GIanlucaRub commited on
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c83ef86
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1 Parent(s): ee6a3f0

Create app.py

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  1. app.py +73 -0
app.py ADDED
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+ ìimport gradio as gr
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+ import numpy as np
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+ from math import ceil
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+ import tensorflow.keras as tfk
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+
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+ model = tfk.models.load_model("autoencoder_model_d_0")
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+ def double_res(input_image):
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+ input_height = input_image.shape[0]
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+ input_width = input_image.shape[1]
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+ height = ceil(input_height/128)
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+ width = ceil(input_width/128)
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+ expanded_input_image = np.zeros((128*height, 128*width,3), dtype=np.uint8)
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+ np.copyto(expanded_input_image[0:input_height, 0:input_width], input_image)
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+
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+ output_image = np.zeros((128*height*2, 128*width*2,3), dtype=np.float32)
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+
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+ for i in range(height):
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+ for j in range(width):
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+ temp_slice = expanded_input_image[i*128:(i+1)*128, j*128:(j+1)*128]/255
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+ upsampled_slice = model.predict(temp_slice[np.newaxis, ...])
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+ np.copyto(output_image[i*256:(i+1)*256, j*256:(j+1)*256], upsampled_slice[0])
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+ if i!= 0 and j!= 0 and i != height-1 and j!=width-1:
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+ # removing inner borders
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+ right_slice = expanded_input_image[i*128:(i+1)*128, (j+1)*128-64:(j+1)*128+64]/255
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+ right_upsampled_slice = model.predict(right_slice[np.newaxis, ...])
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+ resized_right_slice = right_upsampled_slice[0][64:192,64:192]
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+ np.copyto(output_image[i*256+64:(i+1)*256-64, (j+1)*256-64:(j+1)*256+64], resized_right_slice)
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+
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+ left_slice = expanded_input_image[i*128:(i+1)*128, j*128-64:(j)*128+64]/255
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+ left_upsampled_slice = model.predict(left_slice[np.newaxis, ...])
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+ resized_left_slice = left_upsampled_slice[0][64:192,64:192]
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+ np.copyto(output_image[i*256+64:(i+1)*256-64, j*256-64:j*256+64], resized_left_slice)
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+
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+ upper_slice = expanded_input_image[(i+1)*128-64:(i+1)*128+64, j*128:(j+1)*128]/255
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+ upper_upsampled_slice = model.predict(upper_slice[np.newaxis, ...])
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+ resized_upper_slice = upper_upsampled_slice[0][64:192,64:192]
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+ np.copyto(output_image[(i+1)*256-64:(i+1)*256+64, j*256+64:(j+1)*256-64], resized_upper_slice)
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+
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+ lower_slice = expanded_input_image[i*128-64:i*128+64, j*128:(j+1)*128]/255
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+ lower_upsampled_slice = model.predict(lower_slice[np.newaxis, ...])
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+ resized_lower_slice = lower_upsampled_slice[0][64:192,64:192]
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+ np.copyto(output_image[i*256-64:i*256+64, j*256+64:(j+1)*256-64], resized_lower_slice)
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+
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+
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+ # removing angles
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+ lower_right_slice = expanded_input_image[i*128-64:i*128+64, (j+1)*128-64:(j+1)*128+64]/255
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+ lower_right_upsampled_slice = model.predict(lower_right_slice[np.newaxis, ...])
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+ resized_lower_right_slice = lower_right_upsampled_slice[0][64:192,64:192]
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+ np.copyto(output_image[i*256-64:i*256+64, (j+1)*256-64:(j+1)*256+64], resized_lower_right_slice)
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+
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+ lower_left_slice = expanded_input_image[i*128-64:i*128+64, j*128-64:j*128+64]/255
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+ lower_left_upsampled_slice = model.predict(lower_left_slice[np.newaxis, ...])
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+ resized_lower_left_slice = lower_left_upsampled_slice[0][64:192,64:192]
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+ np.copyto(output_image[i*256-64:i*256+64, j*256-64:j*256+64], resized_lower_left_slice)
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+
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+
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+
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+
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+ resized_output_image = output_image[0:input_height*2,0:input_width*2]
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+ return resized_output_image
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+
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+ demo = gr.Interface(
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+ fn=double_res,
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+ title="Double picture resolution",
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+ description="Upload a picture and get the horizontal and vertical resolution doubled (4x pixels)",
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+ allow_flagging="never",
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+ inputs=[
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+ gr.inputs.Image(type="numpy")
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+ ],
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+ outputs=gr.Image(type="numpy"))
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+
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+ demo.launch()
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+