def test_rle_mask(self):
rle_data = random_rle_encoding()
dense = rl.rle_to_dense(rle_data)
mask = np.random.uniform(size=dense.shape) > 0.8
expected = dense[mask]
actual = tuple(rl.rle_mask(rle_data, mask))
np.testing.assert_equal(actual, expected)
def test_brle_to_rle(self):
brle_data = random_brle_encoding()
brle_dense = rl.brle_to_dense(brle_data)
rle_data = rl.brle_to_rle(brle_data)
rle_dense = rl.rle_to_dense(rle_data)
np.testing.assert_equal(brle_dense, rle_dense)
np.testing.assert_equal(rl.brle_to_rle([0, 5, 2, 0]), [1, 5, 0, 2])
def test_rle_encode_decode(self):
small = np.array([3] * 500 + [5] * 1000 + [2], dtype=np.uint8)
rand = (np.random.uniform(size=(10000, )) > 0.05).astype(np.uint8)
for original in [small, rand]:
for dtype in [np.uint8, np.int64]:
enc = rl.dense_to_rle(original, dtype=dtype)
dec = rl.rle_to_dense(enc)
np.testing.assert_equal(original, dec)
def test_rle_to_dense(self):
np.testing.assert_equal(rl.rle_to_dense([5, 3, 4, 10]),
[5] * 3 + [4] * 10)
np.testing.assert_equal(rl.rle_to_dense([5, 300, 4, 100]),
[5] * 300 + [4] * 100)