app.py

import os
os.system("pip install tensorflow==2.3.0")
os.system("pip install tensorflow_hub")
os.system("pip install numpy==1.20.3")
import tensorflow as tf
# Load compressed models from tensorflow_hub
os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'

import numpy as np
import PIL.Image
import time
import functools



def tensor_to_image(tensor):
  tensor = tensor*255
  tensor = np.array(tensor, dtype=np.uint8)
  if np.ndim(tensor)>3:
    assert tensor.shape[0] == 1
    tensor = tensor[0]
  return PIL.Image.fromarray(tensor)

import tensorflow_hub as hub

content_path = tf.keras.utils.get_file('YellowLabradorLooking_new.jpg',
                'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg')
style_path = tf.keras.utils.get_file('kandinsky5.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')

def load_img(path_to_img):
  max_dim = 512
  img = tf.io.read_file(path_to_img)
  img = tf.image.decode_image(img, channels=3)
  img = tf.image.convert_image_dtype(img, tf.float32)

  shape = tf.cast(tf.shape(img)[:-1], tf.float32)
  long_dim = max(shape)
  scale = max_dim / long_dim

  new_shape = tf.cast(shape * scale, tf.int32)

  img = tf.image.resize(img, new_shape)
  img = img[tf.newaxis, :]
  return img

content_image = load_img(content_path)
style_image = load_img(style_path)


hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
print("结果:", stylized_image)


import gradio as gr


def inference(content_image, style_image):
    stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
    img = tensor_to_image(stylized_image)
    return img 
      
  
title = "TTT"

gr.Interface(
    inference, 
    gr.inputs.Image(type="pil", label="content_image"), 
    gr.inputs.Image(type="pil", label="style_image"), 
    gr.outputs.Image(type="pil", label="Output"),
    title=title,
    description="",
    enable_queue=True,
    allow_flagging=False
    ).launch()