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import pytest
import torch
from torch.autograd import gradcheck
import kornia
from kornia.testing import assert_close, tensor_to_gradcheck_var
class TestCam2Pixel:
def _create_intrinsics(self, batch_size, fx, fy, cx, cy, device, dtype):
temp = torch.eye(4, device=device, dtype=dtype)
temp[0, 0], temp[0, 2] = fx, cx
temp[1, 1], temp[1, 2] = fy, cy
intrinsics = temp.expand(batch_size, -1, -1)
return intrinsics
def _create_intrinsics_inv(self, batch_size, fx, fy, cx, cy, device, dtype):
temp = torch.eye(4, device=device, dtype=dtype)
temp[0, 0], temp[0, 2] = 1 / fx, -cx / fx
temp[1, 1], temp[1, 2] = 1 / fy, -cy / fy
intrinsics_inv = temp.expand(batch_size, -1, -1)
return intrinsics_inv
def _get_samples(self, shape, low, high, device, dtype):
"""Return a tensor having the given shape and whose values are in the range [low, high)"""
return ((high - low) * torch.rand(shape, device=device, dtype=dtype)) + low
@pytest.mark.parametrize("batch_size", (1,))
def test_smoke(self, batch_size, device, dtype):
H, W = 250, 500
fx, fy = W, H
cx, cy = W / 2, H / 2
eps = 1e-12
seed = 77
low, high = -500, 500
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
# Setting the projection matrix to the intrinsic matrix for
# simplicity (i.e. assuming that the RT matrix is an identity matrix)
proj_mat = intrinsics
torch.manual_seed(seed)
cam_coords_src = self._get_samples((batch_size, H, W, 3), low, high, device, dtype)
pixel_coords_dst = kornia.geometry.camera.cam2pixel(
cam_coords_src=cam_coords_src, dst_proj_src=proj_mat, eps=eps
)
assert pixel_coords_dst.shape == (batch_size, H, W, 2)
@pytest.mark.parametrize("batch_size", (1, 2, 5))
def test_consistency(self, batch_size, device, dtype):
H, W = 250, 500
fx, fy = W, H
cx, cy = W / 2, H / 2
eps = 1e-12
seed = 77
low, high = -500, 500
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
intrinsics_inv = self._create_intrinsics_inv(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
# Setting the projection matrix to the intrinsic matrix for
# simplicity (i.e. assuming that the RT matrix is an identity matrix)
proj_mat = intrinsics
torch.manual_seed(seed)
cam_coords_input = self._get_samples((batch_size, H, W, 3), low, high, device, dtype)
pixel_coords_output = kornia.geometry.camera.cam2pixel(
cam_coords_src=cam_coords_input, dst_proj_src=proj_mat, eps=eps
)
last_ch = torch.ones((batch_size, H, W, 1), device=device, dtype=dtype)
pixel_coords_concat = torch.cat([pixel_coords_output, last_ch], axis=-1)
depth = cam_coords_input[..., 2:3].permute(0, 3, 1, 2).contiguous()
cam_coords_output = kornia.geometry.camera.pixel2cam(
depth=depth, intrinsics_inv=intrinsics_inv, pixel_coords=pixel_coords_concat
)
assert_close(cam_coords_output, cam_coords_input, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", (1,))
def test_gradcheck(self, batch_size, device, dtype):
H, W = 10, 20
fx, fy = W, H
cx, cy = W / 2, H / 2
eps = 1e-12
seed = 77
low, high = -500, 500
atol, rtol = 1e-5, 1e-3
# Different tolerances for the below case.
if (device.type == "cuda") and (dtype == torch.float64):
atol, rtol = 1e-4, 1e-2
# If contiguous() is not called, gradcheck fails
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype).contiguous()
# Setting the projection matrix to the intrinsic matrix for
# simplicity (i.e. assuming that the RT matrix is an identity matrix)
proj_mat = intrinsics
torch.manual_seed(seed)
cam_coords_src = self._get_samples((batch_size, H, W, 3), low, high, device, dtype)
cam_coords_src = tensor_to_gradcheck_var(cam_coords_src)
proj_mat = tensor_to_gradcheck_var(proj_mat)
assert gradcheck(
kornia.geometry.camera.cam2pixel,
(cam_coords_src, proj_mat, eps),
raise_exception=True,
atol=atol,
rtol=rtol,
)
class TestPixel2Cam:
def _create_intrinsics(self, batch_size, fx, fy, cx, cy, device, dtype):
temp = torch.eye(4, device=device, dtype=dtype)
temp[0, 0], temp[0, 2] = fx, cx
temp[1, 1], temp[1, 2] = fy, cy
intrinsics = temp.expand(batch_size, -1, -1)
return intrinsics
def _create_intrinsics_inv(self, batch_size, fx, fy, cx, cy, device, dtype):
temp = torch.eye(4, device=device, dtype=dtype)
temp[0, 0], temp[0, 2] = 1 / fx, -cx / fx
temp[1, 1], temp[1, 2] = 1 / fy, -cy / fy
intrinsics_inv = temp.expand(batch_size, -1, -1)
return intrinsics_inv
def _get_samples(self, shape, low, high, device, dtype):
"""Return a tensor having the given shape and whose values are in the range [low, high)"""
return ((high - low) * torch.rand(shape, device=device, dtype=dtype)) + low
@pytest.mark.parametrize("batch_size", (1, 2, 5))
def test_smoke(self, batch_size, device, dtype):
H, W = 250, 500
fx, fy = W, H
cx, cy = W / 2, H / 2
seed = 77
low_1, high_1 = -500, 500
low_2, high_2 = -(max(W, H) * 3), (max(W, H) * 3)
torch.manual_seed(seed)
depth = self._get_samples((batch_size, 1, H, W), low_1, high_1, device, dtype)
pixel_coords = self._get_samples((batch_size, H, W, 2), low_2, high_2, device, dtype)
last_ch = torch.ones((batch_size, H, W, 1), device=device, dtype=dtype)
pixel_coords_input = torch.cat([pixel_coords, last_ch], axis=-1)
intrinsics_inv = self._create_intrinsics_inv(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
output = kornia.geometry.camera.pixel2cam(
depth=depth, intrinsics_inv=intrinsics_inv, pixel_coords=pixel_coords_input
)
assert output.shape == (batch_size, H, W, 3)
@pytest.mark.parametrize("batch_size", (1, 2, 5))
def test_consistency(self, batch_size, device, dtype):
H, W = 250, 500
fx, fy = W, H
cx, cy = W / 2, H / 2
eps = 1e-12
seed = 77
low_1, high_1 = -500, 500
low_2, high_2 = -(max(W, H) * 3), (max(W, H) * 3)
torch.manual_seed(seed)
depth = self._get_samples((batch_size, 1, H, W), low_1, high_1, device, dtype)
pixel_coords = self._get_samples((batch_size, H, W, 2), low_2, high_2, device, dtype)
last_ch = torch.ones((batch_size, H, W, 1), device=device, dtype=dtype)
pixel_coords_input = torch.cat([pixel_coords, last_ch], axis=-1)
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
intrinsics_inv = self._create_intrinsics_inv(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
cam_coords = kornia.geometry.camera.pixel2cam(
depth=depth, intrinsics_inv=intrinsics_inv, pixel_coords=pixel_coords_input
)
# Setting the projection matrix to the intrinsic matrix for
# simplicity (i.e. assuming that the RT matrix is an identity matrix)
proj_mat = intrinsics
pixel_coords_output = kornia.geometry.camera.cam2pixel(
cam_coords_src=cam_coords, dst_proj_src=proj_mat, eps=eps
)
pixel_coords_concat = torch.cat([pixel_coords_output, last_ch], axis=-1)
assert_close(pixel_coords_concat, pixel_coords_input, atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", (1,))
def test_gradcheck(self, batch_size, device, dtype):
H, W = 10, 20
fx, fy = W, H
cx, cy = W / 2, H / 2
seed = 77
low_1, high_1 = -500, 500
low_2, high_2 = -(max(W, H) * 3), (max(W, H) * 3)
torch.manual_seed(seed)
depth = self._get_samples((batch_size, 1, H, W), low_1, high_1, device, dtype)
pixel_coords = self._get_samples((batch_size, H, W, 2), low_2, high_2, device, dtype)
last_ch = torch.ones((batch_size, H, W, 1), device=device, dtype=dtype)
pixel_coords_input = torch.cat([pixel_coords, last_ch], axis=-1)
# If contiguous() is not called, gradcheck fails
intrinsics_inv = self._create_intrinsics_inv(
batch_size, fx, fy, cx, cy, device=device, dtype=dtype
).contiguous()
depth = tensor_to_gradcheck_var(depth)
intrinsics_inv = tensor_to_gradcheck_var(intrinsics_inv)
pixel_coords_input = tensor_to_gradcheck_var(pixel_coords_input)
assert gradcheck(
kornia.geometry.camera.pixel2cam, (depth, intrinsics_inv, pixel_coords_input), raise_exception=True
)
class TestPinholeCamera:
def _create_intrinsics(self, batch_size, fx, fy, cx, cy, device, dtype):
intrinsics = torch.eye(4, device=device, dtype=dtype)
intrinsics[..., 0, 0] = fx
intrinsics[..., 1, 1] = fy
intrinsics[..., 0, 2] = cx
intrinsics[..., 1, 2] = cy
return intrinsics.expand(batch_size, -1, -1)
def _create_extrinsics(self, batch_size, tx, ty, tz, device, dtype):
extrinsics = torch.eye(4, device=device, dtype=dtype)
extrinsics[..., 0, -1] = tx
extrinsics[..., 1, -1] = ty
extrinsics[..., 2, -1] = tz
return extrinsics.expand(batch_size, -1, -1)
def test_smoke(self, device, dtype):
intrinsics = torch.eye(4, device=device, dtype=dtype)[None]
extrinsics = torch.eye(4, device=device, dtype=dtype)[None]
height = torch.ones(1, device=device, dtype=dtype)
width = torch.ones(1, device=device, dtype=dtype)
pinhole = kornia.geometry.camera.PinholeCamera(intrinsics, extrinsics, height, width)
assert isinstance(pinhole, kornia.geometry.camera.PinholeCamera)
def test_pinhole_camera_attributes(self, device, dtype):
batch_size = 1
height, width = 4, 6
fx, fy, cx, cy = 1, 2, width / 2, height / 2
tx, ty, tz = 1, 2, 3
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
extrinsics = self._create_extrinsics(batch_size, tx, ty, tz, device=device, dtype=dtype)
height = torch.ones(batch_size, device=device, dtype=dtype) * height
width = torch.ones(batch_size, device=device, dtype=dtype) * width
pinhole = kornia.geometry.camera.PinholeCamera(intrinsics, extrinsics, height, width)
assert pinhole.batch_size == batch_size
assert pinhole.fx.item() == fx
assert pinhole.fy.item() == fy
assert pinhole.cx.item() == cx
assert pinhole.cy.item() == cy
assert pinhole.tx.item() == tx
assert pinhole.ty.item() == ty
assert pinhole.tz.item() == tz
assert pinhole.height.item() == height
assert pinhole.width.item() == width
assert pinhole.rt_matrix.shape == (batch_size, 3, 4)
assert pinhole.camera_matrix.shape == (batch_size, 3, 3)
assert pinhole.rotation_matrix.shape == (batch_size, 3, 3)
assert pinhole.translation_vector.shape == (batch_size, 3, 1)
def test_pinhole_camera_translation_setters(self, device, dtype):
batch_size = 1
height, width = 4, 6
fx, fy, cx, cy = 1, 2, width / 2, height / 2
tx, ty, tz = 1, 2, 3
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
extrinsics = self._create_extrinsics(batch_size, tx, ty, tz, device=device, dtype=dtype)
height = torch.ones(batch_size, device=device, dtype=dtype) * height
width = torch.ones(batch_size, device=device, dtype=dtype) * width
pinhole = kornia.geometry.camera.PinholeCamera(intrinsics, extrinsics, height, width)
assert pinhole.tx.item() == tx
assert pinhole.ty.item() == ty
assert pinhole.tz.item() == tz
# add offset
pinhole.tx += 3.0
pinhole.ty += 2.0
pinhole.tz += 1.0
assert pinhole.tx.item() == tx + 3.0
assert pinhole.ty.item() == ty + 2.0
assert pinhole.tz.item() == tz + 1.0
# set to zero
pinhole.tx = 0.0
pinhole.ty = 0.0
pinhole.tz = 0.0
assert pinhole.tx.item() == 0.0
assert pinhole.ty.item() == 0.0
assert pinhole.tz.item() == 0.0
def test_pinhole_camera_attributes_batch2(self, device, dtype):
batch_size = 2
height, width = 4, 6
fx, fy, cx, cy = 1, 2, width / 2, height / 2
tx, ty, tz = 1, 2, 3
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
extrinsics = self._create_extrinsics(batch_size, tx, ty, tz, device=device, dtype=dtype)
height = torch.ones(batch_size, device=device, dtype=dtype) * height
width = torch.ones(batch_size, device=device, dtype=dtype) * width
pinhole = kornia.geometry.camera.PinholeCamera(intrinsics, extrinsics, height, width)
assert pinhole.batch_size == batch_size
assert pinhole.fx.shape[0] == batch_size
assert pinhole.fy.shape[0] == batch_size
assert pinhole.cx.shape[0] == batch_size
assert pinhole.cy.shape[0] == batch_size
assert pinhole.tx.shape[0] == batch_size
assert pinhole.ty.shape[0] == batch_size
assert pinhole.tz.shape[0] == batch_size
assert pinhole.height.shape[0] == batch_size
assert pinhole.width.shape[0] == batch_size
assert pinhole.rt_matrix.shape == (batch_size, 3, 4)
assert pinhole.camera_matrix.shape == (batch_size, 3, 3)
assert pinhole.rotation_matrix.shape == (batch_size, 3, 3)
assert pinhole.translation_vector.shape == (batch_size, 3, 1)
def test_pinhole_camera_scale(self, device, dtype):
batch_size = 2
height, width = 4, 6
fx, fy, cx, cy = 1, 2, width / 2, height / 2
tx, ty, tz = 1, 2, 3
scale_val = 2.0
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
extrinsics = self._create_extrinsics(batch_size, tx, ty, tz, device=device, dtype=dtype)
height = torch.ones(batch_size, device=device, dtype=dtype) * height
width = torch.ones(batch_size, device=device, dtype=dtype) * width
scale_factor = torch.ones(batch_size, device=device, dtype=dtype) * scale_val
pinhole = kornia.geometry.camera.PinholeCamera(intrinsics, extrinsics, height, width)
pinhole_scale = pinhole.scale(scale_factor)
assert_close(
pinhole_scale.intrinsics[..., 0, 0], pinhole.intrinsics[..., 0, 0] * scale_val, atol=1e-4, rtol=1e-4
) # fx
assert_close(
pinhole_scale.intrinsics[..., 1, 1], pinhole.intrinsics[..., 1, 1] * scale_val, atol=1e-4, rtol=1e-4
) # fy
assert_close(
pinhole_scale.intrinsics[..., 0, 2], pinhole.intrinsics[..., 0, 2] * scale_val, atol=1e-4, rtol=1e-4
) # cx
assert_close(
pinhole_scale.intrinsics[..., 1, 2], pinhole.intrinsics[..., 1, 2] * scale_val, atol=1e-4, rtol=1e-4
) # cy
assert_close(pinhole_scale.height, pinhole.height * scale_val, atol=1e-4, rtol=1e-4)
assert_close(pinhole_scale.width, pinhole.width * scale_val, atol=1e-4, rtol=1e-4)
def test_pinhole_camera_scale_inplace(self, device, dtype):
batch_size = 2
height, width = 4, 6
fx, fy, cx, cy = 1, 2, width / 2, height / 2
tx, ty, tz = 1, 2, 3
scale_val = 2.0
intrinsics = self._create_intrinsics(batch_size, fx, fy, cx, cy, device=device, dtype=dtype)
extrinsics = self._create_extrinsics(batch_size, tx, ty, tz, device=device, dtype=dtype)
height = torch.ones(batch_size, device=device, dtype=dtype) * height
width = torch.ones(batch_size, device=device, dtype=dtype) * width
scale_factor = torch.ones(batch_size, device=device, dtype=dtype) * scale_val
pinhole = kornia.geometry.camera.PinholeCamera(intrinsics, extrinsics, height, width)
pinhole_scale = pinhole.clone()
pinhole_scale.scale_(scale_factor)
assert_close(
pinhole_scale.intrinsics[..., 0, 0], pinhole.intrinsics[..., 0, 0] * scale_val, atol=1e-4, rtol=1e-4
) # fx
assert_close(
pinhole_scale.intrinsics[..., 1, 1], pinhole.intrinsics[..., 1, 1] * scale_val, atol=1e-4, rtol=1e-4
) # fy
assert_close(
pinhole_scale.intrinsics[..., 0, 2], pinhole.intrinsics[..., 0, 2] * scale_val, atol=1e-4, rtol=1e-4
) # cx
assert_close(
pinhole_scale.intrinsics[..., 1, 2], pinhole.intrinsics[..., 1, 2] * scale_val, atol=1e-4, rtol=1e-4
) # cy
assert_close(pinhole_scale.height, pinhole.height * scale_val, atol=1e-4, rtol=1e-4)
assert_close(pinhole_scale.width, pinhole.width * scale_val, atol=1e-4, rtol=1e-4)
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