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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
from math import radians
import torch
from pytorch3d.renderer.camera_utils import camera_to_eye_at_up, rotate_on_spot
from pytorch3d.renderer.cameras import (
get_world_to_view_transform,
look_at_view_transform,
PerspectiveCameras,
)
from pytorch3d.transforms import axis_angle_to_matrix
from torch.nn.functional import normalize
from .common_testing import TestCaseMixin
def _batched_dotprod(x: torch.Tensor, y: torch.Tensor):
"""
Takes two tensors of shape (N,3) and returns their batched
dot product along the last dimension as a tensor of shape
(N,).
"""
return torch.einsum("ij,ij->i", x, y)
class TestCameraUtils(TestCaseMixin, unittest.TestCase):
def setUp(self) -> None:
torch.manual_seed(42)
def test_invert_eye_at_up(self):
# Generate random cameras and check we can reconstruct their eye, at,
# and up vectors.
N = 13
eye = torch.rand(N, 3)
at = torch.rand(N, 3)
up = torch.rand(N, 3)
R, T = look_at_view_transform(eye=eye, at=at, up=up)
cameras = PerspectiveCameras(R=R, T=T)
eye2, at2, up2 = camera_to_eye_at_up(cameras.get_world_to_view_transform())
# The retrieved eye matches
self.assertClose(eye, eye2, atol=1e-5)
self.assertClose(cameras.get_camera_center(), eye)
# at-eye as retrieved must be a vector in the same direction as
# the original.
self.assertClose(normalize(at - eye), normalize(at2 - eye2))
# The up vector as retrieved should be rotated the same amount
# around at-eye as the original. The component in the at-eye
# direction is unimportant, as is the length.
# So check that (up x (at-eye)) as retrieved is in the same
# direction as its original value.
up_check = torch.cross(up, at - eye, dim=-1)
up_check2 = torch.cross(up2, at - eye, dim=-1)
self.assertClose(normalize(up_check), normalize(up_check2))
# Master check that we get the same camera if we reinitialise.
R2, T2 = look_at_view_transform(eye=eye2, at=at2, up=up2)
cameras2 = PerspectiveCameras(R=R2, T=T2)
cam_trans = cameras.get_world_to_view_transform()
cam_trans2 = cameras2.get_world_to_view_transform()
self.assertClose(cam_trans.get_matrix(), cam_trans2.get_matrix(), atol=1e-5)
def test_rotate_on_spot_yaw(self):
N = 14
eye = torch.rand(N, 3)
at = torch.rand(N, 3)
up = torch.rand(N, 3)
R, T = look_at_view_transform(eye=eye, at=at, up=up)
# Moving around the y axis looks left.
angles = torch.FloatTensor([0, -radians(10), 0])
rotation = axis_angle_to_matrix(angles)
R_rot, T_rot = rotate_on_spot(R, T, rotation)
eye_rot, at_rot, up_rot = camera_to_eye_at_up(
get_world_to_view_transform(R=R_rot, T=T_rot)
)
self.assertClose(eye, eye_rot, atol=1e-5)
# Make vectors pointing exactly left and up
left = torch.cross(up, at - eye, dim=-1)
left_rot = torch.cross(up_rot, at_rot - eye_rot, dim=-1)
fully_up = torch.cross(at - eye, left, dim=-1)
fully_up_rot = torch.cross(at_rot - eye_rot, left_rot, dim=-1)
# The up direction is unchanged
self.assertClose(normalize(fully_up), normalize(fully_up_rot), atol=1e-5)
# The camera has moved left
agree = _batched_dotprod(torch.cross(left, left_rot, dim=1), fully_up)
self.assertGreater(agree.min(), 0)
# Batch dimension for rotation
R_rot2, T_rot2 = rotate_on_spot(R, T, rotation.expand(N, 3, 3))
self.assertClose(R_rot, R_rot2)
self.assertClose(T_rot, T_rot2)
# No batch dimension for either
R_rot3, T_rot3 = rotate_on_spot(R[0], T[0], rotation)
self.assertClose(R_rot[:1], R_rot3)
self.assertClose(T_rot[:1], T_rot3)
# No batch dimension for R, T
R_rot4, T_rot4 = rotate_on_spot(R[0], T[0], rotation.expand(N, 3, 3))
self.assertClose(R_rot[:1].expand(N, 3, 3), R_rot4)
self.assertClose(T_rot[:1].expand(N, 3), T_rot4)
def test_rotate_on_spot_pitch(self):
N = 14
eye = torch.rand(N, 3)
at = torch.rand(N, 3)
up = torch.rand(N, 3)
R, T = look_at_view_transform(eye=eye, at=at, up=up)
# Moving around the x axis looks down.
angles = torch.FloatTensor([-radians(10), 0, 0])
rotation = axis_angle_to_matrix(angles)
R_rot, T_rot = rotate_on_spot(R, T, rotation)
eye_rot, at_rot, up_rot = camera_to_eye_at_up(
get_world_to_view_transform(R=R_rot, T=T_rot)
)
self.assertClose(eye, eye_rot, atol=1e-5)
# A vector pointing left is unchanged
left = torch.cross(up, at - eye, dim=-1)
left_rot = torch.cross(up_rot, at_rot - eye_rot, dim=-1)
self.assertClose(normalize(left), normalize(left_rot), atol=1e-5)
# The camera has moved down
fully_up = torch.cross(at - eye, left, dim=-1)
fully_up_rot = torch.cross(at_rot - eye_rot, left_rot, dim=-1)
agree = _batched_dotprod(torch.cross(fully_up, fully_up_rot, dim=1), left)
self.assertGreater(agree.min(), 0)
def test_rotate_on_spot_roll(self):
N = 14
eye = torch.rand(N, 3)
at = torch.rand(N, 3)
up = torch.rand(N, 3)
R, T = look_at_view_transform(eye=eye, at=at, up=up)
# Moving around the z axis rotates the image.
angles = torch.FloatTensor([0, 0, -radians(10)])
rotation = axis_angle_to_matrix(angles)
R_rot, T_rot = rotate_on_spot(R, T, rotation)
eye_rot, at_rot, up_rot = camera_to_eye_at_up(
get_world_to_view_transform(R=R_rot, T=T_rot)
)
self.assertClose(eye, eye_rot, atol=1e-5)
self.assertClose(normalize(at - eye), normalize(at_rot - eye), atol=1e-5)
# The camera has moved clockwise
agree = _batched_dotprod(torch.cross(up, up_rot, dim=1), at - eye)
self.assertGreater(agree.min(), 0)
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