def extract_frustum_data_rgb_detection(det_filename, split, output_filename,
type_whitelist=['Car'],
img_height_threshold=5,
lidar_point_threshold=1):
''' Extract point clouds in frustums extruded from 2D detection boxes.
Update: Lidar points and 3d boxes are in *rect camera* coord system
(as that in 3d box label files)
Input:
det_filename: string, each line is
img_path typeid confidence xmin ymin xmax ymax
split: string, either trianing or testing
output_filename: string, the name for output .pickle file
type_whitelist: a list of strings, object types we are interested in.
img_height_threshold: int, neglect image with height lower than that.
lidar_point_threshold: int, neglect frustum with too few points.
Output:
None (will write a .pickle file to the disk)
'''
dataset = kitti_object(os.path.join(ROOT_DIR, 'data/kitti'), split=split)
if det_filename.split('.')[-1] == 'pkl':
det_id_list, det_type_list, det_box2d_list, det_prob_list = \
read_det_pkl_file(det_filename)
else:
det_id_list, det_type_list, det_box2d_list, det_prob_list = \
read_det_file(det_filename)
cache_id = -1
cache = None
id_list = []
type_list = []
box2d_list = []
prob_list = []
input_list = [] # channel number = 4, xyz,intensity in rect camera coord
frustum_angle_list = [] # angle of 2d box center from pos x-axis
calib_list = []
for det_idx in range(len(det_id_list)):
data_idx = det_id_list[det_idx]
print('det idx: %d/%d, data idx: %d' %
(det_idx, len(det_id_list), data_idx))
if cache_id != data_idx:
calib = dataset.get_calibration(data_idx) # 3 by 4 matrix
pc_velo = dataset.get_lidar(data_idx)
pc_rect = np.zeros_like(pc_velo)
pc_rect[:, 0:3] = calib.project_velo_to_rect(pc_velo[:, 0:3])
pc_rect[:, 3] = pc_velo[:, 3]
img = dataset.get_image(data_idx)
img_height, img_width, img_channel = img.shape
_, pc_image_coord, img_fov_inds = get_lidar_in_image_fov(
pc_velo[:, 0:3], calib, 0, 0, img_width, img_height, True)
cache = [calib, pc_rect, pc_image_coord, img_fov_inds]
cache_id = data_idx
else:
calib, pc_rect, pc_image_coord, img_fov_inds = cache
if det_type_list[det_idx] not in type_whitelist:
continue
# 2D BOX: Get pts rect backprojected
det_box2d = det_box2d_list[det_idx].copy()
det_box2d[[0, 2]] = np.clip(det_box2d[[0, 2]], 0, img_width - 1)
det_box2d[[1, 3]] = np.clip(det_box2d[[1, 3]], 0, img_height - 1)
xmin, ymin, xmax, ymax = det_box2d
box_fov_inds = (pc_image_coord[:, 0] < xmax) & \
(pc_image_coord[:, 0] >= xmin) & \
(pc_image_coord[:, 1] < ymax) & \
(pc_image_coord[:, 1] >= ymin)
box_fov_inds = box_fov_inds & img_fov_inds
pc_in_box_fov = pc_rect[box_fov_inds, :]
pc_box_image_coord = pc_image_coord[box_fov_inds, :]
# Get frustum angle (according to center pixel in 2D BOX)
box2d_center = np.array([(xmin + xmax) / 2.0, (ymin + ymax) / 2.0])
uvdepth = np.zeros((1, 3))
uvdepth[0, 0:2] = box2d_center
uvdepth[0, 2] = 20 # some random depth
box2d_center_rect = calib.project_image_to_rect(uvdepth)
frustum_angle = -1 * np.arctan2(box2d_center_rect[0, 2],
box2d_center_rect[0, 0])
# Pass objects that are too small
if ymax - ymin < img_height_threshold or xmax - xmin < 1 or \
len(pc_in_box_fov) < lidar_point_threshold:
continue
id_list.append(data_idx)
type_list.append(det_type_list[det_idx])
box2d_list.append(np.array([xmin, ymin, xmax, ymax]))
prob_list.append(det_prob_list[det_idx])
input_list.append(pc_in_box_fov.astype(np.float32, copy=False))
frustum_angle_list.append(frustum_angle)
calib_list.append(calib.calib_dict)
with open(output_filename, 'wb') as fp:
pickle.dump(id_list, fp, -1)
pickle.dump(box2d_list, fp, -1)
pickle.dump(input_list, fp, -1)
pickle.dump(type_list, fp, -1)
pickle.dump(frustum_angle_list, fp, -1)
pickle.dump(prob_list, fp, -1)
pickle.dump(calib_list, fp, -1)
print('total_objects %d' % len(id_list))
print('save in {}'.format(output_filename))
def extract_frustum_data(idx_filename, split, output_filename,
perturb_box2d=False, augmentX=1, type_whitelist=['Car']):
''' Extract point clouds and corresponding annotations in frustums
defined generated from 2D bounding boxes
Lidar points and 3d boxes are in *rect camera* coord system
(as that in 3d box label files)
Input:
idx_filename: string, each line of the file is a sample ID
split: string, either trianing or testing
output_filename: string, the name for output .pickle file
viz: bool, whether to visualize extracted data
perturb_box2d: bool, whether to perturb the box2d
(used for data augmentation in train set)
augmentX: scalar, how many augmentations to have for each 2D box.
type_whitelist: a list of strings, object types we are interested in.
Output:
None (will write a .pickle file to the disk)
'''
dataset = kitti_object(os.path.join(ROOT_DIR, 'data/kitti'), split)
data_idx_list = [int(line.rstrip()) for line in open(idx_filename)]
id_list = [] # int number
box2d_list = [] # [xmin,ymin,xmax,ymax]
box3d_list = [] # (8,3) array in rect camera coord
input_list = [] # channel number = 4, xyz,intensity in rect camera coord
label_list = [] # 1 for roi object, 0 for clutter
type_list = [] # string e.g. Car
heading_list = [] # ry (along y-axis in rect camera coord) radius of
# (cont.) clockwise angle from positive x axis in velo coord.
box3d_size_list = [] # array of l,w,h
frustum_angle_list = [] # angle of 2d box center from pos x-axis
gt_box2d_list = []
calib_list = []
pos_cnt = 0
all_cnt = 0
for data_idx in data_idx_list:
print('------------- ', data_idx)
calib = dataset.get_calibration(data_idx) # 3 by 4 matrix
objects = dataset.get_label_objects(data_idx)
pc_velo = dataset.get_lidar(data_idx)
pc_rect = np.zeros_like(pc_velo)
pc_rect[:, 0:3] = calib.project_velo_to_rect(pc_velo[:, 0:3])
pc_rect[:, 3] = pc_velo[:, 3]
img = dataset.get_image(data_idx)
img_height, img_width, img_channel = img.shape
_, pc_image_coord, img_fov_inds = get_lidar_in_image_fov(pc_velo[:, 0:3],
calib, 0, 0, img_width, img_height, True)
for obj_idx in range(len(objects)):
if objects[obj_idx].type not in type_whitelist:
continue
# 2D BOX: Get pts rect backprojected
box2d = objects[obj_idx].box2d
for _ in range(augmentX):
# Augment data by box2d perturbation
if perturb_box2d:
xmin, ymin, xmax, ymax = random_shift_box2d(box2d, img_height, img_width, 0.1)
else:
xmin, ymin, xmax, ymax = box2d
box_fov_inds = (pc_image_coord[:, 0] < xmax) & \
(pc_image_coord[:, 0] >= xmin) & \
(pc_image_coord[:, 1] < ymax) & \
(pc_image_coord[:, 1] >= ymin)
box_fov_inds = box_fov_inds & img_fov_inds
pc_in_box_fov = pc_rect[box_fov_inds, :]
pc_box_image_coord = pc_image_coord[box_fov_inds]
# Get frustum angle (according to center pixel in 2D BOX)
box2d_center = np.array([(xmin + xmax) / 2.0, (ymin + ymax) / 2.0])
uvdepth = np.zeros((1, 3))
uvdepth[0, 0:2] = box2d_center
uvdepth[0, 2] = 20 # some random depth
box2d_center_rect = calib.project_image_to_rect(uvdepth)
frustum_angle = -1 * np.arctan2(box2d_center_rect[0, 2],
box2d_center_rect[0, 0])
# 3D BOX: Get pts velo in 3d box
obj = objects[obj_idx]
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
_, inds = extract_pc_in_box3d(pc_in_box_fov, box3d_pts_3d)
label = np.zeros((pc_in_box_fov.shape[0]))
label[inds] = 1
# Get 3D BOX heading
heading_angle = obj.ry
# Get 3D BOX size
box3d_size = np.array([obj.l, obj.w, obj.h])
# Reject too far away object or object without points
if (box2d[3] - box2d[1]) < 25 or np.sum(label) == 0:
# print(box2d[3] - box2d[1], np.sum(label))
continue
id_list.append(data_idx)
box2d_list.append(np.array([xmin, ymin, xmax, ymax]))
box3d_list.append(box3d_pts_3d)
input_list.append(pc_in_box_fov.astype(np.float32, copy=False))
label_list.append(label)
type_list.append(objects[obj_idx].type)
heading_list.append(heading_angle)
box3d_size_list.append(box3d_size)
frustum_angle_list.append(frustum_angle)
gt_box2d_list.append(box2d)
calib_list.append(calib.calib_dict)
# collect statistics
pos_cnt += np.sum(label)
all_cnt += pc_in_box_fov.shape[0]
print('total_objects %d' % len(id_list))
print('Average pos ratio: %f' % (pos_cnt / float(all_cnt)))
print('Average npoints: %f' % (float(all_cnt) / len(id_list)))
with open(output_filename, 'wb') as fp:
pickle.dump(id_list, fp, -1)
pickle.dump(box2d_list, fp, -1)
pickle.dump(box3d_list, fp, -1)
pickle.dump(input_list, fp, -1)
pickle.dump(label_list, fp, -1)
pickle.dump(type_list, fp, -1)
pickle.dump(heading_list, fp, -1)
pickle.dump(box3d_size_list, fp, -1)
pickle.dump(frustum_angle_list, fp, -1)
pickle.dump(gt_box2d_list, fp, -1)
pickle.dump(calib_list, fp, -1)
print('save in {}'.format(output_filename))
def extract_frustum_det_data(idx_filename, split, output_filename, det_filename,
perturb_box2d=False, augmentX=1, type_whitelist=['Car']):
dataset = kitti_object(os.path.join(ROOT_DIR, 'data/kitti'), split)
data_idx_list = [int(line.rstrip()) for line in open(idx_filename)]
det_id_list, det_type_list, det_box2d_list, det_prob_list = \
read_det_file(det_filename)
all_boxes_2d = {}
for i, det_idx in enumerate(det_id_list):
if det_idx not in all_boxes_2d:
all_boxes_2d[det_idx] = []
all_boxes_2d[det_idx] += [
{
'type': det_type_list[i],
'box2d': det_box2d_list[i],
'prob': det_prob_list[i]
}
]
id_list = [] # int number
box2d_list = [] # [xmin,ymin,xmax,ymax]
box3d_list = [] # (8,3) array in rect camera coord
input_list = [] # channel number = 4, xyz,intensity in rect camera coord
label_list = [] # 1 for roi object, 0 for clutter
type_list = [] # string e.g. Car
heading_list = [] # ry (along y-axis in rect camera coord) radius of
# (cont.) clockwise angle from positive x axis in velo coord.
box3d_size_list = [] # array of l,w,h
frustum_angle_list = [] # angle of 2d box center from pos x-axis
gt_box2d_list = []
calib_list = []
pos_cnt = 0
all_cnt = 0
thresh = 0.5 if 'Car' in type_whitelist else 0.25
for data_idx in data_idx_list:
print('------------- ', data_idx)
calib = dataset.get_calibration(data_idx) # 3 by 4 matrix
gt_objects = dataset.get_label_objects(data_idx)
gt_objects, gt_boxes_2d, gt_boxes_3d = extract_boxes(gt_objects, type_whitelist)
if len(gt_objects) == 0:
continue
pc_velo = dataset.get_lidar(data_idx)
pc_rect = np.zeros_like(pc_velo)
pc_rect[:, 0:3] = calib.project_velo_to_rect(pc_velo[:, 0:3])
pc_rect[:, 3] = pc_velo[:, 3]
img = dataset.get_image(data_idx)
img_height, img_width, img_channel = img.shape
_, pc_image_coord, img_fov_inds = get_lidar_in_image_fov(pc_velo[:, 0:3],
calib, 0, 0, img_width, img_height, True)
det_objects = all_boxes_2d.get(data_idx)
if det_objects is None:
continue
for obj_idx in range(len(det_objects)):
cur_obj = det_objects[obj_idx]
if cur_obj['type'] not in type_whitelist:
continue
overlap = bbox_overlaps_2d(cur_obj['box2d'].reshape(-1, 4), gt_boxes_2d)
overlap = overlap[0]
max_overlap = overlap.max(0)
max_idx = overlap.argmax(0)
if max_overlap < thresh:
continue
assign_obj = gt_objects[max_idx]
# 2D BOX: Get pts rect backprojected
box2d = cur_obj['box2d']
for _ in range(augmentX):
# Augment data by box2d perturbation
if perturb_box2d:
xmin, ymin, xmax, ymax = random_shift_box2d(box2d, img_height, img_width, 0.1)
else:
xmin, ymin, xmax, ymax = box2d
box_fov_inds = (pc_image_coord[:, 0] < xmax) & \
(pc_image_coord[:, 0] >= xmin) & \
(pc_image_coord[:, 1] < ymax) & \
(pc_image_coord[:, 1] >= ymin)
box_fov_inds = box_fov_inds & img_fov_inds
pc_in_box_fov = pc_rect[box_fov_inds, :]
pc_box_image_coord = pc_image_coord[box_fov_inds]
# Get frustum angle (according to center pixel in 2D BOX)
box2d_center = np.array([(xmin + xmax) / 2.0, (ymin + ymax) / 2.0])
uvdepth = np.zeros((1, 3))
uvdepth[0, 0:2] = box2d_center
uvdepth[0, 2] = 20 # some random depth
box2d_center_rect = calib.project_image_to_rect(uvdepth)
frustum_angle = -1 * np.arctan2(box2d_center_rect[0, 2],
box2d_center_rect[0, 0])
# 3D BOX: Get pts velo in 3d box
obj = assign_obj
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
_, inds = extract_pc_in_box3d(pc_in_box_fov, box3d_pts_3d)
label = np.zeros((pc_in_box_fov.shape[0]))
label[inds] = 1
# Get 3D BOX heading
heading_angle = obj.ry
# Get 3D BOX size
box3d_size = np.array([obj.l, obj.w, obj.h])
gt_box2d = obj.box2d
# Reject too far away object or object without points
if (gt_box2d[3] - gt_box2d[1]) < 25 or np.sum(label) == 0:
# print(gt_box2d[3] - gt_box2d[1], np.sum(label))
continue
id_list.append(data_idx)
box2d_list.append(np.array([xmin, ymin, xmax, ymax]))
box3d_list.append(box3d_pts_3d)
input_list.append(pc_in_box_fov.astype(np.float32, copy=False))
label_list.append(label)
type_list.append(obj.type)
heading_list.append(heading_angle)
box3d_size_list.append(box3d_size)
frustum_angle_list.append(frustum_angle)
gt_box2d_list.append(gt_box2d)
calib_list.append(calib.calib_dict)
# collect statistics
pos_cnt += np.sum(label)
all_cnt += pc_in_box_fov.shape[0]
print('total_objects %d' % len(id_list))
print('Average pos ratio: %f' % (pos_cnt / float(all_cnt)))
print('Average npoints: %f' % (float(all_cnt) / len(id_list)))
with open(output_filename, 'wb') as fp:
pickle.dump(id_list, fp, -1)
pickle.dump(box2d_list, fp, -1)
pickle.dump(box3d_list, fp, -1)
pickle.dump(input_list, fp, -1)
pickle.dump(label_list, fp, -1)
pickle.dump(type_list, fp, -1)
pickle.dump(heading_list, fp, -1)
pickle.dump(box3d_size_list, fp, -1)
pickle.dump(frustum_angle_list, fp, -1)
pickle.dump(gt_box2d_list, fp, -1)
pickle.dump(calib_list, fp, -1)
print('save in {}'.format(output_filename))
def extract_frustum_data_rgb_detection(idx_filename, split, output_filename, res_label_dir,
type_whitelist=['Car'],
img_height_threshold=5,
lidar_point_threshold=1):
''' Extract point clouds in frustums extruded from 2D detection boxes.
Update: Lidar points and 3d boxes are in *rect camera* coord system
(as that in 3d box label files)
Input:
det_filename: string, each line is
img_path typeid confidence xmin ymin xmax ymax
split: string, either trianing or testing
output_filename: string, the name for output .pickle file
type_whitelist: a list of strings, object types we are interested in.
img_height_threshold: int, neglect image with height lower than that.
lidar_point_threshold: int, neglect frustum with too few points.
Output:
None (will write a .pickle file to the disk)
'''
dataset = kitti_object(os.path.join(ROOT_DIR, 'data/kitti'), split)
data_idx_list = [int(line.rstrip()) for line in open(idx_filename)]
id_list = []
type_list = []
box2d_list = []
prob_list = []
input_list = [] # channel number = 4, xyz,intensity in rect camera coord
frustum_angle_list = [] # angle of 2d box center from pos x-axis
box3d_pred_list = []
calib_list = []
enlarge_box3d_list = []
enlarge_box3d_size_list = []
enlarge_box3d_angle_list = []
for data_idx in data_idx_list:
print('------------- ', data_idx)
calib = dataset.get_calibration(data_idx) # 3 by 4 matrix
pc_velo = dataset.get_lidar(data_idx)
pc_rect = np.zeros_like(pc_velo)
pc_rect[:, 0:3] = calib.project_velo_to_rect(pc_velo[:, 0:3])
pc_rect[:, 3] = pc_velo[:, 3]
img = dataset.get_image(data_idx)
img_height, img_width, img_channel = img.shape
_, pc_image_coord, img_fov_inds = get_lidar_in_image_fov(pc_velo[:, 0:3],
calib, 0, 0, img_width, img_height, True)
pc_image_coord = pc_image_coord[img_fov_inds]
pc_rect = pc_rect[img_fov_inds]
label_filename = os.path.join(res_label_dir, '%06d.txt' % (data_idx))
objects = utils.read_label(label_filename)
for obj_idx in range(len(objects)):
if objects[obj_idx].type not in type_whitelist:
continue
# 2D BOX: Get pts rect backprojected
box2d = objects[obj_idx].box2d
xmin, ymin, xmax, ymax = box2d
obj = objects[obj_idx]
l, w, h = obj.l, obj.w, obj.h
cx, cy, cz = obj.t
ry = obj.ry
cy = cy - h / 2
obj_array = np.array([cx, cy, cz, l, w, h, ry])
box3d_pts_3d = compute_box_3d_obj_array(obj_array)
ratio = 1.2
enlarge_obj_array = obj_array.copy()
enlarge_obj_array[3:6] = enlarge_obj_array[3:6] * ratio
box3d_pts_3d_l = compute_box_3d_obj_array(enlarge_obj_array)
_, inds = extract_pc_in_box3d(pc_rect, box3d_pts_3d_l)
pc_in_cuboid = pc_rect[inds]
pc_box_image_coord = pc_image_coord[inds]
box3d_center = enlarge_obj_array[:3]
frustum_angle = -1 * np.arctan2(box3d_center[2],
box3d_center[0])
# Pass objects that are too small
if ymax - ymin < img_height_threshold or xmax - xmin < 1 or \
len(pc_in_cuboid) < lidar_point_threshold:
continue
id_list.append(data_idx)
input_list.append(pc_in_cuboid.astype(np.float32, copy=False))
type_list.append(objects[obj_idx].type)
frustum_angle_list.append(frustum_angle)
prob_list.append(obj.score)
box2d_list.append(box2d)
box3d_pred_list.append(box3d_pts_3d)
enlarge_box3d_list.append(box3d_pts_3d_l)
enlarge_box3d_size_list.append(enlarge_obj_array[3:6])
enlarge_box3d_angle_list.append(enlarge_obj_array[-1])
calib_list.append(calib.calib_dict)
with open(output_filename, 'wb') as fp:
pickle.dump(id_list, fp, -1)
pickle.dump(box2d_list, fp, -1)
pickle.dump(input_list, fp, -1)
pickle.dump(type_list, fp, -1)
pickle.dump(frustum_angle_list, fp, -1)
pickle.dump(prob_list, fp, -1)
pickle.dump(calib_list, fp, -1)
pickle.dump(enlarge_box3d_list, fp, -1)
pickle.dump(enlarge_box3d_size_list, fp, -1)
pickle.dump(enlarge_box3d_angle_list, fp, -1)
print(len(id_list))
print('save in {}'.format(output_filename))
def extract_frustum_det_data(idx_filename, split, output_filename, res_label_dir,
perturb_box2d=False, augmentX=1, type_whitelist=['Car'], remove_diff=False):
''' Extract point clouds and corresponding annotations in frustums
defined generated from 2D bounding boxes
Lidar points and 3d boxes are in *rect camera* coord system
(as that in 3d box label files)
Input:
idx_filename: string, each line of the file is a sample ID
split: string, either trianing or testing
output_filename: string, the name for output .pickle file
viz: bool, whether to visualize extracted data
perturb_box2d: bool, whether to perturb the box2d
(used for data augmentation in train set)
augmentX: scalar, how many augmentations to have for each 2D box.
type_whitelist: a list of strings, object types we are interested in.
Output:
None (will write a .pickle file to the disk)
'''
dataset = kitti_object(os.path.join(ROOT_DIR, 'data/kitti'), split)
data_idx_list = [int(line.rstrip()) for line in open(idx_filename)]
id_list = [] # int number
box3d_list = [] # (8,3) array in rect camera coord
input_list = [] # channel number = 4, xyz,intensity in rect camera coord
label_list = [] # 1 for roi object, 0 for clutter
type_list = [] # string e.g. Car
heading_list = [] # ry (along y-axis in rect camera coord) radius of
# (cont.) clockwise angle from positive x axis in velo coord.
box3d_size_list = [] # array of l,w,h
frustum_angle_list = [] # angle of 2d box center from pos x-axis
gt_box2d_list = []
calib_list = []
enlarge_box3d_list = []
enlarge_box3d_size_list = []
enlarge_box3d_angle_list = []
pos_cnt = 0
all_cnt = 0
thresh = 0.5 if 'Car' in type_whitelist else 0.25
for data_idx in data_idx_list:
print('------------- ', data_idx)
calib = dataset.get_calibration(data_idx) # 3 by 4 matrix
# objects = dataset.get_label_objects(data_idx)
gt_objects = dataset.get_label_objects(data_idx)
gt_objects, gt_boxes_2d, gt_boxes_3d = extract_boxes(
gt_objects, type_whitelist, remove_diff)
if len(gt_objects) == 0:
continue
pc_velo = dataset.get_lidar(data_idx)
pc_rect = np.zeros_like(pc_velo)
pc_rect[:, 0:3] = calib.project_velo_to_rect(pc_velo[:, 0:3])
pc_rect[:, 3] = pc_velo[:, 3]
img = dataset.get_image(data_idx)
img_height, img_width, img_channel = img.shape
_, pc_image_coord, img_fov_inds = get_lidar_in_image_fov(pc_velo[:, 0:3],
calib, 0, 0, img_width, img_height, True)
pc_rect = pc_rect[img_fov_inds, :]
pc_image_coord = pc_image_coord[img_fov_inds]
label_filename = os.path.join(res_label_dir, '%06d.txt' % (data_idx))
objects = utils.read_label(label_filename)
for obj_idx in range(len(objects)):
if objects[obj_idx].type not in type_whitelist:
continue
obj = objects[obj_idx]
l, w, h = obj.l, obj.w, obj.h
cx, cy, cz = obj.t
ry = obj.ry
cy = cy - h / 2
obj_array = np.array([cx, cy, cz, l, w, h, ry])
ratio = 1.2
enlarge_obj_array = obj_array.copy()
enlarge_obj_array[3:6] = enlarge_obj_array[3:6] * ratio
overlap = rbbox_iou_3d(obj_array.reshape(-1, 7), gt_boxes_3d)
overlap = overlap[0]
max_overlap = overlap.max(0)
max_idx = overlap.argmax(0)
# print(max_overlap)
if max_overlap < thresh:
continue
gt_obj = gt_objects[max_idx]
gt_box2d = gt_objects[max_idx].box2d
l, w, h = gt_obj.l, gt_obj.w, gt_obj.h
cx, cy, cz = gt_obj.t
ry = gt_obj.ry
cy = cy - h / 2
gt_obj_array = np.array([cx, cy, cz, l, w, h, ry])
box3d_pts_3d = compute_box_3d_obj_array(gt_obj_array)
for _ in range(augmentX):
if perturb_box2d:
# print(box3d_align)
enlarge_obj_array = random_shift_rotate_box3d(
enlarge_obj_array, 0.05)
box3d_corners_enlarge = compute_box_3d_obj_array(
enlarge_obj_array)
else:
box3d_corners_enlarge = compute_box_3d_obj_array(
enlarge_obj_array)
_, inds = extract_pc_in_box3d(pc_rect, box3d_corners_enlarge)
pc_in_cuboid = pc_rect[inds]
# pc_box_image_coord = pc_image_coord[inds]
_, inds = extract_pc_in_box3d(pc_in_cuboid, box3d_pts_3d)
label = np.zeros((pc_in_cuboid.shape[0]))
label[inds] = 1
# _, inds = extract_pc_in_box3d(pc_rect, box3d_pts_3d)
# print(np.sum(label), np.sum(inds))
# Get 3D BOX heading
heading_angle = gt_obj.ry
# Get 3D BOX size
box3d_size = np.array([gt_obj.l, gt_obj.w, gt_obj.h])
# Reject too far away object or object without points
if np.sum(label) == 0:
continue
box3d_center = enlarge_obj_array[:3]
frustum_angle = -1 * np.arctan2(box3d_center[2],
box3d_center[0])
id_list.append(data_idx)
box3d_list.append(box3d_pts_3d)
input_list.append(pc_in_cuboid)
label_list.append(label)
type_list.append(objects[obj_idx].type)
heading_list.append(heading_angle)
box3d_size_list.append(box3d_size)
frustum_angle_list.append(frustum_angle)
gt_box2d_list.append(gt_box2d)
calib_list.append(calib.calib_dict)
enlarge_box3d_list.append(box3d_corners_enlarge)
enlarge_box3d_size_list.append(enlarge_obj_array[3:6])
enlarge_box3d_angle_list.append(enlarge_obj_array[-1])
# collect statistics
pos_cnt += np.sum(label)
all_cnt += pc_in_cuboid.shape[0]
print('total_objects %d' % len(id_list))
print('Average pos ratio: %f' % (pos_cnt / float(all_cnt)))
print('Average npoints: %f' % (float(all_cnt) / len(id_list)))
with open(output_filename, 'wb') as fp:
pickle.dump(id_list, fp, -1)
pickle.dump(box3d_list, fp, -1)
pickle.dump(input_list, fp, -1)
pickle.dump(label_list, fp, -1)
pickle.dump(type_list, fp, -1)
pickle.dump(heading_list, fp, -1)
pickle.dump(box3d_size_list, fp, -1)
pickle.dump(frustum_angle_list, fp, -1)
pickle.dump(gt_box2d_list, fp, -1)
pickle.dump(calib_list, fp, -1)
pickle.dump(enlarge_box3d_list, fp, -1)
pickle.dump(enlarge_box3d_size_list, fp, -1)
pickle.dump(enlarge_box3d_angle_list, fp, -1)
print('save in {}'.format(output_filename))
def extract_frustum_data(
data_name,
object_i,
dataset,
classes_mapper,
perturb_box2d=False,
augmentX=1,
type_whitelist=['Car'],
):
''' Extract point clouds and corresponding annotations in frustums
defined generated from 2D bounding boxes
Lidar points and 3d boxes are in *rect camera* coord system
(as that in 3d box label files)
Input:
data_names_lists: names list
split: string, either trianing or testing
output_filename: string, the name for output .pickle file
viz: bool, whether to visualize extracted data
perturb_box2d: bool, whether to perturb the box2d
(used for data augmentation in train set)
augmentX: scalar, how many augmentations to have for each 2D box.
type_whitelist: a list of strings, object types we are interested in.
Output:
None (will write a .pickle file to the disk)
'''
data = {}
# id_i = []
# box2d_i = [] # [xmin,ymin,xmax,ymax]
# box3d_i = [] # (8,3) array in rect camera coord
# input_i = [] # channel number = 4, xyz,intensity in rect camera coord
# label_i = [] # 1 for roi object, 0 for clutter
# type_i = [] # string e.g. Car
# heading_i = [] # ry (along y-axis in rect camera coord) radius of
# # (cont.) clockwise angle from positive x axis in velo coord.
# box3d_size_i = [] # array of l,w,h
# frustum_angle_i = [] # angle of 2d box center from pos x-axis
# gt_box2d_i = []
# calib_i = []
# print('------------- ', data_name)
calib = dataset.get_calibration(data_name) # 3 by 4 matrix
# objects = dataset.get_label_objects(data_name)
pc_velo = dataset.get_lidar(data_name)
pc_rect = np.zeros_like(pc_velo)
pc_rect[:, 0:3] = calib.project_velo_to_rect(pc_velo[:, 0:3])
pc_rect[:, 3] = pc_velo[:, 3]
img = dataset.get_image(data_name)
if img is None:
print('SKIP IMAGE!')
return None
img_height, img_width, img_channel = img.shape
_, pc_image_coord, img_fov_inds = get_lidar_in_image_fov(
pc_velo[:, 0:3], calib, 0, 0, img_width, img_height, True)
# 2D BOX: Get pts rect backprojected
box2d = object_i.box2d
for _ in range(augmentX):
# Augment data by box2d perturbation
if perturb_box2d:
xmin, ymin, xmax, ymax = random_shift_box2d(
box2d, img_height, img_width, 0.1)
else:
xmin, ymin, xmax, ymax = box2d
box_fov_inds = (pc_image_coord[:, 0] < xmax) & \
(pc_image_coord[:, 0] >= xmin) & \
(pc_image_coord[:, 1] < ymax) & \
(pc_image_coord[:, 1] >= ymin)
box_fov_inds = box_fov_inds & img_fov_inds
pc_in_box_fov = pc_rect[box_fov_inds, :]
pc_box_image_coord = pc_image_coord[box_fov_inds]
# Get frustum angle (according to center pixel in 2D BOX)
box2d_center = np.array([(xmin + xmax) / 2.0, (ymin + ymax) / 2.0])
uvdepth = np.zeros((1, 3))
uvdepth[0, 0:2] = box2d_center
uvdepth[0, 2] = 20 # some random depth
box2d_center_rect = calib.project_image_to_rect(uvdepth)
frustum_angle = -1 * np.arctan2(box2d_center_rect[0, 2],
box2d_center_rect[0, 0])
# 3D BOX: Get pts velo in 3d box
obj = object_i
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
_, inds = extract_pc_in_box3d(pc_in_box_fov, box3d_pts_3d)
label = np.zeros((pc_in_box_fov.shape[0]))
label[inds] = 1
# Get 3D BOX heading
heading_angle = obj.ry
# Get 3D BOX size
box3d_size = np.array([obj.l, obj.w, obj.h])
# Reject too far away object or object without points
if np.sum(label) == 0:
# print(box2d[3] - box2d[1], np.sum(label))
return None
id_i = data_name
box2d_i = np.array([xmin, ymin, xmax, ymax])
box3d_i = box3d_pts_3d
input_i = pc_in_box_fov.astype(np.float32, copy=False)
label_i = label
type_i = object_i.type
heading_i = heading_angle
box3d_size_i = box3d_size
frustum_angle_i = frustum_angle
gt_box2d_i = box2d
calib_i = calib.calib_dict
data = {
'id_i': id_i,
'box2d_i': box2d_i,
'box3d_i': box3d_i,
'input_i': input_i,
'label_i': label_i,
'type_i': classes_mapper[type_i],
'heading_i': heading_i,
'box3d_size_i': box3d_size_i,
'frustum_angle_i': frustum_angle_i,
'gt_box2d_i': gt_box2d_i,
'calib_i': calib_i
}
return data
