def test_non_uniform_scale():
cube_v = create_default_cube_verts()
# Confidence check.
np.testing.assert_array_equal(cube_v[0], [1.0, 0.0, 0.0])
np.testing.assert_array_equal(cube_v[6], [5.0, 4.0, 4.0])
forward, inverse = transform_matrix_for_non_uniform_scale(
1.0, -2.0, 1.0, allow_flipping=True, ret_inverse_matrix=True
)
transformed_cube_v = apply_transform(forward)(cube_v)
np.testing.assert_array_equal(transformed_cube_v[0], [1.0, 0.0, 0.0])
np.testing.assert_array_equal(transformed_cube_v[6], [5.0, -8.0, 4.0])
untransformed_cube_v = apply_transform(inverse)(transformed_cube_v)
np.testing.assert_array_almost_equal(untransformed_cube_v, cube_v)
np.testing.assert_array_equal(
transform_matrix_for_non_uniform_scale(
1.0, -2.0, 1.0, allow_flipping=True, ret_inverse_matrix=False
),
forward,
)
def test_apply_transform():
scale_factor = np.array([3.0, 0.5, 2.0])
transform = np.array(
[
[scale_factor[0], 0, 0, 0],
[0, scale_factor[1], 0, 0],
[0, 0, scale_factor[2], 0],
[0, 0, 0, 1],
]
)
points = np.array([[1.0, 2.0, 3.0], [5.0, 0.0, 1.0]])
expected_points = np.array([[3.0, 1.0, 6.0], [15.0, 0.0, 2.0]])
transformer = apply_transform(transform)
np.testing.assert_array_equal(transformer(points), expected_points)
np.testing.assert_array_equal(transformer(points[1]), expected_points[1])
expected_points_discarding_z = np.array([[3.0, 1.0], [15.0, 0.0]])
np.testing.assert_array_equal(
transformer(points, discard_z_coord=True),
expected_points_discarding_z,
)
np.testing.assert_array_equal(
transformer(points[1], discard_z_coord=True),
expected_points_discarding_z[1],
)
def test_inverse_orthographic_projection():
world_coords = teapot_verts()
canvas_coords = apply_transform(
world_to_canvas_orthographic_projection(
width=800,
height=600,
position=np.array([0.5, 1.5, 0.0]),
target=np.array([-0.5, 1.0, -2.0]),
zoom=100,
))(world_coords)
untransformed_world_coords = apply_transform(
world_to_canvas_orthographic_projection(
width=800,
height=600,
position=np.array([0.5, 1.5, 0.0]),
target=np.array([-0.5, 1.0, -2.0]),
zoom=100,
inverse=True,
))(canvas_coords)
np.testing.assert_array_almost_equal(untransformed_world_coords,
world_coords)
def test_uniform_scale():
cube_v = create_default_cube_verts()
# Confidence check.
np.testing.assert_array_equal(cube_v[0], [1.0, 0.0, 0.0])
np.testing.assert_array_equal(cube_v[6], [5.0, 4.0, 4.0])
transformed_cube_v = apply_transform(
transform_matrix_for_uniform_scale(-10.0, allow_flipping=True)
)(cube_v)
np.testing.assert_array_equal(transformed_cube_v[0], [-10.0, 0.0, 0.0])
np.testing.assert_array_equal(transformed_cube_v[6], [-50.0, -40.0, -40.0])
def test_translate():
cube_v = create_default_cube_verts()
# Confidence check.
np.testing.assert_array_equal(cube_v[0], [1.0, 0.0, 0.0])
np.testing.assert_array_equal(cube_v[6], [5.0, 4.0, 4.0])
transformed_cube_v = apply_transform(
transform_matrix_for_translation(np.array([8.0, 6.0, 7.0]))
)(cube_v)
np.testing.assert_array_equal(transformed_cube_v[0], [9.0, 6.0, 7.0])
np.testing.assert_array_equal(transformed_cube_v[6], [13.0, 10.0, 11.0])
def generate_teapot_image():
from polliwog.transform._testing_helper import write_canvas_points_to_png
world_coords = teapot_verts()
canvas_coords = apply_transform(
world_to_canvas_orthographic_projection(
width=800,
height=600,
position=np.array([0.5, 1.5, 0.0]),
target=np.array([-0.5, 1.0, -2.0]),
zoom=100,
))(world_coords, discard_z_coord=True)
write_canvas_points_to_png(canvas_coords, 800, 600, "projected.png")
def test_rotate():
cube_v = create_default_cube_verts()
ways_to_rotate_around_y_a_quarter_turn = [
np.array([[0, 0, 1], [0, 1, 0], [-1, 0, 0]]),
np.array([0, np.pi / 2, 0]),
]
for rot in ways_to_rotate_around_y_a_quarter_turn:
# Confidence check.
np.testing.assert_array_equal(cube_v[0], [1.0, 0.0, 0.0])
np.testing.assert_array_equal(cube_v[6], [5.0, 4.0, 4.0])
transformed_cube_v = apply_transform(transform_matrix_for_rotation(rot))(cube_v)
np.testing.assert_array_almost_equal(transformed_cube_v[0], [0.0, 0.0, -1.0])
np.testing.assert_array_almost_equal(transformed_cube_v[6], [4, 4.0, -5.0])
def test_teapot_orthographic_projection():
world_coords = teapot_verts()
canvas_coords = apply_transform(
world_to_canvas_orthographic_projection(
width=800,
height=600,
position=np.array([0.5, 1.5, 0.0]),
target=np.array([-0.5, 1.0, -2.0]),
zoom=100,
))(world_coords, discard_z_coord=True)
expected_head = np.array([[86.950483, 236.566495], [91.324232, 235.067513],
[84.079372, 238.229429]])
expected_tail = np.array([[661.899462,
233.196566], [661.899462, 233.196566],
[662.424938, 233.68759]])
np.testing.assert_array_almost_equal(canvas_coords[0:len(expected_head)],
expected_head)
np.testing.assert_array_almost_equal(canvas_coords[-len(expected_tail):],
expected_tail)
def test_apply_transform_for_vectors():
translation = np.array([3.0, 0.5, 2.0])
transform = np.array(
[
[1, 0, 0, translation[0]],
[0, 1, 0, translation[1]],
[0, 0, 1, translation[2]],
[0, 0, 0, 1],
]
)
points = np.array([[1.0, 2.0, 3.0], [5.0, 0.0, 1.0]])
expected_points = np.array([[4.0, 2.5, 5.0], [8.0, 0.5, 3.0]])
# Confidence check.
transformer = apply_transform(transform)
np.testing.assert_array_equal(transformer(points), expected_points)
np.testing.assert_array_equal(
transformer(points, treat_input_as_vector=True), points
)
