Spaces:
Build error
Build error
File size: 2,678 Bytes
f3695f1 65798e0 f3695f1 24f9235 f3695f1 ae96dc5 f3695f1 d4e4b48 8d7d705 f3695f1 65798e0 93326ce 65798e0 f3695f1 93326ce f3695f1 0fa9601 f3695f1 0fa9601 f3695f1 5b908f1 f3695f1 5b908f1 f3695f1 c7cf0ea 0fa9601 46269f7 f3695f1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
'''NEURAL STYLE TRANSFER'''
import gradio as gr
import tensorflow as tf
import tensorflow_hub as hub
import PIL
from PIL import Image
import numpy as np
# import time
# import requests
#import cv2
# !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)
"""## 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
##Transformation
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(label="Content Image") #CONTENT IMAGE
image2 = gr.inputs.Image(label="Style Image") #STYLE IMAGE
stylizedimg=gr.outputs.Image(label="Result")
gr.Interface(fn=mymodel, inputs= [image1,image2] , outputs= stylizedimg,title='Style Transfer',theme='seafoam',examples=[['Content_Images/contnt12.jpg','VG516.jpg']],article="References-\n\nExploring the structure of a real-time, arbitrary neural artistic stylization network. Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin.").launch()
"""The function will be launched both Inline and Outline where u need to add a content and style image."""
gradio_intrface(transform_mymodel)
|