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| import gradio as gr | |
| import numpy as np | |
| from math import ceil | |
| from huggingface_hub import from_pretrained_keras | |
| import requests | |
| x = requests.get( | |
| 'https://api.nasa.gov/planetary/apod?api_key=0eyGPKWmJmE5Z0Ijx25oG56ydbTKWE2H75xuEefx') | |
| print(x.url) | |
| 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() | |