def post_process_image(self, image):
"""Post process image of size (H, W, 512) to a coarse RGB image."""
image = base_utils.bins_to_labels(
image, num_symbols_per_channel=self.num_symbols_per_channel)
image = base_utils.convert_bits(image, n_bits_in=3, n_bits_out=8)
image = tf.cast(image, dtype=tf.uint8)
return image
def test_bins_to_labels_random(self):
labels_t = tf.random.uniform(shape=(1, 64, 64, 3), minval=0, maxval=8,
dtype=tf.int32)
labels_np = labels_t.numpy()
bins_t = base_utils.labels_to_bins(labels_t, num_symbols_per_channel=8)
inv_labels_t = base_utils.bins_to_labels(bins_t, num_symbols_per_channel=8)
inv_labels_np = inv_labels_t.numpy()
self.assertTrue(np.allclose(inv_labels_np, labels_np))
def test_labels_to_bins(self):
n_bits = 3
bins = np.arange(2**n_bits)
triplets = itertools.product(bins, bins, bins)
labels = np.array(list(triplets))
labels_t = tf.convert_to_tensor(labels, dtype=tf.float32)
bins_t = base_utils.labels_to_bins(labels_t, num_symbols_per_channel=8)
bins_np = bins_t.numpy()
self.assertTrue(np.allclose(bins_np, np.arange(512)))
inv_labels_t = base_utils.bins_to_labels(bins_t, num_symbols_per_channel=8)
inv_labels_np = inv_labels_t.numpy()
self.assertTrue(np.allclose(labels, inv_labels_np))
