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Commit 0f85fae5 authored by zhangwenwei's avatar zhangwenwei
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Full unittest for cam box

parent 6d37a3d0
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Showing with 272 additions and 14 deletions
mmdet3d/core/bbox/structures/base_box3d.py
View file @ 0f85fae5
...@@ -117,7 +117,6 @@ class BaseInstance3DBoxes(object): ...@@ -117,7 +117,6 @@ class BaseInstance3DBoxes(object):
Args: Args:
trans_vector (torch.Tensor): translation vector of size 1x3 trans_vector (torch.Tensor): translation vector of size 1x3
""" """
if not isinstance(trans_vector, torch.Tensor): if not isinstance(trans_vector, torch.Tensor):
trans_vector = self.tensor.new_tensor(trans_vector) trans_vector = self.tensor.new_tensor(trans_vector)
......
mmdet3d/core/bbox/structures/box_3d_mode.py
View file @ 0f85fae5
...@@ -3,6 +3,10 @@ from enum import IntEnum, unique ...@@ -3,6 +3,10 @@ from enum import IntEnum, unique
import numpy as np import numpy as np
import torch import torch
from .base_box3d import BaseInstance3DBoxes
from .cam_box3d import CameraInstance3DBoxes
from .lidar_box3d import LiDARInstance3DBoxes
@unique @unique
class Box3DMode(IntEnum): class Box3DMode(IntEnum):
...@@ -16,6 +20,7 @@ class Box3DMode(IntEnum): ...@@ -16,6 +20,7 @@ class Box3DMode(IntEnum):
| / | /
| / | /
left y <------ 0 left y <------ 0
The relative coordinate of bottom center in a LiDAR box is [0.5, 0.5, 0], The relative coordinate of bottom center in a LiDAR box is [0.5, 0.5, 0],
and the yaw is around the z axis, thus the rotation axis=2. and the yaw is around the z axis, thus the rotation axis=2.
...@@ -30,6 +35,7 @@ class Box3DMode(IntEnum): ...@@ -30,6 +35,7 @@ class Box3DMode(IntEnum):
| |
v v
down y down y
The relative coordinate of bottom center in a CAM box is [0.5, 1.0, 0.5], The relative coordinate of bottom center in a CAM box is [0.5, 1.0, 0.5],
and the yaw is around the y axis, thus the rotation axis=1. and the yaw is around the y axis, thus the rotation axis=1.
...@@ -41,6 +47,7 @@ class Box3DMode(IntEnum): ...@@ -41,6 +47,7 @@ class Box3DMode(IntEnum):
| / | /
| / | /
0 ------> x right 0 ------> x right
The relative coordinate of bottom center in a DEPTH box is [0.5, 0.5, 0], The relative coordinate of bottom center in a DEPTH box is [0.5, 0.5, 0],
and the yaw is around the z axis, thus the rotation axis=2. and the yaw is around the z axis, thus the rotation axis=2.
""" """
...@@ -72,6 +79,7 @@ class Box3DMode(IntEnum): ...@@ -72,6 +79,7 @@ class Box3DMode(IntEnum):
return box return box
is_numpy = isinstance(box, np.ndarray) is_numpy = isinstance(box, np.ndarray)
is_Instance3DBoxes = isinstance(box, BaseInstance3DBoxes)
single_box = isinstance(box, (list, tuple)) single_box = isinstance(box, (list, tuple))
if single_box: if single_box:
assert len(box) >= 7, ( assert len(box) >= 7, (
...@@ -82,6 +90,8 @@ class Box3DMode(IntEnum): ...@@ -82,6 +90,8 @@ class Box3DMode(IntEnum):
# avoid modifying the input box # avoid modifying the input box
if is_numpy: if is_numpy:
arr = torch.from_numpy(np.asarray(box)).clone() arr = torch.from_numpy(np.asarray(box)).clone()
elif is_Instance3DBoxes:
arr = box.tensor.clone()
else: else:
arr = box.clone() arr = box.clone()
...@@ -126,5 +136,15 @@ class Box3DMode(IntEnum): ...@@ -126,5 +136,15 @@ class Box3DMode(IntEnum):
return original_type(arr.flatten().tolist()) return original_type(arr.flatten().tolist())
if is_numpy: if is_numpy:
return arr.numpy() return arr.numpy()
elif is_Instance3DBoxes:
if dst == Box3DMode.CAM:
target_type = CameraInstance3DBoxes
elif dst == Box3DMode.LIDAR:
target_type = LiDARInstance3DBoxes
else:
raise NotImplementedError(
f'Conversion to {dst} through {original_type}'
' is not supported yet')
return target_type(arr, box_dim=arr.size(-1))
else: else:
return arr return arr
mmdet3d/core/bbox/structures/cam_box3d.py
View file @ 0f85fae5
...@@ -11,10 +11,10 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes): ...@@ -11,10 +11,10 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
Coordinates in camera: Coordinates in camera:
.. code-block:: none .. code-block:: none
x right z front
/ /
/ /
front z <------ 0 0 ------> x right
| |
| |
v v
...@@ -39,7 +39,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes): ...@@ -39,7 +39,7 @@ class CameraInstance3DBoxes(BaseInstance3DBoxes):
bottom_center = self.bottom_center bottom_center = self.bottom_center
gravity_center = torch.zeros_like(bottom_center) gravity_center = torch.zeros_like(bottom_center)
gravity_center[:, [0, 2]] = bottom_center[:, [0, 2]] gravity_center[:, [0, 2]] = bottom_center[:, [0, 2]]
gravity_center[:, 1] = bottom_center[:, 1] - self.tensor[:, 5] * 0.5 gravity_center[:, 1] = bottom_center[:, 1] - self.tensor[:, 4] * 0.5
return gravity_center return gravity_center
@property @property
......
mmdet3d/core/bbox/structures/lidar_box3d.py
View file @ 0f85fae5
...@@ -16,6 +16,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes): ...@@ -16,6 +16,7 @@ class LiDARInstance3DBoxes(BaseInstance3DBoxes):
| / | /
| / | /
left y <------ 0 left y <------ 0
The relative coordinate of bottom center in a LiDAR box is [0.5, 0.5, 0], The relative coordinate of bottom center in a LiDAR box is [0.5, 0.5, 0],
and the yaw is around the z axis, thus the rotation axis=2. and the yaw is around the z axis, thus the rotation axis=2.
......
tests/test_box3d.py
View file @ 0f85fae5
...@@ -292,6 +292,11 @@ def test_boxes_conversion(): ...@@ -292,6 +292,11 @@ def test_boxes_conversion():
with pytest.raises(NotImplementedError): with pytest.raises(NotImplementedError):
Box3DMode.convert(depth_box_tensor, Box3DMode.DEPTH, Box3DMode.LIDAR) Box3DMode.convert(depth_box_tensor, Box3DMode.DEPTH, Box3DMode.LIDAR)
# test similar mode conversion
same_results = Box3DMode.convert(depth_box_tensor, Box3DMode.DEPTH,
Box3DMode.DEPTH)
assert (same_results == depth_box_tensor).all()
# test conversion with a given rt_mat # test conversion with a given rt_mat
camera_boxes = CameraInstance3DBoxes( camera_boxes = CameraInstance3DBoxes(
[[0.06, 1.77, 21.4, 3.2, 1.61, 1.66, -1.54], [[0.06, 1.77, 21.4, 3.2, 1.61, 1.66, -1.54],
...@@ -337,10 +342,243 @@ def test_boxes_conversion(): ...@@ -337,10 +342,243 @@ def test_boxes_conversion():
dtype=torch.float32) dtype=torch.float32)
rt_mat = rect @ Trv2c rt_mat = rect @ Trv2c
cam_to_lidar_box = Box3DMode.convert(camera_boxes.tensor, Box3DMode.CAM, # test coversion with Box type
cam_to_lidar_box = Box3DMode.convert(camera_boxes, Box3DMode.CAM,
Box3DMode.LIDAR, rt_mat.inverse()) Box3DMode.LIDAR, rt_mat.inverse())
assert torch.allclose(cam_to_lidar_box, expected_tensor) assert torch.allclose(cam_to_lidar_box.tensor, expected_tensor)
lidar_to_cam_box = Box3DMode.convert(cam_to_lidar_box, Box3DMode.LIDAR, lidar_to_cam_box = Box3DMode.convert(cam_to_lidar_box.tensor,
Box3DMode.CAM, rt_mat) Box3DMode.LIDAR, Box3DMode.CAM,
rt_mat)
assert torch.allclose(lidar_to_cam_box, camera_boxes.tensor) assert torch.allclose(lidar_to_cam_box, camera_boxes.tensor)
# test numpy convert
cam_to_lidar_box = Box3DMode.convert(camera_boxes.tensor.numpy(),
Box3DMode.CAM, Box3DMode.LIDAR,
rt_mat.inverse().numpy())
assert np.allclose(cam_to_lidar_box, expected_tensor.numpy())
# test list convert
cam_to_lidar_box = Box3DMode.convert(
camera_boxes.tensor[0].numpy().tolist(), Box3DMode.CAM,
Box3DMode.LIDAR,
rt_mat.inverse().numpy())
assert np.allclose(np.array(cam_to_lidar_box), expected_tensor[0].numpy())
def test_camera_boxes3d():
# Test init with numpy array
np_boxes = np.array(
[[1.7802081, 2.516249, -1.7501148, 1.75, 3.39, 1.65, 1.48],
[8.959413, 2.4567227, -1.6357126, 1.54, 4.01, 1.57, 1.62]],
dtype=np.float32)
boxes_1 = Box3DMode.convert(
LiDARInstance3DBoxes(np_boxes), Box3DMode.LIDAR, Box3DMode.CAM)
assert isinstance(boxes_1, CameraInstance3DBoxes)
cam_np_boxes = Box3DMode.convert(np_boxes, Box3DMode.LIDAR, Box3DMode.CAM)
assert torch.allclose(boxes_1.tensor,
boxes_1.tensor.new_tensor(cam_np_boxes))
# test init with torch.Tensor
th_boxes = torch.tensor(
[[
28.29669987, -0.5557558, -1.30332506, 1.47000003, 2.23000002,
1.48000002, -1.57000005
],
[
26.66901946, 21.82302134, -1.73605708, 1.55999994, 3.48000002,
1.39999998, -1.69000006
],
[
31.31977974, 8.16214412, -1.62177875, 1.74000001, 3.76999998,
1.48000002, 2.78999996
]],
dtype=torch.float32)
cam_th_boxes = Box3DMode.convert(th_boxes, Box3DMode.LIDAR, Box3DMode.CAM)
boxes_2 = CameraInstance3DBoxes(cam_th_boxes)
assert torch.allclose(boxes_2.tensor, cam_th_boxes)
# test clone/to/device
boxes_2 = boxes_2.clone()
boxes_1 = boxes_1.to(boxes_2.device)
# test box concatenation
expected_tensor = Box3DMode.convert(
torch.tensor(
[[1.7802081, 2.516249, -1.7501148, 1.75, 3.39, 1.65, 1.48],
[8.959413, 2.4567227, -1.6357126, 1.54, 4.01, 1.57, 1.62],
[28.2967, -0.5557558, -1.303325, 1.47, 2.23, 1.48, -1.57],
[26.66902, 21.82302, -1.736057, 1.56, 3.48, 1.4, -1.69],
[31.31978, 8.162144, -1.6217787, 1.74, 3.77, 1.48, 2.79]]),
Box3DMode.LIDAR, Box3DMode.CAM)
boxes = CameraInstance3DBoxes.cat([boxes_1, boxes_2])
assert torch.allclose(boxes.tensor, expected_tensor)
# test box flip
expected_tensor = Box3DMode.convert(
torch.tensor(
[[1.7802081, -2.516249, -1.7501148, 1.75, 3.39, 1.65, 1.6615927],
[8.959413, -2.4567227, -1.6357126, 1.54, 4.01, 1.57, 1.5215927],
[28.2967, 0.5557558, -1.303325, 1.47, 2.23, 1.48, 4.7115927],
[26.66902, -21.82302, -1.736057, 1.56, 3.48, 1.4, 4.8315926],
[31.31978, -8.162144, -1.6217787, 1.74, 3.77, 1.48, 0.35159278]]),
Box3DMode.LIDAR, Box3DMode.CAM)
boxes.flip()
assert torch.allclose(boxes.tensor, expected_tensor)
# test box rotation
expected_tensor = Box3DMode.convert(
torch.tensor(
[[1.0385344, -2.9020846, -1.7501148, 1.75, 3.39, 1.65, 1.9336663],
[7.969653, -4.774011, -1.6357126, 1.54, 4.01, 1.57, 1.7936664],
[27.405172, -7.0688415, -1.303325, 1.47, 2.23, 1.48, 4.9836664],
[19.823532, -28.187025, -1.736057, 1.56, 3.48, 1.4, 5.1036663],
[27.974297, -16.27845, -1.6217787, 1.74, 3.77, 1.48, 0.6236664]]),
Box3DMode.LIDAR, Box3DMode.CAM)
boxes.rotate(0.27207362796436096)
assert torch.allclose(boxes.tensor, expected_tensor)
# test box scaling
expected_tensor = Box3DMode.convert(
torch.tensor([[
1.0443488, -2.9183323, -1.7599131, 1.7597977, 3.4089797, 1.6592377,
1.9336663
],
[
8.014273, -4.8007393, -1.6448704, 1.5486219,
4.0324507, 1.57879, 1.7936664
],
[
27.558605, -7.1084175, -1.310622, 1.4782301,
2.242485, 1.488286, 4.9836664
],
[
19.934517, -28.344835, -1.7457767, 1.5687338,
3.4994833, 1.4078381, 5.1036663
],
[
28.130915, -16.369587, -1.6308585, 1.7497417,
3.791107, 1.488286, 0.6236664
]]), Box3DMode.LIDAR, Box3DMode.CAM)
boxes.scale(1.00559866335275)
assert torch.allclose(boxes.tensor, expected_tensor)
# test box translation
expected_tensor = Box3DMode.convert(
torch.tensor([[
1.1281544, -3.0507944, -1.9169292, 1.7597977, 3.4089797, 1.6592377,
1.9336663
],
[
8.098079, -4.9332013, -1.8018866, 1.5486219,
4.0324507, 1.57879, 1.7936664
],
[
27.64241, -7.2408795, -1.4676381, 1.4782301,
2.242485, 1.488286, 4.9836664
],
[
20.018322, -28.477297, -1.9027928, 1.5687338,
3.4994833, 1.4078381, 5.1036663
],
[
28.21472, -16.502048, -1.7878747, 1.7497417,
3.791107, 1.488286, 0.6236664
]]), Box3DMode.LIDAR, Box3DMode.CAM)
boxes.translate([0.13246193, 0.15701613, 0.0838056])
assert torch.allclose(boxes.tensor, expected_tensor)
# test bbox in_range_bev
expected_tensor = torch.tensor([1, 1, 1, 1, 1], dtype=torch.bool)
mask = boxes.in_range_bev([0., -40., 70.4, 40.])
assert (mask == expected_tensor).all()
mask = boxes.nonempty()
assert (mask == expected_tensor).all()
# test bbox in_range
expected_tensor = torch.tensor([1, 1, 0, 0, 0], dtype=torch.bool)
mask = boxes.in_range_3d([-2, -5, 0, 20, 2, 22])
assert (mask == expected_tensor).all()
# test properties
assert torch.allclose(boxes.bottom_center, boxes.tensor[:, :3])
expected_tensor = (
boxes.tensor[:, :3] - boxes.tensor[:, 3:6] *
(torch.tensor([0.5, 1.0, 0.5]) - torch.tensor([0.5, 0.5, 0.5])))
assert torch.allclose(boxes.gravity_center, expected_tensor)
boxes.limit_yaw()
assert (boxes.tensor[:, 6] <= np.pi / 2).all()
assert (boxes.tensor[:, 6] >= -np.pi / 2).all()
Box3DMode.convert(boxes, Box3DMode.LIDAR, Box3DMode.LIDAR)
expected_tesor = boxes.tensor.clone()
assert torch.allclose(expected_tesor, boxes.tensor)
boxes.flip()
boxes.flip()
boxes.limit_yaw()
assert torch.allclose(expected_tesor, boxes.tensor)
# test nearest_bev
# BEV box in lidar coordinates (x, y)
lidar_expected_tensor = torch.tensor(
[[-0.5763, -3.9307, 2.8326, -2.1709],
[6.0819, -5.7075, 10.1143, -4.1589],
[26.5212, -7.9800, 28.7637, -6.5018],
[18.2686, -29.2617, 21.7681, -27.6929],
[27.3398, -18.3976, 29.0896, -14.6065]])
# BEV box in camera coordinate (-y, x)
expected_tensor = lidar_expected_tensor.clone()
expected_tensor[:, 0::2] = -lidar_expected_tensor[:, [3, 1]]
expected_tensor[:, 1::2] = lidar_expected_tensor[:, 0::2]
# the pytorch print loses some precision
assert torch.allclose(
boxes.nearset_bev, expected_tensor, rtol=1e-4, atol=1e-7)
# obtained by the print of the original implementation
expected_tensor = torch.tensor([[[3.2684e+00, 2.5769e-01, -7.7767e-01],
[1.6232e+00, 2.5769e-01, -1.5301e-01],
[1.6232e+00, 1.9169e+00, -1.5301e-01],
[3.2684e+00, 1.9169e+00, -7.7767e-01],
[4.4784e+00, 2.5769e-01, 2.4093e+00],
[2.8332e+00, 2.5769e-01, 3.0340e+00],
[2.8332e+00, 1.9169e+00, 3.0340e+00],
[4.4784e+00, 1.9169e+00, 2.4093e+00]],
[[5.2427e+00, 2.2310e-01, 5.9606e+00],
[3.7324e+00, 2.2310e-01, 6.3029e+00],
[3.7324e+00, 1.8019e+00, 6.3029e+00],
[5.2427e+00, 1.8019e+00, 5.9606e+00],
[6.1340e+00, 2.2310e-01, 9.8933e+00],
[4.6237e+00, 2.2310e-01, 1.0236e+01],
[4.6237e+00, 1.8019e+00, 1.0236e+01],
[6.1340e+00, 1.8019e+00, 9.8933e+00]],
[[7.6525e+00, -2.0648e-02, 2.6364e+01],
[6.2283e+00, -2.0648e-02, 2.6760e+01],
[6.2283e+00, 1.4676e+00, 2.6760e+01],
[7.6525e+00, 1.4676e+00, 2.6364e+01],
[8.2534e+00, -2.0648e-02, 2.8525e+01],
[6.8292e+00, -2.0648e-02, 2.8921e+01],
[6.8292e+00, 1.4676e+00, 2.8921e+01],
[8.2534e+00, 1.4676e+00, 2.8525e+01]],
[[2.8535e+01, 4.9495e-01, 1.8102e+01],
[2.7085e+01, 4.9495e-01, 1.8700e+01],
[2.7085e+01, 1.9028e+00, 1.8700e+01],
[2.8535e+01, 1.9028e+00, 1.8102e+01],
[2.9870e+01, 4.9495e-01, 2.1337e+01],
[2.8420e+01, 4.9495e-01, 2.1935e+01],
[2.8420e+01, 1.9028e+00, 2.1935e+01],
[2.9870e+01, 1.9028e+00, 2.1337e+01]],
[[1.4452e+01, 2.9959e-01, 2.8612e+01],
[1.5474e+01, 2.9959e-01, 3.0032e+01],
[1.5474e+01, 1.7879e+00, 3.0032e+01],
[1.4452e+01, 1.7879e+00, 2.8612e+01],
[1.7530e+01, 2.9959e-01, 2.6398e+01],
[1.8552e+01, 2.9959e-01, 2.7818e+01],
[1.8552e+01, 1.7879e+00, 2.7818e+01],
[1.7530e+01, 1.7879e+00, 2.6398e+01]]])
# the pytorch print loses some precision
assert torch.allclose(boxes.corners, expected_tensor, rtol=1e-4, atol=1e-7)
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