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