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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/doubleResFinal")
# 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)
to_predict = []
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
to_predict.append(temp_slice)
# removing inner borders
for i in range(height):
for j in range(width):
if i != 0 and j != 0 and i != height-1 and j != width-1:
right_slice = expanded_input_image[i *
128:(i+1)*128, (j+1)*128-64:(j+1)*128+64]/255
to_predict.append(right_slice)
left_slice = expanded_input_image[i *
128:(i+1)*128, j*128-64:(j)*128+64]/255
to_predict.append(left_slice)
upper_slice = expanded_input_image[(
i+1)*128-64:(i+1)*128+64, j*128:(j+1)*128]/255
to_predict.append(upper_slice)
lower_slice = expanded_input_image[i *
128-64:i*128+64, j*128:(j+1)*128]/255
to_predict.append(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
to_predict.append(lower_right_slice)
lower_left_slice = expanded_input_image[i *
128-64:i*128+64, j*128-64:j*128+64]/255
to_predict.append(lower_left_slice)
# predicting all images at once
completed = False
n = 16
# n = 1
while not completed:
try:
print("attempting with "+ str(n))
predicted = model.predict(np.array(to_predict),batch_size = n)
completed = True
print("completed with "+ str(n))
except:
print("attempt with " + str(n) + " failed")
n += -1
if n <= 0:
n = 1
counter = 0
for i in range(height):
for j in range(width):
np.copyto(output_image[i*256:(i+1)*256, j *
256:(j+1)*256], predicted[counter])
counter+=1
for i in range(height):
for j in range(width):
if i != 0 and j != 0 and i != height-1 and j != width-1:
right_upsampled_slice = predicted[counter]
counter+=1
resized_right_slice = right_upsampled_slice[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_upsampled_slice = predicted[counter]
counter+=1
resized_left_slice = left_upsampled_slice[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_upsampled_slice = predicted[counter]
counter+=1
resized_upper_slice = upper_upsampled_slice[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_upsampled_slice = predicted[counter]
counter+=1
resized_lower_slice = lower_upsampled_slice[64:192, 64:192]
np.copyto(output_image[i*256-64:i*256+64,
j*256+64:(j+1)*256-64], resized_lower_slice)
lower_right_upsampled_slice = predicted[counter]
counter+=1
resized_lower_right_slice = lower_right_upsampled_slice[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_upsampled_slice = predicted[counter]
counter+=1
resized_lower_left_slice = lower_left_upsampled_slice[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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