def test_sum(self):
descriptors = [
dbow.ORB([0, 1, 0, 0, 1]),
dbow.ORB([1, 1, 1, 1, 1]),
dbow.ORB([0, 0, 0, 1, 0]),
dbow.ORB([0, 1, 0, 0, 1]),
]
np.allclose(np.sum(descriptors).features, np.array([1, 3, 1, 2, 3]))
def test_mean(self):
descriptors = [
dbow.ORB([0, 1, 0, 0, 1]),
dbow.ORB([1, 1, 1, 1, 1]),
dbow.ORB([0, 0, 0, 1, 0]),
dbow.ORB([0, 1, 0, 0, 1]),
]
self.assertEqual(dbow.mean_value(descriptors),
dbow.ORB([0, 1, 0, 1, 1]))
def test_weight(self):
descriptors = [
dbow.ORB([0, 1, 0, 0, 1]),
dbow.ORB([1, 1, 1, 1, 1]),
dbow.ORB([0, 0, 0, 1, 0]),
dbow.ORB([0, 1, 0, 0, 1]),
]
word = dbow.Word(descriptors)
word.update_weight(4)
self.assertEqual(word.weight, 0.0)
def test_tree(self):
descriptors = [
dbow.ORB([0, 1, 0, 0, 1]),
dbow.ORB([1, 1, 1, 1, 1]),
dbow.ORB([0, 0, 0, 1, 0]),
dbow.ORB([0, 1, 0, 0, 1]),
]
root = dbow.Node(descriptors)
words = dbow.initialize_tree(root, 4, 4)
for i, word in enumerate(words):
self.assertEqual(word.idx, i)
def test_distance(self):
desc1 = dbow.ORB([0, 1, 0, 0, 1])
desc2 = dbow.ORB([1, 1, 1, 1, 1])
self.assertEqual(desc1.distance(desc2), 3)