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| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| from glob import glob | |
| import pandas as pd | |
| 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") | |
| model.summary() | |
| def infer(image): | |
| nx=image.shape[0] | |
| ny=image.shape[1] | |
| img = Image.fromarray(image) | |
| # img = img.resize((100,100)) | |
| # img = img.crop((0,100,0,100)) | |
| 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) | |
| nxo = out.squeeze().shape[0] | |
| nyo = out.squeeze().shape[1] | |
| 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" | |
| ) | |
| out_img.save('output.png') | |
| out = {} | |
| out.update( {'input image size x': nx } ) | |
| out.update( {'output image size x': nxo } ) | |
| out.update( {'input image size y': ny } ) | |
| out.update( {'output image size y': nyo } ) | |
| return (pd.DataFrame(data=out.values(), index=out.keys()).transpose(), img,out_img, 'output.png') | |
| 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>" | |
| examples= [[l] for l in glob('examples/tiles/*.jpg')] | |
| out1 = gr.outputs.Dataframe(label='Summary', headers=["Input X (px)", "Output X (px)", "Input Y (px)", "Output Y (px)"], type='pandas') | |
| out2 = gr.outputs.Image(label="Cropped input image", type='pil') | |
| out3 = gr.outputs.Image(label="Super-resolution x3 image", type='pil') | |
| out4 = gr.outputs.File(label='Click to download super-resolved image') | |
| iface = gr.Interface( | |
| fn=infer, | |
| title = " Satellite Super-resolution", | |
| description = "This space is a demo of Satellite image Super-Resolution using a Sub-Pixel Convolutional Neural Network", | |
| article = article, | |
| inputs=gr.inputs.Image(label="Input Image"), | |
| outputs=[out1,out2,out3,out4], | |
| examples=examples, | |
| ).launch() |