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import pytest
import torch
from torch.autograd import gradcheck
import kornia
import kornia.testing as utils # test utils
from kornia.geometry.conversions import normalize_pixel_coordinates
from kornia.testing import assert_close
class TestDepthWarper:
eps = 1e-6
def _create_pinhole_pair(self, batch_size, device, dtype):
# prepare data
fx, fy = 1.0, 1.0
height, width = 3, 5
cx, cy = width / 2, height / 2
tx, ty, tz = 0, 0, 0
# create pinhole cameras
pinhole_src = kornia.geometry.camera.PinholeCamera.from_parameters(
fx, fy, cx, cy, height, width, tx, ty, tz, batch_size, device=device, dtype=dtype
)
pinhole_dst = kornia.geometry.camera.PinholeCamera.from_parameters(
fx, fy, cx, cy, height, width, tx, ty, tz, batch_size, device=device, dtype=dtype
)
return pinhole_src, pinhole_dst
@pytest.mark.parametrize("batch_size", (1, 2))
def test_compute_projection_matrix(self, batch_size, device, dtype):
height, width = 3, 5 # output shape
pinhole_src, pinhole_dst = self._create_pinhole_pair(batch_size, device, dtype)
pinhole_dst.tx += 1.0 # apply offset to tx
# create warper
warper = kornia.geometry.depth.DepthWarper(pinhole_dst, height, width)
assert warper._dst_proj_src is None
# initialize projection matrices
warper.compute_projection_matrix(pinhole_src)
assert warper._dst_proj_src is not None
# retrieve computed projection matrix and compare to expected
dst_proj_src = warper._dst_proj_src
dst_proj_src_expected = torch.eye(4, device=device, dtype=dtype)[None].repeat(batch_size, 1, 1) # Bx4x4
dst_proj_src_expected[..., 0, -2] += pinhole_src.cx
dst_proj_src_expected[..., 1, -2] += pinhole_src.cy
dst_proj_src_expected[..., 0, -1] += 1.0 # offset to x-axis
assert_close(dst_proj_src, dst_proj_src_expected)
@pytest.mark.parametrize("batch_size", (1, 2))
def test_warp_grid_offset_x1_depth1(self, batch_size, device, dtype):
height, width = 3, 5 # output shape
pinhole_src, pinhole_dst = self._create_pinhole_pair(batch_size, device, dtype)
pinhole_dst.tx += 1.0 # apply offset to tx
# initialize depth to one
depth_src = torch.ones(batch_size, 1, height, width, device=device, dtype=dtype)
# create warper, initialize projection matrices and warp grid
warper = kornia.geometry.depth.DepthWarper(pinhole_dst, height, width)
warper.compute_projection_matrix(pinhole_src)
grid_warped = warper.warp_grid(depth_src)
assert grid_warped.shape == (batch_size, height, width, 2)
# normalize base meshgrid
grid = warper.grid[..., :2].to(device=device, dtype=dtype)
grid_norm = normalize_pixel_coordinates(grid, height, width)
# check offset in x-axis
assert_close(grid_warped[..., -2, 0], grid_norm[..., -1, 0].repeat(batch_size, 1), atol=1e-4, rtol=1e-4)
# check that y-axis remain the same
assert_close(grid_warped[..., -1, 1], grid_norm[..., -1, 1].repeat(batch_size, 1), rtol=1e-4, atol=1e-4)
@pytest.mark.parametrize("batch_size", (1, 2))
def test_warp_grid_offset_x1y1_depth1(self, batch_size, device, dtype):
height, width = 3, 5 # output shape
pinhole_src, pinhole_dst = self._create_pinhole_pair(batch_size, device, dtype)
pinhole_dst.tx += 1.0 # apply offset to tx
pinhole_dst.ty += 1.0 # apply offset to ty
# initialize depth to one
depth_src = torch.ones(batch_size, 1, height, width, device=device, dtype=dtype)
# create warper, initialize projection matrices and warp grid
warper = kornia.geometry.depth.DepthWarper(pinhole_dst, height, width)
warper.compute_projection_matrix(pinhole_src)
grid_warped = warper.warp_grid(depth_src)
assert grid_warped.shape == (batch_size, height, width, 2)
# normalize base meshgrid
grid = warper.grid[..., :2].to(device=device, dtype=dtype)
grid_norm = normalize_pixel_coordinates(grid, height, width)
# check offset in x-axis
assert_close(grid_warped[..., -2, 0], grid_norm[..., -1, 0].repeat(batch_size, 1), atol=1e-4, rtol=1e-4)
# check that y-axis remain the same
assert_close(grid_warped[..., -2, :, 1], grid_norm[..., -1, :, 1].repeat(batch_size, 1), rtol=1e-4, atol=1e-4)
@pytest.mark.parametrize("batch_size", (1, 2))
def test_warp_tensor_offset_x1y1(self, batch_size, device, dtype):
channels, height, width = 3, 3, 5 # output shape
pinhole_src, pinhole_dst = self._create_pinhole_pair(batch_size, device, dtype)
pinhole_dst.tx += 1.0 # apply offset to tx
pinhole_dst.ty += 1.0 # apply offset to ty
# initialize depth to one
depth_src = torch.ones(batch_size, 1, height, width, device=device, dtype=dtype)
# create warper, initialize projection matrices and warp grid
warper = kornia.geometry.depth.DepthWarper(pinhole_dst, height, width)
warper.compute_projection_matrix(pinhole_src)
# create patch to warp
patch_dst = (
torch.arange(float(height * width), device=device, dtype=dtype)
.view(1, 1, height, width)
.expand(batch_size, channels, -1, -1)
)
# warpd source patch by depth
patch_src = warper(depth_src, patch_dst)
# compare patches
assert_close(patch_dst[..., 1:, 1:], patch_src[..., :2, :4], atol=1e-4, rtol=1e-4)
@pytest.mark.parametrize("batch_size", (1, 2))
def test_compute_projection(self, batch_size, device, dtype):
height, width = 3, 5 # output shape
pinhole_src, pinhole_dst = self._create_pinhole_pair(batch_size, device, dtype)
# create warper, initialize projection matrices and warp grid
warper = kornia.geometry.depth.DepthWarper(pinhole_dst, height, width)
warper.compute_projection_matrix(pinhole_src)
# test compute_projection
xy_projected = warper._compute_projection(0.0, 0.0, 1.0)
assert xy_projected.shape == (batch_size, 2)
@pytest.mark.parametrize("batch_size", (1, 2))
def test_compute_subpixel_step(self, batch_size, device, dtype):
height, width = 3, 5 # output shape
pinhole_src, pinhole_dst = self._create_pinhole_pair(batch_size, device, dtype)
# create warper, initialize projection matrices and warp grid
warper = kornia.geometry.depth.DepthWarper(pinhole_dst, height, width)
warper.compute_projection_matrix(pinhole_src)
# test compute_subpixel_step
subpixel_step = warper.compute_subpixel_step()
assert pytest.approx(subpixel_step.item(), 0.3536)
@pytest.mark.parametrize("batch_size", (1, 2))
def test_gradcheck(self, batch_size, device, dtype):
# prepare data
channels, height, width = 3, 3, 5 # output shape
pinhole_src, pinhole_dst = self._create_pinhole_pair(batch_size, device, dtype)
# initialize depth to one
depth_src = torch.ones(batch_size, 1, height, width, device=device, dtype=dtype)
depth_src = utils.tensor_to_gradcheck_var(depth_src) # to var
# create patch to warp
img_dst = torch.ones(batch_size, channels, height, width, device=device, dtype=dtype)
img_dst = utils.tensor_to_gradcheck_var(img_dst) # to var
# evaluate function gradient
assert gradcheck(
kornia.geometry.depth.depth_warp,
(pinhole_dst, pinhole_src, depth_src, img_dst, height, width),
raise_exception=True,
)
# TODO(edgar): we should include a test showing some kind of occlusion
# def test_warp_with_occlusion(self):
# pass
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