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import requests
from PIL import Image
from io import BytesIO
from numpy import asarray
import gradio as gr
import numpy as np
from math import ceil
from huggingface_hub import from_pretrained_keras
r = requests.get(
'https://api.nasa.gov/planetary/apod?api_key=0eyGPKWmJmE5Z0Ijx25oG56ydbTKWE2H75xuEefx')
result = r.json()
receive = requests.get(result['url'])
img = Image.open(BytesIO(receive.content)).convert('RGB')
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
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
gr.Label("Original image")
input_img = gr.Image(img)
with gr.Column():
gr.Label("Image with resolution doubled")
numpydata = asarray(img)
output = double_res(numpydata) # numpy.ndarray
input_img = gr.Image(output)
demo.launch()
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