''' Prepare KITTI data for 3D object detection.
Author: Charles R. Qi
Date: September 2017
Modified by Zhixin Wang
'''
import argparse
import os
import pickle
import sys
import cv2
import numpy as np
from PIL import Image
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT_DIR = os.path.dirname(BASE_DIR)
sys.path.append(BASE_DIR)
sys.path.append(ROOT_DIR)
import kitti_util as utils
from kitti_object import kitti_object
from draw_util import get_lidar_in_image_fov
from ops.pybind11.rbbox_iou import bbox_overlaps_2d
def in_hull(p, hull):
from scipy.spatial import Delaunay
if not isinstance(hull, Delaunay):
hull = Delaunay(hull)
return hull.find_simplex(p) >= 0
def extract_pc_in_box3d(pc, box3d):
''' pc: (N,3), box3d: (8,3) '''
box3d_roi_inds = in_hull(pc[:, 0:3], box3d)
return pc[box3d_roi_inds, :], box3d_roi_inds
def extract_pc_in_box2d(pc, box2d):
''' pc: (N,2), box2d: (xmin,ymin,xmax,ymax) '''
box2d_corners = np.zeros((4, 2))
box2d_corners[0, :] = [box2d[0], box2d[1]]
box2d_corners[1, :] = [box2d[2], box2d[1]]
box2d_corners[2, :] = [box2d[2], box2d[3]]
box2d_corners[3, :] = [box2d[0], box2d[3]]
box2d_roi_inds = in_hull(pc[:, 0:2], box2d_corners)
return pc[box2d_roi_inds, :], box2d_roi_inds
def random_shift_box2d(box2d, img_height, img_width, shift_ratio=0.1):
''' Randomly shift box center, randomly scale width and height
'''
r = shift_ratio
xmin, ymin, xmax, ymax = box2d
h = ymax - ymin
w = xmax - xmin
cx = (xmin + xmax) / 2.0
cy = (ymin + ymax) / 2.0
assert xmin < xmax and ymin < ymax
while True:
cx2 = cx + w * r * (np.random.random() * 2 - 1)
cy2 = cy + h * r * (np.random.random() * 2 - 1)
h2 = h * (1 + np.random.random() * 2 * r - r) # 0.9 to 1.1
w2 = w * (1 + np.random.random() * 2 * r - r) # 0.9 to 1.1
new_box2d = np.array([cx2 - w2 / 2.0, cy2 - h2 / 2.0, cx2 + w2 / 2.0, cy2 + h2 / 2.0])
new_box2d[[0, 2]] = np.clip(new_box2d[[0, 2]], 0, img_width - 1)
new_box2d[[1, 3]] = np.clip(new_box2d[[1, 3]], 0, img_height - 1)
if new_box2d[0] < new_box2d[2] and new_box2d[1] < new_box2d[3]:
return new_box2d
def extract_boxes(objects, type_whitelist):
boxes_2d = []
boxes_3d = []
filter_objects = []
for obj_idx in range(len(objects)):
obj = objects[obj_idx]
if obj.type not in type_whitelist:
continue
boxes_2d += [obj.box2d]
boxes_3d += [np.array([obj.t[0], obj.t[1], obj.t[2], obj.l, obj.w, obj.h, obj.ry])]
filter_objects += [obj]
if len(boxes_3d) != 0:
boxes_3d = np.stack(boxes_3d, 0)
boxes_2d = np.stack(boxes_2d, 0)
return filter_objects, boxes_2d, boxes_3d
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(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 get_box3d_dim_statistics(idx_filename):
''' Collect and dump 3D bounding box statistics '''
dataset = kitti_object(os.path.join(ROOT_DIR, 'data/kitti'))
dimension_list = []
type_list = []
ry_list = []
data_idx_list = [int(line.rstrip()) for line in open(idx_filename)]
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)
for obj_idx in range(len(objects)):
obj = objects[obj_idx]
if obj.type == 'DontCare':
continue
dimension_list.append(np.array([obj.l, obj.w, obj.h]))
type_list.append(obj.type)
ry_list.append(obj.ry)
with open('box3d_dimensions.pickle', 'wb') as fp:
pickle.dump(type_list, fp)
pickle.dump(dimension_list, fp)
pickle.dump(ry_list, fp)
def read_det_file(det_filename):
''' Parse lines in 2D detection output files '''
det_id2str = {1: 'Pedestrian', 2: 'Car', 3: 'Cyclist'}
id_list = []
type_list = []
prob_list = []
box2d_list = []
for line in open(det_filename, 'r'):
t = line.rstrip().split(" ")
id_list.append(int(os.path.basename(t[0]).rstrip('.png')))
type_list.append(det_id2str[int(t[1])])
prob_list.append(float(t[2]))
box2d_list.append(np.array([float(t[i]) for i in range(3, 7)]))
return id_list, type_list, box2d_list, prob_list
def read_det_pkl_file(det_filename):
''' Parse lines in 2D detection output files '''
with open(det_filename, 'r') as fn:
results = pickle.load(fn)
id_list = results['id_list']
type_list = results['type_list']
box2d_list = results['box2d_list']
prob_list = results['prob_list']
return id_list, type_list, box2d_list, prob_list
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 write_2d_rgb_detection(det_filename, split, result_dir):
''' Write 2D detection results for KITTI evaluation.
Convert from Wei's format to KITTI format.
Input:
det_filename: string, each line is
img_path typeid confidence xmin ymin xmax ymax
split: string, either trianing or testing
result_dir: string, folder path for results dumping
Output:
None (will write <xxx>.txt files to disk)
Usage:
write_2d_rgb_detection("val_det.txt", "training", "results")
'''
dataset = kitti_object(os.path.join(ROOT_DIR, 'data/kitti'), split)
det_id_list, det_type_list, det_box2d_list, det_prob_list = \
read_det_file(det_filename)
# map from idx to list of strings, each string is a line without \n
results = {}
for i in range(len(det_id_list)):
idx = det_id_list[i]
typename = det_type_list[i]
box2d = det_box2d_list[i]
prob = det_prob_list[i]
output_str = typename + " -1 -1 -10 "
output_str += "%f %f %f %f " % (box2d[0], box2d[1], box2d[2], box2d[3])
output_str += "-1 -1 -1 -1000 -1000 -1000 -10 %f" % (prob)
if idx not in results:
results[idx] = []
results[idx].append(output_str)
if not os.path.exists(result_dir):
os.mkdir(result_dir)
output_dir = os.path.join(result_dir, 'data')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
for idx in results:
pred_filename = os.path.join(output_dir, '%06d.txt' % (idx))
fout = open(pred_filename, 'w')
for line in results[idx]:
fout.write(line + '\n')
fout.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gen_train', action='store_true',
help='Generate train split frustum data with perturbed GT 2D boxes')
parser.add_argument('--gen_val', action='store_true', help='Generate val split frustum data with GT 2D boxes')
parser.add_argument('--gen_val_rgb_detection', action='store_true',
help='Generate val split frustum data with RGB detection 2D boxes')
parser.add_argument('--car_only', action='store_true', help='Only generate cars')
parser.add_argument('--people_only', action='store_true', help='Only generate peds and cycs')
parser.add_argument('--save_dir', default=None, type=str, help='data directory to save data')
args = parser.parse_args()
np.random.seed(3)
if args.save_dir is None:
save_dir = 'kitti/data/pickle_data'
else:
save_dir = args.save_dir
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if args.car_only:
type_whitelist = ['Car']
output_prefix = 'frustum_caronly_'
elif args.people_only:
type_whitelist = ['Pedestrian', 'Cyclist']
output_prefix = 'frustum_pedcyc_'
else:
type_whitelist = ['Car', 'Pedestrian', 'Cyclist']
output_prefix = 'frustum_carpedcyc_'
if args.gen_train:
extract_frustum_data(
os.path.join(BASE_DIR, 'image_sets/train.txt'),
'training',
os.path.join(save_dir, output_prefix + 'train.pickle'),
perturb_box2d=True, augmentX=5,
type_whitelist=type_whitelist)
if args.gen_val:
extract_frustum_data(
os.path.join(BASE_DIR, 'image_sets/val.txt'),
'training',
os.path.join(save_dir, output_prefix + 'val.pickle'),
perturb_box2d=False, augmentX=1,
type_whitelist=type_whitelist)
if args.gen_val_rgb_detection:
extract_frustum_data_rgb_detection(
os.path.join(BASE_DIR, 'rgb_detections/rgb_detection_val.txt'),
'training',
os.path.join(save_dir, output_prefix + 'val_rgb_detection.pickle'),
type_whitelist=type_whitelist)
