Duplicate from dbuscombe/SatelliteSuperResolution

Co-authored-by: Daniel Buscombe <[email protected]>

.gitattributes

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README.md

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+---
+title: SatelliteSuperResolution
+emoji: 🏢
+colorFrom: yellow
+colorTo: gray
+sdk: gradio
+sdk_version: 2.9.4
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: dbuscombe/SatelliteSuperResolution
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

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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()

requirements.txt

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+scikit-image
+numpy
+tensorflow
+pandas