def test_brle_to_dense(self):
np.testing.assert_equal(
rl.brle_to_dense(np.array([300, 200, 1000, 0], dtype=np.int64)),
[False] * 300 + [True] * 200 + [False] * 1000)
np.testing.assert_equal(
rl.brle_to_dense(
np.array([255, 0, 45, 200, 255, 0, 255, 0, 255, 0, 235, 0],
dtype=np.int64)),
[False] * 300 + [True] * 200 + [False] * 1000)
def test_brle_mask(self):
brle_data = random_brle_encoding()
dense = rl.brle_to_dense(brle_data)
mask = np.random.uniform(size=dense.shape) > 0.8
expected = dense[mask]
actual = tuple(rl.brle_mask(brle_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_brle_decode_encode(self):
small = [3, 5, 5, 10, 2, 1]
rand = random_brle_encoding()
for original in [small, rand]:
for dtype in [np.uint8, np.int64]:
dec = rl.brle_to_dense(original)
enc = rl.dense_to_brle(dec, dtype=dtype)
np.testing.assert_equal(original, enc)
def test_brle_encode_decode(self):
small = np.array([False] * 500 + [True] * 1000 + [False], dtype=bool)
rand = np.random.uniform(size=(10000, )) > 0.05
for original in [small, rand]:
for dtype in [np.uint8, np.int64]:
enc = rl.dense_to_brle(original, dtype=dtype)
dec = rl.brle_to_dense(enc)
np.testing.assert_equal(original, dec)
def test_brle_logical_not(self):
original = random_brle_encoding(dtype=np.int64)
notted = rl.brle_logical_not(original)
dense_notted = rl.brle_to_dense(notted)
dense_original = rl.brle_to_dense(original)
np.testing.assert_equal(dense_notted, np.logical_not(dense_original))
def test_brle_length(self):
enc = random_brle_encoding(dtype=np.int64)
dec = rl.brle_to_dense(enc)
np.testing.assert_equal(len(dec), rl.brle_length(enc))