def test_thin_should_determine_percision_if_none_specified_for_diferent_scale(self):
PT = PointThinner()
points = np.array([[1000.0, 0.0, 0.0], [1000.1, 0.0, 0.0], [2000.0, 0.0, 0.0], [2001.0, 0.0, 0.0], ])
expected = np.array([[1000.0, 0.0, 0.0], [2000.0, 0.0, 0.0], [2001.0, 0.0, 0.0]])
result = PT.thin(points)
result.sort(axis=0)
self.assertTrue(np.array_equal(result, expected))
def test_thin_thins_large_arrays_fast(self):
percision = 2
points = np.random.random([1000000, 3]).astype('float16')
PT = PointThinner(percision)
start = time.time()
result = PT.thin(points)
end = time.time() - start
self.assertLess(end, 1)
def test_thin_thins_large_arrays_fast(self):
percision = 2
points = np.random.random([1000000, 3]).astype('float16')
PT = PointThinner(percision)
start = time.time()
result = PT.thin(points)
end = time.time() - start
self.assertLess(end, 1)
def test_thin_removes_points_within_a_specified_distance_in_single_plane_percision_2(self):
points = np.array([[1.0, 0.0, 0.0], [1.001, 0.0, 0.0], [2.0, 0.0, 0.0], [2.01, 0.0, 0.0], ])
expected = np.array([[1.0, 0.0, 0.0], [2.0, 0.0, 0.0], [2.01, 0.0, 0.0]])
percision = 2
PT = PointThinner(percision)
result = PT.thin(points, )
result.sort(axis=0)
self.assertTrue(np.array_equal(result, expected))
def test_thin_thins_large_arrays(self):
percision = 0
points = np.random.random([10000, 3]).astype('float16')
PT = PointThinner(percision)
result = PT.thin(points)
self.assertEqual(len(result), 8)
percision = 1
PT = PointThinner(percision)
result = PT.thin(points)
self.assertTrue(len(result) < 10000)
def test_thin_should_determine_percision_if_none_specified(self):
PT = PointThinner()
points = np.array([
[1.0, 0.0, 0.0],
[1.0001, 0.0, 0.0],
[2.0, 0.0, 0.0],
[2.001, 0.0, 0.0],
])
expected = np.array([[1.0, 0.0, 0.0], [2.0, 0.0, 0.0],
[2.001, 0.0, 0.0]])
result = PT.thin(points)
result.sort(axis=0)
self.assertTrue(np.array_equal(result, expected))
def test_thin_removes_points_within_a_specified_distance_in_single_plane_percision_2(
self):
points = np.array([
[1.0, 0.0, 0.0],
[1.001, 0.0, 0.0],
[2.0, 0.0, 0.0],
[2.01, 0.0, 0.0],
])
expected = np.array([[1.0, 0.0, 0.0], [2.0, 0.0, 0.0],
[2.01, 0.0, 0.0]])
percision = 2
PT = PointThinner(percision)
result = PT.thin(points, )
result.sort(axis=0)
self.assertTrue(np.array_equal(result, expected))
def test_thin_thins_large_arrays(self):
percision = 0
points = np.random.random([10000, 3]).astype('float16')
PT = PointThinner(percision)
result = PT.thin(points)
self.assertEqual(len(result), 8)
percision = 1
PT = PointThinner(percision)
result = PT.thin(points)
self.assertTrue(len(result) < 10000)
def save_points(self, path, filename):
with open(os.path.join(path, filename), 'w') as afile:
thinner = PointThinner()
converter = GLConverter()
PLYWriter(converter, thinner).write_cartisien_points(afile, self.raw_points_xyz)
self.dismiss_popup()
