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"""
Visualize 3D boxes in Image space.
Align the setting in mmdetection3d:
* Convert 3D box in nuplan coordinates to camera coordinates.
* draw 3D box in camera.
"""
import cv2
import numpy as np
from navsim.common.extraction.helpers import transformation
def rotation_3d_in_axis(points, angles, axis=0):
"""Rotate points by angles according to axis.
Args:
points (torch.Tensor): Points of shape (N, M, 3).
angles (torch.Tensor): Vector of angles in shape (N,)
axis (int, optional): The axis to be rotated. Defaults to 0.
Raises:
ValueError: when the axis is not in range [0, 1, 2], it will \
raise value error.
Returns:
torch.Tensor: Rotated points in shape (N, M, 3)
"""
rot_sin = np.sin(angles)
rot_cos = np.cos(angles)
ones = np.ones_like(rot_cos)
zeros = np.zeros_like(rot_cos)
if axis == 1:
rot_mat_T = np.stack(
[
np.stack([rot_cos, zeros, -rot_sin]),
np.stack([zeros, ones, zeros]),
np.stack([rot_sin, zeros, rot_cos]),
]
)
elif axis == 2 or axis == -1:
rot_mat_T = np.stack(
[
np.stack([rot_cos, -rot_sin, zeros]),
np.stack([rot_sin, rot_cos, zeros]),
np.stack([zeros, zeros, ones]),
]
)
elif axis == 0:
rot_mat_T = np.stack(
[
np.stack([zeros, rot_cos, -rot_sin]),
np.stack([zeros, rot_sin, rot_cos]),
np.stack([ones, zeros, zeros]),
]
)
else:
raise ValueError(f"axis should in range [0, 1, 2], got {axis}")
return np.einsum("aij,jka->aik", points, rot_mat_T)
def plot_rect3d_on_img(img, num_rects, rect_corners, color=(0, 255, 0), thickness=1):
"""Plot the boundary lines of 3D rectangular on 2D images.
Args:
img (numpy.array): The numpy array of image.
num_rects (int): Number of 3D rectangulars.
rect_corners (numpy.array): Coordinates of the corners of 3D
rectangulars. Should be in the shape of [num_rect, 8, 2].
color (tuple[int]): The color to draw bboxes. Default: (0, 255, 0).
thickness (int, optional): The thickness of bboxes. Default: 1.
"""
line_indices = (
(0, 1),
(0, 3),
(0, 4),
(1, 2),
(1, 5),
(3, 2),
(3, 7),
(4, 5),
(4, 7),
(2, 6),
(5, 6),
(6, 7),
)
for i in range(num_rects):
corners = rect_corners[i].astype(np.int)
for start, end in line_indices:
cv2.line(
img,
(corners[start, 0], corners[start, 1]),
(corners[end, 0], corners[end, 1]),
color,
thickness,
cv2.LINE_AA,
)
return img.astype(np.uint8)
def draw_boxes_nuplan_on_img(gt_boxes_nuplan, cam_infos, eps=1e-3):
for cam_type, cam_info in cam_infos.items():
cur_img_path = cam_info["data_path"]
cur_img = cv2.imread(cur_img_path)
cur_img_h, cur_img_w = cur_img.shape[:2]
gt_boxes_cams = transformation.transform_nuplan_boxes_to_cam(
gt_boxes_nuplan,
cam_info["sensor2lidar_rotation"],
cam_info["sensor2lidar_translation"],
)
# Then convert gt_boxes_cams to corners.
cur_locs, cur_dims, cur_rots = (
gt_boxes_cams[:, :3],
gt_boxes_cams[:, 3:6],
gt_boxes_cams[:, 6:],
)
corners_norm = np.stack(np.unravel_index(np.arange(8), [2] * 3), axis=1)
corners_norm = corners_norm[[0, 1, 3, 2, 4, 5, 7, 6]]
corners_norm = corners_norm - np.array([0.5, 0.5, 0.5])
corners = cur_dims.reshape([-1, 1, 3]) * corners_norm.reshape([1, 8, 3])
corners = rotation_3d_in_axis(corners, cur_rots.squeeze(-1), axis=1)
corners += cur_locs.reshape(-1, 1, 3)
# Then draw project corners to image.
corners_img, corners_pc_in_fov = transformation.transform_cam_to_img(
corners.reshape(-1, 3), cam_info["cam_intrinsic"], img_shape=(cur_img_h, cur_img_w)
)
corners_img = corners_img.reshape(-1, 8, 2)
corners_pc_in_fov = corners_pc_in_fov.reshape(-1, 8)
valid_corners = corners_pc_in_fov.all(-1)
corners_img = corners_img[valid_corners]
cur_img = plot_rect3d_on_img(cur_img, len(corners_img), corners_img)
cv2.imwrite(f"dbg/{cam_type}.png", cur_img)
return None
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