| import numpy as np |
| import scipy |
| import torch |
| import copy |
| from scipy.spatial import Delaunay |
|
|
| from ..ops.roiaware_pool3d import roiaware_pool3d_utils |
| from . import common_utils |
|
|
|
|
| def in_hull(p, hull): |
| """ |
| :param p: (N, K) test points |
| :param hull: (M, K) M corners of a box |
| :return (N) bool |
| """ |
| try: |
| if not isinstance(hull, Delaunay): |
| hull = Delaunay(hull) |
| flag = hull.find_simplex(p) >= 0 |
| except scipy.spatial.qhull.QhullError: |
| print('Warning: not a hull %s' % str(hull)) |
| flag = np.zeros(p.shape[0], dtype=np.bool) |
|
|
| return flag |
|
|
|
|
| def boxes_to_corners_3d(boxes3d): |
| """ |
| 7 -------- 4 |
| /| /| |
| 6 -------- 5 . |
| | | | | |
| . 3 -------- 0 |
| |/ |/ |
| 2 -------- 1 |
| Args: |
| boxes3d: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center |
| |
| Returns: |
| """ |
| boxes3d, is_numpy = common_utils.check_numpy_to_torch(boxes3d) |
|
|
| template = boxes3d.new_tensor(( |
| [1, 1, -1], [1, -1, -1], [-1, -1, -1], [-1, 1, -1], |
| [1, 1, 1], [1, -1, 1], [-1, -1, 1], [-1, 1, 1], |
| )) / 2 |
|
|
| corners3d = boxes3d[:, None, 3:6].repeat(1, 8, 1) * template[None, :, :] |
| corners3d = common_utils.rotate_points_along_z(corners3d.view(-1, 8, 3), boxes3d[:, 6]).view(-1, 8, 3) |
| corners3d += boxes3d[:, None, 0:3] |
|
|
| return corners3d.numpy() if is_numpy else corners3d |
|
|
| def corners_rect_to_camera(corners): |
| """ |
| 7 -------- 4 |
| /| /| |
| 6 -------- 5 . |
| | | | | |
| . 3 -------- 0 |
| |/ |/ |
| 2 -------- 1 |
| Args: |
| corners: (8, 3) [x0, y0, z0, ...], (x, y, z) is the point coordinate in image rect |
| |
| Returns: |
| boxes_rect: (7,) [x, y, z, l, h, w, r] in rect camera coords |
| """ |
| height_group = [(0, 4), (1, 5), (2, 6), (3, 7)] |
| width_group = [(0, 1), (2, 3), (4, 5), (6, 7)] |
| length_group = [(0, 3), (1, 2), (4, 7), (5, 6)] |
| vector_group = [(0, 3), (1, 2), (4, 7), (5, 6)] |
| height, width, length = 0., 0., 0. |
| vector = np.zeros(2, dtype=np.float32) |
| for index_h, index_w, index_l, index_v in zip(height_group, width_group, length_group, vector_group): |
| height += np.linalg.norm(corners[index_h[0], :] - corners[index_h[1], :]) |
| width += np.linalg.norm(corners[index_w[0], :] - corners[index_w[1], :]) |
| length += np.linalg.norm(corners[index_l[0], :] - corners[index_l[1], :]) |
| vector[0] += (corners[index_v[0], :] - corners[index_v[1], :])[0] |
| vector[1] += (corners[index_v[0], :] - corners[index_v[1], :])[2] |
|
|
| height, width, length = height*1.0/4, width*1.0/4, length*1.0/4 |
| rotation_y = -np.arctan2(vector[1], vector[0]) |
|
|
| center_point = corners.mean(axis=0) |
| center_point[1] += height/2 |
| camera_rect = np.concatenate([center_point, np.array([length, height, width, rotation_y])]) |
|
|
| return camera_rect |
|
|
|
|
| def mask_boxes_outside_range_numpy(boxes, limit_range, min_num_corners=1, use_center_to_filter=True): |
| """ |
| Args: |
| boxes: (N, 7) [x, y, z, dx, dy, dz, heading, ...], (x, y, z) is the box center |
| limit_range: [minx, miny, minz, maxx, maxy, maxz] |
| min_num_corners: |
| |
| Returns: |
| |
| """ |
| if boxes.shape[1] > 7: |
| boxes = boxes[:, 0:7] |
| if use_center_to_filter: |
| box_centers = boxes[:, 0:3] |
| mask = ((box_centers >= limit_range[0:3]) & (box_centers <= limit_range[3:6])).all(axis=-1) |
| else: |
| corners = boxes_to_corners_3d(boxes) |
| corners = corners[:, :, 0:2] |
| mask = ((corners >= limit_range[0:2]) & (corners <= limit_range[3:5])).all(axis=2) |
| mask = mask.sum(axis=1) >= min_num_corners |
|
|
| return mask |
|
|
|
|
| def remove_points_in_boxes3d(points, boxes3d): |
| """ |
| Args: |
| points: (num_points, 3 + C) |
| boxes3d: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center, each box DO NOT overlaps |
| |
| Returns: |
| |
| """ |
| boxes3d, is_numpy = common_utils.check_numpy_to_torch(boxes3d) |
| points, is_numpy = common_utils.check_numpy_to_torch(points) |
| point_masks = roiaware_pool3d_utils.points_in_boxes_cpu(points[:, 0:3], boxes3d) |
| points = points[point_masks.sum(dim=0) == 0] |
|
|
| return points.numpy() if is_numpy else points |
|
|
|
|
| def boxes3d_kitti_camera_to_lidar(boxes3d_camera, calib): |
| """ |
| Args: |
| boxes3d_camera: (N, 7) [x, y, z, l, h, w, r] in rect camera coords |
| calib: |
| |
| Returns: |
| boxes3d_lidar: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center |
| |
| """ |
| boxes3d_camera_copy = copy.deepcopy(boxes3d_camera) |
| xyz_camera, r = boxes3d_camera_copy[:, 0:3], boxes3d_camera_copy[:, 6:7] |
| l, h, w = boxes3d_camera_copy[:, 3:4], boxes3d_camera_copy[:, 4:5], boxes3d_camera_copy[:, 5:6] |
|
|
| xyz_lidar = calib.rect_to_lidar(xyz_camera) |
| xyz_lidar[:, 2] += h[:, 0] / 2 |
| return np.concatenate([xyz_lidar, l, w, h, -(r + np.pi / 2)], axis=-1) |
|
|
|
|
| def boxes3d_kitti_fakelidar_to_lidar(boxes3d_lidar): |
| """ |
| Args: |
| boxes3d_fakelidar: (N, 7) [x, y, z, w, l, h, r] in old LiDAR coordinates, z is bottom center |
| |
| Returns: |
| boxes3d_lidar: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center |
| |
| """ |
| boxes3d_lidar_copy = copy.deepcopy(boxes3d_lidar) |
| w, l, h = boxes3d_lidar_copy[:, 3:4], boxes3d_lidar_copy[:, 4:5], boxes3d_lidar_copy[:, 5:6] |
| r = boxes3d_lidar_copy[:, 6:7] |
|
|
| boxes3d_lidar_copy[:, 2] += h[:, 0] / 2 |
| return np.concatenate([boxes3d_lidar_copy[:, 0:3], l, w, h, -(r + np.pi / 2)], axis=-1) |
|
|
|
|
| def boxes3d_kitti_lidar_to_fakelidar(boxes3d_lidar): |
| """ |
| Args: |
| boxes3d_lidar: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center |
| |
| Returns: |
| boxes3d_fakelidar: [x, y, z, w, l, h, r] in old LiDAR coordinates, z is bottom center |
| |
| """ |
| boxes3d_lidar_copy = copy.deepcopy(boxes3d_lidar) |
| dx, dy, dz = boxes3d_lidar_copy[:, 3:4], boxes3d_lidar_copy[:, 4:5], boxes3d_lidar_copy[:, 5:6] |
| heading = boxes3d_lidar_copy[:, 6:7] |
|
|
| boxes3d_lidar_copy[:, 2] -= dz[:, 0] / 2 |
| return np.concatenate([boxes3d_lidar_copy[:, 0:3], dy, dx, dz, -heading - np.pi / 2], axis=-1) |
|
|
|
|
| def enlarge_box3d(boxes3d, extra_width=(0, 0, 0)): |
| """ |
| Args: |
| boxes3d: [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center |
| extra_width: [extra_x, extra_y, extra_z] |
| |
| Returns: |
| |
| """ |
| boxes3d, is_numpy = common_utils.check_numpy_to_torch(boxes3d) |
| large_boxes3d = boxes3d.clone() |
|
|
| large_boxes3d[:, 3:6] += boxes3d.new_tensor(extra_width)[None, :] |
| return large_boxes3d |
|
|
|
|
| def boxes3d_lidar_to_kitti_camera(boxes3d_lidar, calib): |
| """ |
| :param boxes3d_lidar: (N, 7) [x, y, z, dx, dy, dz, heading], (x, y, z) is the box center |
| :param calib: |
| :return: |
| boxes3d_camera: (N, 7) [x, y, z, l, h, w, r] in rect camera coords |
| """ |
| boxes3d_lidar_copy = copy.deepcopy(boxes3d_lidar) |
| xyz_lidar = boxes3d_lidar_copy[:, 0:3] |
| l, w, h = boxes3d_lidar_copy[:, 3:4], boxes3d_lidar_copy[:, 4:5], boxes3d_lidar_copy[:, 5:6] |
| r = boxes3d_lidar_copy[:, 6:7] |
|
|
| xyz_lidar[:, 2] -= h.reshape(-1) / 2 |
| xyz_cam = calib.lidar_to_rect(xyz_lidar) |
| |
| r = -r - np.pi / 2 |
| return np.concatenate([xyz_cam, l, h, w, r], axis=-1) |
|
|
|
|
| def boxes3d_to_corners3d_kitti_camera(boxes3d, bottom_center=True): |
| """ |
| :param boxes3d: (N, 7) [x, y, z, l, h, w, ry] in camera coords, see the definition of ry in KITTI dataset |
| :param bottom_center: whether y is on the bottom center of object |
| :return: corners3d: (N, 8, 3) |
| 7 -------- 4 |
| /| /| |
| 6 -------- 5 . |
| | | | | |
| . 3 -------- 0 |
| |/ |/ |
| 2 -------- 1 |
| """ |
| boxes_num = boxes3d.shape[0] |
| l, h, w = boxes3d[:, 3], boxes3d[:, 4], boxes3d[:, 5] |
| x_corners = np.array([l / 2., l / 2., -l / 2., -l / 2., l / 2., l / 2., -l / 2., -l / 2], dtype=np.float32).T |
| z_corners = np.array([w / 2., -w / 2., -w / 2., w / 2., w / 2., -w / 2., -w / 2., w / 2.], dtype=np.float32).T |
| if bottom_center: |
| y_corners = np.zeros((boxes_num, 8), dtype=np.float32) |
| y_corners[:, 4:8] = -h.reshape(boxes_num, 1).repeat(4, axis=1) |
| else: |
| y_corners = np.array([h / 2., h / 2., h / 2., h / 2., -h / 2., -h / 2., -h / 2., -h / 2.], dtype=np.float32).T |
|
|
| ry = boxes3d[:, 6] |
| zeros, ones = np.zeros(ry.size, dtype=np.float32), np.ones(ry.size, dtype=np.float32) |
| rot_list = np.array([[np.cos(ry), zeros, -np.sin(ry)], |
| [zeros, ones, zeros], |
| [np.sin(ry), zeros, np.cos(ry)]]) |
| R_list = np.transpose(rot_list, (2, 0, 1)) |
|
|
| temp_corners = np.concatenate((x_corners.reshape(-1, 8, 1), y_corners.reshape(-1, 8, 1), |
| z_corners.reshape(-1, 8, 1)), axis=2) |
| rotated_corners = np.matmul(temp_corners, R_list) |
| x_corners, y_corners, z_corners = rotated_corners[:, :, 0], rotated_corners[:, :, 1], rotated_corners[:, :, 2] |
|
|
| x_loc, y_loc, z_loc = boxes3d[:, 0], boxes3d[:, 1], boxes3d[:, 2] |
|
|
| x = x_loc.reshape(-1, 1) + x_corners.reshape(-1, 8) |
| y = y_loc.reshape(-1, 1) + y_corners.reshape(-1, 8) |
| z = z_loc.reshape(-1, 1) + z_corners.reshape(-1, 8) |
|
|
| corners = np.concatenate((x.reshape(-1, 8, 1), y.reshape(-1, 8, 1), z.reshape(-1, 8, 1)), axis=2) |
|
|
| return corners.astype(np.float32) |
|
|
|
|
| def boxes3d_kitti_camera_to_imageboxes(boxes3d, calib, image_shape=None): |
| """ |
| :param boxes3d: (N, 7) [x, y, z, l, h, w, r] in rect camera coords |
| :param calib: |
| :return: |
| box_2d_preds: (N, 4) [x1, y1, x2, y2] |
| """ |
| corners3d = boxes3d_to_corners3d_kitti_camera(boxes3d) |
| pts_img, _ = calib.rect_to_img(corners3d.reshape(-1, 3)) |
| corners_in_image = pts_img.reshape(-1, 8, 2) |
|
|
| min_uv = np.min(corners_in_image, axis=1) |
| max_uv = np.max(corners_in_image, axis=1) |
| boxes2d_image = np.concatenate([min_uv, max_uv], axis=1) |
| if image_shape is not None: |
| boxes2d_image[:, 0] = np.clip(boxes2d_image[:, 0], a_min=0, a_max=image_shape[1] - 1) |
| boxes2d_image[:, 1] = np.clip(boxes2d_image[:, 1], a_min=0, a_max=image_shape[0] - 1) |
| boxes2d_image[:, 2] = np.clip(boxes2d_image[:, 2], a_min=0, a_max=image_shape[1] - 1) |
| boxes2d_image[:, 3] = np.clip(boxes2d_image[:, 3], a_min=0, a_max=image_shape[0] - 1) |
|
|
| return boxes2d_image |
|
|
|
|
| def boxes_iou_normal(boxes_a, boxes_b): |
| """ |
| Args: |
| boxes_a: (N, 4) [x1, y1, x2, y2] |
| boxes_b: (M, 4) [x1, y1, x2, y2] |
| |
| Returns: |
| |
| """ |
| assert boxes_a.shape[1] == boxes_b.shape[1] == 4 |
| x_min = torch.max(boxes_a[:, 0, None], boxes_b[None, :, 0]) |
| x_max = torch.min(boxes_a[:, 2, None], boxes_b[None, :, 2]) |
| y_min = torch.max(boxes_a[:, 1, None], boxes_b[None, :, 1]) |
| y_max = torch.min(boxes_a[:, 3, None], boxes_b[None, :, 3]) |
| x_len = torch.clamp_min(x_max - x_min, min=0) |
| y_len = torch.clamp_min(y_max - y_min, min=0) |
| area_a = (boxes_a[:, 2] - boxes_a[:, 0]) * (boxes_a[:, 3] - boxes_a[:, 1]) |
| area_b = (boxes_b[:, 2] - boxes_b[:, 0]) * (boxes_b[:, 3] - boxes_b[:, 1]) |
| a_intersect_b = x_len * y_len |
| iou = a_intersect_b / torch.clamp_min(area_a[:, None] + area_b[None, :] - a_intersect_b, min=1e-6) |
| return iou |
|
|
|
|
| def boxes3d_lidar_to_aligned_bev_boxes(boxes3d): |
| """ |
| Args: |
| boxes3d: (N, 7 + C) [x, y, z, dx, dy, dz, heading] in lidar coordinate |
| |
| Returns: |
| aligned_bev_boxes: (N, 4) [x1, y1, x2, y2] in the above lidar coordinate |
| """ |
| rot_angle = common_utils.limit_period(boxes3d[:, 6], offset=0.5, period=np.pi).abs() |
| choose_dims = torch.where(rot_angle[:, None] < np.pi / 4, boxes3d[:, [3, 4]], boxes3d[:, [4, 3]]) |
| aligned_bev_boxes = torch.cat((boxes3d[:, 0:2] - choose_dims / 2, boxes3d[:, 0:2] + choose_dims / 2), dim=1) |
| return aligned_bev_boxes |
|
|
|
|
| def boxes3d_nearest_bev_iou(boxes_a, boxes_b): |
| """ |
| Args: |
| boxes_a: (N, 7) [x, y, z, dx, dy, dz, heading] |
| boxes_b: (N, 7) [x, y, z, dx, dy, dz, heading] |
| |
| Returns: |
| |
| """ |
| boxes_bev_a = boxes3d_lidar_to_aligned_bev_boxes(boxes_a) |
| boxes_bev_b = boxes3d_lidar_to_aligned_bev_boxes(boxes_b) |
|
|
| return boxes_iou_normal(boxes_bev_a, boxes_bev_b) |
|
|
|
|
| def area(box) -> torch.Tensor: |
| """ |
| Computes the area of all the boxes. |
| |
| Returns: |
| torch.Tensor: a vector with areas of each box. |
| """ |
| area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1]) |
| return area |
|
|
|
|
| |
| |
| def pairwise_iou(boxes1, boxes2) -> torch.Tensor: |
| """ |
| Given two lists of boxes of size N and M, |
| compute the IoU (intersection over union) |
| between __all__ N x M pairs of boxes. |
| The box order must be (xmin, ymin, xmax, ymax). |
| |
| Args: |
| boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. |
| |
| Returns: |
| Tensor: IoU, sized [N,M]. |
| """ |
| area1 = area(boxes1) |
| area2 = area(boxes2) |
|
|
| width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max( |
| boxes1[:, None, :2], boxes2[:, :2] |
| ) |
|
|
| width_height.clamp_(min=0) |
| inter = width_height.prod(dim=2) |
| del width_height |
|
|
| |
| iou = torch.where( |
| inter > 0, |
| inter / (area1[:, None] + area2 - inter), |
| torch.zeros(1, dtype=inter.dtype, device=inter.device), |
| ) |
| return iou |
|
|
|
|
| def center_to_corner2d(center, dim): |
| corners_norm = torch.tensor([[-0.5, -0.5], [-0.5, 0.5], [0.5, 0.5], [0.5, -0.5]], device=dim.device).type_as(center) |
| corners = dim.view([-1, 1, 2]) * corners_norm.view([1, 4, 2]) |
| corners = corners + center.view(-1, 1, 2) |
| return corners |
|
|
|
|
| def bbox3d_overlaps_diou(pred_boxes, gt_boxes): |
| """ |
| https://github.com/agent-sgs/PillarNet/blob/master/det3d/core/utils/center_utils.py |
| Args: |
| pred_boxes (N, 7): |
| gt_boxes (N, 7): |
| |
| Returns: |
| _type_: _description_ |
| """ |
| assert pred_boxes.shape[0] == gt_boxes.shape[0] |
|
|
| qcorners = center_to_corner2d(pred_boxes[:, :2], pred_boxes[:, 3:5]) |
| gcorners = center_to_corner2d(gt_boxes[:, :2], gt_boxes[:, 3:5]) |
|
|
| inter_max_xy = torch.minimum(qcorners[:, 2], gcorners[:, 2]) |
| inter_min_xy = torch.maximum(qcorners[:, 0], gcorners[:, 0]) |
| out_max_xy = torch.maximum(qcorners[:, 2], gcorners[:, 2]) |
| out_min_xy = torch.minimum(qcorners[:, 0], gcorners[:, 0]) |
|
|
| |
| volume_pred_boxes = pred_boxes[:, 3] * pred_boxes[:, 4] * pred_boxes[:, 5] |
| volume_gt_boxes = gt_boxes[:, 3] * gt_boxes[:, 4] * gt_boxes[:, 5] |
|
|
| inter_h = torch.minimum(pred_boxes[:, 2] + 0.5 * pred_boxes[:, 5], gt_boxes[:, 2] + 0.5 * gt_boxes[:, 5]) - \ |
| torch.maximum(pred_boxes[:, 2] - 0.5 * pred_boxes[:, 5], gt_boxes[:, 2] - 0.5 * gt_boxes[:, 5]) |
| inter_h = torch.clamp(inter_h, min=0) |
|
|
| inter = torch.clamp((inter_max_xy - inter_min_xy), min=0) |
| volume_inter = inter[:, 0] * inter[:, 1] * inter_h |
| volume_union = volume_gt_boxes + volume_pred_boxes - volume_inter |
|
|
| |
| inter_diag = torch.pow(gt_boxes[:, 0:3] - pred_boxes[:, 0:3], 2).sum(-1) |
|
|
| outer_h = torch.maximum(gt_boxes[:, 2] + 0.5 * gt_boxes[:, 5], pred_boxes[:, 2] + 0.5 * pred_boxes[:, 5]) - \ |
| torch.minimum(gt_boxes[:, 2] - 0.5 * gt_boxes[:, 5], pred_boxes[:, 2] - 0.5 * pred_boxes[:, 5]) |
| outer_h = torch.clamp(outer_h, min=0) |
| outer = torch.clamp((out_max_xy - out_min_xy), min=0) |
| outer_diag = outer[:, 0] ** 2 + outer[:, 1] ** 2 + outer_h ** 2 |
|
|
| dious = volume_inter / volume_union - inter_diag / outer_diag |
| dious = torch.clamp(dious, min=-1.0, max=1.0) |
|
|
| return dious |
