def test_images_as_moving_sequence(self):
h, w = (28, 28)
sequence_length = 8
vh = 1 / (sequence_length)
vw = 1 / (2*(sequence_length))
image = tf.ones((28, 28, 1), dtype=tf.uint8)
velocity = tf.constant([vh, vw], dtype=tf.float32)
out_size = (h + sequence_length, w + sequence_length)
start_position = tf.constant([0, 0], dtype=tf.float32)
sequence = ms.image_as_moving_sequence(
image, start_position=start_position, velocity=velocity,
output_size=out_size, sequence_length=sequence_length)
sequence = tf.cast(sequence.image_sequence, tf.float32)
self.assertAllEqual(*self.evaluate([
tf.reduce_sum(sequence, axis=(1, 2, 3)),
tf.fill((sequence_length,),
tf.reduce_sum(tf.cast(image, tf.float32)))
]))
for i, full_image in enumerate(tf.unstack(sequence, axis=0)):
j = i // 2
subimage = full_image[i:i+h, j:j+w]
n_true = tf.reduce_sum(subimage)
# allow for pixel rounding errors in each dimension
self.assertGreaterEqual(self.evaluate(n_true), (h-1)*(w-1))
def map_fn(self, image, label):
sequence = moving_sequence.image_as_moving_sequence(image,
sequence_length=20)
return sequence.image_sequence
