Duplicate from keras-io/super-resolution

Co-authored-by: Julien Chaumond <[email protected]>

.gitattributes

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README.md

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+---
+title: Super Resolution
+emoji: 🖼
+colorFrom: red
+colorTo: green
+sdk: gradio
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: keras-io/super-resolution
+---
+
+# Configuration
+
+`Super-resolution using Efficient Sub pixel net`: _string_
+Display title for the Space
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
+
+`models`: _List[string]_
+HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`datasets`: _List[string]_
+HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
+Will be parsed automatically from your code if not specified here.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app.py

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+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()

requirements.txt

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+tensorflow>2.6