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kitti_dataset.py
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kitti_dataset.py
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"""This file defines a class to interact with KITTI dataset. """
import os
import time
from os.path import isfile, join
import random
from collections import namedtuple, defaultdict
import numpy as np
import open3d
import cv2
Points = namedtuple('Points', ['xyz', 'attr'])
def downsample_by_average_voxel(points, voxel_size):
"""Voxel downsampling using average function.
points: a Points namedtuple containing "xyz" and "attr".
voxel_size: the size of voxel cells used for downsampling.
"""
# create voxel grid
xmax, ymax, zmax = np.amax(points.xyz, axis=0)
xmin, ymin, zmin = np.amin(points.xyz, axis=0)
xyz_offset = np.asarray([[xmin, ymin, zmin]])
xyz_zeros = np.asarray([0, 0, 0], dtype=np.float32)
xyz_idx = (points.xyz - xyz_offset) // voxel_size
xyz_idx = xyz_idx.astype(np.int32)
dim_x, dim_y, dim_z = np.amax(xyz_idx, axis=0) + 1
keys = xyz_idx[:, 0] + xyz_idx[:, 1]*dim_x + xyz_idx[:, 2]*dim_y*dim_x
order = np.argsort(keys)
keys = keys[order]
points_xyz = points.xyz[order]
unique_keys, lens = np.unique(keys, return_counts=True)
indices = np.hstack([[0], lens[:-1]]).cumsum()
downsampled_xyz = np.add.reduceat(
points_xyz, indices, axis=0)/lens[:,np.newaxis]
include_attr = points.attr is not None
if include_attr:
attr = points.attr[order]
downsampled_attr = np.add.reduceat(
attr, indices, axis=0)/lens[:,np.newaxis]
if include_attr:
return Points(xyz=downsampled_xyz,
attr=downsampled_attr)
else:
return Points(xyz=downsampled_xyz,
attr=None)
def downsample_by_random_voxel(points, voxel_size, add_rnd3d=False):
"""Downsample the points using base_voxel_size at different scales"""
xmax, ymax, zmax = np.amax(points.xyz, axis=0)
xmin, ymin, zmin = np.amin(points.xyz, axis=0)
xyz_offset = np.asarray([[xmin, ymin, zmin]])
xyz_zeros = np.asarray([0, 0, 0], dtype=np.float32)
if not add_rnd3d:
xyz_idx = (points.xyz - xyz_offset) // voxel_size
else:
xyz_idx = (points.xyz - xyz_offset +
voxel_size*np.random.random((1,3))) // voxel_size
dim_x, dim_y, dim_z = np.amax(xyz_idx, axis=0) + 1
keys = xyz_idx[:, 0] + xyz_idx[:, 1]*dim_x + xyz_idx[:, 2]*dim_y*dim_x
num_points = xyz_idx.shape[0]
voxels_idx = {}
for pidx in range(len(points.xyz)):
key = keys[pidx]
if key in voxels_idx:
voxels_idx[key].append(pidx)
else:
voxels_idx[key] = [pidx]
downsampled_xyz = []
downsampled_attr = []
for key in voxels_idx:
center_idx = random.choice(voxels_idx[key])
downsampled_xyz.append(points.xyz[center_idx])
downsampled_attr.append(points.attr[center_idx])
return Points(xyz=np.array(downsampled_xyz),
attr=np.array(downsampled_attr))
def box3d_to_cam_points(label, expend_factor=(1.0, 1.0, 1.0)):
"""Project 3D box into camera coordinates.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw", "height"
"width", "length".
Returns: a numpy array [8, 3] representing the corners of the 3d box in
camera coordinates.
"""
yaw = label['yaw']
R = np.array([[np.cos(yaw), 0, np.sin(yaw)],
[0, 1, 0 ],
[-np.sin(yaw), 0, np.cos(yaw)]]);
h = label['height']
delta_h = h*(expend_factor[0]-1)
w = label['width']*expend_factor[1]
l = label['length']*expend_factor[2]
corners = np.array([[ l/2, delta_h/2, w/2], # front up right
[ l/2, delta_h/2, -w/2], # front up left
[-l/2, delta_h/2, -w/2], # back up left
[-l/2, delta_h/2, w/2], # back up right
[ l/2, -h-delta_h/2, w/2], # front down right
[ l/2, -h-delta_h/2, -w/2], # front down left
[-l/2, -h-delta_h/2, -w/2], # back down left
[-l/2, -h-delta_h/2, w/2]]) # back down right
r_corners = corners.dot(np.transpose(R))
tx = label['x3d']
ty = label['y3d']
tz = label['z3d']
cam_points_xyz = r_corners+np.array([tx, ty, tz])
return Points(xyz = cam_points_xyz, attr = None)
def box3d_to_normals(label, expend_factor=(1.0, 1.0, 1.0)):
"""Project a 3D box into camera coordinates, compute the center
of the box and normals.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw",
"height", "width", "lenth".
Returns: a numpy array [3, 3] containing [wx, wy, wz]^T, a [3] lower
bound and a [3] upper bound.
"""
box3d_points = box3d_to_cam_points(label, expend_factor)
box3d_points_xyz = box3d_points.xyz
wx = box3d_points_xyz[[0], :] - box3d_points_xyz[[4], :]
lx = np.matmul(wx, box3d_points_xyz[4, :])
ux = np.matmul(wx, box3d_points_xyz[0, :])
wy = box3d_points_xyz[[0], :] - box3d_points_xyz[[1], :]
ly = np.matmul(wy, box3d_points_xyz[1, :])
uy = np.matmul(wy, box3d_points_xyz[0, :])
wz = box3d_points_xyz[[0], :] - box3d_points_xyz[[3], :]
lz = np.matmul(wz, box3d_points_xyz[3, :])
uz = np.matmul(wz, box3d_points_xyz[0, :])
return(np.concatenate([wx, wy, wz], axis=0),
np.concatenate([lx, ly, lz]), np.concatenate([ux, uy, uz]))
def sel_xyz_in_box3d(label, xyz, expend_factor=(1.0, 1.0, 1.0)):
"""Select points in a 3D box.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw",
"height", "width", "lenth".
Returns: a bool mask indicating points inside a 3D box.
"""
normals, lower, upper = box3d_to_normals(label, expend_factor)
projected = np.matmul(xyz, np.transpose(normals))
points_in_x = np.logical_and(projected[:, 0] > lower[0],
projected[:, 0] < upper[0])
points_in_y = np.logical_and(projected[:, 1] > lower[1],
projected[:, 1] < upper[1])
points_in_z = np.logical_and(projected[:, 2] > lower[2],
projected[:, 2] < upper[2])
mask = np.logical_and.reduce((points_in_x, points_in_y, points_in_z))
return mask
def sel_xyz_in_box2d(label, xyz, expend_factor=(1.0, 1.0, 1.0)):
"""Select points in a 3D box.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw",
"height", "width", "lenth".
Returns: a bool mask indicating points inside a 3D box.
"""
normals, lower, upper = box3d_to_normals(label, expend_factor)
normals, lower, upper = normals[1:], lower[1:], upper[1:]
projected = np.matmul(xyz, np.transpose(normals))
points_in_y = np.logical_and(projected[:, 0] > lower[0],
projected[:, 0] < upper[0])
points_in_z = np.logical_and(projected[:, 1] > lower[1],
projected[:, 1] < upper[1])
mask = np.logical_and.reduce((points_in_y, points_in_z))
return mask
class KittiDataset(object):
"""A class to interact with KITTI dataset."""
def __init__(self, image_dir, point_dir, calib_dir, label_dir,
index_filename=None, is_training=True, is_raw=False, difficulty=-100,
num_classes=8):
"""
Args:
image_dir: a string of the path to image folder.
point_dir: a string of the path to point cloud data folder.
calib_dir: a string of the path to the calibration matrices.
label_dir: a string of the path to the label folder.
index_filename: a string containing a path an index file.
"""
self._image_dir = image_dir
self._point_dir = point_dir
self._calib_dir = calib_dir
self._label_dir = label_dir
self._index_filename = index_filename
if index_filename:
self._file_list = self._read_index_file(index_filename)
else:
self._file_list = self._get_file_list(self._image_dir)
self._verify_file_list(
self._image_dir, self._point_dir, self._label_dir, self._calib_dir,
self._file_list, is_training, is_raw)
self._is_training = is_training
self._is_raw = is_raw
self.num_classes = num_classes
self.difficulty = difficulty
self._max_image_height = 376
self._max_image_width = 1242
def __str__(self):
"""Generate a string summary of the dataset"""
summary_string = ('Dataset Summary:\n'
+'image_dir=%s\n' % self._image_dir
+'point_dir=%s\n' % self._point_dir
+'calib_dir=%s\n' % self._calib_dir
+'label_dir=%s\n' % self._label_dir
+'index_filename=%s\n' % self._index_filename
+'Total number of sampels: %d\n' % self.num_files)
statics = self.get_statics()
return summary_string + statics
def get_statics(self):
import matplotlib.pyplot as plt
"""Get statics of objects inside the dataset"""
x_dict = defaultdict(list)
y_dict = defaultdict(list)
z_dict = defaultdict(list)
h_dict = defaultdict(list)
w_dict = defaultdict(list)
l_dict = defaultdict(list)
view_angle_dict = defaultdict(list)
yaw_dict = defaultdict(list)
for frame_idx in range(self.num_files):
labels = self.get_label(frame_idx)
for label in labels:
if label['ymin'] > 0:
if label['ymax'] - label['ymin'] > 25:
object_name = label['name']
h_dict[object_name].append(label['height'])
w_dict[object_name].append(label['width'])
l_dict[object_name].append(label['length'])
x_dict[object_name].append(label['x3d'])
y_dict[object_name].append(label['y3d'])
z_dict[object_name].append(label['z3d'])
view_angle_dict[object_name].append(
np.arctan(label['x3d']/label['z3d']))
yaw_dict[object_name].append(label['yaw'])
plt.scatter(z_dict['Pedestrian'], np.array(l_dict['Pedestrian']))
plt.title('Scatter plot pythonspot.com')
plt.show()
# compute ingore statics
import nms
truncation_rates = []
no_truncation_rates = []
image_height = []
image_width = []
for frame_idx in range(self.num_files):
labels = self.get_label(frame_idx)
calib = self.get_calib(frame_idx)
image = self.get_image(frame_idx)
image_height.append(image.shape[0])
image_width.append(image.shape[1])
for label in labels:
if label['name'] == 'Car':
# too small
if label['ymax'] - label['ymin'] < 25:
object_name = label['name']
h_dict['ignored_by_height'].append(label['height'])
w_dict['ignored_by_height'].append(label['width'])
l_dict['ignored_by_height'].append(label['length'])
x_dict['ignored_by_height'].append(label['x3d'])
y_dict['ignored_by_height'].append(label['y3d'])
z_dict['ignored_by_height'].append(label['z3d'])
view_angle_dict['ignored_by_height'].append(
np.arctan(label['x3d']/label['z3d']))
yaw_dict['ignored_by_height'].append(label['yaw'])
if label['truncation'] > 0.5:
h_dict['ignored_by_truncation'].append(label['height'])
w_dict['ignored_by_truncation'].append(label['width'])
l_dict['ignored_by_truncation'].append(label['length'])
x_dict['ignored_by_truncation'].append(label['x3d'])
y_dict['ignored_by_truncation'].append(label['y3d'])
z_dict['ignored_by_truncation'].append(label['z3d'])
view_angle_dict['ignored_by_truncation'].append(
np.arctan(label['x3d']/label['z3d']))
yaw_dict['ignored_by_truncation'].append(label['yaw'])
detection_boxes_3d = np.array(
[[label['x3d'], label['y3d'], label['z3d'],
label['length'], label['height'], label['width'],
label['yaw']]])
detection_boxes_3d_corners = nms.boxes_3d_to_corners(
detection_boxes_3d)
corners_cam_points = Points(
xyz=detection_boxes_3d_corners[0], attr=None)
corners_img_points = self.cam_points_to_image(
corners_cam_points, calib)
corners_xy = corners_img_points.xyz[:, :2]
xmin, ymin = np.amin(corners_xy, axis=0)
xmax, ymax = np.amax(corners_xy, axis=0)
clip_xmin = max(xmin, 0.0)
clip_ymin = max(ymin, 0.0)
clip_xmax = min(xmax, 1242.0)
clip_ymax = min(ymax, 375.0)
height = clip_ymax - clip_ymin
truncation_rate = 1.0 - \
(clip_ymax - clip_ymin)*(clip_xmax - clip_xmin)\
/((ymax - ymin)*(xmax - xmin))
if label['truncation'] > 0.5:
truncation_rates.append(truncation_rate)
else:
no_truncation_rates.append(truncation_rate)
if label['occlusion'] > 2:
h_dict['ignored_by_occlusion'].append(label['height'])
w_dict['ignored_by_occlusion'].append(label['width'])
l_dict['ignored_by_occlusion'].append(label['length'])
x_dict['ignored_by_occlusion'].append(label['x3d'])
y_dict['ignored_by_occlusion'].append(label['y3d'])
z_dict['ignored_by_occlusion'].append(label['z3d'])
view_angle_dict['ignored_by_occlusion'].append(
np.arctan(label['x3d']/label['z3d']))
yaw_dict['ignored_by_occlusion'].append(label['yaw'])
statics = ""
for object_name in h_dict:
print(object_name+"l="+str(
np.histogram(l_dict[object_name], 10, density=True)))
if len(h_dict[object_name]) == 0:
continue
statics += (str(len(h_dict[object_name]))+ " "+ str(object_name)
+ " "
+ "mh=" + str(np.min(h_dict[object_name])) + " "
+ str(np.median(h_dict[object_name])) + " "
+ str(np.max(h_dict[object_name])) + "; "
+ "mw=" + str(np.min(w_dict[object_name])) + " "
+ str(np.median(w_dict[object_name])) + " "
+ str(np.max(w_dict[object_name])) + "; "
+ "ml=" + str(np.min(l_dict[object_name])) + " "
+ str(np.median(l_dict[object_name])) + " "
+ str(np.max(l_dict[object_name])) + "; "
+ "mx=" + str(np.min(x_dict[object_name])) + " "
+ str(np.median(x_dict[object_name])) + " "
+ str(np.max(x_dict[object_name])) + "; "
+ "my=" + str(np.min(y_dict[object_name])) + " "
+ str(np.median(y_dict[object_name])) + " "
+ str(np.max(y_dict[object_name])) + "; "
+ "mz=" + str(np.min(z_dict[object_name])) + " "
+ str(np.median(z_dict[object_name])) + " "
+ str(np.max(z_dict[object_name])) + "; "
+ "mA=" + str(np.min(view_angle_dict[object_name]))
+ " "
+ str(np.median(view_angle_dict[object_name]))
+ " "
+ str(np.max(view_angle_dict[object_name])) + "; "
+ "mY=" + str(np.min(yaw_dict[object_name])) + " "
+ str(np.median(yaw_dict[object_name])) + " "
+ str(np.max(yaw_dict[object_name])) + "; "
+ "image_height" + str(np.min(image_height)) + " "
+ str(np.max(image_height)) +" "
+ "image_width" + str(np.min(image_width)) + " "
+ str(np.max(image_width)) + ";"
"\n")
return statics
@property
def num_files(self):
return len(self._file_list)
def _read_index_file(self, index_filename):
"""Read an index file containing the filenames.
Args:
index_filename: a string containing the path to an index file.
Returns: a list of filenames.
"""
file_list = []
with open(index_filename, 'r') as f:
for line in f:
file_list.append(line.rstrip('\n').split('.')[0])
return file_list
def _get_file_list(self, image_dir):
"""Load all filenames from image_dir.
Args:
image_dir: a string of path to the image folder.
Returns: a list of filenames.
"""
file_list = [f.split('.')[0]
for f in os.listdir(image_dir) if isfile(join(image_dir, f))]
file_list.sort()
return file_list
def _verify_file_list(
self, image_dir, point_dir, label_dir, calib_dir, file_list,
is_training, is_raw):
"""Varify the files in file_list exist.
Args:
image_dir: a string of the path to image folder.
point_dir: a string of the path to point cloud data folder.
label_dir: a string of the path to the label folder.
calib_dir: a string of the path to the calibration folder.
file_list: a list of filenames.
is_training: if False, label_dir is not verified.
Raise: assertion error when file in file_list is not complete.
"""
for f in file_list:
image_file = join(image_dir, f)+'.png'
point_file = join(point_dir, f)+'.bin'
label_file = join(label_dir, f)+'.txt'
calib_file = join(calib_dir, f)+'.txt'
assert isfile(image_file), "Image %s does not exist" % image_file
assert isfile(point_file), "Point %s does not exist" % point_file
if not is_raw:
assert isfile(calib_file), \
"Calib %s does not exist" % calib_file
if is_training:
assert isfile(label_file), \
"Label %s does not exist" % label_file
def downsample_by_voxel(self, points, voxel_size, method='AVERAGE'):
"""Downsample point cloud by voxel.
points: a Points namedtuple containing "xyz" and "attr".
voxel_size: the size of voxel cells used for downsampling.
method: 'AVERAGE', all points inside a voxel cell are averaged
including xyz and attr.
"""
# create voxel grid
xmax, ymax, zmax = np.amax(points.xyz, axis=0)
xmin, ymin, zmin = np.amin(points.xyz, axis=0)
dim_x = int((xmax - xmin) / voxel_size + 1)
dim_y = int((ymax - ymin) / voxel_size + 1)
dim_z = int((zmax - zmin) / voxel_size + 1)
voxel_account = {}
xyz_idx = np.int32(
(points.xyz - np.asarray([[xmin, ymin, zmin]])) / voxel_size)
for pidx in range(xyz_idx.shape[0]):
x_idx = xyz_idx[pidx, 0]
y_idx = xyz_idx[pidx, 1]
z_idx = xyz_idx[pidx, 2]
# TODO check bug impact
key = x_idx + y_idx*dim_x + z_idx*dim_y*dim_x
if key in voxel_account:
voxel_account[key].append(pidx)
else:
voxel_account[key] = [pidx]
# compute voxel points
downsampled_xyz_list = []
if points.attr is not None:
downsampled_attr_list = []
if method == 'AVERAGE':
for idx, pidx_list in voxel_account.iteritems():
if len(pidx_list) > 0:
downsampled_xyz_list.append(
np.mean(points.xyz[pidx_list, :],
axis=0, keepdims=True))
if points.attr is not None:
downsampled_attr_list.append(
np.mean(points.attr[pidx_list, :],
axis=0, keepdims=True))
if points.attr is not None:
return Points(xyz=np.vstack(downsampled_xyz_list),
attr=np.vstack(downsampled_attr_list))
else:
return Points(xyz=np.vstack(downsampled_xyz_list),
attr=None)
def get_calib(self, frame_idx):
"""Load calibration matrices and compute calibrations.
Args:
frame_idx: the index of the frame to read.
Returns: a dictionary of calibrations.
"""
calib_file = join(self._calib_dir, self._file_list[frame_idx])+'.txt'
with open(calib_file, 'r') as f:
calib = {}
for line in f:
fields = line.split(' ')
matrix_name = fields[0].rstrip(':')
matrix = np.array(fields[1:], dtype=np.float32)
calib[matrix_name] = matrix
calib['P2'] = calib['P2'].reshape(3, 4)
calib['R0_rect'] = calib['R0_rect'].reshape(3,3)
calib['Tr_velo_to_cam'] = calib['Tr_velo_to_cam'].reshape(3,4)
R0_rect = np.eye(4)
R0_rect[:3, :3] = calib['R0_rect']
calib['velo_to_rect'] = np.vstack([calib['Tr_velo_to_cam'],[0,0,0,1]])
calib['cam_to_image'] = np.hstack([calib['P2'][:, 0:3], [[0],[0],[0]]])
calib['rect_to_cam'] = np.hstack([
calib['R0_rect'],
np.matmul(
np.linalg.inv(calib['P2'][:, 0:3]), calib['P2'][:, [3]])])
calib['rect_to_cam'] = np.vstack([calib['rect_to_cam'],
[0,0,0,1]])
calib['velo_to_cam'] = np.matmul(calib['rect_to_cam'],
calib['velo_to_rect'])
calib['cam_to_velo'] = np.linalg.inv(calib['velo_to_cam'])
# senity check
calib['velo_to_image'] = np.matmul(calib['cam_to_image'],
calib['velo_to_cam'])
assert np.isclose(calib['velo_to_image'],
np.matmul(np.matmul(calib['P2'], R0_rect),
calib['velo_to_rect'])).all()
return calib
def get_raw_calib(self, calib_velo_to_cam_path, calib_cam_to_cam_path):
"""Read calibrations in kitti raw dataset."""
with open(calib_cam_to_cam_path, 'r') as f:
calib = {}
for line in f:
line = line.rstrip('\n')
fields = line.split(':')
calib[fields[0]] = fields[1]
calib['corner_dist'] = np.array(
calib['corner_dist'], dtype=np.float32)
for i in range(4):
calib['S_0%d'%i] = np.array(
calib['S_0%d'%i].split(' ')[1:], dtype=np.float32).reshape(1,2)
calib['K_0%d'%i] = np.array(
calib['K_0%d'%i].split(' ')[1:], dtype=np.float32).reshape(3,3)
calib['D_0%d'%i] = np.array(
calib['D_0%d'%i].split(' ')[1:], dtype=np.float32).reshape(1,5)
calib['R_0%d'%i] = np.array(
calib['R_0%d'%i].split(' ')[1:], dtype=np.float32).reshape(3,3)
calib['T_0%d'%i] = np.array(
calib['T_0%d'%i].split(' ')[1:], dtype=np.float32).reshape(3,1)
calib['S_rect_0%d'%i] = np.array(
calib['S_rect_0%d'%i].split(' ')[1:],
dtype=np.float32).reshape(1,2)
calib['R_rect_0%d'%i] = np.array(
calib['R_rect_0%d'%i].split(' ')[1:],
dtype=np.float32).reshape(3,3)
calib['P_rect_0%d'%i] = np.array(
calib['P_rect_0%d'%i].split(' ')[1:],
dtype=np.float32).reshape(3,4)
with open(calib_velo_to_cam_path, 'r') as f:
for line in f:
line = line.rstrip('\n')
fields = line.split(':')
calib[fields[0]] = fields[1]
calib['R'] = np.array(
calib['R'].split(' ')[1:], dtype=np.float32).reshape(3,3)
calib['T'] = np.array(
calib['T'].split(' ')[1:], dtype=np.float32).reshape(3,1)
calib['Tr_velo_to_cam'] = np.vstack(
[np.hstack([calib['R'], calib['T']]),[0,0,0,1]])
R0_rect = np.eye(4)
R0_rect[:3, :3] = calib['R_rect_00']
T2 = np.eye(4)
T2[0, 3] = calib['P_rect_02'][0, 3]/calib['P_rect_02'][0, 0]
calib['velo_to_cam'] = T2.dot(R0_rect.dot(calib['Tr_velo_to_cam']))
calib['cam_to_image'] = np.hstack(
[calib['P_rect_02'][:, 0:3], [[0],[0],[0]]])
calib['velo_to_image'] = np.matmul(calib['cam_to_image'],
calib['velo_to_cam'])
return calib
def get_filename(self, frame_idx):
"""Get the filename based on frame_idx.
Args:
frame_idx: the index of the frame to get.
Returns: a string containing the filename.
"""
return self._file_list[frame_idx]
def get_velo_points(self, frame_idx, xyz_range=None):
"""Load velo points from frame_idx.
Args:
frame_idx: the index of the frame to read.
Returns: Points.
"""
point_file = join(self._point_dir, self._file_list[frame_idx])+'.bin'
velo_data = np.fromfile(point_file, dtype=np.float32).reshape(-1, 4)
velo_points = velo_data[:,:3]
reflections = velo_data[:,[3]]
if xyz_range is not None:
x_range, y_range, z_range = xyz_range
mask =(
velo_points[:, 0] > x_range[0])*(velo_points[:, 0] < x_range[1])
mask *=(
velo_points[:, 1] > y_range[0])*(velo_points[:, 1] < y_range[1])
mask *=(
velo_points[:, 2] > z_range[0])*(velo_points[:, 2] < z_range[1])
return Points(xyz = velo_points[mask], attr = reflections[mask])
return Points(xyz = velo_points, attr = reflections)
def get_cam_points(self, frame_idx,
downsample_voxel_size=None, calib=None, xyz_range=None):
"""Load velo points and convert them to camera coordinates.
Args:
frame_idx: the index of the frame to read.
Returns: Points.
"""
velo_points = self.get_velo_points(frame_idx, xyz_range=xyz_range)
if calib is None:
calib = self.get_calib(frame_idx)
cam_points = self.velo_points_to_cam(velo_points, calib)
if downsample_voxel_size is not None:
cam_points = downsample_by_average_voxel(cam_points,
downsample_voxel_size)
return cam_points
def calc_distances(self, p0, points):
return ((p0 - points)**2).sum(axis=1)
def farthest_first(self, pts, K):
farthest_pts = np.zeros((K, 3))
farthest_pts[0] = pts[np.random.randint(len(pts))]
distances = self.calc_distances(farthest_pts[0], pts)
for i in range(1, K):
farthest_pts[i] = pts[np.argmax(distances)]
distances = np.minimum(distances,
self.calc_distances(farthest_pts[i], pts))
return farthest_pts
def get_cam_points_in_image(self, frame_idx, downsample_voxel_size=None,
calib=None, xyz_range=None):
"""Load velo points and remove points that are not observed by camera.
"""
if calib is None:
calib = self.get_calib(frame_idx)
cam_points = self.get_cam_points(frame_idx, downsample_voxel_size,
calib=calib, xyz_range=xyz_range)
image = self.get_image(frame_idx)
height = image.shape[0]
width = image.shape[1]
front_cam_points_idx = cam_points.xyz[:,2] > 0.1
front_cam_points = Points(cam_points.xyz[front_cam_points_idx, :],
cam_points.attr[front_cam_points_idx, :])
img_points = self.cam_points_to_image(front_cam_points, calib)
img_points_in_image_idx = np.logical_and.reduce(
[img_points.xyz[:,0]>0, img_points.xyz[:,0]<width,
img_points.xyz[:,1]>0, img_points.xyz[:,1]<height])
cam_points_in_img = Points(
xyz = front_cam_points.xyz[img_points_in_image_idx,:],
attr = front_cam_points.attr[img_points_in_image_idx,:])
return cam_points_in_img
def get_cam_points_in_image_with_rgb(self, frame_idx,
downsample_voxel_size=None, calib=None, xyz_range=None):
"""Get camera points that are visible in image and append image color
to the points as attributes."""
if calib is None:
calib = self.get_calib(frame_idx)
cam_points = self.get_cam_points(frame_idx, downsample_voxel_size,
calib = calib, xyz_range=xyz_range)
front_cam_points_idx = cam_points.xyz[:,2] > 0.1
front_cam_points = Points(cam_points.xyz[front_cam_points_idx, :],
cam_points.attr[front_cam_points_idx, :])
image = self.get_image(frame_idx)
height = image.shape[0]
width = image.shape[1]
img_points = self.cam_points_to_image(front_cam_points, calib)
img_points_in_image_idx = np.logical_and.reduce(
[img_points.xyz[:,0]>0, img_points.xyz[:,0]<width,
img_points.xyz[:,1]>0, img_points.xyz[:,1]<height])
cam_points_in_img = Points(
xyz = front_cam_points.xyz[img_points_in_image_idx,:],
attr = front_cam_points.attr[img_points_in_image_idx,:])
cam_points_in_img_with_rgb = self.rgb_to_cam_points(cam_points_in_img,
image, calib)
return cam_points_in_img_with_rgb
def get_image(self, frame_idx):
"""Load the image from frame_idx.
Args:
frame_idx: the index of the frame to read.
Returns: cv2.matrix
"""
image_file = join(self._image_dir, self._file_list[frame_idx])+'.png'
return cv2.imread(image_file)
def get_label(self, frame_idx, no_orientation=False):
"""Load bbox labels from frame_idx frame.
Args:
frame_idx: the index of the frame to read.
Returns: a list of object label dictionaries.
"""
MIN_HEIGHT = [40, 25, 25]
MAX_OCCLUSION = [0, 1, 2]
MAX_TRUNCATION = [0.15, 0.3, 0.5]
label_file = join(self._label_dir, self._file_list[frame_idx])+'.txt'
label_list = []
with open(label_file, 'r') as f:
for line in f:
label={}
line = line.strip()
if line == '':
continue
fields = line.split(' ')
label['name'] = fields[0]
# 0=visible 1=partly occluded, 2=fully occluded, 3=unknown
label['truncation'] = float(fields[1])
label['occlusion'] = int(fields[2])
label['alpha'] = float(fields[3])
label['xmin'] = float(fields[4])
label['ymin'] = float(fields[5])
label['xmax'] = float(fields[6])
label['ymax'] = float(fields[7])
label['height'] = float(fields[8])
label['width'] = float(fields[9])
label['length'] = float(fields[10])
label['x3d'] = float(fields[11])
label['y3d'] = float(fields[12])
label['z3d'] = float(fields[13])
label['yaw'] = float(fields[14])
if len(fields) > 15:
label['score'] = float(fields[15])
if self.difficulty > -1:
if label['truncation'] > MAX_TRUNCATION[self.difficulty]:
continue
if label['occlusion'] > MAX_OCCLUSION[self.difficulty]:
continue
if (label['ymax'] - label['ymin']
) < MIN_HEIGHT[self.difficulty]:
continue
label_list.append(label)
return label_list
def box3d_to_cam_points(self, label, expend_factor=(1.0, 1.0, 1.0)):
"""Project 3D box into camera coordinates.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw", "height"
"width", "length".
Returns: a numpy array [8, 3] representing the corners of the 3d box in
camera coordinates.
"""
yaw = label['yaw']
R = np.array([[np.cos(yaw), 0, np.sin(yaw)],
[0, 1, 0 ],
[-np.sin(yaw), 0, np.cos(yaw)]]);
h = label['height']
delta_h = h*(expend_factor[0]-1)
w = label['width']*expend_factor[1]
l = label['length']*expend_factor[2]
corners = np.array([[ l/2, delta_h/2, w/2], # front up right
[ l/2, delta_h/2, -w/2], # front up left
[-l/2, delta_h/2, -w/2], # back up left
[-l/2, delta_h/2, w/2], # back up right
[ l/2, -h-delta_h/2, w/2], # front down right
[ l/2, -h-delta_h/2, -w/2], # front down left
[-l/2, -h-delta_h/2, -w/2], # back down left
[-l/2, -h-delta_h/2, w/2]]) # back down right
r_corners = corners.dot(np.transpose(R))
tx = label['x3d']
ty = label['y3d']
tz = label['z3d']
cam_points_xyz = r_corners+np.array([tx, ty, tz])
return Points(xyz = cam_points_xyz, attr = None)
def boxes_3d_to_line_set(self, boxes_3d, boxes_color=None):
points = []
edges = []
colors = []
for i, box_3d in enumerate(boxes_3d):
x3d, y3d, z3d, l, h, w, yaw = box_3d
R = np.array([[np.cos(yaw), 0, np.sin(yaw)],
[0, 1, 0 ],
[-np.sin(yaw), 0, np.cos(yaw)]]);
corners = np.array([[ l/2, 0.0, w/2], # front up right
[ l/2, 0.0, -w/2], # front up left
[-l/2, 0.0, -w/2], # back up left
[-l/2, 0.0, w/2], # back up right
[ l/2, -h, w/2], # front down right
[ l/2, -h, -w/2], # front down left
[-l/2, -h, -w/2], # back down left
[-l/2, -h, w/2]]) # back down right
r_corners = corners.dot(np.transpose(R))
cam_points_xyz = r_corners+np.array([x3d, y3d, z3d])
points.append(cam_points_xyz)
edges.append(
np.array([[0, 1], [0, 4], [0, 3],
[1, 2], [1, 5], [2, 3],
[2, 6], [3, 7], [4, 5],
[4, 7], [5, 6], [6, 7]])+i*8)
if boxes_color is None:
colors.append(np.tile([[1.0, 0.0, 0.0]], [12, 1]))
else:
colors.append(np.tile(boxes_color[[i], :], [12, 1]))
if len(points) == 0:
return None, None, None
return np.vstack(points), np.vstack(edges), np.vstack(colors)
def draw_open3D_box(self, label, expend_factor=(1.0, 1.0, 1.0)):
"""Draw a 3d box using open3d.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw",
"height", "width", "lenth".
returns: a open3d mesh object.
"""
yaw = label['yaw']
R = np.array([[np.cos(yaw), 0, np.sin(yaw)],
[0, 1, 0 ],
[-np.sin(yaw), 0, np.cos(yaw)]]);
Rh = np.array([ [1, 0, 0],
[0, 0, 1],
[0, 1, 0]])
Rl = np.array([ [0, 0, 1],
[0, 1, 0],
[1, 0, 0]])
h = label['height']
delta_h = h*(expend_factor[0]-1)
w = label['width']*expend_factor[1]
l = label['length']*expend_factor[2]
print((l, w, h))
tx = label['x3d']
ty = label['y3d']
tz = label['z3d']
box_offset = np.array([ [ l/2, -h/2-delta_h/2, w/2],
[ l/2, -h/2-delta_h/2, -w/2],
[-l/2, -h/2-delta_h/2, -w/2],
[-l/2, -h/2-delta_h/2, w/2],
[ l/2, delta_h/2, 0],
[ -l/2, delta_h/2, 0],
[l/2, -h-delta_h/2, 0],
[-l/2, -h-delta_h/2, 0],
[0, delta_h/2, w/2],
[0, delta_h/2, -w/2],
[0, -h-delta_h/2, w/2],
[0, -h-delta_h/2, -w/2]])
transform = np.matmul(R, np.transpose(box_offset))
transform = transform + np.array([[tx], [ty], [tz]])
transform = np.vstack((transform, np.ones((1, 12))))
hrotation = np.vstack((R.dot(Rh), np.zeros((1,3))))
lrotation = np.vstack((R.dot(Rl), np.zeros((1,3))))
wrotation = np.vstack((R, np.zeros((1,3))))
h1_cylinder = open3d.create_mesh_cylinder(radius = h/100, height = h)
h1_cylinder.paint_uniform_color([0.1, 0.9, 0.1])
h1_cylinder.transform(np.hstack((hrotation, transform[:, [0]])))
h2_cylinder = open3d.create_mesh_cylinder(radius = h/100, height = h)
h2_cylinder.paint_uniform_color([0.1, 0.9, 0.1])
h2_cylinder.transform(np.hstack((hrotation, transform[:, [1]])))
h3_cylinder = open3d.create_mesh_cylinder(radius = h/100, height = h)
h3_cylinder.paint_uniform_color([0.1, 0.9, 0.1])
h3_cylinder.transform(np.hstack((hrotation, transform[:, [2]])))
h4_cylinder = open3d.create_mesh_cylinder(radius = h/100, height = h)
h4_cylinder.paint_uniform_color([0.1, 0.9, 0.1])
h4_cylinder.transform(np.hstack((hrotation, transform[:, [3]])))
w1_cylinder = open3d.create_mesh_cylinder(radius = w/100, height = w)
w1_cylinder.paint_uniform_color([0.9, 0.1, 0.1])
w1_cylinder.transform(np.hstack((wrotation, transform[:, [4]])))
w2_cylinder = open3d.create_mesh_cylinder(radius = w/100, height = w)
w2_cylinder.paint_uniform_color([0.9, 0.1, 0.1])
w2_cylinder.transform(np.hstack((wrotation, transform[:, [5]])))
w3_cylinder = open3d.create_mesh_cylinder(radius = w/100, height = w)
w3_cylinder.paint_uniform_color([0.9, 0.1, 0.1])
w3_cylinder.transform(np.hstack((wrotation, transform[:, [6]])))
w4_cylinder = open3d.create_mesh_cylinder(radius = w/100, height = w)
w4_cylinder.paint_uniform_color([0.9, 0.1, 0.1])
w4_cylinder.transform(np.hstack((wrotation, transform[:, [7]])))
l1_cylinder = open3d.create_mesh_cylinder(radius = l/100, height = l)
l1_cylinder.paint_uniform_color([0.1, 0.1, 0.9])
l1_cylinder.transform(np.hstack((lrotation, transform[:, [8]])))
l2_cylinder = open3d.create_mesh_cylinder(radius = l/100, height = l)
l2_cylinder.paint_uniform_color([0.1, 0.1, 0.9])
l2_cylinder.transform(np.hstack((lrotation, transform[:, [9]])))
l3_cylinder = open3d.create_mesh_cylinder(radius = l/100, height = l)
l3_cylinder.paint_uniform_color([0.1, 0.1, 0.9])
l3_cylinder.transform(np.hstack((lrotation, transform[:, [10]])))
l4_cylinder = open3d.create_mesh_cylinder(radius = l/100, height = l)
l4_cylinder.paint_uniform_color([0.1, 0.1, 0.9])
l4_cylinder.transform(np.hstack((lrotation, transform[:, [11]])))
return [h1_cylinder, h2_cylinder, h3_cylinder, h4_cylinder,
w1_cylinder, w2_cylinder, w3_cylinder, w4_cylinder,
l1_cylinder, l2_cylinder, l3_cylinder, l4_cylinder]
def box3d_to_normals(self, label, expend_factor=(1.0, 1.0, 1.0)):
"""Project a 3D box into camera coordinates, compute the center
of the box and normals.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw",
"height", "width", "lenth".
Returns: a numpy array [3, 3] containing [wx, wy, wz]^T, a [3] lower
bound and a [3] upper bound.
"""
box3d_points = self.box3d_to_cam_points(label, expend_factor)
box3d_points_xyz = box3d_points.xyz
wx = box3d_points_xyz[[0], :] - box3d_points_xyz[[4], :]
lx = np.matmul(wx, box3d_points_xyz[4, :])
ux = np.matmul(wx, box3d_points_xyz[0, :])
wy = box3d_points_xyz[[0], :] - box3d_points_xyz[[1], :]
ly = np.matmul(wy, box3d_points_xyz[1, :])
uy = np.matmul(wy, box3d_points_xyz[0, :])
wz = box3d_points_xyz[[0], :] - box3d_points_xyz[[3], :]
lz = np.matmul(wz, box3d_points_xyz[3, :])
uz = np.matmul(wz, box3d_points_xyz[0, :])
return(np.concatenate([wx, wy, wz], axis=0),
np.concatenate([lx, ly, lz]), np.concatenate([ux, uy, uz]))
def sel_points_in_box3d(self, label, points, expend_factor=(1.0, 1.0, 1.0)):
"""Select points in a 3D box.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw",
"height", "width", "lenth".
Returns: a bool mask indicating points inside a 3D box.
"""
normals, lower, upper = self.box3d_to_normals(label, expend_factor)
projected = np.matmul(points.xyz, np.transpose(normals))
points_in_x = np.logical_and(projected[:, 0] > lower[0],
projected[:, 0] < upper[0])
points_in_y = np.logical_and(projected[:, 1] > lower[1],
projected[:, 1] < upper[1])
points_in_z = np.logical_and(projected[:, 2] > lower[2],
projected[:, 2] < upper[2])
mask = np.logical_and.reduce((points_in_x, points_in_y, points_in_z))
return mask
def sel_xyz_in_box3d(self, label, xyz, expend_factor=(1.0, 1.0, 1.0)):
"""Select points in a 3D box.
Args:
label: a dictionary containing "x3d", "y3d", "z3d", "yaw",
"height", "width", "lenth".
Returns: a bool mask indicating points inside a 3D box.
"""
normals, lower, upper = self.box3d_to_normals(label, expend_factor)
projected = np.matmul(xyz, np.transpose(normals))
points_in_x = np.logical_and(projected[:, 0] > lower[0],
projected[:, 0] < upper[0])
points_in_y = np.logical_and(projected[:, 1] > lower[1],
projected[:, 1] < upper[1])
points_in_z = np.logical_and(projected[:, 2] > lower[2],
projected[:, 2] < upper[2])
mask = np.logical_and.reduce((points_in_x, points_in_y, points_in_z))
return mask
def rgb_to_cam_points(self, points, image, calib):
"""Append rgb info to camera points"""
img_points = self.cam_points_to_image(points, calib)
rgb = image[np.int32(img_points.xyz[:,1]),
np.int32(img_points.xyz[:,0]),::-1].astype(np.float32)/255
return Points(points.xyz, np.hstack([points.attr, rgb]))
def velo_points_to_cam(self, points, calib):
"""Convert points in velodyne coordinates to camera coordinates.