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Fix coordinate system conventions in renderer

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Summary:
## Updates

- Defined the world and camera coordinates according to this figure. The world coordinates are defined as having +Y up, +X left and +Z in.

{F230888499}

- Removed all flipping from blending functions.
- Updated the rasterizer to return images with +Y up and +X left.
- Updated all the mesh rasterizer tests
    - The expected values are now defined in terms of the default +Y up, +X left
    - Added tests where the triangles in the meshes are non symmetrical so that it is clear which direction +X and +Y are

## Questions:
- Should we have **scene settings** instead of raster settings?
    - To be more correct we should be [z clipping in the rasterizer based on the far/near clipping planes](https://github.com/ShichenLiu/SoftRas/blob/master/soft_renderer/cuda/soft_rasterize_cuda_kernel.cu#L400) - these values are also required in the blending functions so should we make these scene level parameters and have a scene settings tuple which is available to the rasterizer and shader?

Reviewed By: gkioxari

Differential Revision: D20208604

fbshipit-source-id: 55787301b1bffa0afa9618f0a0886cc681da51f3
This commit is contained in:
Nikhila Ravi
2020-03-06 06:48:31 -08:00
committed by Facebook Github Bot
parent 767d68a3af
commit 15c72be444
27 changed files with 526 additions and 486 deletions
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6
tests/test_blending.py
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@@ -41,7 +41,7 @@ def sigmoid_blend_naive_loop(colors, fragments, blend_params):
pixel_colors[n, h, w, :3] = colors[n, h, w, 0, :]
pixel_colors[n, h, w, 3] = 1.0 - alpha
return torch.flip(pixel_colors, [1])
return pixel_colors
def sigmoid_blend_naive_loop_backward(
@@ -54,8 +54,6 @@ def sigmoid_blend_naive_loop_backward(
N, H, W, K = pix_to_face.shape
device = pix_to_face.device
grad_distances = torch.zeros((N, H, W, K), dtype=dists.dtype, device=device)
images = torch.flip(images, [1])
grad_images = torch.flip(grad_images, [1])
for n in range(N):
for h in range(H):
@@ -130,7 +128,7 @@ def softmax_blend_naive(colors, fragments, blend_params):
pixel_colors[n, h, w, :3] += (delta / denom) * bk_color
pixel_colors[n, h, w, 3] = 1.0 - alpha
return torch.flip(pixel_colors, [1])
return pixel_colors
class TestBlending(unittest.TestCase):

8
tests/test_cameras.py
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@@ -173,12 +173,12 @@ class TestCameraHelpers(unittest.TestCase):
grad_dist = (
torch.cos(elev) * torch.sin(azim)
+ torch.sin(elev)
- torch.cos(elev) * torch.cos(azim)
+ torch.cos(elev) * torch.cos(azim)
)
grad_elev = (
-torch.sin(elev) * torch.sin(azim)
+ torch.cos(elev)
+ torch.sin(elev) * torch.cos(azim)
- torch.sin(elev) * torch.cos(azim)
)
grad_elev = dist * (math.pi / 180.0) * grad_elev
self.assertTrue(torch.allclose(elev_grad, grad_elev))
@@ -232,12 +232,12 @@ class TestCameraHelpers(unittest.TestCase):
grad_dist = (
torch.cos(elev) * torch.sin(azim)
+ torch.sin(elev)
- torch.cos(elev) * torch.cos(azim)
+ torch.cos(elev) * torch.cos(azim)
)
grad_elev = (
-torch.sin(elev) * torch.sin(azim)
+ torch.cos(elev)
+ torch.sin(elev) * torch.cos(azim)
- torch.sin(elev) * torch.cos(azim)
)
grad_elev = (dist * (math.pi / 180.0) * grad_elev).sum()
self.assertTrue(torch.allclose(elev_grad, grad_elev))

344
tests/test_rasterize_meshes.py
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@@ -19,7 +19,7 @@ class TestRasterizeMeshes(unittest.TestCase):
device = torch.device("cpu")
self._simple_triangle_raster(
rasterize_meshes_python, device, bin_size=-1
) # don't set binsize
)
self._simple_blurry_raster(rasterize_meshes_python, device, bin_size=-1)
self._test_behind_camera(rasterize_meshes_python, device, bin_size=-1)
self._test_perspective_correct(
@@ -28,10 +28,10 @@ class TestRasterizeMeshes(unittest.TestCase):
def test_simple_cpu_naive(self):
device = torch.device("cpu")
self._simple_triangle_raster(rasterize_meshes, device)
self._simple_blurry_raster(rasterize_meshes, device)
self._test_behind_camera(rasterize_meshes, device)
self._test_perspective_correct(rasterize_meshes, device)
self._simple_triangle_raster(rasterize_meshes, device, bin_size=0)
self._simple_blurry_raster(rasterize_meshes, device, bin_size=0)
self._test_behind_camera(rasterize_meshes, device, bin_size=0)
self._test_perspective_correct(rasterize_meshes, device, bin_size=0)
def test_simple_cuda_naive(self):
device = torch.device("cuda:0")
@@ -285,14 +285,15 @@ class TestRasterizeMeshes(unittest.TestCase):
max_faces_per_bin = 20
device = torch.device("cpu")
meshes = ico_sphere(2, device)
meshes = ico_sphere(2, device)
faces = meshes.faces_packed()
verts = meshes.verts_packed()
faces_verts = verts[faces]
num_faces_per_mesh = meshes.num_faces_per_mesh()
mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
args = (
bin_faces_cpu = _C._rasterize_meshes_coarse(
faces_verts,
mesh_to_face_first_idx,
num_faces_per_mesh,
@@ -301,17 +302,16 @@ class TestRasterizeMeshes(unittest.TestCase):
bin_size,
max_faces_per_bin,
)
bin_faces_cpu = _C._rasterize_meshes_coarse(*args)
device = torch.device("cuda:0")
meshes = ico_sphere(2, device)
meshes = meshes.clone().to(device)
faces = meshes.faces_packed()
verts = meshes.verts_packed()
faces_verts = verts[faces]
num_faces_per_mesh = meshes.num_faces_per_mesh()
mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
args = (
bin_faces_cuda = _C._rasterize_meshes_coarse(
faces_verts,
mesh_to_face_first_idx,
num_faces_per_mesh,
@@ -320,11 +320,11 @@ class TestRasterizeMeshes(unittest.TestCase):
bin_size,
max_faces_per_bin,
)
bin_faces_cuda = _C._rasterize_meshes_coarse(*args)
# Bin faces might not be the same: CUDA version might write them in
# any order. But if we sort the non-(-1) elements of the CUDA output
# then they should be the same.
for n in range(N):
for by in range(bin_faces_cpu.shape[1]):
for bx in range(bin_faces_cpu.shape[2]):
@@ -456,64 +456,68 @@ class TestRasterizeMeshes(unittest.TestCase):
# Run with and without perspective correction
idx_f, zbuf_f, bary_f, dists_f = rasterize_meshes_fn(**kwargs)
kwargs["perspective_correct"] = True
idx_t, zbuf_t, bary_t, dists_t = rasterize_meshes_fn(**kwargs)
# Expected output tensors in the format with axes +X left, +Y up, +Z in
# idx and dists should be the same with or without perspecitve correction
# fmt: off
idx_expected = torch.tensor([
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
], dtype=torch.int64, device=device).view(1, 11, 11, 1)
dists_expected = torch.tensor([
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 0.1283, 0.1071, 0.1071, 0.1071, 0.1071, 0.1071, 0.1283, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, 0.1283, 0.0423, 0.0212, 0.0212, 0.0212, 0.0212, 0.0212, 0.0423, 0.1283, -1.0000], # noqa: E241, E201
[-1.0000, 0.1084, 0.0225, -0.0003, -0.0013, -0.0013, -0.0013, -0.0003, 0.0225, 0.1084, -1.0000], # noqa: E241, E201
[-1.0000, 0.1523, 0.0518, 0.0042, -0.0095, -0.0476, -0.0095, 0.0042, 0.0518, 0.1523, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 0.0955, 0.0214, -0.0003, -0.0320, -0.0003, 0.0214, 0.0955, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 0.1523, 0.0518, 0.0042, -0.0095, 0.0042, 0.0518, 0.1523, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, 0.0955, 0.0214, -0.0003, 0.0214, 0.0955, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, 0.1523, 0.0542, 0.0212, 0.0542, 0.1523, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, -1.0000, 0.1402, 0.1071, 0.1402, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., 0.1402, 0.1071, 0.1402, -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., - 1., 0.1523, 0.0542, 0.0212, 0.0542, 0.1523, -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 0.0955, 0.0214, -0.0003, 0.0214, 0.0955, -1., -1., -1.], # noqa: E241, E201
[-1., -1., 0.1523, 0.0518, 0.0042, -0.0095, 0.0042, 0.0518, 0.1523, -1., -1.], # noqa: E241, E201
[-1., -1., 0.0955, 0.0214, -0.0003, -0.032, -0.0003, 0.0214, 0.0955, -1., -1.], # noqa: E241, E201
[-1., 0.1523, 0.0518, 0.0042, -0.0095, -0.0476, -0.0095, 0.0042, 0.0518, 0.1523, -1.], # noqa: E241, E201
[-1., 0.1084, 0.0225, -0.0003, -0.0013, -0.0013, -0.0013, -0.0003, 0.0225, 0.1084, -1.], # noqa: E241, E201
[-1., 0.1283, 0.0423, 0.0212, 0.0212, 0.0212, 0.0212, 0.0212, 0.0423, 0.1283, -1.], # noqa: E241, E201
[-1., -1., 0.1283, 0.1071, 0.1071, 0.1071, 0.1071, 0.1071, 0.1283, -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
], dtype=torch.float32, device=device).view(1, 11, 11, 1)
# zbuf and barycentric will be different with perspective correction
zbuf_f_expected = torch.tensor([
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, -1.0000], # noqa: E241, E201
[-1.0000, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, -1.0000], # noqa: E241, E201
[-1.0000, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, 15.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, 19.5455, 19.5455, 19.5455, 19.5455, 19.5455, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, 21.8182, 21.8182, 21.8182, 21.8182, 21.8182, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, -1.0000, 24.0909, 24.0909, 24.0909, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., 24.0909, 24.0909, 24.0909, -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 21.8182, 21.8182, 21.8182, 21.8182, 21.8182, -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 19.5455, 19.5455, 19.5455, 19.5455, 19.5455, -1., -1., -1.], # noqa: E241, E201
[-1., -1., 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, 17.2727, -1., -1.], # noqa: E241, E201
[-1., -1., 15., 15., 15., 15., 15., 15., 15., -1., -1.], # noqa: E241, E201
[-1., 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, 12.7273, -1.], # noqa: E241, E201
[-1., 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, 10.4545, -1.], # noqa: E241, E201
[-1., 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, 8.1818, -1.], # noqa: E241, E201
[-1., -1., 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, 5.9091, -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
], dtype=torch.float32, device=device).view(1, 11, 11, 1)
zbuf_t_expected = torch.tensor([
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, -1.0000], # noqa: E241, E201
[-1.0000, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, -1.0000], # noqa: E241, E201
[-1.0000, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, 19.1304, 19.1304, 19.1304, 19.1304, 19.1304, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, 24.4444, 24.4444, 24.4444, 24.4444, 24.4444, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, -1.0000, 33.8462, 33.8462, 33.8461, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., 33.8461, 33.8462, 33.8462, -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 24.4444, 24.4444, 24.4444, 24.4444, 24.4444, -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 19.1304, 19.1304, 19.1304, 19.1304, 19.1304, -1., -1., -1.], # noqa: E241, E201
[-1., -1., 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, 15.7143, -1., -1.], # noqa: E241, E201
[-1., -1., 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, 13.3333, -1., -1.], # noqa: E241, E201
[-1., 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, 11.5789, -1.], # noqa: E241, E201
[-1., 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, 10.2326, -1.], # noqa: E241, E201
[-1., 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, 9.1667, -1.], # noqa: E241, E201
[-1., -1., 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, 8.3019, -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
], dtype=torch.float32, device=device).view(1, 11, 11, 1)
# fmt: on
@@ -618,9 +622,10 @@ class TestRasterizeMeshes(unittest.TestCase):
def _simple_triangle_raster(self, raster_fn, device, bin_size=None):
image_size = 10
# Mesh with a single face.
# Mesh with a single non-symmetrical face - this will help
# check that the XY directions are correctly oriented.
verts0 = torch.tensor(
[[-0.7, -0.4, 0.1], [0.0, 0.6, 0.1], [0.7, -0.4, 0.1]],
[[-0.3, -0.4, 0.1], [0.0, 0.6, 0.1], [0.9, -0.4, 0.1]],
dtype=torch.float32,
device=device,
)
@@ -630,7 +635,7 @@ class TestRasterizeMeshes(unittest.TestCase):
# fmt: off
verts1 = torch.tensor(
[
[-0.7, -0.4, 0.1], # noqa: E241, E201
[-0.9, -0.2, 0.1], # noqa: E241, E201
[ 0.0, 0.6, 0.1], # noqa: E241, E201
[ 0.7, -0.4, 0.1], # noqa: E241, E201
[-0.7, 0.4, 0.5], # noqa: E241, E201
@@ -645,6 +650,8 @@ class TestRasterizeMeshes(unittest.TestCase):
[[1, 0, 2], [3, 4, 5]], dtype=torch.int64, device=device
)
# Expected output tensors in the format with axes +X left, +Y up, +Z in
# k = 0, closest point.
# fmt off
expected_p2face_k0 = torch.tensor(
[
@@ -652,10 +659,10 @@ class TestRasterizeMeshes(unittest.TestCase):
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
@@ -663,11 +670,11 @@ class TestRasterizeMeshes(unittest.TestCase):
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 1, 1, 1, 1, 1, 1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 1, 1, 1, 1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 1, 1, 1, 1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 2, 2, 1, 1, 2, 2, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, 1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 2, 2, 1, 1, 1, 2, -1, -1], # noqa: E241, E201
[-1, -1, -1, 1, 1, 1, 1, 1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 1, 1, 1, 1, 1, 1, -1], # noqa: E241, E201
[-1, -1, 1, 1, 1, 2, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
@@ -677,49 +684,37 @@ class TestRasterizeMeshes(unittest.TestCase):
device=device,
)
expected_zbuf_k0 = torch.tensor(
[
[
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
],
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.5, 0.5, 0.1, 0.1, 0.5, 0.5, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
],
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0.1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
],
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, 0.1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.5, 0.5, 0.1, 0.1, 0.1, 0.5, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.5, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
]
],
device=device,
)
# fmt: on
meshes = Meshes(verts=[verts0, verts1], faces=[faces0, faces1])
if bin_size == -1:
# simple python case with no binning
p2face, zbuf, bary, pix_dists = raster_fn(
meshes, image_size, 0.0, 2
)
else:
p2face, zbuf, bary, pix_dists = raster_fn(
meshes, image_size, 0.0, 2, bin_size
)
# k = 0, closest point.
self.assertTrue(torch.allclose(p2face[..., 0], expected_p2face_k0))
self.assertTrue(torch.allclose(zbuf[..., 0], expected_zbuf_k0))
# k = 1, second closest point.
expected_p2face_k1 = expected_p2face_k0.clone()
@@ -729,18 +724,18 @@ class TestRasterizeMeshes(unittest.TestCase):
# fmt: off
expected_p2face_k1[1, :] = torch.tensor(
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 2, 2, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 2, 2, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
],
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 2, 2, 2, 2, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 2, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] # noqa: E241, E201
],
dtype=torch.int64,
device=device,
)
@@ -748,21 +743,35 @@ class TestRasterizeMeshes(unittest.TestCase):
expected_zbuf_k1[0, :] = torch.ones_like(expected_zbuf_k1[0, :]) * -1
expected_zbuf_k1[1, :] = torch.tensor(
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0.5, 0.5, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.5, 0.5, 0.5, 0.5, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, 0.5, 0.5, 0.5, 0.5, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0.5, 0.5, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., 0.5, 0.5, 0.5, -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 0.5, 0.5, 0.5, 0.5, -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., 0.5, 0.5, 0.5, 0.5, -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., 0.5, -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
],
dtype=torch.float32,
device=device,
)
# fmt: on
# Coordinate conventions +Y up, +Z in, +X left
if bin_size == -1:
# simple python, no bin_size
p2face, zbuf, bary, pix_dists = raster_fn(
meshes, image_size, 0.0, 2
)
else:
p2face, zbuf, bary, pix_dists = raster_fn(
meshes, image_size, 0.0, 2, bin_size
)
self.assertTrue(torch.allclose(p2face[..., 0], expected_p2face_k0))
self.assertTrue(torch.allclose(zbuf[..., 0], expected_zbuf_k0))
self.assertTrue(torch.allclose(p2face[..., 1], expected_p2face_k1))
self.assertTrue(torch.allclose(zbuf[..., 1], expected_zbuf_k1))
@@ -778,9 +787,9 @@ class TestRasterizeMeshes(unittest.TestCase):
# fmt: off
verts = torch.tensor(
[
[ -0.5, 0.0, 0.1], # noqa: E241, E201
[ -0.3, 0.0, 0.1], # noqa: E241, E201
[ 0.0, 0.6, 0.1], # noqa: E241, E201
[ 0.5, 0.0, 0.1], # noqa: E241, E201
[ 0.8, 0.0, 0.1], # noqa: E241, E201
[-0.25, 0.0, 0.9], # noqa: E241, E201
[0.25, 0.5, 0.9], # noqa: E241, E201
[0.75, 0.0, 0.9], # noqa: E241, E201
@@ -794,6 +803,8 @@ class TestRasterizeMeshes(unittest.TestCase):
dtype=torch.float32,
device=device,
)
# Face with index 0 is non symmetric about the X and Y axis to
# test that the positive Y and X directions are correct in the output.
faces_packed = torch.tensor(
[[1, 0, 2], [4, 3, 5], [7, 6, 8], [10, 9, 11]],
dtype=torch.int64,
@@ -803,12 +814,12 @@ class TestRasterizeMeshes(unittest.TestCase):
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, 2, 2, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
[-1, 2, 0, 0, 0, 0, -1, -1, -1, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
[-1, 0, 0, 0, 0, 0, 0, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, 2, -1], # noqa: E241, E201
[-1, -1, 0, 0, 0, 0, 0, 0, 2, -1], # noqa: E241, E201
[-1, -1, -1, 0, 0, 0, 0, 2, 2, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0, 0, 2, 2, 2, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
],
@@ -817,16 +828,16 @@ class TestRasterizeMeshes(unittest.TestCase):
)
expected_zbuf = torch.tensor(
[
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.5, -1], # noqa: E241, E201
[-1, -1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.5, -1], # noqa: E241, E201
[-1, -1, -1, 0.1, 0.1, 0.1, 0.1, 0.5, 0.5, -1], # noqa: E241, E201
[-1, -1, -1, -1, 0.1, 0.1, 0.5, 0.5, 0.5, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., 0.5, 0.5, 0.1, 0.1, 0.1, -1., -1., -1., -1.], # noqa: E241, E201
[-1., 0.5, 0.1, 0.1, 0.1, 0.1, -1., -1., -1., -1.], # noqa: E241, E201
[-1., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1., -1., -1.], # noqa: E241, E201
[-1., 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.], # noqa: E241, E201
[-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.] # noqa: E241, E201
],
dtype=torch.float32,
device=device,
@@ -837,7 +848,7 @@ class TestRasterizeMeshes(unittest.TestCase):
faces = faces_packed[order] # rearrange order of faces.
mesh = Meshes(verts=[verts], faces=[faces])
if bin_size == -1:
# simple python case with no binning
# simple python, no bin size arg
pix_to_face, zbuf, bary_coords, dists = raster_fn(
mesh, image_size, blur_radius, faces_per_pixel
)
@@ -845,7 +856,6 @@ class TestRasterizeMeshes(unittest.TestCase):
pix_to_face, zbuf, bary_coords, dists = raster_fn(
mesh, image_size, blur_radius, faces_per_pixel, bin_size
)
if i == 0:
expected_dists = dists
p2f = expected_p2f.clone()
@@ -861,33 +871,37 @@ class TestRasterizeMeshes(unittest.TestCase):
def _test_coarse_rasterize(self, device):
image_size = 16
blur_radius = 0.2 ** 2
# No blurring. This test checks that the XY directions are
# correctly oriented.
blur_radius = 0.0
bin_size = 8
max_faces_per_bin = 3
# fmt: off
verts = torch.tensor(
[
[-0.5, 0.0, 0.1], # noqa: E241, E201
[ 0.0, 0.6, 0.1], # noqa: E241, E201
[ 0.5, 0.0, 0.1], # noqa: E241, E201
[-0.3, 0.0, 0.4], # noqa: E241, E201
[ 0.3, 0.5, 0.4], # noqa: E241, E201
[0.75, 0.0, 0.4], # noqa: E241, E201
[-0.4, -0.3, 0.9], # noqa: E241, E201
[ 0.2, -0.7, 0.9], # noqa: E241, E201
[ 0.4, -0.3, 0.9], # noqa: E241, E201
[-0.4, 0.0, -1.5], # noqa: E241, E201
[ 0.6, 0.6, -1.5], # noqa: E241, E201
[ 0.8, 0.0, -1.5], # noqa: E241, E201
[-0.5, 0.1, 0.1], # noqa: E241, E201
[-0.3, 0.6, 0.1], # noqa: E241, E201
[-0.1, 0.1, 0.1], # noqa: E241, E201
[-0.3, -0.1, 0.4], # noqa: E241, E201
[ 0.3, 0.5, 0.4], # noqa: E241, E201
[0.75, -0.1, 0.4], # noqa: E241, E201
[ 0.2, -0.3, 0.9], # noqa: E241, E201
[ 0.3, -0.7, 0.9], # noqa: E241, E201
[ 0.6, -0.3, 0.9], # noqa: E241, E201
[-0.4, 0.0, -1.5], # noqa: E241, E201
[ 0.6, 0.6, -1.5], # noqa: E241, E201
[ 0.8, 0.0, -1.5], # noqa: E241, E201
],
device=device,
)
# Expected faces using axes convention +Y down, + X right, +Z in
# Non symmetrical triangles i.e face 0 and 3 are in one bin only
faces = torch.tensor(
[
[ 1, 0, 2], # noqa: E241, E201 bin 00 and bin 01
[ 4, 3, 5], # noqa: E241, E201 bin 00 and bin 01
[ 7, 6, 8], # noqa: E241, E201 bin 10 and bin 11
[ 1, 0, 2], # noqa: E241, E201 bin 01 only
[ 4, 3, 5], # noqa: E241, E201 all bins
[ 7, 6, 8], # noqa: E241, E201 bin 10 only
[10, 9, 11], # noqa: E241, E201 negative z, should not appear.
],
dtype=torch.int64,
@@ -900,16 +914,19 @@ class TestRasterizeMeshes(unittest.TestCase):
num_faces_per_mesh = meshes.num_faces_per_mesh()
mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
# Expected faces using axes convention +Y down, + X right, + Z in
bin_faces_expected = (
torch.ones(
(1, 2, 2, max_faces_per_bin), dtype=torch.int32, device=device
)
* -1
)
bin_faces_expected[0, 0, 0, 0:2] = torch.tensor([0, 1])
bin_faces_expected[0, 0, 0, 0] = torch.tensor([1])
bin_faces_expected[0, 1, 0, 0:2] = torch.tensor([1, 2])
bin_faces_expected[0, 0, 1, 0:2] = torch.tensor([0, 1])
bin_faces_expected[0, 1, 0, 0:3] = torch.tensor([0, 1, 2])
bin_faces_expected[0, 1, 1, 0:3] = torch.tensor([0, 1, 2])
bin_faces_expected[0, 1, 1, 0] = torch.tensor([1])
# +Y up, +X left, +Z in
bin_faces = _C._rasterize_meshes_coarse(
faces_verts,
mesh_to_face_first_idx,
@@ -919,9 +936,8 @@ class TestRasterizeMeshes(unittest.TestCase):
bin_size,
max_faces_per_bin,
)
bin_faces_same = (
bin_faces.squeeze().flip(dims=[0]) == bin_faces_expected
).all()
# Flip x and y axis of output before comparing to expected
bin_faces_same = (bin_faces.squeeze() == bin_faces_expected).all()
self.assertTrue(bin_faces_same.item() == 1)
@staticmethod

84
tests/test_rendering_meshes.py
View File

@@ -72,20 +72,25 @@ class TestRenderingMeshes(unittest.TestCase):
# Init rasterizer settings
if elevated_camera:
R, T = look_at_view_transform(2.7, 45.0, 0.0)
# Elevated and rotated camera
R, T = look_at_view_transform(dist=2.7, elev=45.0, azim=45.0)
postfix = "_elevated_camera"
# If y axis is up, the spot of light should
# be on the bottom left of the sphere.
else:
# No elevation or azimuth rotation
R, T = look_at_view_transform(2.7, 0.0, 0.0)
postfix = ""
cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
raster_settings = RasterizationSettings(
image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
)
# Init shader settings
materials = Materials(device=device)
lights = PointLights(device=device)
lights.location = torch.tensor([0.0, 0.0, -2.0], device=device)[None]
lights.location = torch.tensor([0.0, 0.0, +2.0], device=device)[None]
raster_settings = RasterizationSettings(
image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
)
# Init renderer
rasterizer = MeshRasterizer(
@@ -107,15 +112,16 @@ class TestRenderingMeshes(unittest.TestCase):
# Load reference image
image_ref_phong = load_rgb_image(
"test_simple_sphere_illuminated%s.png" % postfix
"test_simple_sphere_light%s.png" % postfix
)
self.assertTrue(torch.allclose(rgb, image_ref_phong, atol=0.05))
###################################
# Move the light behind the object
###################################
# Check the image is dark
lights.location[..., 2] = +2.0
########################################################
# Move the light to the +z axis in world space so it is
# behind the sphere. Note that +Z is in, +Y up,
# +X left for both world and camera space.
########################################################
lights.location[..., 2] = -2.0
images = renderer(sphere_mesh, lights=lights)
rgb = images[0, ..., :3].squeeze().cpu()
if DEBUG:
@@ -133,7 +139,7 @@ class TestRenderingMeshes(unittest.TestCase):
######################################
# Change the shader to a GouraudShader
######################################
lights.location = torch.tensor([0.0, 0.0, -2.0], device=device)[None]
lights.location = torch.tensor([0.0, 0.0, +2.0], device=device)[None]
renderer = MeshRenderer(
rasterizer=rasterizer,
shader=HardGouraudShader(
@@ -143,7 +149,7 @@ class TestRenderingMeshes(unittest.TestCase):
images = renderer(sphere_mesh)
rgb = images[0, ..., :3].squeeze().cpu()
if DEBUG:
filename = "DEBUG_simple_sphere_light_gourad%s.png" % postfix
filename = "DEBUG_simple_sphere_light_gouraud%s.png" % postfix
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
DATA_DIR / filename
)
@@ -157,7 +163,6 @@ class TestRenderingMeshes(unittest.TestCase):
######################################
# Change the shader to a HardFlatShader
######################################
lights.location = torch.tensor([0.0, 0.0, -2.0], device=device)[None]
renderer = MeshRenderer(
rasterizer=rasterizer,
shader=HardFlatShader(
@@ -217,7 +222,7 @@ class TestRenderingMeshes(unittest.TestCase):
# Init shader settings
materials = Materials(device=device)
lights = PointLights(device=device)
lights.location = torch.tensor([0.0, 0.0, -2.0], device=device)[None]
lights.location = torch.tensor([0.0, 0.0, +2.0], device=device)[None]
# Init renderer
renderer = MeshRenderer(
@@ -231,7 +236,7 @@ class TestRenderingMeshes(unittest.TestCase):
images = renderer(sphere_meshes)
# Load ref image
image_ref = load_rgb_image("test_simple_sphere_illuminated.png")
image_ref = load_rgb_image("test_simple_sphere_light.png")
for i in range(batch_size):
rgb = images[i, ..., :3].squeeze().cpu()
@@ -261,7 +266,7 @@ class TestRenderingMeshes(unittest.TestCase):
)
# Init rasterizer settings
R, T = look_at_view_transform(2.7, 10, 20)
R, T = look_at_view_transform(2.7, 0, 0)
cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
# Init renderer
@@ -275,7 +280,7 @@ class TestRenderingMeshes(unittest.TestCase):
alpha = images[0, ..., 3].squeeze().cpu()
if DEBUG:
Image.fromarray((alpha.numpy() * 255).astype(np.uint8)).save(
DATA_DIR / "DEBUG_silhouette_grad.png"
DATA_DIR / "DEBUG_silhouette.png"
)
with Image.open(image_ref_filename) as raw_image_ref:
@@ -292,7 +297,8 @@ class TestRenderingMeshes(unittest.TestCase):
def test_texture_map(self):
"""
Test a mesh with a texture map is loaded and rendered correctly
Test a mesh with a texture map is loaded and rendered correctly.
The pupils in the eyes of the cow should always be looking to the left.
"""
device = torch.device("cuda:0")
DATA_DIR = (
@@ -304,7 +310,7 @@ class TestRenderingMeshes(unittest.TestCase):
mesh = load_objs_as_meshes([obj_filename], device=device)
# Init rasterizer settings
R, T = look_at_view_transform(2.7, 10, 20)
R, T = look_at_view_transform(2.7, 0, 0)
cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
raster_settings = RasterizationSettings(
image_size=512, blur_radius=0.0, faces_per_pixel=1, bin_size=0
@@ -313,7 +319,10 @@ class TestRenderingMeshes(unittest.TestCase):
# Init shader settings
materials = Materials(device=device)
lights = PointLights(device=device)
lights.location = torch.tensor([0.0, 0.0, -2.0], device=device)[None]
# Place light behind the cow in world space. The front of
# the cow is facing the -z direction.
lights.location = torch.tensor([0.0, 0.0, 2.0], device=device)[None]
# Init renderer
renderer = MeshRenderer(
@@ -328,18 +337,13 @@ class TestRenderingMeshes(unittest.TestCase):
rgb = images[0, ..., :3].squeeze().cpu()
# Load reference image
image_ref = load_rgb_image("test_texture_map.png")
image_ref = load_rgb_image("test_texture_map_back.png")
if DEBUG:
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
DATA_DIR / "DEBUG_texture_map.png"
DATA_DIR / "DEBUG_texture_map_back.png"
)
# There's a calculation instability on the corner of the ear of the cow.
# We ignore that pixel.
image_ref[137, 166] = 0
rgb[137, 166] = 0
self.assertTrue(torch.allclose(rgb, image_ref, atol=0.05))
# Check grad exists
@@ -352,10 +356,31 @@ class TestRenderingMeshes(unittest.TestCase):
images[0, ...].sum().backward()
self.assertIsNotNone(verts.grad)
##########################################
# Check rendering of the front of the cow
##########################################
R, T = look_at_view_transform(2.7, 0, 180)
cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
# Move light to the front of the cow in world space
lights.location = torch.tensor([0.0, 0.0, -2.0], device=device)[None]
images = renderer(mesh, cameras=cameras, lights=lights)
rgb = images[0, ..., :3].squeeze().cpu()
# Load reference image
image_ref = load_rgb_image("test_texture_map_front.png")
if DEBUG:
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
DATA_DIR / "DEBUG_texture_map_front.png"
)
#################################
# Add blurring to rasterization
#################################
R, T = look_at_view_transform(2.7, 0, 180)
cameras = OpenGLPerspectiveCameras(device=device, R=R, T=T)
blend_params = BlendParams(sigma=5e-4, gamma=1e-4)
raster_settings = RasterizationSettings(
image_size=512,
@@ -366,6 +391,7 @@ class TestRenderingMeshes(unittest.TestCase):
images = renderer(
mesh.clone(),
cameras=cameras,
raster_settings=raster_settings,
blend_params=blend_params,
)