70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
import unittest as ut
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class TestSubWin(ut.TestCase):
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def test_display_numpy(self):
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from displayarray import display
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import numpy as np
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s, vids = display(np.random.normal(0.5, 0.1, (500, 500, 3)))
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s.end()
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print("ended")
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def test_display_numpy_callback(self):
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from displayarray import display
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import numpy as np
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arr = np.random.normal(0.5, 0.1, (500, 500, 3))
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def fix_arr_cv(arr_in):
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arr_in[:] += np.random.normal(0.01, 0.005, (500, 500, 3))
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arr_in %= 1.0
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display(arr, callbacks=fix_arr_cv, blocking=True)
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def test_display_numpy_loop(self):
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from displayarray import display
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import numpy as np
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arr = np.random.normal(0.5, 0.1, (500, 500, 3))
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displayer, ids = display(arr, blocking=False)
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while True:
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arr[:] += np.random.normal(0.01, 0.005, (500, 500, 3))
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arr %= 1.0
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displayer.update(arr, ids[0])
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displayer.end()
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def test_display_tensorflow(self):
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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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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, ids = display(0, blocking=False)
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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(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
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autoencoder.compile(loss="mse", optimizer="adam")
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while True:
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grab = tf.convert_to_tensor(
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displayer.FRAME_DICT["0frame"][np.newaxis, ...].astype(np.float32)
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/ 255.0
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)
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autoencoder.fit(grab, grab, steps_per_epoch=1, epochs=1)
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output_image = autoencoder.predict(grab, steps=1)
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displayer.update(
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(output_image[0] * 255.0).astype(np.uint8), "uid for autoencoder output"
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)
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