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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 | |
| ''' | |
| url = "https://storage.googleapis.com/tfhub-modules/google/magenta/arbitrary-image-stylization-v1-256/2.tar.gz" | |
| response = requests.get(url,stream=True) | |
| path_input="./" | |
| urllib.request.urlretrieve(url, filename=path_input) | |
| file = tarfile.open(fileobj=response.raw, mode="r|gz") | |
| file.extractall(path="./nst_model") | |
| ''' | |
| 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) | |
| save_image(stylized_image,'stylized') | |
| 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) | |