42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
from displayarray import display
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import numpy as np
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from tensorflow.keras import layers, models
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import tensorflow as tf
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from examples.videos import test_video_2
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for gpu in tf.config.experimental.list_physical_devices("GPU"):
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tf.compat.v2.config.experimental.set_memory_growth(gpu, True)
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displayer = display(test_video_2)
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displayer.wait_for_init()
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autoencoder = models.Sequential()
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autoencoder.add(
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layers.Conv2D(
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20, (3, 3), activation="sigmoid", input_shape=displayer.frames[0].shape
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)
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)
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autoencoder.add(
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layers.Conv2D(
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20, (3, 3), activation="sigmoid", input_shape=displayer.frames[0].shape
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)
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)
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autoencoder.add(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
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autoencoder.add(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
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autoencoder.compile(loss="mse", optimizer="adam")
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while displayer:
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displayer.update()
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grab = tf.convert_to_tensor(
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next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(np.float32) / 255.0
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)
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grab_noise = tf.convert_to_tensor(
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((next(iter(displayer.FRAME_DICT.values()))[np.newaxis, ...].astype(
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np.float32) + np.random.uniform(0, 255, grab.shape)) / 2)
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/ 255.0
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)
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displayer.update((grab_noise.numpy()[0] * 255.0).astype(np.uint8), "uid for grab noise")
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autoencoder.fit(grab_noise, grab, steps_per_epoch=1, epochs=1)
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output_image = autoencoder.predict(grab, steps=1)
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displayer.update((output_image[0] * 255.0).astype(np.uint8), "uid for autoencoder output")
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