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pytorch3d/tests/test_cameras.py
Eduardo Henrique Arnold d0cec028c7 Fix look_at_view_transform when object location is not (0,0,0) (#230) 
Summary:
The look_at_view_transform did not give the correct results when the object location `at` was not (0,0,0).

The problem was on computing the cameras' location in world's coordinate `C`. It only took into account the camera position from spherical angles, but ignored the object location in the world's coordinate system. I simply modified the C tensor to take into account the object's location which is not necessarily in the origin.

I ran unit tests and all but 4 failed with the same error message: `RuntimeError: CUDA error: invalid device ordinal`. However the same happens before this patch, so I believe these errors are unrelated.

Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/230

Reviewed By: gkioxari

Differential Revision: D23278126

Pulled By: nikhilaravi

fbshipit-source-id: c06e891bc46de8222325ee7b37aa43cde44648e8
2020-08-21 23:43:47 -07:00

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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
# Some of the code below is adapted from Soft Rasterizer (SoftRas)
#
# Copyright (c) 2017 Hiroharu Kato
# Copyright (c) 2018 Nikos Kolotouros
# Copyright (c) 2019 Shichen Liu
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import math
import unittest
import numpy as np
import torch
from common_testing import TestCaseMixin
from pytorch3d.renderer.cameras import OpenGLOrthographicCameras # deprecated
from pytorch3d.renderer.cameras import OpenGLPerspectiveCameras # deprecated
from pytorch3d.renderer.cameras import SfMOrthographicCameras # deprecated
from pytorch3d.renderer.cameras import SfMPerspectiveCameras # deprecated
from pytorch3d.renderer.cameras import (
CamerasBase,
FoVOrthographicCameras,
FoVPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
camera_position_from_spherical_angles,
get_world_to_view_transform,
look_at_rotation,
look_at_view_transform,
)
from pytorch3d.transforms import Transform3d
from pytorch3d.transforms.so3 import so3_exponential_map
# Naive function adapted from SoftRasterizer for test purposes.
def perspective_project_naive(points, fov=60.0):
"""
Compute perspective projection from a given viewing angle.
Args:
points: (N, V, 3) representing the padded points.
viewing angle: degrees
Returns:
(N, V, 3) tensor of projected points preserving the view space z
coordinate (no z renormalization)
"""
device = points.device
halfFov = torch.tensor((fov / 2) / 180 * np.pi, dtype=torch.float32, device=device)
scale = torch.tan(halfFov[None])
scale = scale[:, None]
z = points[:, :, 2]
x = points[:, :, 0] / z / scale
y = points[:, :, 1] / z / scale
points = torch.stack((x, y, z), dim=2)
return points
def sfm_perspective_project_naive(points, fx=1.0, fy=1.0, p0x=0.0, p0y=0.0):
"""
Compute perspective projection using focal length and principal point.
Args:
points: (N, V, 3) representing the padded points.
fx: world units
fy: world units
p0x: pixels
p0y: pixels
Returns:
(N, V, 3) tensor of projected points.
"""
z = points[:, :, 2]
x = (points[:, :, 0] * fx) / z + p0x
y = (points[:, :, 1] * fy) / z + p0y
points = torch.stack((x, y, 1.0 / z), dim=2)
return points
# Naive function adapted from SoftRasterizer for test purposes.
def orthographic_project_naive(points, scale_xyz=(1.0, 1.0, 1.0)):
"""
Compute orthographic projection from a given angle
Args:
points: (N, V, 3) representing the padded points.
scaled: (N, 3) scaling factors for each of xyz directions
Returns:
(N, V, 3) tensor of projected points preserving the view space z
coordinate (no z renormalization).
"""
if not torch.is_tensor(scale_xyz):
scale_xyz = torch.tensor(scale_xyz)
scale_xyz = scale_xyz.view(-1, 3)
z = points[:, :, 2]
x = points[:, :, 0] * scale_xyz[:, 0]
y = points[:, :, 1] * scale_xyz[:, 1]
points = torch.stack((x, y, z), dim=2)
return points
def ndc_to_screen_points_naive(points, imsize):
"""
Transforms points from PyTorch3D's NDC space to screen space
Args:
points: (N, V, 3) representing padded points
imsize: (N, 2) image size = (width, height)
Returns:
(N, V, 3) tensor of transformed points
"""
imwidth, imheight = imsize.unbind(1)
imwidth = imwidth.view(-1, 1)
imheight = imheight.view(-1, 1)
x, y, z = points.unbind(2)
x = (1.0 - x) * (imwidth - 1) / 2.0
y = (1.0 - y) * (imheight - 1) / 2.0
return torch.stack((x, y, z), dim=2)
class TestCameraHelpers(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
super().setUp()
torch.manual_seed(42)
np.random.seed(42)
def test_look_at_view_transform_from_eye_point_tuple(self):
dist = math.sqrt(2)
elev = math.pi / 4
azim = 0.0
eye = ((0.0, 1.0, 1.0),)
# using passed values for dist, elev, azim
R, t = look_at_view_transform(dist, elev, azim, degrees=False)
# using other values for dist, elev, azim - eye overrides
R_eye, t_eye = look_at_view_transform(dist=3, elev=2, azim=1, eye=eye)
# using only eye value
R_eye_only, t_eye_only = look_at_view_transform(eye=eye)
self.assertTrue(torch.allclose(R, R_eye, atol=2e-7))
self.assertTrue(torch.allclose(t, t_eye, atol=2e-7))
self.assertTrue(torch.allclose(R, R_eye_only, atol=2e-7))
self.assertTrue(torch.allclose(t, t_eye_only, atol=2e-7))
def test_look_at_view_transform_default_values(self):
dist = 1.0
elev = 0.0
azim = 0.0
# Using passed values for dist, elev, azim
R, t = look_at_view_transform(dist, elev, azim)
# Using default dist=1.0, elev=0.0, azim=0.0
R_default, t_default = look_at_view_transform()
# test default = passed = expected
self.assertTrue(torch.allclose(R, R_default, atol=2e-7))
self.assertTrue(torch.allclose(t, t_default, atol=2e-7))
def test_look_at_view_transform_non_default_at_position(self):
dist = 1.0
elev = 0.0
azim = 0.0
at = ((1, 1, 1),)
# Using passed values for dist, elev, azim, at
R, t = look_at_view_transform(dist, elev, azim, at=at)
# Using default dist=1.0, elev=0.0, azim=0.0
R_default, t_default = look_at_view_transform()
# test default = passed = expected
# R must be the same, t must be translated by (1,-1,1) with respect to t_default
t_trans = torch.tensor([1, -1, 1], dtype=torch.float32).view(1, 3)
self.assertTrue(torch.allclose(R, R_default, atol=2e-7))
self.assertTrue(torch.allclose(t, t_default + t_trans, atol=2e-7))
def test_camera_position_from_angles_python_scalar(self):
dist = 2.7
elev = 90.0
azim = 0.0
expected_position = torch.tensor([0.0, 2.7, 0.0], dtype=torch.float32).view(
1, 3
)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=2e-7)
def test_camera_position_from_angles_python_scalar_radians(self):
dist = 2.7
elev = math.pi / 2
azim = 0.0
expected_position = torch.tensor([0.0, 2.7, 0.0], dtype=torch.float32)
expected_position = expected_position.view(1, 3)
position = camera_position_from_spherical_angles(
dist, elev, azim, degrees=False
)
self.assertClose(position, expected_position, atol=2e-7)
def test_camera_position_from_angles_torch_scalars(self):
dist = torch.tensor(2.7)
elev = torch.tensor(0.0)
azim = torch.tensor(90.0)
expected_position = torch.tensor([2.7, 0.0, 0.0], dtype=torch.float32).view(
1, 3
)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=2e-7)
def test_camera_position_from_angles_mixed_scalars(self):
dist = 2.7
elev = torch.tensor(0.0)
azim = 90.0
expected_position = torch.tensor([2.7, 0.0, 0.0], dtype=torch.float32).view(
1, 3
)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=2e-7)
def test_camera_position_from_angles_torch_scalar_grads(self):
dist = torch.tensor(2.7, requires_grad=True)
elev = torch.tensor(45.0, requires_grad=True)
azim = torch.tensor(45.0)
position = camera_position_from_spherical_angles(dist, elev, azim)
position.sum().backward()
self.assertTrue(hasattr(elev, "grad"))
self.assertTrue(hasattr(dist, "grad"))
elev_grad = elev.grad.clone()
dist_grad = dist.grad.clone()
elev = math.pi / 180.0 * elev.detach()
azim = math.pi / 180.0 * azim
grad_dist = (
torch.cos(elev) * torch.sin(azim)
+ torch.sin(elev)
+ torch.cos(elev) * torch.cos(azim)
)
grad_elev = (
-(torch.sin(elev)) * torch.sin(azim)
+ torch.cos(elev)
- torch.sin(elev) * torch.cos(azim)
)
grad_elev = dist * (math.pi / 180.0) * grad_elev
self.assertClose(elev_grad, grad_elev)
self.assertClose(dist_grad, grad_dist)
def test_camera_position_from_angles_vectors(self):
dist = torch.tensor([2.0, 2.0])
elev = torch.tensor([0.0, 90.0])
azim = torch.tensor([90.0, 0.0])
expected_position = torch.tensor(
[[2.0, 0.0, 0.0], [0.0, 2.0, 0.0]], dtype=torch.float32
)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=2e-7)
def test_camera_position_from_angles_vectors_broadcast(self):
dist = torch.tensor([2.0, 3.0, 5.0])
elev = torch.tensor([0.0])
azim = torch.tensor([90.0])
expected_position = torch.tensor(
[[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]], dtype=torch.float32
)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=3e-7)
def test_camera_position_from_angles_vectors_mixed_broadcast(self):
dist = torch.tensor([2.0, 3.0, 5.0])
elev = 0.0
azim = torch.tensor(90.0)
expected_position = torch.tensor(
[[2.0, 0.0, 0.0], [3.0, 0.0, 0.0], [5.0, 0.0, 0.0]], dtype=torch.float32
)
position = camera_position_from_spherical_angles(dist, elev, azim)
self.assertClose(position, expected_position, atol=3e-7)
def test_camera_position_from_angles_vectors_mixed_broadcast_grads(self):
dist = torch.tensor([2.0, 3.0, 5.0], requires_grad=True)
elev = torch.tensor(45.0, requires_grad=True)
azim = 45.0
position = camera_position_from_spherical_angles(dist, elev, azim)
position.sum().backward()
self.assertTrue(hasattr(elev, "grad"))
self.assertTrue(hasattr(dist, "grad"))
elev_grad = elev.grad.clone()
dist_grad = dist.grad.clone()
azim = torch.tensor(azim)
elev = math.pi / 180.0 * elev.detach()
azim = math.pi / 180.0 * azim
grad_dist = (
torch.cos(elev) * torch.sin(azim)
+ torch.sin(elev)
+ torch.cos(elev) * torch.cos(azim)
)
grad_elev = (
-(torch.sin(elev)) * torch.sin(azim)
+ torch.cos(elev)
- torch.sin(elev) * torch.cos(azim)
)
grad_elev = (dist * (math.pi / 180.0) * grad_elev).sum()
self.assertClose(elev_grad, grad_elev)
self.assertClose(dist_grad, torch.full([3], grad_dist))
def test_camera_position_from_angles_vectors_bad_broadcast(self):
# Batch dim for broadcast must be N or 1
dist = torch.tensor([2.0, 3.0, 5.0])
elev = torch.tensor([0.0, 90.0])
azim = torch.tensor([90.0])
with self.assertRaises(ValueError):
camera_position_from_spherical_angles(dist, elev, azim)
def test_look_at_rotation_python_list(self):
camera_position = [[0.0, 0.0, -1.0]] # camera pointing along negative z
rot_mat = look_at_rotation(camera_position)
self.assertClose(rot_mat, torch.eye(3)[None], atol=2e-7)
def test_look_at_rotation_input_fail(self):
camera_position = [-1.0] # expected to have xyz positions
with self.assertRaises(ValueError):
look_at_rotation(camera_position)
def test_look_at_rotation_list_broadcast(self):
# fmt: off
camera_positions = [[0.0, 0.0, -1.0], [0.0, 0.0, 1.0]]
rot_mats_expected = torch.tensor(
[
[
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]
],
[
[-1.0, 0.0, 0.0], # noqa: E241, E201
[ 0.0, 1.0, 0.0], # noqa: E241, E201
[ 0.0, 0.0, -1.0] # noqa: E241, E201
],
],
dtype=torch.float32
)
# fmt: on
rot_mats = look_at_rotation(camera_positions)
self.assertClose(rot_mats, rot_mats_expected, atol=2e-7)
def test_look_at_rotation_tensor_broadcast(self):
# fmt: off
camera_positions = torch.tensor([
[0.0, 0.0, -1.0],
[0.0, 0.0, 1.0] # noqa: E241, E201
], dtype=torch.float32)
rot_mats_expected = torch.tensor(
[
[
[1.0, 0.0, 0.0],
[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]
],
[
[-1.0, 0.0, 0.0], # noqa: E241, E201
[ 0.0, 1.0, 0.0], # noqa: E241, E201
[ 0.0, 0.0, -1.0] # noqa: E241, E201
],
],
dtype=torch.float32
)
# fmt: on
rot_mats = look_at_rotation(camera_positions)
self.assertClose(rot_mats, rot_mats_expected, atol=2e-7)
def test_look_at_rotation_tensor_grad(self):
camera_position = torch.tensor([[0.0, 0.0, -1.0]], requires_grad=True)
rot_mat = look_at_rotation(camera_position)
rot_mat.sum().backward()
self.assertTrue(hasattr(camera_position, "grad"))
self.assertClose(
camera_position.grad, torch.zeros_like(camera_position), atol=2e-7
)
def test_view_transform(self):
T = torch.tensor([0.0, 0.0, -1.0], requires_grad=True).view(1, -1)
R = look_at_rotation(T)
RT = get_world_to_view_transform(R=R, T=T)
self.assertTrue(isinstance(RT, Transform3d))
class TestCamerasCommon(TestCaseMixin, unittest.TestCase):
def test_view_transform_class_method(self):
T = torch.tensor([0.0, 0.0, -1.0], requires_grad=True).view(1, -1)
R = look_at_rotation(T)
RT = get_world_to_view_transform(R=R, T=T)
for cam_type in (
OpenGLPerspectiveCameras,
OpenGLOrthographicCameras,
SfMOrthographicCameras,
SfMPerspectiveCameras,
FoVOrthographicCameras,
FoVPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
):
cam = cam_type(R=R, T=T)
RT_class = cam.get_world_to_view_transform()
self.assertTrue(torch.allclose(RT.get_matrix(), RT_class.get_matrix()))
self.assertTrue(isinstance(RT, Transform3d))
def test_get_camera_center(self, batch_size=10):
T = torch.randn(batch_size, 3)
R = so3_exponential_map(torch.randn(batch_size, 3) * 3.0)
for cam_type in (
OpenGLPerspectiveCameras,
OpenGLOrthographicCameras,
SfMOrthographicCameras,
SfMPerspectiveCameras,
FoVOrthographicCameras,
FoVPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
):
cam = cam_type(R=R, T=T)
C = cam.get_camera_center()
C_ = -torch.bmm(R, T[:, :, None])[:, :, 0]
self.assertTrue(torch.allclose(C, C_, atol=1e-05))
@staticmethod
def init_random_cameras(cam_type: CamerasBase, batch_size: int):
cam_params = {}
T = torch.randn(batch_size, 3) * 0.03
T[:, 2] = 4
R = so3_exponential_map(torch.randn(batch_size, 3) * 3.0)
cam_params = {"R": R, "T": T}
if cam_type in (OpenGLPerspectiveCameras, OpenGLOrthographicCameras):
cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1
cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"]
if cam_type == OpenGLPerspectiveCameras:
cam_params["fov"] = torch.rand(batch_size) * 60 + 30
cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5
else:
cam_params["top"] = torch.rand(batch_size) * 0.2 + 0.9
cam_params["bottom"] = -(torch.rand(batch_size)) * 0.2 - 0.9
cam_params["left"] = -(torch.rand(batch_size)) * 0.2 - 0.9
cam_params["right"] = torch.rand(batch_size) * 0.2 + 0.9
elif cam_type in (FoVPerspectiveCameras, FoVOrthographicCameras):
cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1
cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"]
if cam_type == FoVPerspectiveCameras:
cam_params["fov"] = torch.rand(batch_size) * 60 + 30
cam_params["aspect_ratio"] = torch.rand(batch_size) * 0.5 + 0.5
else:
cam_params["max_y"] = torch.rand(batch_size) * 0.2 + 0.9
cam_params["min_y"] = -(torch.rand(batch_size)) * 0.2 - 0.9
cam_params["min_x"] = -(torch.rand(batch_size)) * 0.2 - 0.9
cam_params["max_x"] = torch.rand(batch_size) * 0.2 + 0.9
elif cam_type in (
SfMOrthographicCameras,
SfMPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
):
cam_params["focal_length"] = torch.rand(batch_size) * 10 + 0.1
cam_params["principal_point"] = torch.randn((batch_size, 2))
else:
raise ValueError(str(cam_type))
return cam_type(**cam_params)
@staticmethod
def init_equiv_cameras_ndc_screen(cam_type: CamerasBase, batch_size: int):
T = torch.randn(batch_size, 3) * 0.03
T[:, 2] = 4
R = so3_exponential_map(torch.randn(batch_size, 3) * 3.0)
screen_cam_params = {"R": R, "T": T}
ndc_cam_params = {"R": R, "T": T}
if cam_type in (OrthographicCameras, PerspectiveCameras):
ndc_cam_params["focal_length"] = torch.rand((batch_size, 2)) * 3.0
ndc_cam_params["principal_point"] = torch.randn((batch_size, 2))
image_size = torch.randint(low=2, high=64, size=(batch_size, 2))
screen_cam_params["image_size"] = image_size
screen_cam_params["focal_length"] = (
ndc_cam_params["focal_length"] * image_size / 2.0
)
screen_cam_params["principal_point"] = (
(1.0 - ndc_cam_params["principal_point"]) * image_size / 2.0
)
else:
raise ValueError(str(cam_type))
return cam_type(**ndc_cam_params), cam_type(**screen_cam_params)
def test_unproject_points(self, batch_size=50, num_points=100):
"""
Checks that an unprojection of a randomly projected point cloud
stays the same.
"""
for cam_type in (
SfMOrthographicCameras,
OpenGLPerspectiveCameras,
OpenGLOrthographicCameras,
SfMPerspectiveCameras,
FoVOrthographicCameras,
FoVPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
):
# init the cameras
cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
# xyz - the ground truth point cloud
xyz = torch.randn(batch_size, num_points, 3) * 0.3
# xyz in camera coordinates
xyz_cam = cameras.get_world_to_view_transform().transform_points(xyz)
# depth = z-component of xyz_cam
depth = xyz_cam[:, :, 2:]
# project xyz
xyz_proj = cameras.transform_points(xyz)
xy, cam_depth = xyz_proj.split(2, dim=2)
# input to the unprojection function
xy_depth = torch.cat((xy, depth), dim=2)
for to_world in (False, True):
if to_world:
matching_xyz = xyz
else:
matching_xyz = xyz_cam
# if we have FoV (= OpenGL) cameras
# test for scaled_depth_input=True/False
if cam_type in (
OpenGLPerspectiveCameras,
OpenGLOrthographicCameras,
FoVPerspectiveCameras,
FoVOrthographicCameras,
):
for scaled_depth_input in (True, False):
if scaled_depth_input:
xy_depth_ = xyz_proj
else:
xy_depth_ = xy_depth
xyz_unproj = cameras.unproject_points(
xy_depth_,
world_coordinates=to_world,
scaled_depth_input=scaled_depth_input,
)
self.assertTrue(
torch.allclose(xyz_unproj, matching_xyz, atol=1e-4)
)
else:
xyz_unproj = cameras.unproject_points(
xy_depth, world_coordinates=to_world
)
self.assertTrue(torch.allclose(xyz_unproj, matching_xyz, atol=1e-4))
def test_project_points_screen(self, batch_size=50, num_points=100):
"""
Checks that an unprojection of a randomly projected point cloud
stays the same.
"""
for cam_type in (
OpenGLOrthographicCameras,
OpenGLPerspectiveCameras,
SfMOrthographicCameras,
SfMPerspectiveCameras,
FoVOrthographicCameras,
FoVPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
):
# init the cameras
cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
# xyz - the ground truth point cloud
xyz = torch.randn(batch_size, num_points, 3) * 0.3
# image size
image_size = torch.randint(low=2, high=64, size=(batch_size, 2))
# project points
xyz_project_ndc = cameras.transform_points(xyz)
xyz_project_screen = cameras.transform_points_screen(xyz, image_size)
# naive
xyz_project_screen_naive = ndc_to_screen_points_naive(
xyz_project_ndc, image_size
)
self.assertClose(xyz_project_screen, xyz_project_screen_naive)
def test_equiv_project_points(self, batch_size=50, num_points=100):
"""
Checks that NDC and screen cameras project points to ndc correctly.
Applies only to OrthographicCameras and PerspectiveCameras.
"""
for cam_type in (OrthographicCameras, PerspectiveCameras):
# init the cameras
(
ndc_cameras,
screen_cameras,
) = TestCamerasCommon.init_equiv_cameras_ndc_screen(cam_type, batch_size)
# xyz - the ground truth point cloud
xyz = torch.randn(batch_size, num_points, 3) * 0.3
# project points
xyz_ndc_cam = ndc_cameras.transform_points(xyz)
xyz_screen_cam = screen_cameras.transform_points(xyz)
self.assertClose(xyz_ndc_cam, xyz_screen_cam, atol=1e-6)
def test_clone(self, batch_size: int = 10):
"""
Checks the clone function of the cameras.
"""
for cam_type in (
SfMOrthographicCameras,
OpenGLPerspectiveCameras,
OpenGLOrthographicCameras,
SfMPerspectiveCameras,
FoVOrthographicCameras,
FoVPerspectiveCameras,
OrthographicCameras,
PerspectiveCameras,
):
cameras = TestCamerasCommon.init_random_cameras(cam_type, batch_size)
cameras = cameras.to(torch.device("cpu"))
cameras_clone = cameras.clone()
for var in cameras.__dict__.keys():
val = getattr(cameras, var)
val_clone = getattr(cameras_clone, var)
if torch.is_tensor(val):
self.assertClose(val, val_clone)
self.assertSeparate(val, val_clone)
else:
self.assertTrue(val == val_clone)
############################################################
# FoVPerspective Camera #
############################################################
class TestFoVPerspectiveProjection(TestCaseMixin, unittest.TestCase):
def test_perspective(self):
far = 10.0
near = 1.0
cameras = FoVPerspectiveCameras(znear=near, zfar=far, fov=60.0)
P = cameras.get_projection_transform()
# vertices are at the far clipping plane so z gets mapped to 1.
vertices = torch.tensor([1, 2, far], dtype=torch.float32)
projected_verts = torch.tensor(
[np.sqrt(3) / far, 2 * np.sqrt(3) / far, 1.0], dtype=torch.float32
)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
v2 = perspective_project_naive(vertices, fov=60.0)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(far * v1[..., 2], v2[..., 2])
self.assertClose(v1.squeeze(), projected_verts)
# vertices are at the near clipping plane so z gets mapped to 0.0.
vertices[..., 2] = near
projected_verts = torch.tensor(
[np.sqrt(3) / near, 2 * np.sqrt(3) / near, 0.0], dtype=torch.float32
)
v1 = P.transform_points(vertices)
v2 = perspective_project_naive(vertices, fov=60.0)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v1.squeeze(), projected_verts)
def test_perspective_kwargs(self):
cameras = FoVPerspectiveCameras(znear=5.0, zfar=100.0, fov=0.0)
# Override defaults by passing in values to get_projection_transform
far = 10.0
P = cameras.get_projection_transform(znear=1.0, zfar=far, fov=60.0)
vertices = torch.tensor([1, 2, far], dtype=torch.float32)
projected_verts = torch.tensor(
[np.sqrt(3) / far, 2 * np.sqrt(3) / far, 1.0], dtype=torch.float32
)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
self.assertClose(v1.squeeze(), projected_verts)
def test_perspective_mixed_inputs_broadcast(self):
far = torch.tensor([10.0, 20.0], dtype=torch.float32)
near = 1.0
fov = torch.tensor(60.0)
cameras = FoVPerspectiveCameras(znear=near, zfar=far, fov=fov)
P = cameras.get_projection_transform()
vertices = torch.tensor([1, 2, 10], dtype=torch.float32)
z1 = 1.0 # vertices at far clipping plane so z = 1.0
z2 = (20.0 / (20.0 - 1.0) * 10.0 + -20.0 / (20.0 - 1.0)) / 10.0
projected_verts = torch.tensor(
[
[np.sqrt(3) / 10.0, 2 * np.sqrt(3) / 10.0, z1],
[np.sqrt(3) / 10.0, 2 * np.sqrt(3) / 10.0, z2],
],
dtype=torch.float32,
)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
v2 = perspective_project_naive(vertices, fov=60.0)
self.assertClose(v1[..., :2], torch.cat([v2, v2])[..., :2])
self.assertClose(v1.squeeze(), projected_verts)
def test_perspective_mixed_inputs_grad(self):
far = torch.tensor([10.0])
near = 1.0
fov = torch.tensor(60.0, requires_grad=True)
cameras = FoVPerspectiveCameras(znear=near, zfar=far, fov=fov)
P = cameras.get_projection_transform()
vertices = torch.tensor([1, 2, 10], dtype=torch.float32)
vertices_batch = vertices[None, None, :]
v1 = P.transform_points(vertices_batch).squeeze()
v1.sum().backward()
self.assertTrue(hasattr(fov, "grad"))
fov_grad = fov.grad.clone()
half_fov_rad = (math.pi / 180.0) * fov.detach() / 2.0
grad_cotan = -(1.0 / (torch.sin(half_fov_rad) ** 2.0) * 1 / 2.0)
grad_fov = (math.pi / 180.0) * grad_cotan
grad_fov = (vertices[0] + vertices[1]) * grad_fov / 10.0
self.assertClose(fov_grad, grad_fov)
def test_camera_class_init(self):
device = torch.device("cuda:0")
cam = FoVPerspectiveCameras(znear=10.0, zfar=(100.0, 200.0))
# Check broadcasting
self.assertTrue(cam.znear.shape == (2,))
self.assertTrue(cam.zfar.shape == (2,))
# update znear element 1
cam[1].znear = 20.0
self.assertTrue(cam.znear[1] == 20.0)
# Get item and get value
c0 = cam[0]
self.assertTrue(c0.zfar == 100.0)
# Test to
new_cam = cam.to(device=device)
self.assertTrue(new_cam.device == device)
def test_get_full_transform(self):
cam = FoVPerspectiveCameras()
T = torch.tensor([0.0, 0.0, 1.0]).view(1, -1)
R = look_at_rotation(T)
P = cam.get_full_projection_transform(R=R, T=T)
self.assertTrue(isinstance(P, Transform3d))
self.assertClose(cam.R, R)
self.assertClose(cam.T, T)
def test_transform_points(self):
# Check transform_points methods works with default settings for
# RT and P
far = 10.0
cam = FoVPerspectiveCameras(znear=1.0, zfar=far, fov=60.0)
points = torch.tensor([1, 2, far], dtype=torch.float32)
points = points.view(1, 1, 3).expand(5, 10, -1)
projected_points = torch.tensor(
[np.sqrt(3) / far, 2 * np.sqrt(3) / far, 1.0], dtype=torch.float32
)
projected_points = projected_points.view(1, 1, 3).expand(5, 10, -1)
new_points = cam.transform_points(points)
self.assertClose(new_points, projected_points)
############################################################
# FoVOrthographic Camera #
############################################################
class TestFoVOrthographicProjection(TestCaseMixin, unittest.TestCase):
def test_orthographic(self):
far = 10.0
near = 1.0
cameras = FoVOrthographicCameras(znear=near, zfar=far)
P = cameras.get_projection_transform()
vertices = torch.tensor([1, 2, far], dtype=torch.float32)
projected_verts = torch.tensor([1, 2, 1], dtype=torch.float32)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v1.squeeze(), projected_verts)
vertices[..., 2] = near
projected_verts[2] = 0.0
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v1.squeeze(), projected_verts)
def test_orthographic_scaled(self):
vertices = torch.tensor([1, 2, 0.5], dtype=torch.float32)
vertices = vertices[None, None, :]
scale = torch.tensor([[2.0, 0.5, 20]])
# applying the scale puts the z coordinate at the far clipping plane
# so the z is mapped to 1.0
projected_verts = torch.tensor([2, 1, 1], dtype=torch.float32)
cameras = FoVOrthographicCameras(znear=1.0, zfar=10.0, scale_xyz=scale)
P = cameras.get_projection_transform()
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices, scale)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v1, projected_verts[None, None])
def test_orthographic_kwargs(self):
cameras = FoVOrthographicCameras(znear=5.0, zfar=100.0)
far = 10.0
P = cameras.get_projection_transform(znear=1.0, zfar=far)
vertices = torch.tensor([1, 2, far], dtype=torch.float32)
projected_verts = torch.tensor([1, 2, 1], dtype=torch.float32)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
self.assertClose(v1.squeeze(), projected_verts)
def test_orthographic_mixed_inputs_broadcast(self):
far = torch.tensor([10.0, 20.0])
near = 1.0
cameras = FoVOrthographicCameras(znear=near, zfar=far)
P = cameras.get_projection_transform()
vertices = torch.tensor([1.0, 2.0, 10.0], dtype=torch.float32)
z2 = 1.0 / (20.0 - 1.0) * 10.0 + -1.0 / (20.0 - 1.0)
projected_verts = torch.tensor(
[[1.0, 2.0, 1.0], [1.0, 2.0, z2]], dtype=torch.float32
)
vertices = vertices[None, None, :]
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices)
self.assertClose(v1[..., :2], torch.cat([v2, v2])[..., :2])
self.assertClose(v1.squeeze(), projected_verts)
def test_orthographic_mixed_inputs_grad(self):
far = torch.tensor([10.0])
near = 1.0
scale = torch.tensor([[1.0, 1.0, 1.0]], requires_grad=True)
cameras = FoVOrthographicCameras(znear=near, zfar=far, scale_xyz=scale)
P = cameras.get_projection_transform()
vertices = torch.tensor([1.0, 2.0, 10.0], dtype=torch.float32)
vertices_batch = vertices[None, None, :]
v1 = P.transform_points(vertices_batch)
v1.sum().backward()
self.assertTrue(hasattr(scale, "grad"))
scale_grad = scale.grad.clone()
grad_scale = torch.tensor(
[
[
vertices[0] * P._matrix[:, 0, 0],
vertices[1] * P._matrix[:, 1, 1],
vertices[2] * P._matrix[:, 2, 2],
]
]
)
self.assertClose(scale_grad, grad_scale)
############################################################
# Orthographic Camera #
############################################################
class TestOrthographicProjection(TestCaseMixin, unittest.TestCase):
def test_orthographic(self):
cameras = OrthographicCameras()
P = cameras.get_projection_transform()
vertices = torch.randn([3, 4, 3], dtype=torch.float32)
projected_verts = vertices.clone()
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(vertices)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v1, projected_verts)
def test_orthographic_scaled(self):
focal_length_x = 10.0
focal_length_y = 15.0
cameras = OrthographicCameras(focal_length=((focal_length_x, focal_length_y),))
P = cameras.get_projection_transform()
vertices = torch.randn([3, 4, 3], dtype=torch.float32)
projected_verts = vertices.clone()
projected_verts[:, :, 0] *= focal_length_x
projected_verts[:, :, 1] *= focal_length_y
v1 = P.transform_points(vertices)
v2 = orthographic_project_naive(
vertices, scale_xyz=(focal_length_x, focal_length_y, 1.0)
)
v3 = cameras.transform_points(vertices)
self.assertClose(v1[..., :2], v2[..., :2])
self.assertClose(v3[..., :2], v2[..., :2])
self.assertClose(v1, projected_verts)
def test_orthographic_kwargs(self):
cameras = OrthographicCameras(focal_length=5.0, principal_point=((2.5, 2.5),))
P = cameras.get_projection_transform(
focal_length=2.0, principal_point=((2.5, 3.5),)
)
vertices = torch.randn([3, 4, 3], dtype=torch.float32)
projected_verts = vertices.clone()
projected_verts[:, :, :2] *= 2.0
projected_verts[:, :, 0] += 2.5
projected_verts[:, :, 1] += 3.5
v1 = P.transform_points(vertices)
self.assertClose(v1, projected_verts)
############################################################
# Perspective Camera #
############################################################
class TestPerspectiveProjection(TestCaseMixin, unittest.TestCase):
def test_perspective(self):
cameras = PerspectiveCameras()
P = cameras.get_projection_transform()
vertices = torch.randn([3, 4, 3], dtype=torch.float32)
v1 = P.transform_points(vertices)
v2 = sfm_perspective_project_naive(vertices)
self.assertClose(v1, v2)
def test_perspective_scaled(self):
focal_length_x = 10.0
focal_length_y = 15.0
p0x = 15.0
p0y = 30.0
cameras = PerspectiveCameras(
focal_length=((focal_length_x, focal_length_y),),
principal_point=((p0x, p0y),),
)
P = cameras.get_projection_transform()
vertices = torch.randn([3, 4, 3], dtype=torch.float32)
v1 = P.transform_points(vertices)
v2 = sfm_perspective_project_naive(
vertices, fx=focal_length_x, fy=focal_length_y, p0x=p0x, p0y=p0y
)
v3 = cameras.transform_points(vertices)
self.assertClose(v1, v2)
self.assertClose(v3[..., :2], v2[..., :2])
def test_perspective_kwargs(self):
cameras = PerspectiveCameras(focal_length=5.0, principal_point=((2.5, 2.5),))
P = cameras.get_projection_transform(
focal_length=2.0, principal_point=((2.5, 3.5),)
)
vertices = torch.randn([3, 4, 3], dtype=torch.float32)
v1 = P.transform_points(vertices)
v2 = sfm_perspective_project_naive(vertices, fx=2.0, fy=2.0, p0x=2.5, p0y=3.5)
self.assertClose(v1, v2, atol=1e-6)
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