Spaces:
Runtime error
Runtime error
File size: 1,326 Bytes
6f5fe00 792d179 6f5fe00 3567bbb 6f5fe00 792d179 6f5fe00 7e60fc1 abf1433 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
import gradio as gr
import numpy as np
import tensorflow as tf
from PIL import Image
from tensorflow import keras
from tensorflow.keras.applications.resnet50 import preprocess_input
autoencoder = keras.models.load_model("./models/denoising_autoencoder_weights.h5")
encoder = keras.models.load_model("./models/encoder.h5")
decoder = keras.models.load_model("./models/decoder.h5")
# Define the Gradio interface
def denoise_image(input_image):
# Open the image
input_array = np.array(input_image)
input_array = preprocess_input(input_array)
input_array = np.expand_dims(input_array, axis=0)
hash = encoder.predict(input_array)
output = decoder.predict(hash)
hash_image = Image.fromarray((hash[0].reshape(32,32) * 255).astype(np.uint8))
output_image = Image.fromarray((output[0] * 255).astype(np.uint8))
return [input_image, hash_image, output_image]
iface = gr.Interface(
fn=denoise_image,
inputs= [
gr.Image (label = "Original Image", shape=(32,32))
],
outputs=[
gr.Image (label = "Decoded Output"),
gr.Image (label= "Hash Output"),
],
title="Denoising Autoencoder",
description="Upload an image and see its denoised version using a denoising autoencoder.",
examples=[
["./example.jpg"]
],
)
iface.launch()
|