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Upload 6 files
Browse files- app.py +42 -0
- example.jpg +0 -0
- models/decoder.h5 +3 -0
- models/denoising_autoencoder_weights.h5 +3 -0
- models/encoder.h5 +3 -0
- requirements.txt.txt +2 -0
app.py
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import gradio as gr
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import numpy as np
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import tensorflow as tf
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from PIL import Image
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from tensorflow import keras
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from tensorflow.keras.applications.resnet50 import preprocess_input
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autoencoder = keras.models.load_model("D:/midterm/autoencoder/models/denoising_autoencoder_weights.h5")
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encoder = keras.models.load_model("D:/midterm/autoencoder/models/encoder.h5")
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decoder = keras.models.load_model("D:/midterm/autoencoder/models/decoder.h5")
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# Define the Gradio interface
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def denoise_image(input_image):
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# Open the image
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input_image= np.resize(input_image,(32,32,3))
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input_array = np.array(input_image)
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input_array = preprocess_input(input_array)
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input_array = np.expand_dims(input_array, axis=0)
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hash = encoder.predict(input_array)
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output = decoder.predict(hash)
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hash_image = Image.fromarray((hash[0].reshape(32,32) * 255).astype(np.uint8))
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output_image = Image.fromarray((output[0] * 255).astype(np.uint8))
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return [input_image, hash_image, output_image]
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iface = gr.Interface(
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fn=denoise_image,
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inputs= [
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gr.Image (label = "Original Image")
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],
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outputs=[
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gr.Image (label = "Decoded Output"),
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gr.Image (label= "Hash Output"),
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],
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title="Denoising Autoencoder",
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description="Upload an image and see its denoised version using a denoising autoencoder.",
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examples=[
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["D:/midterm/autoencoder/example.jpg"]
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],
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)
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iface.launch(share = True, server_port=3001)
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example.jpg
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models/decoder.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:31b9f9c61821bfb9d11ea704ea678fc54ac832707eb904e3f1bd9c47fe310959
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size 53152
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models/denoising_autoencoder_weights.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:c862437b0e1ca05565a9fc022aca396ed080d56b3e339a6ee5637a7167a7db8c
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size 212960
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models/encoder.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:6ef0f37772624e096970712fe43224ea648f44622a814f44d24c6235dccf20fd
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size 41224
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requirements.txt.txt
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tensorflow
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Pillow
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