62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
import unittest as ut
|
|
|
|
class TestSubWin(ut.TestCase):
|
|
|
|
def test_display_numpy(self):
|
|
from cvpubsubs import display
|
|
import numpy as np
|
|
|
|
display(np.random.normal(0.5, .1, (500,500,3)))
|
|
|
|
def test_display_numpy_callback(self):
|
|
from cvpubsubs import display
|
|
import numpy as np
|
|
|
|
arr = np.random.normal(0.5, .1, (500, 500, 3))
|
|
|
|
def fix_arr_cv(arr_in):
|
|
arr_in[:] += np.random.normal(0.01, .005, (500, 500, 3))
|
|
arr_in%=1.0
|
|
|
|
display(arr, callbacks= fix_arr_cv, blocking=True)
|
|
|
|
def test_display_numpy_loop(self):
|
|
from cvpubsubs import display
|
|
import numpy as np
|
|
|
|
arr = np.random.normal(0.5, .1, (500, 500, 3))
|
|
|
|
displayer, ids = display(arr, blocking = False)
|
|
|
|
while True:
|
|
arr[:] += np.random.normal(0.01, .005, (500, 500, 3))
|
|
arr %= 1.0
|
|
displayer.update(arr, ids[0])
|
|
displayer.end()
|
|
|
|
def test_display_tensorflow(self):
|
|
from cvpubsubs import display
|
|
import numpy as np
|
|
from tensorflow.keras import layers, models
|
|
import tensorflow as tf
|
|
|
|
for gpu in tf.config.experimental.list_physical_devices("GPU"):
|
|
tf.compat.v2.config.experimental.set_memory_growth(gpu, True)
|
|
#tf.keras.backend.set_floatx("float16")
|
|
|
|
displayer, ids = display(0, blocking = False)
|
|
displayer.wait_for_init()
|
|
autoencoder = models.Sequential()
|
|
autoencoder.add(
|
|
layers.Conv2D(20, (3, 3), activation="sigmoid", input_shape=displayer.frames[0].shape)
|
|
)
|
|
autoencoder.add(layers.Conv2DTranspose(3, (3, 3), activation="sigmoid"))
|
|
|
|
autoencoder.compile(loss="mse", optimizer="adam")
|
|
|
|
while True:
|
|
grab = tf.convert_to_tensor(displayer.frame_dict['0frame'][np.newaxis, ...].astype(np.float32)/255.0)
|
|
autoencoder.fit(grab, grab, steps_per_epoch=1, epochs=1)
|
|
output_image = autoencoder.predict(grab, steps=1)
|
|
displayer.update((output_image[0]*255.0).astype(np.uint8), "uid for autoencoder output")
|