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import gradio as gr
import numpy as np
from math import ceil
from huggingface_hub import from_pretrained_keras
model = from_pretrained_keras("GIanlucaRub/autoencoder_model_d_0")
def double_res(input_image):
input_height = input_image.shape[0]
input_width = input_image.shape[1]
height = ceil(input_height/128)
width = ceil(input_width/128)
expanded_input_image = np.zeros((128*height, 128*width,3), dtype=np.uint8)
np.copyto(expanded_input_image[0:input_height, 0:input_width], input_image)
output_image = np.zeros((128*height*2, 128*width*2,3), dtype=np.float32)
for i in range(height):
for j in range(width):
temp_slice = expanded_input_image[i*128:(i+1)*128, j*128:(j+1)*128]/255
upsampled_slice = model.predict(temp_slice[np.newaxis, ...])
np.copyto(output_image[i*256:(i+1)*256, j*256:(j+1)*256], upsampled_slice[0])
if i!= 0 and j!= 0 and i != height-1 and j!=width-1:
# removing inner borders
right_slice = expanded_input_image[i*128:(i+1)*128, (j+1)*128-64:(j+1)*128+64]/255
right_upsampled_slice = model.predict(right_slice[np.newaxis, ...])
resized_right_slice = right_upsampled_slice[0][64:192,64:192]
np.copyto(output_image[i*256+64:(i+1)*256-64, (j+1)*256-64:(j+1)*256+64], resized_right_slice)
left_slice = expanded_input_image[i*128:(i+1)*128, j*128-64:(j)*128+64]/255
left_upsampled_slice = model.predict(left_slice[np.newaxis, ...])
resized_left_slice = left_upsampled_slice[0][64:192,64:192]
np.copyto(output_image[i*256+64:(i+1)*256-64, j*256-64:j*256+64], resized_left_slice)
upper_slice = expanded_input_image[(i+1)*128-64:(i+1)*128+64, j*128:(j+1)*128]/255
upper_upsampled_slice = model.predict(upper_slice[np.newaxis, ...])
resized_upper_slice = upper_upsampled_slice[0][64:192,64:192]
np.copyto(output_image[(i+1)*256-64:(i+1)*256+64, j*256+64:(j+1)*256-64], resized_upper_slice)
lower_slice = expanded_input_image[i*128-64:i*128+64, j*128:(j+1)*128]/255
lower_upsampled_slice = model.predict(lower_slice[np.newaxis, ...])
resized_lower_slice = lower_upsampled_slice[0][64:192,64:192]
np.copyto(output_image[i*256-64:i*256+64, j*256+64:(j+1)*256-64], resized_lower_slice)
# removing angles
lower_right_slice = expanded_input_image[i*128-64:i*128+64, (j+1)*128-64:(j+1)*128+64]/255
lower_right_upsampled_slice = model.predict(lower_right_slice[np.newaxis, ...])
resized_lower_right_slice = lower_right_upsampled_slice[0][64:192,64:192]
np.copyto(output_image[i*256-64:i*256+64, (j+1)*256-64:(j+1)*256+64], resized_lower_right_slice)
lower_left_slice = expanded_input_image[i*128-64:i*128+64, j*128-64:j*128+64]/255
lower_left_upsampled_slice = model.predict(lower_left_slice[np.newaxis, ...])
resized_lower_left_slice = lower_left_upsampled_slice[0][64:192,64:192]
np.copyto(output_image[i*256-64:i*256+64, j*256-64:j*256+64], resized_lower_left_slice)
resized_output_image = output_image[0:input_height*2,0:input_width*2]
return resized_output_image
demo = gr.Interface(
fn=double_res,
title="Double picture resolution",
description="Upload a picture and get the horizontal and vertical resolution doubled (4x pixels)",
allow_flagging="never",
inputs=[
gr.inputs.Image(type="numpy")
],
outputs=gr.Image(type="numpy"))
demo.launch()
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