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import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.testing import assert_close
class TestDepthTo3d:
def test_smoke(self, device, dtype):
depth = torch.rand(1, 1, 3, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
points3d = kornia.geometry.depth.depth_to_3d(depth, camera_matrix)
assert points3d.shape == (1, 3, 3, 4)
@pytest.mark.parametrize("batch_size", [2, 4, 5])
def test_shapes(self, batch_size, device, dtype):
depth = torch.rand(batch_size, 1, 3, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(batch_size, 3, 3, device=device, dtype=dtype)
points3d = kornia.geometry.depth.depth_to_3d(depth, camera_matrix)
assert points3d.shape == (batch_size, 3, 3, 4)
@pytest.mark.parametrize("batch_size", [1, 2, 4, 5])
def test_shapes_broadcast(self, batch_size, device, dtype):
depth = torch.rand(batch_size, 1, 3, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
points3d = kornia.geometry.depth.depth_to_3d(depth, camera_matrix)
assert points3d.shape == (batch_size, 3, 3, 4)
def test_unproject_denormalized(self, device, dtype):
# this is for default normalize_points=False
depth = 2 * torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], device=device, dtype=dtype
)
camera_matrix = torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
points3d_expected = torch.tensor(
[
[
[[0.0, 2.0, 4.0], [0.0, 2.0, 4.0], [0.0, 2.0, 4.0], [0.0, 2.0, 4.0]],
[[0.0, 0.0, 0.0], [2.0, 2.0, 2.0], [4.0, 4.0, 4.0], [6.0, 6.0, 6.0]],
[[2.0, 2.0, 2.0], [2.0, 2.0, 2.0], [2.0, 2.0, 2.0], [2.0, 2.0, 2.0]],
]
],
device=device,
dtype=dtype,
)
points3d = kornia.geometry.depth.depth_to_3d(depth, camera_matrix) # default is normalize_points=False
assert_close(points3d, points3d_expected, atol=1e-4, rtol=1e-4)
def test_unproject_normalized(self, device, dtype):
# this is for normalize_points=True
depth = 2 * torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], device=device, dtype=dtype
)
camera_matrix = torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
points3d_expected = torch.tensor(
[
[
[
[0.0000, 1.4142, 1.7889],
[0.0000, 1.1547, 1.6330],
[0.0000, 0.8165, 1.3333],
[0.0000, 0.6030, 1.0690],
],
[
[0.0000, 0.0000, 0.0000],
[1.4142, 1.1547, 0.8165],
[1.7889, 1.6330, 1.3333],
[1.8974, 1.8091, 1.6036],
],
[
[2.0000, 1.4142, 0.8944],
[1.4142, 1.1547, 0.8165],
[0.8944, 0.8165, 0.6667],
[0.6325, 0.6030, 0.5345],
],
]
],
device=device,
dtype=dtype,
)
points3d = kornia.geometry.depth.depth_to_3d(depth, camera_matrix, normalize_points=True)
assert_close(points3d, points3d_expected, atol=1e-4, rtol=1e-4)
def test_unproject_and_project(self, device, dtype):
depth = 2 * torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], device=device, dtype=dtype
)
camera_matrix = torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
points3d = kornia.geometry.depth.depth_to_3d(depth, camera_matrix)
points2d = kornia.geometry.camera.project_points(points3d.permute(0, 2, 3, 1), camera_matrix[:, None, None])
points2d_expected = kornia.utils.create_meshgrid(4, 3, False, device=device).to(dtype=dtype)
assert_close(points2d, points2d_expected, atol=1e-4, rtol=1e-4)
def test_gradcheck(self, device, dtype):
# generate input data
depth = torch.rand(1, 1, 3, 4, device=device, dtype=dtype)
depth = utils.tensor_to_gradcheck_var(depth) # to var
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
camera_matrix = utils.tensor_to_gradcheck_var(camera_matrix) # to var
# evaluate function gradient
assert gradcheck(kornia.geometry.depth.depth_to_3d, (depth, camera_matrix), raise_exception=True)
class TestDepthToNormals:
def test_smoke(self, device, dtype):
depth = torch.rand(1, 1, 3, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
points3d = kornia.geometry.depth.depth_to_normals(depth, camera_matrix)
assert points3d.shape == (1, 3, 3, 4)
@pytest.mark.parametrize("batch_size", [2, 4, 5])
def test_shapes(self, batch_size, device, dtype):
depth = torch.rand(batch_size, 1, 3, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(batch_size, 3, 3, device=device, dtype=dtype)
points3d = kornia.geometry.depth.depth_to_normals(depth, camera_matrix)
assert points3d.shape == (batch_size, 3, 3, 4)
@pytest.mark.parametrize("batch_size", [2, 4, 5])
def test_shapes_broadcast(self, batch_size, device, dtype):
depth = torch.rand(batch_size, 1, 3, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
points3d = kornia.geometry.depth.depth_to_normals(depth, camera_matrix)
assert points3d.shape == (batch_size, 3, 3, 4)
def test_simple(self, device, dtype):
# this is for default normalize_points=False
depth = 2 * torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], device=device, dtype=dtype
)
camera_matrix = torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
normals_expected = torch.tensor(
[
[
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
]
],
device=device,
dtype=dtype,
)
normals = kornia.geometry.depth.depth_to_normals(depth, camera_matrix) # default is normalize_points=False
assert_close(normals, normals_expected, rtol=1e-3, atol=1e-3)
def test_simple_normalized(self, device, dtype):
# this is for default normalize_points=False
depth = 2 * torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], device=device, dtype=dtype
)
camera_matrix = torch.tensor([[[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]], device=device, dtype=dtype)
normals_expected = torch.tensor(
[
[
[
[0.3432, 0.4861, 0.7628],
[0.2873, 0.4260, 0.6672],
[0.2284, 0.3683, 0.5596],
[0.1695, 0.2980, 0.4496],
],
[
[0.3432, 0.2873, 0.2363],
[0.4861, 0.4260, 0.3785],
[0.8079, 0.7261, 0.6529],
[0.8948, 0.8237, 0.7543],
],
[
[0.8743, 0.8253, 0.6019],
[0.8253, 0.7981, 0.6415],
[0.5432, 0.5807, 0.5105],
[0.4129, 0.4824, 0.4784],
],
]
],
device=device,
dtype=dtype,
)
normals = kornia.geometry.depth.depth_to_normals(depth, camera_matrix, normalize_points=True)
assert_close(normals, normals_expected, rtol=1e-3, atol=1e-3)
def test_gradcheck(self, device, dtype):
# generate input data
depth = torch.rand(1, 1, 3, 4, device=device, dtype=dtype)
depth = utils.tensor_to_gradcheck_var(depth) # to var
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
camera_matrix = utils.tensor_to_gradcheck_var(camera_matrix) # to var
# evaluate function gradient
assert gradcheck(kornia.geometry.depth.depth_to_normals, (depth, camera_matrix), raise_exception=True)
class TestWarpFrameDepth:
def test_smoke(self, device, dtype):
image_src = torch.rand(1, 3, 3, 4, device=device, dtype=dtype)
depth_dst = torch.rand(1, 1, 3, 4, device=device, dtype=dtype)
src_trans_dst = torch.rand(1, 4, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
image_dst = kornia.geometry.depth.warp_frame_depth(image_src, depth_dst, src_trans_dst, camera_matrix)
assert image_dst.shape == (1, 3, 3, 4)
@pytest.mark.parametrize("batch_size", [2, 4, 5])
@pytest.mark.parametrize("num_features", [1, 3, 5])
def test_shape(self, batch_size, num_features, device, dtype):
image_src = torch.rand(batch_size, num_features, 3, 4, device=device, dtype=dtype)
depth_dst = torch.rand(batch_size, 1, 3, 4, device=device, dtype=dtype)
src_trans_dst = torch.rand(batch_size, 4, 4, device=device, dtype=dtype)
camera_matrix = torch.rand(batch_size, 3, 3, device=device, dtype=dtype)
image_dst = kornia.geometry.depth.warp_frame_depth(image_src, depth_dst, src_trans_dst, camera_matrix)
assert image_dst.shape == (batch_size, num_features, 3, 4)
def test_translation(self, device, dtype):
# this is for normalize_points=False
image_src = torch.tensor(
[[[[1.0, 2.0, 3.0], [1.0, 2.0, 3.0], [1.0, 2.0, 3.0], [1.0, 2.0, 3.0]]]], device=device, dtype=dtype
)
depth_dst = torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], device=device, dtype=dtype
)
src_trans_dst = torch.tensor(
[[[1.0, 0.0, 0.0, 1.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
)
h, w = image_src.shape[-2:]
camera_matrix = torch.tensor(
[[[1.0, 0.0, w / 2], [0.0, 1.0, h / 2], [0.0, 0.0, 1.0]]], device=device, dtype=dtype
)
image_dst_expected = torch.tensor(
[[[[2.0, 3.0, 0.0], [2.0, 3.0, 0.0], [2.0, 3.0, 0.0], [2.0, 3.0, 0.0]]]], device=device, dtype=dtype
)
image_dst = kornia.geometry.depth.warp_frame_depth(
image_src, depth_dst, src_trans_dst, camera_matrix
) # default is normalize_points=False
assert_close(image_dst, image_dst_expected, rtol=1e-3, atol=1e-3)
def test_translation_normalized(self, device, dtype):
# this is for normalize_points=True
image_src = torch.tensor(
[[[[1.0, 2.0, 3.0], [1.0, 2.0, 3.0], [1.0, 2.0, 3.0], [1.0, 2.0, 3.0]]]], device=device, dtype=dtype
)
depth_dst = torch.tensor(
[[[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]]], device=device, dtype=dtype
)
src_trans_dst = torch.tensor(
[[[1.0, 0.0, 0.0, 1.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]],
device=device,
dtype=dtype,
)
h, w = image_src.shape[-2:]
camera_matrix = torch.tensor(
[[[1.0, 0.0, w / 2], [0.0, 1.0, h / 2], [0.0, 0.0, 1.0]]], device=device, dtype=dtype
)
image_dst_expected = torch.tensor(
[
[
[
[0.9223, 0.0000, 0.0000],
[2.8153, 1.5000, 0.0000],
[2.8028, 2.6459, 0.0000],
[2.8153, 1.5000, 0.0000],
]
]
],
device=device,
dtype=dtype,
)
image_dst = kornia.geometry.depth.warp_frame_depth(
image_src, depth_dst, src_trans_dst, camera_matrix, normalize_points=True
)
assert_close(image_dst, image_dst_expected, rtol=1e-3, atol=1e-3)
def test_gradcheck(self, device, dtype):
image_src = torch.rand(1, 3, 3, 4, device=device, dtype=dtype)
image_src = utils.tensor_to_gradcheck_var(image_src) # to var
depth_dst = torch.rand(1, 1, 3, 4, device=device, dtype=dtype)
depth_dst = utils.tensor_to_gradcheck_var(depth_dst) # to var
src_trans_dst = torch.rand(1, 4, 4, device=device, dtype=dtype)
src_trans_dst = utils.tensor_to_gradcheck_var(src_trans_dst) # to var
camera_matrix = torch.rand(1, 3, 3, device=device, dtype=dtype)
camera_matrix = utils.tensor_to_gradcheck_var(camera_matrix) # to var
# evaluate function gradient
assert gradcheck(
kornia.geometry.depth.warp_frame_depth,
(image_src, depth_dst, src_trans_dst, camera_matrix),
raise_exception=True,
)
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