import tensorflow as tf import math import numpy as np from PIL import Image from tensorflow.keras.preprocessing.image import img_to_array from huggingface_hub import from_pretrained_keras import gradio as gr model = from_pretrained_keras("keras-io/super-resolution") def infer(image): img = Image.fromarray(image) img = img.resize((100,100)) ycbcr = img.convert("YCbCr") y, cb, cr = ycbcr.split() y = img_to_array(y) y = y.astype("float32") / 255.0 input = np.expand_dims(y, axis=0) out = model.predict(input) out_img_y = out[0] out_img_y *= 255.0 # Restore the image in RGB color space. out_img_y = out_img_y.clip(0, 255) out_img_y = out_img_y.reshape((np.shape(out_img_y)[0], np.shape(out_img_y)[1])) out_img_y = Image.fromarray(np.uint8(out_img_y), mode="L") out_img_cb = cb.resize(out_img_y.size, Image.BICUBIC) out_img_cr = cr.resize(out_img_y.size, Image.BICUBIC) out_img = Image.merge("YCbCr", (out_img_y, out_img_cb, out_img_cr)).convert( "RGB" ) return (img,out_img) article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1609.05158' target='_blank'>Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network</a></p><center> <a href='https://keras.io/examples/vision/super_resolution_sub_pixel/' target='_blank'>Image Super-Resolution using an Efficient Sub-Pixel CNN</a></p> <center>Contributors: <a href='https://twitter.com/Cr0wley_zz'>Devjyoti Chakraborty</a>|<a href='https://twitter.com/ritwik_raha'>Ritwik Raha</a>|<a href='https://twitter.com/ariG23498'>Aritra Roy Gosthipaty</a></center>" iface = gr.Interface( fn=infer, title = " Image Super-resolution", description = "This space is a demo of the keras tutorial 'Image Super-Resolution using an Efficient Sub-Pixel CNN' based on the paper 'Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network' 👀", article = article, inputs=gr.inputs.Image(label="Input Image"), outputs=[gr.outputs.Image(label="Resized 100x100 image"), gr.outputs.Image(label="Super-resolution 300x300 image") ], examples=[["camel.jpg"], ["pokemon.jpg"]], ).launch()