| """ |
| 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"], |
| ) |
|
|
| |
| 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) |
|
|
| |
| 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 |
|
|
