage

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model_path='https://tfhub.dev/google/'

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model_dict={'magenta':model_path+\

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            'magenta/arbitrary-image-stylization-v1-256/2'}

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@interact(layout=dict(

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              top=[['tfhub_model'],

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                   ['max_image_size','rotate_original_image',

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                   'rotate_style_image']]))

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def _style_transfer(tfhub_model=selector(

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                        list(model_dict.keys()),default='magenta'),

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                    max_image_size=selector(

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                        [256,320,..,1024],default=512),

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                    rotate_original_image=selector(

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                        [True,False],default=True),

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                    rotate_style_image=selector(

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                        [True,False],default=True)):

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    global original,style,original_img

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    global style_img,stylized_img,model_dict

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    original_img=load_img(

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        original[original.find('/')+int(1):],

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        rotate=rotate_original_image,max_dim=int(max_image_size))

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    style_img=load_img(

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        style[style.find('/')+int(1):],

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        rotate=rotate_style_image,max_dim=int(max_image_size))

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    hub_model=hub.load(model_dict[tfhub_model])

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    stylized_img=hub_model(

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        tf.constant(original_img),tf.constant(style_img))[int(0)]

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    matrix_plot(stylized_img[int(0)],figsize=7).show()

Language: Sage Gap GP HTML Macaulay2 Maxima Octave Python R Singular

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Messages

✒️   Animation

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steps,imgs=0,[]

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@interact(layout=dict(

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              top=[['max_image_size','iter_steps']]))

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def _interpolate_resize(max_image_size=selector(

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                            [256,320,..,512],default=256),

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                        iter_steps=selector(

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                            [10,20,..,60],default=30)):

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    global imgs,original_img,stylized_img

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    max_dim=int(max_image_size); steps=int(iter_steps)

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    for img in [original_img,stylized_img]:

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        shape=tf.cast(tf.shape(img)[int(1):int(-1)],tf.float32)

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        new_shape=tf.cast(shape*max_dim/max(shape),tf.int32)

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        img=tf.image.resize(img,new_shape)

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    original_img_norm=tf.norm(original_img) 

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    stylized_img_norm=tf.norm(stylized_img)

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    vectors=[]

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    stylized_img_normalized=stylized_img*\

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    (original_img_norm/stylized_img_norm)

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    for step in range(steps):

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        interpolated=original_img+\

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        (stylized_img_normalized-original_img)*step/(steps-int(1))

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        interpolated_norm=tf.norm(interpolated)

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        interpolated_normalized=interpolated*\

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        (original_img_norm/interpolated_norm)

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        vectors.append(interpolated_normalized)

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    imgs=tf.squeeze(tf.stack(vectors)).numpy()

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    animate([matrix_plot(imgs[i],figsize=5,frame=False) 

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             for i in range(steps)]).show()

Language: Sage Gap GP HTML Macaulay2 Maxima Octave Python R Singular

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