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'''NEURAL STYLE TRANSFER'''
import gradio as gr
import tensorflow as tf
# import os
import tensorflow_hub as hub
import PIL
from PIL import Image,ImageOps
import numpy as np
# import time
# import requests
import cv2
from cv2 import *
# !mkdir nstmodel
# !wget -c https://storage.googleapis.com/tfhub-modules/google/magenta/arbitrary-image-stylization-v1-256/2.tar.gz -O - | tar -xz -C /nstmodel
# import tensorflow.keras
# from PIL import Image, ImageOps
#import requests
#import tarfile
MODEL_PATH='Nst_model'
# Disable scientific notation for clarity
np.set_printoptions(suppress=True)
# Load model from TF-Hub
#model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
# Load the model
model = tf.keras.models.load_model(MODEL_PATH)
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)
"""##Saving unscaled Tensor images."""
def save_image(image, filename):
"""
Saves unscaled Tensor Images.
Args:
image: 3D image tensor. [height, width, channels]
filename: Name of the file to save to.
"""
if not isinstance(image, Image.Image):
image = tf.clip_by_value(image, 0, 255)
image = Image.fromarray(tf.cast(image, tf.uint8).numpy())
image.save("%s.jpg" % filename)
print("Saved as %s.jpg" % filename)
"""## Grayscaling image for testing purpose to check if we could get better results."""
def gray_scaled(inp_img):
gray = cv2.cvtColor(inp_img, cv2.COLOR_BGR2GRAY)
gray_img = np.zeros_like(inp_img)
gray_img[:,:,0] = gray
gray_img[:,:,1] = gray
gray_img[:,:,2] = gray
return gray_img
def transform_mymodel(content_image,style_image):
# Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]
content_image=gray_scaled(content_image)
content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.0
style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.0
#Resizing image
style_image = tf.image.resize(style_image, (256, 256))
# Stylize image
outputs = model(tf.constant(content_image), tf.constant(style_image))
stylized_image = outputs[0]
# stylized = tf.image.resize(stylized_image, (356, 356))
stylized_image =tensor_to_image(stylized_image)
return stylized_image
def gradio_intrface(mymodel):
# Initializing the input component
image1 = gr.inputs.Image() #CONTENT IMAGE
image2 = gr.inputs.Image() #STYLE IMAGE
stylizedimg=gr.outputs.Image()
gr.Interface(fn=mymodel, inputs= [image1,image2] , outputs= stylizedimg,title='Style Transfer').launch()
"""The function will be launched both Inline and Outline where u need to add a content and style image."""
gradio_intrface(transform_mymodel)
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