simleek
98 lines
3.5 KiB
Python
98 lines
3.5 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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display(np.random.normal(0.5, 0.1, (500, 500, 3)))
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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, (100, 100, 3))
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with display(arr) as displayer:
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while displayer:
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arr[:] += np.random.normal(0.001, 0.0005, (100, 100, 3))
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arr %= 1.0
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def test_display_camera(self):
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from displayarray import display
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import numpy as np
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def black_and_white(arr):
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return (np.sum(arr, axis=-1) / 3).astype(np.uint8)
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display(0, callbacks=black_and_white, blocking=True)
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def test_display_video(self):
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from displayarray import display
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import math as m
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def forest_color(arr):
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forest_color.i += 1
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arr[..., 0] = (m.sin(forest_color.i * (2 * m.pi) * .4 / 360) * 255 + arr[..., 0]) % 255
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arr[..., 1] = (m.sin((forest_color.i * (2 * m.pi) * .5 + 45) / 360) * 255 + arr[..., 1]) % 255
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arr[..., 2] = (m.cos(forest_color.i * (2 * m.pi) * .3 / 360) * 255 + arr[..., 2]) % 255
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forest_color.i = 0
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display("fractal test.mp4", callbacks=forest_color, blocking=True, fps_limit=120)
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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 = display("fractal test.mp4")
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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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grab = tf.convert_to_tensor(
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displayer.FRAME_DICT["fractal test.mp4frame"][np.newaxis, ...].astype(np.float32)
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/ 255.0
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)
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grab_noise = tf.convert_to_tensor(
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(((displayer.FRAME_DICT["fractal test.mp4frame"][np.newaxis, ...].astype(
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np.float32) + np.random.uniform(0, 255, grab.shape)) / 2) % 255)
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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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