🏙 Thebe Experiments with Live Python Coding 02

✒️   Cell #1

!pip install tensorflow tensorflow_hub -q
import tensorflow as tf,tensorflow_hub as hub
import pylab as pl,numpy as np
def get_resize_img(img_path,img_size=50):
    img_path=tf.keras.utils.get_file(
        'img'+str(np.random.randint(1,99999))+'.png',img_path)
    lr=tf.io.read_file(img_path)
    lr=tf.image.decode_jpeg(lr)
    print('mean: %f'%lr.numpy().mean())
    lr=tf.image.resize(lr,[img_size,img_size])
    lr=tf.expand_dims(lr.numpy()[:,:,:3],axis=0)
    return tf.cast(lr,tf.float32)

✒️   Cell #2

def esrgantf2_superresolution(lr):
    if len(lr.shape) < 4:
        lr=tf.expand_dims(lr.numpy()[:,:,:3],axis=0)
    lr=tf.cast(lr,tf.float32)
    img_size=lr.shape[1]
    model=hub.load("https://tfhub.dev/captain-pool/esrgan-tf2/1")
    concrete_func=model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
    @tf.function(input_signature=[tf.TensorSpec(
        shape=[1,img_size,img_size,3],dtype=tf.float32)])
    def f(input): return concrete_func(input)
    converter=tf.lite.TFLiteConverter.from_concrete_functions(
        [f.get_concrete_function()],model)
    converter.optimizations=[tf.lite.Optimize.DEFAULT]
    tflite_model = converter.convert()
    with tf.io.gfile.GFile('ESRGAN.tflite','wb') as f:
        f.write(tflite_model)
    esrgan_model_path='./ESRGAN.tflite'
    interpreter=tf.lite.Interpreter(model_path=esrgan_model_path)
    interpreter.allocate_tensors()
    input_details=interpreter.get_input_details()
    output_details=interpreter.get_output_details()
    interpreter.set_tensor(input_details[0]['index'],lr)
    interpreter.invoke()
    output_data=interpreter.get_tensor(output_details[0]['index'])
    sr=tf.squeeze(output_data,axis=0)
    sr=tf.round(tf.clip_by_value(sr,0,255)) 
    sr=tf.cast(sr,tf.uint8)
    lr=tf.cast(tf.squeeze(lr,axis=0),tf.uint8)
    return lr,sr

✒️   Cell #3

def low2bicubic_low2super(lr0,sr1,sr2):
    img_size=lr0.shape[1]
    lr0=tf.cast(tf.squeeze(lr0,axis=0),tf.uint8)
    pl.figure(figsize=(9,3))
    pl.subplot(1,3,1); pl.title('LR0')
    pl.imshow(lr0.numpy())
    bicubic4=tf.image.resize(
        lr0,[img_size*4,img_size*4],
        tf.image.ResizeMethod.BICUBIC)
    bicubic4=tf.cast(bicubic4,tf.uint8)
    pl.subplot(1,3,2); pl.title('Bicubic x4')
    pl.imshow(bicubic4.numpy())
    bicubic16=tf.image.resize(
        bicubic4,[img_size*16,img_size*16],
        tf.image.ResizeMethod.BICUBIC)
    bicubic16=tf.cast(bicubic16,tf.uint8)
    pl.subplot(1,3,3); pl.title('Bicubic x16')
    pl.imshow(bicubic16.numpy())
    pl.tight_layout(); pl.show()
    pl.figure(figsize=(9,3))
    pl.subplot(1,3,1); pl.title('LR0')
    pl.imshow(lr0.numpy())   
    pl.subplot(1,3,2); pl.title('ESRGAN x4')
    pl.imshow(sr1.numpy())
    pl.subplot(1,3,3); pl.title('ESRGAN x16')
    pl.imshow(sr2.numpy())
    pl.tight_layout(); pl.show()

✒️   Cell #4

file_path1=('https://raw.githubusercontent.com/OlgaBelitskaya/'
            'data/main/pictograms/')
file_name1='00_02_012.png'
lr0=get_resize_img(file_path1+file_name1,50)
print(lr0.shape)
lr1,sr1=esrgantf2_superresolution(lr0)
print(lr1.shape,sr1.shape)
lr2,sr2=esrgantf2_superresolution(sr1)
print(lr2.shape,sr2.shape)
low2bicubic_low2super(lr0,sr1,sr2)